Method in Multi-Grounded Distribution Networks
A thesis submitted for the degree of Doctor of Engineering Sciences
Alicia Valero Masa
BEAMS - Energy Group
Ecole polytechnique de Bruxelles ´ Universit´e Libre de Bruxelles
Thesis Advisor: Academic Year:
Prof. Jean-Claude Maun 2012-2013
President of the PhD Committee: Prof. Michael Kinnaert Members of the PhD Committee: Dr. Siegfried Lemmer
Prof. Christine Decaestecker Prof. Johan Gyselinck Prof. Bertrand Raison
Day of the defense: 23rd November 2012
Signature from the President of the PhD committee:
Prof. Michael Kinnaert
This PhD dissertation is the result of my work during the last four years, work that would not have been possible without the support and contribution of many people. I would like to take the opportunity to thank them.
First of all, I am very grateful to my thesis promoter, Prof. Jean-Claude Maun, who gave me the opportunity of becoming a researcher of BEAMS. His knowledge of the subject and valuable advices have been indispensable for this thesis.
I especially thank Siemens for financing this project and particularly to Stefan Werben and to Matthias Kereit for their support, encouragement and efforts they have done for carrying out this project.
I also express my gratitude to the professors Michael Kinnaert and Christine De- caestecker for their advices, and to I˜naki Ojanguren, Flavio Costa, Sylvain Douce- ment, Alonso Arregoces and Jairo Aguirre for assisting me by providing data.
Obviously, kind thanks go to my colleagues of the Beams Energy. Especially to the persons who welcomed me in the department: Jacques, Benjamin, Erwan, David, Fabien, Michael, Jawwad, Pascal Ariane, and Prof. Joan Gyselinck, and to those who join later and lived up the department: Quentin, M´elik, Olivier, Matthieu, Pierre, Mircea and Vlad. The great working atmosphere certainly contributed to my motiva- tion to work at the ULB.
I am particularly grateful to the friends who gave me help by reviewing and cor- recting this thesis: Eduardo, Luc´ıa, Ana, Kenny, Yiyus, Marina and Jawwad.
Finally, many thanks to all my friends in Gent and in Brussels who made me feel at home, to my family and friends in Spain who supported me in distance, and to my boyfriend Marcos who cheered me up every day of my PhD.
Nowadays, the main goal of electrical engineers is to modernize the power system by making it more intelligent, reliable and sustainable. The integration of distributed generation, energy storage, electronic devices, and electric vehicles is changing the power network. There is an expectation and a requirement of new protection func- tions according to the up-to-date network. Protection technology still has important limitations; faults that cannot be detected by current devices. Therefore, developing modern functions is a challenging and neecessary topic in the emerging environment of smart grids.
The aim of this thesis is to study the high impedance faults (HIFs), faults that un- der certain conditions cannot be detected by conventional technology. The first part of the work is directed to understand the problem, by identifying the characteristics of the fault and the networks that make the detection critical. A list of novel indicators that reveal the characteristics of the HIFs was obtained by analyzing fault recordings.
A study of different configurations of the distribution network was also made to un- derstand and explain the importance of the detection problem in the American power system. The second part is focused on the development of an HIF detection algorithm.
The algorithm must recognize events suspected of being HIFs and, after applying sev- eral criteria, it must declare them as a fault or as another type of event. Techniques from knowledge discovery in databases and machine learning were adopted to extract the necessary knowledge to perform the classification.
The first achievement of this thesis is to present a complete description of the high impedance fault detection problem, specifying the influence factors and the crit- ical cases. From this study it is inferred that the typical configuration of American networks is the worst-case scenario in case of HIFs, hence a practical detection func- tion is highly valuable. The second achievement is to create original and specific indicators for each of the HIF characteristics. Consequently, all needed information about HIFs is expressed by means of magnitudes, which can be use to detect the fault. The third achievement is to incorporate machine learning techniques into the protection technology of power systems. The approach of this thesis benefits from
sons and property by detecting HIFs in multi-grounded distribution networks when conventional devices are insufficient.
Although the high impedance fault detection problem is critical, there has not been a definitive solution due to the limitation of the present protection technology.
This thesis provides a solution using an innovative methodology based on knowledge discovery in databases, methodology that can be applied to other fault detection prob- lems to attain more reliable protection devices.
List of Figures ix
List of Tables xvii
Introduction 1
I CHARACTERIZATION OF HIGH IMPEDANCE FAULTS 13
1 Description and Approach of the High Impedance Fault Detection Problem 15
1.1 Description of High Impedance Faults (HIFs) . . . 15
1.2 Difficulties in Detecting HIFs and Influence of the Network Configuration . . . . 17
1.2.1 Detection of HIFs in American Distribution Networks . . . 17
1.2.2 Detection of HIFs in European Distribution Networks . . . 18
1.3 Approaching the High Impedance Fault detection Problem . . . 19
1.3.1 Learning about HIFs: Identifying the Difficulties of the Detection and the Typical Characteristics of the Fault . . . 20
1.3.2 Collecting HIF Currents by Simulation, Laboratory Tests and Field Recordings . . . 20
1.3.3 Analyzing and Processing the Database: HIF Characterization . . . 22
2 Simulation of High Impedance Faults 23 2.1 Introduction to the Simulation of High Impedance Faults . . . 23
2.2 Limitation and Objective of the Simulation . . . 23
2.3 Simulation Model . . . 24
2.3.1 Simulation Model of the Distribution Network . . . 24
2.3.2 Simulation Model of the Electrical Arc . . . 31
2.3.2.1 Mathematical Model . . . 31
2.3.2.2 Implementation of the Models . . . 32
2.4 Use of the Arc Model for Fitting Experimental Arcs . . . 35
2.4.1 Study of the Model Parameters . . . 35
2.4.1.1 Time Constantτ . . . 38
2.4.1.2 Constant Arc VoltageVarcand Constant Voltage per Lengthu0 Parameters . . . 41
2.4.1.3 Random CoefficientCR . . . 43
2.4.1.4 Asymmetry . . . 44
2.4.1.5 Minimum Current for Arc PresenceImin . . . 44
2.4.1.6 Conductance between the Extinction and the ReignitionGreig andgreig . . . 45
2.4.2 Performing 380V Laboratory Arcs . . . 46
2.5 The HIF Simulation: Discussion . . . 49
2.5.1 Comparison of the Two Arc Simulation Models . . . 50
2.5.2 Importance of the Simulation: Study of the Arcing Current . . . 51
2.5.3 Limitation of Simulation: Lack of Predictability . . . 51
3 High Impedance Fault Laboratory Tests 53 3.1 Need of Performing HIF Tests . . . 53
3.2 Tests in the High Voltage Laboratory of ULB to Develop a Test Procedure . . . . 54
3.2.1 Introduction to the High Voltage Laboratory of ULB . . . 54
3.2.2 Determining the Capacity of the Laboratory . . . 55
3.2.2.1 Saturation of the Voltage Transformers: Voltage Limitation of the Setup . . . 55
3.2.2.2 Internal Loss of the Voltage Regulator and the Power Trans- former: Current Limitation of the Setup . . . 57
3.2.3 Estimating the Impedance Value of Typical Surfaces Involved in HIF. . . 59
3.2.4 Recording HIF Currents and Voltages in the Proposed Laboratory Setup . 60 3.3 Tests in the High Power Testing Laboratory of Siemens AG in Berlin . . . 63
3.3.1 Preparation of the Tests According to the Proposed Procedure . . . 63
3.3.2 Results of HIF Tests in the High Power Testing Laboratory . . . 64
3.3.3 Validation of the Test Results using Field Fault Recordings . . . 68
3.4 Towards the Characterization of HIFs . . . 70
4 Characterizing High Impedance Faults 71
4.1 Introducing the High Impedance Fault Characterization . . . 71
4.2 Indicators of High Impedance Faults . . . 72
4.3 Searching for the HIF Indicators . . . 73
4.3.1 Frequency Analysis . . . 74
4.3.2 Difference Cycle per Cycle . . . 75
4.3.3 Study of the Waveform . . . 78
4.3.3.1 Arc Effect at the current-zero-crossing . . . 78
4.3.3.2 Asymmetry of the waveform . . . 79
4.4 Characterization of HIFs based on the Indicators . . . 81
II DEVELOPMENT OF THE HIGH IMPEDANCE FAULT DETECTION METHOD 85 5 Design of the HIF Detection Method 87 5.1 Introduction to the Detection Method . . . 87
5.1.1 Objectives, Specifications, and Methodology of the HIF Detection . . . . 87
5.1.2 Structure of the Method . . . 88
5.2 Building the Algorithm for Triggering and Extracting the Suspicious-Event Current 90 5.2.1 Building a Database of Residual Currents . . . 90
5.2.1.1 Contacting Distribution System Operators in America: 3I0 un- der No-Fault Conditions . . . 90
5.2.1.2 Building 3I0 under HIF Conditions Signals by Superimposition 95 5.2.1.3 Contact with Federal University of Campina Grande: 3I0 under HIF Conditions . . . 96
5.2.2 Triggering at Recognizing Suspicious Events . . . 99
5.2.2.1 Interpreting the Accumulated Absolute Differences (AAD) . . . 99
5.2.2.2 Proposed Triggering Algorithm based on theAAD . . . 101
5.2.3 Extraction of the Suspicious-Event Current . . . 102
5.3 Testing and Evaluating theTriggering and Extracting Algorithm . . . 103
5.4 Discussion . . . 113
5.4.1 Limits of the Algorithm regarding the Background Randomness and the Error of Frequency . . . 114
5.4.2 Limits of the Detection Inherent to the HIFs . . . 116
5.4.3 Other Information Drawn from the Triggering . . . 117
6 Incorporation of Knowledge Discovery in Databases in HIF Detection 119
6.1 Introduction to the Application ofKnowledge Discovery in Databases . . . 119
6.2 Knowledge Discovery in Databasesoriented to HIF Detection . . . 120
6.2.1 The Basics ofKnowledge Discovery in Databases . . . 120
6.2.2 Application ofKnowledge Discovery in Databasesto the HIF Detection . 120 6.2.2.1 Data Selection . . . 121
6.2.2.2 Preprocessing . . . 121
6.2.2.3 Incorporation of Appropriate Prior Knowledge . . . 121
6.2.2.4 Data-Mining . . . 122
6.2.2.5 Interpretation and Evaluation of the Results . . . 123
6.2.3 Data-MiningOverview . . . 124
6.3 One-ClassData-MiningTechnique: Support Vector Machine . . . 126
6.3.1 Two-class Classification or One-class Classification? . . . 126
6.3.2 Introduction toSupport Vector Machines (SVMs) . . . 127
6.3.3 Formulation ofOne-class SVMsfor Linear Discriminants . . . 128
6.3.4 Use of Kernels for Non-linear Functions . . . 130
6.4 Implementation Softwares . . . 132
7 Implementation and Results of the HIF/Other Event Classifier 135 7.1 Introduction to theHIF/Other event Classifier . . . 135
7.2 Decision Trees and Rules Built withWeka . . . 136
7.2.1 AboutWeka. . . 136
7.2.2 Building the Classifier inWeka. . . 136
7.2.2.1 Preprocessing . . . 136
7.2.2.2 Classify . . . 138
7.2.3 Results and Evaluation of two-class Classifiers developed inWeka . . . . 139
7.2.4 Conclusion regarding the Two-class Classifiers Built inWeka. . . 141
7.3 One-Class SVM ClassifierDeveloped withScikits-learn. . . 143
7.3.1 AboutScikits-learn . . . 143
7.3.2 Building a Classifier usingScikits-learninPython . . . 144
7.3.3 One-class SVM Classifierand Results . . . 145
7.3.4 Evaluation of the Classifier . . . 148
8 HIF Detection Method 151
8.1 Description of the Detection Method . . . 151
8.2 Parameters of the Method . . . 156
8.3 How to Update and Adapt the Method . . . 160
Conclusions 161 References 165 A Material used to Design the HIF Detection Method 171 B Manual to Update the Database and the Classifier 175 B.1 Databases in the Working Folder . . . 175
B.2 Steps for Adding New Recordings to the Database . . . 176
B.3 Updating the Classifier . . . 178
B.4 Deleting Recordings from the Database . . . 180
1 Block Diagram of the HIF Detection method Digital Feeder Monitor . . . 5
2 Block diagram of the HIF detection method SDI applied to the current of the phase A 7 3 Calculation of the magnitude Sum of Difference Current (SDI) . . . 7
4 Simple block diagram of the High Impedance Fault Detection system of ABB . . 9
1.1 HIF current obtained by laboratory test in the MV Testing laboratory of Siemens presenting several typical characteristics of HIFs. . . 16
1.2 Solidly multi-grounded 4-wire distribution system using single-phase distribution transformers (typical in America). . . 18
1.3 Solidly grounded distribution system in Europe, using three-phase big transformer for supplying the loads. . . 19
1.4 Steps for approaching the HIF detection problem: learning about the faults and characterizing them. . . 20
2.1 Simple network model designed in ATPDraw for simulating HIFs. . . 25
2.2 Model of the generator of the network for simulating HIFs in ATPDraw. . . 25
2.3 Theoretical model of a saturable transformer in EMTP/ATP. . . 26
2.4 ATPDraw model of a single-phase saturable transformer. . . 27
2.5 Exciting characteristics of CTs from where obtaining the saturation data for the 1200/5 CT model . . . 28
2.6 Test circuit for evaluating the saturation and the resulting magnetizing curve plot- ting the peak value of the flux density against the peak value of the magnetizing current, in logarithm scale. . . 28
2.7 Simulated currents measured by the ATP CT model at different voltages (500V, 800V, 1200V), so the saturation process is visible. . . 28
2.8 ATPDraw model of a generic distribution network used for HIF simulation. . . . 30
2.9 General structure of a HIF arc model in ATP/EMTP. . . 33
2.10 ATP/EMTP model developed based on the research carried out in the Helsinki University of Technology. . . 34 2.11 Experimental voltage and current characteristic of a HIF obtained in the laboratory
of the Helsinki University of Technology. . . 34 2.12 Simulated voltage and current characteristic obtained with our arcing model using
the same parameters than of the group of the Helsinki University of Technology. . 36 2.13 Simulated voltage and current of a HIF at the branch and at the trunk, obtained in
the laboratory of the Helsinki University of Technology. . . 36 2.14 Simulated voltage and current of a HIF at the branch and at the trunk, obtained by
simulation in the Helsinki University of Technology. . . 36 2.15 Simulated voltage and current obtained with our arcing model using the same
parameters as the group of the Helsinki University of Technology. . . 37 2.16 ATP/EMTP model develop based on the research carried out in the Wroclaw Uni-
versity of Technology. . . 37 2.17 Simulated signals of voltage and current obtained by the Wroclaw University of
Technology group. . . 37 2.18 Simulated signals of voltage and current obtained used our model with the same
parameters than the group of the Wroclaw University of Technology group. . . . 39 2.19 Time constant values estimated empirically by the group of Elkalashy. . . 39 2.20 Arcing voltage and current simulated with Elkalashy model withτ =15µs. . . . 39 2.21 Arcing voltage and current simulated with Elkalashy model withτ = 1ms. . . 40 2.22 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withτ =15µs. . . 40 2.23 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withτ = 1ms. . . 40 2.24 Arcing voltage and current simulated with Elkalashy model withVarc=2520V. . 41 2.25 Arcing voltage and current simulated with Elkalashy modelVarc=200V. . . 41 2.26 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withu0= 5000V. . . 42 2.27 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withu0= 2000V. . . 42 2.28 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withCR= 0.5. . . 43
2.29 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model withCR= 1.5. . . 43 2.30 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withu0p= 5000V andu0n= 7000 V. . . 44 2.31 Arcing voltage and current simulated with Elkalashy model withImin=10µA. . . 45 2.32 Arcing voltage and current simulated with Elkalashy model withImin=10mA. . . 45 2.33 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withGreig= 1E-5S. . . 46 2.34 Arcing voltage and current simulated with Michalik, Rebizant and Lukowicz model
withGreig= 1E-9S. . . 46 2.35 Laboratory Setup for producing an arc at 380V. . . 47 2.36 Dynamic and instable voltage and current during a 8A laboratory arc at 380V. . . 47 2.37 Current and voltage characteristic of a 8A laboratory arc at 380V showing insta-
bility and unpredictability. . . 48 2.38 Zoom of Figure 2.37 focused on the stable part of the arc, without the unpre-
dictable peaks of voltage and current present in the signals. . . 48 2.39 Simulated arcing voltage and current fitting the experimental 8A arc at 380V. . . 49 2.40 Simulated arcing current and voltage characteristic fitting the experimental 8A arc
at 380V. . . 49 3.1 Diagram of the laboratory set-up to produce a HIF . . . 55 3.2 Diagram of the laboratory setup used to study the saturation of the VTs. . . 56 3.3 Recording ofVarc,Vf ault andI when the voltage of the voltage regulator is 100V
andRlimis 6570Ω. . . 56 3.4 Recording ofVarc,Vf ault andI when the voltage of the voltage regulator is 125V
andRlimis 6570Ω. . . 57 3.5 Diagram of the laboratory set up used to study the influence ofZg0 andZt0 in the
transmission of power to the arc. . . 58 3.6 Diagram of the laboratory test to study the value of the contact surfaces causing
the HIF. . . 59 3.7 Recording of Vtest, VHIF and I of a HIF on a soil test surface using a limiting
resistanceRlimof 1510Ω. . . 60 3.8 Diagram of the setup used in the HV Laboratory of ULB to perform HIFs. . . 61 3.9 The insulated rod is operated for bringing the conductor over the soil surface, the
sand surface, the branches, the trunk and the concrete floor tile. . . 61
3.10 Voltage and current of a HIF test on sand performed in the laboratory of ULB. . . 62 3.11 Voltage and current of a HIF test due to the contact with a tree trunk performed in
the laboratory of ULB. . . 62 3.12 Voltage and current of a HIF test due to the contact with tree branches performed
in the laboratory of ULB. . . 63 3.13 Representation of the setup for HIF tests in the High Power Laboratory of Siemens
AG in Berlin. . . 63 3.14 Picture of the setup addressed to perform HIFs in the High Power Laboratory of
Siemens AG in Berlin. . . 64 3.15 Watering the sand test surface to reproduce different moisture conditions. . . 65 3.16 Fault current resulting by recording a HIF test using a wet sand test surface. . . . 66 3.17 Detail of the current waveform resulting by recording a HIF test using a sand test
surface. . . 67 3.18 Using a tree as test surface we could perform HIF tests caused by the contact
between a conductor and a tree. . . 67 3.19 Fault current resulting by recording a HIF test using a wet tree as test surface. . . 68 3.20 Detail of the current waveform resulting by recording a HIF test using a wet tree
as test surface. . . 68 3.21 Piece of sidewalk used for performing HIF tests, highlighting the smelted material
that is formed at the contacting point due to the heat released by the fault. . . 69 3.22 Fault current resulting by recording a HIF test using a piece of brick and concrete
sidewalk as test surface. . . 69 3.23 HIF current recorded by the sensitive neutral unit of the protection installed by
IBERDROLA in the substation of Riolanza. . . 70 4.1 Current from a HIF test using a piece of sidewalk as test surface, frequency anal-
ysis of the current showing the 3rd harmonic dominance and phase of the 3rd har- monic, which is constant and close to 180◦. . . 75 4.2 Correlation between the 3rdharmonic amplitude in values per unit of fundamental
current (p.u) and the fundamental current amplitude of the current of a HIF test on sand. The correlation is measured by the slope of the linear-fitting using least- squares method. . . 76 4.3 Accumulated Difference cycle per cycle (AAD)calculated for a HIF test on con-
crete surface. . . 76
4.4 Current andAADof a HIF test on concrete, presenting the typical peaks of AAD that are used for defining the HIF indicator related to the dynamics of the current. 77 4.5 Waveform of a HIF test on concrete surface, showing the arc effect at the zero-
crossing and asymmetry in the cycles. . . 78 4.6 The presence of arc effect at the zero-crossing is identified when the function
dI/dthas a minimum in the quarter-cycle-long window centered in the maximum ofdI50Hz/dt. . . 79 4.7 Positive and negative half-cycle asymmetry, visible by comparing the HIF current
with the fundamental component. . . 80 4.8 Even and odd quarter-cycle asymmetry of HIF current, due to the arc effect at a
moment different to the current-zero-crossing. . . 80 4.9 Creating two signals out of the HIF recording for studying the even/odd quarter-
cycle asymmetry. . . 81 4.10 The two signals created from the same HIF recording and the correspondent fre-
quency analysis on which the comparison is based. . . 82 5.1 Scheme of the approach followed to develop the HIF detection method, based on
the knowledge previously learned. . . 88 5.2 Proposed structure of the of the HIF detection method. . . 89 5.3 Simple scheme illustrating the split-phase connection system, whose use is ex-
tended in North America and Brazil. . . 91 5.4 Phase currents and neutral current of a recording from a DSO in Brazil illustrating
transients in 3I0 due to normal operation of residential loads. . . 92 5.5 Phase currents and neutral current of the recording from a DSO in Brazil illustrat-
ing peaks in 3I0 probably due to saturation in the CTs. . . 92 5.6 Phase currents and neutral current from the networkMalvinasprovided byElec-
tricaribe, illustrating the supplying of small loads trough single-phase distribution transformers. . . 93 5.7 Phase currents and neutral current from the networkColinasprovided byEmcali,
supplying single-phase unbalanced loads with high harmonic content. . . 94 5.8 Scheme of a rural network of Hydro-Qu´ebec where the residual current under
normal conditions is so important that the value of the fuses and the triggering threshold of the switches are unable to detect HIFs. . . 95 5.9 Estimation of residual currents under HIF conditions by superimposing a HIF test
recoding on a residual current due to load unbalance. . . 97
5.10 Recording of a 3I0 showing the pre-fault situation, the HIF, and the end of the fault of a HIF on wet sand at 11km from the test location. . . 98 5.11 Recording of a 3I0 during a HIF on dry sand and some seconds of pre-fault, due
to the randomness of the load, the start of the fault is not easily visible. . . 98 5.12 Magnitude ofAADcalculated for a residual current before, during, and after a HIF. 100 5.13 Calculation ofAAD threshold and the triggers for a residual current in presence
of HIF. . . 101 5.14 Summary of thetriggering and extracting algorithmthat is the first step of the HIF
detection algorithm, estimating a suspicious-event current that will be classified in the next step. . . 104 5.15 Performance of the triggering algorithm that detects three suspicious events during
a recording of a HIF on wet sand provided by UFCG. . . 105 5.16 Procedure for extracting the suspicious-event current for the first trigger of the
recording: selected windows, subtracted currents and result. . . 105 5.17 Subtraction and extracted current for the second trigger of the recording. The
extracted current presents the typical characteristics of the HIFs. . . 106 5.18 Subtraction and extracted current for the third trigger of the recording. The ex-
tracted current is close to zero because it is the end of the fault. . . 106 5.19 Performance of the triggering algorithm that detects two suspicious events during
a recording of a HIF on dry sand provided by UFCG. . . 107 5.20 Subtraction and extracted current for the first trigger of the HIF recording on dry
sand. The extracted current presents the typical characteristics of the HIFs. . . 108 5.21 Subtraction and extracted current for the second trigger of the HIF recording on
dry sand. The extracted current presents the typical characteristics of the HIFs. . 108 5.22 Performance of the triggering algorithm that detects four suspicious events in a
recording created by superimposing a HIF laboratory test current on a residual current under normal conditions. . . 109 5.23 Subtraction and extracted current for the first trigger of a current obtained by su-
perimposition. The extracted current estimates the HIF current of a laboratory test on concrete and sand surface. . . 110 5.24 extract of the HIF laboratory test recording that is being estimated by the extraction
shown in Figure 5.23. . . 110
5.25 Subtraction and extracted current for the second trigger of a current obtained by superimposition. The extracted current estimates the HIF current of a laboratory test on concrete and sand surface. . . 112 5.26 Extract of the HIF laboratory test recording that is being estimated by the extrac-
tion shown in Figure 5.25. . . 112 5.27 Creating a residual current under other event conditions by superimposition, so
the triggering algorithm triggers for indicating the presence of a suspicious event. 113 5.28 Subtraction and extracted current for the first trigger of a connection of a load. The
extraction is evaluated by comparing the resulted signal with the superimposed original one. . . 114 5.29 Load current presenting a random behaviour that produces a trigger. The current
component causing the triggering cannot be extracted because the background current cannot be estimated. . . 115 5.30 Fault current recorded during the HIF tests in the laboratory of Siemens, showing
that it is possible to find HIFs with almost sinusoidal current. . . 116 6.1 Representation of a conventional two-class classifier, which defines two patterns
and a line separating them. . . 126 6.2 Representation of a one-class classifier, which distinguishes one class of objects
from all other possible object. . . 127 7.1 Window forPreprocesstask ofExplorer, indicating the way of loading the database,
the filters to choose, and the overview of the database and attributes. . . 137 7.2 Window forClassi f ytask ofExplorer, indicating the classifier methods to choose,
the test options, the output, and a list of the previous results. . . 137 7.3 Database of extracted currents inARFF format, the format used inWeka. . . 138 7.4 Histograms of some of the attributes, separating the classesHIFsandother events. 138 7.5 Window ofClassi f yshowing the list of classification methods inWeka. . . 139 7.6 Window ofClassi f yafter running the training and testing of the J48 decision tree
model, showing the generated outputs. . . 140 7.7 Decision tree obtained applying the J48 decision tree model to theHIF/Other event
classificationproblem. . . 142 7.8 Decision tree obtained applying the random tree model ofWekato theHIF/Other
event classificationproblem, producing a detection rate of 99% and a false alarm rate of 17.2%. . . 142
7.9 Decision rule obtained applying the Nearest-neighbour decision rule model of Wekato theHIF/Other event classificationproblem. . . 143 7.10 Extract of the program in Python describing how to transform theHIFs/Other events
database into the required format. . . 145 7.11 Extract of the program in Python that calculate the suitable values of the parameters.146 7.12 Output of the program regarding the suitable parameters and the demand to choose. 147 7.13 Extract of the program in Python that trains and tests the classifier, and obtains the
performance ratios. . . 148 8.1 Block diagram of the HIF detection method. . . 152 A.1 Data flow diagram of the process followed to design the detection method. . . 171 B.1 Structure of the working folder for updating the database and the classifier. . . 176 B.2 Triggers after computingExtract Isuspicious.mat for ‘New HIF1’. . . 177 B.3 Extraction after computingExtract Isuspicious.matfor ‘New HIF1’. . . 178 B.4 Triggers after computing Extract Isuspicious.mat for ‘New HIF2’; there is no
trigger. . . 179 B.5 Block diagram of the steps to follow for updating the database with new recordings. 180 B.6 Variables inclassi f ier.mat, output of the process of designing the classifier. . . . 181
1.1 Estimated HIF current levels in a 12.5kV Distribution Feeder. . . 16 2.1 Characteristics defining the HV/MV transformer of the HIF simulation model in
ATPDraw. . . 26 2.2 Description of the CT model used in the simulation. . . 27 2.3 Parameters used by Michalik, Rebizant and Lukowicz in their arc model [35]. . . 33 3.1 Measurements for calculating the drop of voltage atVg0 =100V, using different
values ofRlim. The angle ofVf ault is used as reference angle. . . 57 3.2 Conditions of the HIF tests performed in the High Power Testing Laboratory. . . 65 4.1 Characteristics of HIFs, indicators of HIFs and analysis techniques used for this
calculation. . . 73 5.1 Comparison of the values of the indicators of the original HIF current and the
estimated HIF current for the first trigger of the HIF recording on dry sand. . . . 111 5.2 Comparison of the values of the indicators of the original HIF current and the
estimated HIF current for the second trigger of the HIF recording on dry sand. . . 111 8.1 Definition and expressions of the variables used in the description of the HIF de-
tection method. . . 153
Framework of the Research
High Impedance Faults (HIFs) are undetectable by conventional protection technology under cer- tain conditions. These faults occur when an energized conductor makes undesired contact with a quasi-insulating object, such as a tree or a road. This contact restricts the level of the fault current to a very low value, from a few mA up to 75A [49]. In solidly grounded distribution networks where the value of the residual current under normal conditions is considerable, overcurrent de- vices do not protect against HIFs. However, such a protection is essential for guaranteeing public security, because of the possibility of reaching the fallen conductor and the risk of fire.
The interest and demand for HIF detection functions are basically limited to America. The working group B5.94 of Cigr´e 2009 [37] explained this by the assumption that some aspects of the American distribution system should be different, so the risk when producing HIF is higher.
In Europe, however, HIFs are not distinguished from other high resistance ground faults, given that most of them are detected by conventional neutral over-current protection. Certain features of the configuration of distribution networks determine the difficulty of the HIF detection; there- fore, the utilities have to evaluate the necessity of using specific HIF protection for each network.
The worst scenario for HIF detection is multiple-grounded distribution system using single-phase transformers. In this situation, which is very common in America, there is the clear need of using new and specific HIF detection functions.
Traditionally, the technology for detecting phase-to-ground faults in distribution networks is overcurrent protection. Abnormal situations create excess of current that could damage the system.
Overcurrent protection devices identify that unusual high level of current and indicate fault. Their performance is successful in case of low impedance faults, when fault current is important; but not in case of high impedance faults, when fault current can be lower than the triggering threshold of the overcurrent protection. In European distribution networks the residual current under normal conditions is nearly zero, so ground fault currents above 5A or 10A are interpreted as faults by the protection. However, the residual current in American distribution networks can reach 150A or
200A, so the current tap setting of the protection cannot be set below this value, and consequently HIF currents are not detected.
In the last two decades the concern for HIF has taken on importance. Despite the lack of doc- umentation, recordings, and experience, there has been a significant, but no enough, advance in knowledge regarding the HIF detection problem. Even some detection products have been com- mercialized and tested by utilities that recognized the need to address the problem. Nevertheless, the results seem to be not satisfying regarding the detection rate, the security and the reliabil- ity [37]. Hence the development of new functions able to efficiently detect HIF still remains a challenge; functions that are more and more a requirement regarding the protection of people, properties and animals.
State of the Art
Research has been conducted to describe the characteristics of HIFs and to develop detection meth- ods. The motive of the research is the same: to face the demand for HIF information and detection methods. But the resulting techniques are very diverse: mechanical and electrical solutions, using current or also voltage, etc.
Given that the final objective of this work is to implement the detection function in a protection device, this literature review is focused on the HIF detection methods which were commercialized.
They are grouped in two sections: the old solutions and the present solutions. The old solutions are not an option nowadays due to the out-of-date characteristics, the elevated cost or the poor detection rate. On the other hand, the present solutions are implemented in protection devices that are available in the market.
Old Solutions
• Pennsylvania Power and Light Co. (PP&L) and Westinghouse Advanced Systems Technol- ogy developedthe Ratio Ground Relay (RGR) in the 1980s [3]. The detection principle of this induction disk type electromechanical relay is based on the concept of ratio current de- tection. The device contains two main elements: the operating unit which uses the squared of the residual current, and the restraint unit which uses the difference between the square of the positive-sequence current and the square of the negative-sequence current. Some staged fault tests [9] concluded that the performance of the RGR does not present any advantage compared to overcurrent protection. The device detected the faults only if the fault current was higher than 70A and if the residual current under normal conditions was very low. In
• The Falgard device, invented by Irving Reedy, was commercialized in the mid 1980’s by Al- coa Conductor Accessories [49]. This mechanical device consists in a pendulum-mounted aluminum rod with hooked ends that is suspended from the neutral conductor. The de- vice catches and grounds a falling phase wire, causing a low impedance fault when a high impedance fault occurs. As a consequence, overcurrent protection operates and detects the fault. One of the main drawbacks of this method was the high probability of false alarms, due to, for example, wind or ice. Nowadays the product is not available in the market and the company does not work in this area anymore.
• Electric Safety Products, Inc. developed a mechanical detection system calledSafety Yoke [49]. TheSafety Yokeis a device mounted to a cross arm or pole whose aim is to produce solid ground faults by intercepting falling conductors with a grounded bus bar. The force of the descending conductor triggers the internal spring and ejects the bus bar to make contact with the fallen wire, creating a low impedance ground fault so operating the overcurrent protection. An important disadvantage of theSafety Yokeis the high cost of its installation and maintenance.
• The Kearney Company developed in the 1990sthe Open Conductor Detection system (OCD) [15]. The method of this electromechanical relay is based on the use of a transmitter to an- alyze the loss of potential in case of broken conductor. A loss of voltage on the line causes the transmitter to emit a specific frequency on the neutral system, so the reception of the signal at the substation indicates broken conductor. The system has been under test since 1992, but given that the radio transmitters have to be installed at the fuses of the houses, the cost of this method is extremely high. Besides that, the system is designed to cover only the HIFs that imply broken conductor, not down conductors or conductors contacting a tree, which are in fact the most usual HIFs.
• Nordon Technologies developed the methodHigh Impedance Fault Alarm System (HIFAS) [49] and put into service some units in the early 1990’s. The method of HIFAS measures the third harmonic current phase angle with respect to the fundamental voltage. The average ambient third harmonic current is calculated and accumulated, and when a fault occurs, the device evaluates the change of this current component. A high impedance fault is declared if the magnitude passes a threshold and if the angle matches a predetermined value. Nev- ertheless, even if the third harmonic is an indicator of HIF, it is not enough for a detection
method. Basing the decision on the third-harmonic is not secure, since lots of loads generate this harmonic and that may cause false detections. The Taiwan Power Company expressed this conclusion after performing field tests and comparative studies from 1977 until 1980 [9].
Present Solutions
• Texas A&M researchers spent two decades characterizing HIFs and developing an algorithm for detecting them, called the Digital Feeder Monitor (DFM) [44]. In the mid 1990’s, Gen- eral Electric commercialized the algorithm by incorporating it into the monitoring device F60 Feeder Management Relay [11]
The Digital Feeder Monitor (DFM) takes the detection decision after evaluating several al- gorithms that are executed using as inputs frequency components of the currents and energy magnitudes [38]. The frequency components are harmonic and non-harmonic currents be- tween 30 and 780 Hz. These values of currents and energy calculated are inputs to a system of 9 algorithms that determines the presence of arcing on the feeder. Other complementary functions are the detection of load loss, overcurrent conditions and persisting arcing. The general scheme of the method is shown in Figure 1. A description of each algorithm is as follows:
– Energy Algorithm: The Energy Algorithm monitors a specific set of non-fundamental frequency component energies of the phase and neutral currents. It establishes an aver- age value for the component energies, and indicates arcing in case of sudden and sus- tained increase in the value of that component. The DFM runs the Energy Algorithm on each phase current and neutral current, and for even harmonics, odd harmonics, and non-harmonics.
– Randomness Algorithm: The Randomness Algorithm identifies the moments when the energy magnitudes vary considerably from one half-cycle to the next. It monitors the same set of component energies as the Energy Algorithm, looking for a sudden increase in a component followed by highly erratic behaviour. If a suspicious event is detected, it is reported this to the Expert Arc Detector Algorithm.
– Expert Arc Detector Algorithm: The purpose of the Expert Arc Detector Algorithm is to assimilate the outputs of the Energy, Random and Load Event Detector Algorithms into one belief-in-arcing confidence level. The number of belief-in-arcing indications is counted, and certain pre-defined weights are assigned to each one.
– Load Event Detector Algorithm: The Load Event Detector Algorithm examines the RMS of the phase currents and the neutral current each two-cycles. Depending on that value the algorithm can indicate: 1) overcurrent condition, 2) precipitous loss of load, 3) high rate-of-change, 4) significant three-phase event, and 5) open condition. These flags are examined by the Load Analysis Algorithm.
– Load Analysis Algorithm: The Load Analysis Algorithm aims to differentiate between arcing broken conductors and arcing intact conductors by looking for precipitous loss of load and/or overcurrent disturbance at the beginning of an arcing episode. The presence of arcing on the system is given by the output of the Expert Arc Detector Algorithm. This algorithm also attempts to determine the phase on which the HIF exists. If there was a significant loss of load or an overcurrent condition on only one of the phases, that phase is identified as the faulty phase.
– Load Extraction Algorithm: The Load Extraction Algorithm is designed to find a qui- escent period during an arcing fault, so the background neutral current corresponding to normal load can be determined. If this search is successful, it then removes the as- sumed background load from the total measured neutral current, resulting in a signal
which would consist only of the fault component of the neutral current. This informa- tion is provided as input to the Arc Burst Pattern Analysis Algorithm.
– Arc Burst Pattern Analysis Algorithm: If the Load Analysis Algorithm fails in identi- fying the faulty phase, the Arc Burst Pattern Analysis Algorithm tries to provide this information. The algorithm uses for this objective the correlation between the fault component of the neutral current (received from the Load Extraction Algorithm) and the feeder.
– Spectral Analysis Algorithm: The Spectral Analysis Algorithm analyzes the non- harmonic components of the neutral current and correlates the shape of their spectrum to an ideal arcing spectrum. A high correlation provides confirmation of the DFM’s belief in arcing on the power system.
– Arcing Suspected Identifier Algorithm: The purpose of the Arcing Suspected Identi- fier Algorithm is to detect multiple, sporadic arcing events. Only if these events are cumulatively, the HIF alarm is activated.
Once the method was implemented in a device it was tested by some utilities. The best documented test campaign was the one carried out by Potomac Electric Power Company (Pepco). Pepco installed several devices on 280 feeders and studied the performance of the technology developed by Texas A&M University over a period of two years. As a result they obtained a detection rate of 58%, so they suggested that some areas needed potential improvement.
• The Arc Sense Technology is the HIF detection technology commercialized by Schweitzer Engineering Laboratories (SEL) since 2005, implemented in the device SEL-451 Protection, Automation and Control system [47]. The method was described and patented under the name of The Sum of Difference Current (SDI) [27]. The block diagram in Figure 2 presents the structure of the method, which is described in the next paragraphs block per block [26].
– The first block calculates the magnitude of Sum of Difference Current (SDI), which is used as input to the HIF detection algorithm. The system uses a one-cycle differ- ence filter to calculate difference current (DIk) and obtains SDI by accumulating the absolute values of the DIk during a cycles, shown in Figure 3. For ideal sinusoidal waveforms SDI would be zero; so magnitudes different to zero reveals random situa- tions, possibly a HIF. The SDI magnitude can be interpreted as the average measure of the total non-harmonic content and the changes in the harmonic component amplitude over a window of a cycles.
Figure 3:Calculation of the magnitude Sum of Difference Current (SDI)
– Other block is the Infinite-Impulse-Response (IIR) Limiting Averager, aimed to es- tablish a reference for SDI. The output magnitude SDI REFk is adaptive, adjusting its value depending on the present SDI, the last value of SDI REF, and certain parameters.
– The Trending and Memory block calculates the ratio between SDI and SDI REF, and in the case it overpasses a predefined threshold, this ratio and the time is memorized.
The objective is to identify unusual SDI changes. The information of how often and by how much SDI departs from SDI REF plus a margin is transferred to the decision logic.
– The Adaptive Tuning block tries to estimate the feeder background noise during nor- mal system operations and determine the margin above the SDI that set the limit of
normal operation. This adaptive margin is the threshold used in the Trending and Memory block and it is also used by the IIR Limiting Averager.
– The Decision Logic uses the results from the Trending and Memory block to determine the existence of HIF on the processed phase (the algorithm runs for each phase current and the residual current). This decision logic uses relatively simple comparators and counters; separating HIF alarms decision from trip decision.
– Apart from the functions themselves, there is the blocking condition block. It detects system conditions that discard the possibility of HIF. In other words, it creates an output that disables the decision logic and breaks the operation of some of the blocks.
SEL claims that the real field tests performed to evaluate this technology provided results that guarantee the security. However, the utilities that carried out testing campaign declared to prefer not to operate this function of the protection device [37]. One remark of the func- tion is that there are no settings, meaning that the detection method has empirical basis more than theoretical basis, which makes it less robust against new events.
• The high impedance fault detection system developed by ABB U.S.A is HIF DetectTM.
ABB teamed up with Lafayette College in Easton, Pennsylvania, for carrying out the re- search and performing detection tests. Having completed the laboratory and field tests, the technology HIF Detect TM was implemented in the protection and control devices ABB REF 550, REF 615, and REF 630 [2].
The HIF DetectTM detection technique [14] is based on a multi algorithm approach that works using the 4 currents of the system (3 phase and the neutral) and harmonic and non- harmonic current components. After acquiring the signals, a filter removes the 5th, 6th and 7th harmonic components due to the belief that they contribute to erroneous HIF detection.
Then, three individual detection algorithms execute, and the detection logic processes their outputs to provide the final decision. The diagram in Figure 4 was published by ABB for describing the detection method. As seen, the description is not detailed so it was not possible to fully understand how the method works.
The ABB HIF DetectTM algorithm has been tested between 1998 and 2000 [4] using data generated at the laboratory, obtaining a detection rate around 80%. However, when it was tested with HIF field data in 2002, they realized the need of modifying the algorithm to get an acceptable performance. After adding the modifications to the technology, they claimed to have had encouraging results. The published rates are 86.6% as detection rate and 17.06%
as false alarm rate, in absence of welding loads. It is possible to get different values by changing the thresholds, but, obviously, improving one rate is at the expense of the other.
While important progress has been made, a complete solution to the HIF detection problem does not exist at the moment. It is essential that utilities become aware of this necessity and take steps to address the problem.
Aim of the Work
This thesis is a deep and complete study of High Impedance Faults directed to explain the char- acteristics of these faults, analyze the detection problem, describe a methodology to develop a detection function, and present a solution.
The question tried to be solved is ’How can we be protected against HIFs?’ One of the mo- tivations of this research is the hazard produced when a conductor falls on the ground or a tree contacts a line; or in other words, when a HIF occurs. This work has the last objective of devel- oping a detection method that could be implemented in the present protection technology. The process followed for reaching the objective of developing the detection method consists of a series of tasks that are important goals themselves. These tasks are:
• Describe the HIFs in different scenarios, indicating the detection technology used in each situation,
• Explain the necessity of designing a modern detection function different to the traditional ones
• Present a methodology for building a detection algorithm based on an extensive study of a fault and on a reliable database, and
• Apply the methodology to the HIF detection problem.
Structure of the Document
The document is organized in 8 chapters. The four first are focused in the characterization of HIFs, and the last ones deal with the development of the detection method. The outline of the thesis is as follows:
Part I: HIF Characterization
Chapter 1 defines the high impedance faults and explains the reason why the difficulty of the detection is different depending on the configuration of the network. The problem to solve and the restrictions it is subject to are presented.
Chapter 2 provides a first basic study of the fault by simulation. Even if the simulation is not enough for representing all the characteristics of the fault, it allows us to observe the influence of some factors in the fault current.
Given that simulation is not the most adequate tool for analyzing these faults, the study is complemented by experimental work. Chapter 3 describes the planning of the tests, the laboratory setup, and the performance of HIFs. It concludes discussing the results and future use of the obtained recordings.
Chapter 4 details how to extract useful information from recordings of HIFs. This informa- tion is a list of indicators, magnitudes that reveal distinguishing features of HIFs after analyzing a current. The features able to accurately describe the fault and distinguish it from other events are used for the HIF characterization.
Part II: HIF Detection
Chapter 5 introduces the concept of Knowledge Discovery in Databases (KDD), the process of computing sciences for turning data into knowledge. The application of KDD to our database allows discovering patterns that are highly convenient for developing a detection method. Conse- quently, the HIF detection problem is analyzed following the procedure of KDD.
Chapter 6 is dedicated to explain the proposed detection method: the inputs, the calculation of the decision magnitudes, and the criteria which give the information needed for the decision. A
One of the main blocks of the detection method is the classifier, obtained with machine learn- ing. Chapter 7 is focused on the application of machine learning to the problem of classifying HIF and other events.
Finally, Chapter 8 makes the detail and practical description of the algorithm, dedicating one section to the settings of the method and the default and future values. In Conclusions, the author enhances the contribution of this dissertation to the modernization of electrical protections.
Publications
The publications made during the research are numerous internal reports ULB-Siemens, three papers submitted and admitted in international conferences and a patent of the method, property of Siemens. The references are indicated below.
• Internal reports:
– A.Valero,High Impedance Fault Detection: State of the Art, Internal Report ULB/Siemens, December 2008.
– A.Valero,High Impedance Fault Detection: Development of an EMTP/ATP simulation model, Internal Report ULB/Siemens, June 2009.
– A.Valero, High Impedance Fault Test in the High Voltage Laboratory of the ULB, Internal Report ULB/Siemens, June 2009.
– A.Valero,Recordings of Critical Loads, Internal Report ULB/Siemens, June 2009.
– A.Valero,Towards a High Impedance Fault Detection Algorithm, Internal Report ULB/Siemens, February 2011.
– A.Valero,Load Recordings for Studying the Operational Neutral Current in America, Internal Report ULB/Siemens, April 2011.
– A.Valero,Indicators and Characterization of High Impedance Faults, Internal Report ULB/Siemens, July 2011.
– A.Valero,Critical Residual Current for High Impedance Fault Detection, Internal Re- port ULB/Siemens, August 2011.
– A.Valero,Data Mining for High Impedance Fault Detection using Weka, Internal Re- port ULB/Siemens, October 2011.
– A.Valero, Facing the Difficulties at Projecting the High Impedance Fault Detection Algorithm, Internal Report ULB/Siemens, October 2011.
– A.Valero,Triggering Method and Extraction of the Fault Current Component for High Impedance Fault Detection, Internal Report ULB/Siemens, December 2011.
– A.Valero, Evaluation of the Proposed Method for Extracting the Suspicious-event- current Component, Internal Report ULB/Siemens, January 2012.
– A.Valero,Envisaging the HIF Detection Method, Internal Report ULB/Siemens, April 2012.
– A.Valero,Implementation of the Classifier HIFs Other events, Internal Report ULB/
Siemens, June 2012.
• Papers submitted and accepted in conferences:
– A.Valero, J-C Maun, S. Werben, Methodology to Describe High Impedance Faults in Solidly Grounded Networks, CIRED 21th International Conference on Electricity Distribution, Frankfurt, June 2011.
– A.Valero, J-C Maun, S. Werben,Characterization of High Impedance Faults in Solidly Grounded Distribution Networks, 17th Power Systems Computation Conference, Stock- holm, August 2011
– A.Valero, S. Werben, J-C Maun,Incorporation of Data-Mining in Protection Technol- ogy for High Impedance Fault Detection, 2012 IEEE Power & Energy Society General Meeting, San Diego, July 2012.
• Patent:
– A.Valero Masa, J-C. Maun,Detection of High Impendance Faults, International appli- cation number PCT/EP2012/067838, Europaisches Patentant.
CHARACTERIZATION OF HIGH
IMPEDANCE FAULTS
Description and Approach of the High Impedance Fault Detection Problem
1.1 Description of High Impedance Faults (HIFs)
High impedance faults (HIFs) happen when an energized conductor of the distribution network falls on the ground, making unwanted electrical contact with a road, a sidewalk, or some other surface with high resistive value. HIFs can also be caused by trees growing near lines, whose branches might get in contact to an energized conductor.
This contact restricts the flow of fault current to a level below the one detectable by conven- tional overcurrent devices [49]. As a consequence, the energized conductor on the ground surface can pose public danger, as well as risk of fire due to the probable arc ignition. The damage de- rived from HIF concerns people, animals, and properties rather than electrical equipment of the network.
Typically, HIFs are described by the characteristics of the fault current that they produce.
Those characteristics are listed below:
• The current level is low and depends mainly on the surface of contact. Some reference values published [49] by the Power System Relaying Committee of IEEE PES are shown in Table 1.1.
• The great majority of HIFs occurs on a single phase. This is determined by the causes that produce HIFs.
• The current exhibits random behavior with unstable and unpredictable fluctuations in the amplitude.
Table 1.1:Estimated HIF current levels in a 12.5kV Distribution Feeder.
Surface Current (A)
Dry asphalt ≈0
Concrete non reinforced ≈0
Dry sand ≈0
Wet sand 15
Dry soil 20
Dry grass 25
Wet sod 40
Wet grass 50
Concrete reinforced 75
• They are usually intermittent, since the contact between the conductor and the quasi-insulated surface is hardly ever constant.
• The sinusoid is distorted by the presence of electric arc. The electric arc implies production of harmonic components, especially important is the 3rd harmonic; and very often, asym- metry in the cycle.
• The fault is dynamic as a result of the changing resistivity of the surface of contact. A representative example is the build-up (progressive increase of current until reaching steady- state) and the shoulder (interruption of the build-up increase for a few cycles) of some HIF currents.
Figure 1.1:HIF current obtained by laboratory test in the MV Testing laboratory of Siemens present- ing several typical characteristics of HIFs.
Several of the mentioned characteristics are illustrated in the HIF current in Figure 1.1, ob- tained via laboratory test. The laboratory tests are presented and described in Chapter 3.
1.2 Difficulties in Detecting HIFs and Influence of the Network Con- figuration
The difficulty of detecting HIFs is determined by the configuration of the distribution network and by the loads connected to the system. Hence in some countries and under some circumstances, HIF detection is a considerable concern, while in others it remains a secondary issue.
Detecting electrical faults implies to extract information that reveals presence of fault from some electrical signal. The difficulty in detecting faults remains in how easy is extracting that information. Regarding HIFs, the crucial task is to recognize relevant characteristics of HIFs in the residual current. The more unfavorable cases for the HIF detection are those where the residual current contains several components and the HIF component is minimal, or where the residual current under normal conditions presents characteristics similar to HIFs.
Before describing in detail the detection problem, it is necessary to make the distinction be- tween two scenarios: America and Europe [34]. The configuration of the distribution networks shows important differences. Thus, the HIF detection problem has to be considered under different conditions.
1.2.1 Detection of HIFs in American Distribution Networks
Most of distribution networks in Canada, USA and Latin America are solidly multi-grounded, meaning that all system and load neutrals are connected to ground without any intentional impedance between ground and the neutral. In case of high impedance phase-to-ground fault, the neutral of the system is not displaced and the residual voltage is imperceptible. Therefore, the detection is based on the current.
The complication arises from the lack of sensitivity of the overcurrent ground fault protection.
The level of residual current created by load unbalance and stray currents is usually higher than the level of residual current produced by HIFs. The setting of the protection is fixed above the residual current under normal conditions, so the sensitivity is not enough to protect against HIFs.
The use of single-phase distribution transformers is extended in rural and residential areas.
As a consequence of this practice, the load unbalance in the three phases creates a return current in the primary distribution system. The return current has an amplitude equal to the sum of the
Figure 1.2: Solidly multi-grounded 4-wire distribution system using single-phase distribution trans- formers (typical in America).
three-phase currents but inverse direction, and it flows through the neutral conductor (Ineutral) and the earth (Istray). Figure 1.2 illustrates the flux of the return current and how stray currents are driven into the earth each time that the neutral is grounded.
In multi-grounded systems there are numerous neutral connections creating paths for stray currents, thus the current flows uncontrolled with an important magnitude [34]. The attention to the study of stray currents and stray voltages has increased in the last years due to evident technical and legal reasons. One of the conclusions is that the neutral conductors are not always at zero- potential with respect to the earth [30] [34]. As a result, the sensitivity of the overcurrent neutral protection units is limited. HIFs whose current is lower than the residual current present in the network under normal conditions cannot be detected using the overcurrent function.
1.2.2 Detection of HIFs in European Distribution Networks
In Europe, several methods are used for grounding the distribution networks; the most common are low impedance grounding (resistance or reactor), resonant grounding, and isolated neutral.
These grounding systems combined with the use of delta/wye three-phase transformers to supply the loads make HIF detection easily achievable.
The method for detecting phase-to-ground faults in low impedance grounded networks is the same that the one used in multi-grounded networks, with the difference that the sensitivity is 10 to 50 times better. Figure 1.3 illustrate a solidly distribution system using a transformer delta/wye for supplying single and three-phase loads. The only residual current under normal conditions in European distribution networks is the capacitive neutral current, which is less than 1A. Conse-
quently, overcurrent neutral protection units can be set to detect faults above 5 or 10A, covering most of HIFs. In resonant networks the detection is done by wattmetric method or incremental conductance method. The sensitivity of the incremental conductance method is good, allowing detecting small fault currents. The case of isolated networks is similar; the detection of single- phase-to-ground faults is also based on the use of the zero-sequence voltage and current, being a method enough sensitive for detecting a great number of HIFs.
Figure 1.3: Solidly grounded distribution system in Europe, using three-phase big transformer for supplying the loads.
As conclusion, European distribution systems are much more favourable regarding the HIF detection than American ones, since phase-to-ground fault detection methods are sensitive enough to detect low fault currents. The typical configuration of distribution system in America is the worst scenario for HIF detection. The significant level of residual current under normal conditions prevents overcurrent protection from being able to detect low fault currents, such as HIF currents.
Therefore, the major problem related to HIF is the detection in multi-grounded networks, or in other words, doing a generalization, HIF detection in American distribution networks. This is the reason why, historically, HIF research has been located in America.
1.3 Approaching the High Impedance Fault detection Problem
The general lack of knowledge of HIFs is the first difficulty for developing a practical detection method. Consequently, the initial stage of our study is aimed to understand and explain the HIF
detection problem.
HIF research is not yet a well-known subject. For this reason it is needed to accomplish some stages before being capable of developing a detection method. The three stages of the procedure that we propose to characterize HIFs (represented in Figure 1.4) are:
1. Learning about HIFs, identifying the difficulties of the detection and reviewing the typical characteristics.
2. Collecting HIF current recordings to conduct a study of its characteristics. Simulation, laboratory tests, and requesting field data are the three practices for collecting recordings.
3. Analysing and processing the mentioned database in order to develop a list of indicators able to characterize HIFs.
Figure 1.4:Steps for approaching the HIF detection problem: learning about the faults and character- izing them.
1.3.1 Learning about HIFs: Identifying the Difficulties of the Detection and the Typical Characteristics of the Fault
As it was explained, the difficulty of the HIF detection depends on the configuration of the dis- tribution network. The most unfavourable case, and obviously, where HIF detection methods are demanded, is multi-grounded networks using single-phase distribution transformers. The acquisi- tion of the required prior knowledge for addressing the problem is complete after learning about the fault by studying the state-of-the-art and assimilating the complexity of the detection.
1.3.2 Collecting HIF Currents by Simulation, Laboratory Tests and Field Recordings
Given that HIFs are rarely recorded, a simulation model and a laboratory test procedure were developed to obtain the needed data. Iberdrola Distribucion Electrica SAUand theFederal Uni-
versity of Campina Grande (UFCG) teamed up withEnergisa provided us with recordings and information that have been used to validate and complete the present HIF database.
a) Simulation
We use the simulation program Alternative Transients Program (ATP) [40] to model HIFs in solidly grounded distribution networks. The model is constituted by the network components (generator, power transformer, distribution lines and cables, and saturable current transformers) and by the HIF resistance.
The assumptions of the HIF model are the presence of electrical arc in the fault and a constant value of the contact surface resistance (Rcs). The presence of arc is expected due to the imperfect contact between the conductor and the surface. The assumption of a constantRcsis, however, a simplification.
By adjusting the parameters, the model is capable of describing the arcing component of a given HIF. Nevertheless, the contact surface resistance at the beginning of a HIF is highly random and dynamic. This aspect avoided to get a suitable mathematical or simulation model. Even if the model is limited concerning the dynamic of the fault, it allows studying the effect of the arc in the fault current. The aim of the simulation is therefore, not to generate a database where to base the research, but to study particular factors of HIFs. Experimental tools, such as laboratory tests and fault recordings, were used for fitting parameters of the model and validating the results.
b) Laboratory Tests
Given the limitation of the simulations regarding the dynamic and randomness of HIFs, the next step to obtain recordings was to perform laboratory tests. The material of the contact surface, its moisture content, or the arc length are some of the factors that experimental tests aim to study.
The first obstacle for performing HIF tests is that there is no standard that guides the experi- ments, so the procedure had to be designed and validated by an independent agency of inspection and certification. First tests were staged in the High Voltage Laboratory of theULB, and later in the High Power Testing Laboratory ofSiemens.
The test results display the influence of the most significant factors on the fault current.
Through processing and analyzing the obtained currents, the information and magnitudes that make possible the HIF characterization are extracted.
c) Real Fault Recordings
The optimal practice for collecting recordings is to access field recording databases or to per- form faults in the operating network. Iberdrola Distribuci´on El´ectrica SAU (Spain) provided us with some currents of detected HIFs. Besides, the contact with theFederal University of Campina Grande (UFCG)resulted in an interchange of data, so we received current recordings from HIF test performed in a multi-grounded network of Energisa (Brasil). These recordings allow us to validate the results of the laboratory tests and to increase and reinforce the HIF database.
1.3.3 Analyzing and Processing the Database: HIF Characterization
Studying the database we identified the most distinctive HIF current characteristics, such as highly dynamic behaviour, waveform asymmetry, and presence of arc. Applying suitable analysis tech- niques such as Fast Fourier Transform or difference cycle by cycle, we could extract information from the HIF currents and define a list of 13 magnitudes able to represent the distinctive charac- teristics. The magnitudes of the list contain the needed information that the detection method will use for taking the decision whether indicating HIF or not.
After learning about HIFs, collecting HIF currents, and performing the analysis and data pro- cessing, the problem of the HIF detection will be accurately formulated and all the elements needed for developing a detection method will be at our disposal. Part I of this thesis disser- tation leads us to this point. Then, once the problem is approached, the research is focused on developing a detection method, as will be described in detail in Part II.
Simulation of High Impedance Faults
2.1 Introduction to the Simulation of High Impedance Faults
We use the simulation program Alternative Transients Program [1] to model HIFs in solidly grounded distribution networks. Our model comprises the network components (generator, power transformer, distribution lines and cables, and current transformers) and an estimated HIF resis- tance.
The model is capable of describing the arcing component of a given HIF by adjusting the model parameters. Nevertheless, in our opinion, the dominant component of HIFs is the variable resistance of the contact surface, whose behaviour is not ruled by a known equation. For that reason, the objective of the simulation is limited to the arc effect study. This chapter contains the description of the simulation model, a study of the factors influencing the arc, and the conclusions drawn from the simulation regarding the study of HIFs.
2.2 Limitation and Objective of the Simulation
Alternative Transients Program / Electro-Magnetic Transients Program (ATP/EMTP)is the soft- ware used for designing the HIF model used in the simulation step. This simulation program is capable of accurately modeling lines and cables and non-linear elements, such as saturable transformers and electric arcs [13]. Despite the capabilities of the software, simulating HIF has limitations related to the random nature of the fault; its random behaviour has no accurate model.
Therefore, the aim of the simulation is not to reproduce currents that can be used for HIF pattern recognition, but to study particular factors of the fault.
The proposed HIF model accepts two assumptions: arc ignition at the fault point and constant resistance of the contact surface. The first assumption is explained by the fact that the contact
