Disaggregation of Electrical Appliances using Non-Intrusive Load Monitoring

162 Pages·2016·11.35 MB·English

by  Thomas Bier

Disaggregation of Electrical Appliances using
Non-Intrusive Load Monitoring
Thomas Bier
To cite this version:
Thomas Bier. Disaggregation of Electrical Appliances using Non-Intrusive Load Monitoring. Other.
Université de Haute Alsace - Mulhouse, 2014. English. ￿NNT: 2014MULH8860￿. ￿tel-01314432￿
HAL Id: tel-01314432
https://theses.hal.science/tel-01314432
Submitted on 11 May 2016
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Année 2014
Université de Haute-Alsace, Mulhouse
École Doctorale Jean-Henri Lambert
Laboratoire MIPS
Thèse
présentée par
Thomas Bier
pour obtenir le grade de
Docteur de l’Université de Haute-Alsace
Discipline : Électronique, Électrotechnique et Automatique
Disaggregation of Electrical Appliances
using Non-Intrusive Load Monitoring
(Arrêté Ministériel du 30 mars 1992)
Soutenue publiquement le 17 décembre 2014 devant le jury composé de:
Pr. Yassine Ruichek Université de Technologie de Belfort-Montbéliard Rapporteur
Pr. Michel Paindavoine Université de Bourgogne Rapporteur
Pr. Alain Dieterlen Université de Haute-Alsace Examinateur
Pr. Dirk Benyoucef University of Furtwangen Examinateur
Dr. Djaffar Ould-Abdeslam Université de Haute-Alsace Co-encadrant
Pr. Jean Mercklé Université de Haute-Alsace Directeur
Pr. Pierre Raymond European Laboratory for Sensory Intelligence Invité

Acknowledgment
A PhD thesis cannot be performed without the help of many people. During my
PhD, many people have contributed to the success of this. Especially some people be
thanked here.
First,Iwouldliketothankmysupervisor,Prof. JeanMercklefortheirexcellentadvice
and constant support. His experience in the training of graduate students helped me
a lot.
I acknowledge my advisor Prof. Djaffar Ould-Abdeslam from TROP for the assistance
and guidance. His administrative support has simplified much.
A special thanks goes to Prof. Dirk Benyocuef for his support and motivation. Since
the beginning of my studies, he has always given me constructive approaches.
IwouldalsoliketogivethankstomylabcolleaguesfromReSPinGermanyandTROP
in France, especially to my fellow student Philipp Klein.
I would like to thank my parents for their unremitting support. Since I have started
to study electrical engineering they stay by my side.
Finally, I would like to acknowledge the financial support provided by the state of
Baden-Wuerttemberg through the Smart Metering projects, without which my PhD
would not have been possible.
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Acronyms
ADALINE ADAptive LInear NEuron
ANN Artificial Neural Network
AWGN Additive white Gaussian Noise
ED Event Detection
FP False Positives
FPR False Positive Rate
FT Fourier Transformation
IALM Intrusive Appliance Load Monitoring
MLP Multi Layer Perceptron
MS Measurement System
MSE Mean Squared Error
NALM Non-Intrusive Appliance Load Monitoring
NILM Non-Intrusive Load Monitoring
PDF Probability Density Function
PDS Power Density Spectrum
PPV Positive Predicted Value
SNR Signal to Noise Ratio
ST S-Transformation
STFT Short-Time Fourier Transformation
TP True Positives
TPR True Positive Rate
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Contents
1 Introduction 1
1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Systems, Approaches and Applications . . . . . . . . . . . . . . . . . . 2
1.3 Research of the ReSP . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Problem Statement in the Thesis . . . . . . . . . . . . . . . . . . . . . 6
1.5 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Pattern Recognition for NALM 9
2.1 General Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Types of Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Evaluation of Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.2 Statistical Quality Criteria. . . . . . . . . . . . . . . . . . . . . 12
2.3.2.1 Recall, True Positive Rate . . . . . . . . . . . . . . . . 13
2.3.2.2 Precision, Positive Predicted Value . . . . . . . . . . . 13
2.3.2.3 Combined Measures . . . . . . . . . . . . . . . . . . . 13
2.3.3 Determining a Rate . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Time Frequency Analyze . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4.1 Fourier Transformation . . . . . . . . . . . . . . . . . . . . . . 14
2.4.2 Short-Time Fourier Transformation . . . . . . . . . . . . . . . . 15
2.4.2.1 Transformation . . . . . . . . . . . . . . . . . . . . . . 15
2.4.2.2 Power Density Spectrum . . . . . . . . . . . . . . . . 16
2.4.3 S-Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 State of Art for NALM 19
3.1 State of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 Steady State . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Transient State . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.3 Newer Approaches for NALM Systems . . . . . . . . . . . . . . 24
3.2 State of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 Analyse of the Signals for NALM 27
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2.1 Appliance Model . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2.2 Normalization of the Signals P, Q and S . . . . . . . . . . . . . 32
4.3 Real Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3.1 Additive White Gaussian Noise . . . . . . . . . . . . . . . . . . 34
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4.3.2 Signal to Noise Ratio . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3.3 Quantization Error . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.4 Measurement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.4.1 Existing Databases . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.4.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.4.3 3-Phase Measurement System . . . . . . . . . . . . . . . . . . . 38
4.4.3.1 Measurement Boxes . . . . . . . . . . . . . . . . . . . 38
4.4.3.2 Switching and Detection Box . . . . . . . . . . . . . . 44
4.4.4 One-Phase Measurement System . . . . . . . . . . . . . . . . . 45
4.4.5 Validation of the Measurements . . . . . . . . . . . . . . . . . . 45
4.5 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.5.1 Appliances in Residential Buildings . . . . . . . . . . . . . . . . 47
4.5.2 Own Measurements . . . . . . . . . . . . . . . . . . . . . . . . 48
4.5.2.1 One-Phase Measurements Stand Alone. . . . . . . . . 48
4.5.2.2 One-Phase Measurements Simulation . . . . . . . . . 49
4.5.2.3 Measurements in Residential Buildings. . . . . . . . . 50
4.6 Analysis of the Appliance Signals . . . . . . . . . . . . . . . . . . . . . 50
4.6.1 Transient Response . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.6.2 P, Q and S of the Appliances . . . . . . . . . . . . . . . . . . . 52
4.6.3 Steady State Behavior of Automates . . . . . . . . . . . . . . . 52
4.7 Filtering of the Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5 Event Detection and Detector 59
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.4 Event Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 68
5.4.1 General Structure of the Event Detector . . . . . . . . . . . . . 68
5.4.2 High-Pass Filter for the Event Detection . . . . . . . . . . . . . 69
5.4.2.1 Approach of Hart . . . . . . . . . . . . . . . . . . . . 69
5.4.2.2 High-Pass Filters from Image Processing . . . . . . . 70
5.4.2.3 The Gradient Operators . . . . . . . . . . . . . . . . . 71
5.4.2.4 Laplace-Operator . . . . . . . . . . . . . . . . . . . . 73
5.4.2.5 Simulation of the High-Pass Filters . . . . . . . . . . . 74
5.4.3 Complex Filter Structure . . . . . . . . . . . . . . . . . . . . . 76
5.4.3.1 Approach of Canny . . . . . . . . . . . . . . . . . . . 76
5.4.3.2 Improvement of Canny Filter by Perona . . . . . . . . 78
5.4.3.3 Simulation of Peronas Filter . . . . . . . . . . . . . . 81
5.4.4 Short Time Fourier Transformation . . . . . . . . . . . . . . . . 83
5.4.4.1 Simulation of the STFT . . . . . . . . . . . . . . . . . 84
5.4.5 Classification Function . . . . . . . . . . . . . . . . . . . . . . . 87
5.4.5.1 Threshold for the Classifier . . . . . . . . . . . . . . . 88
5.4.5.2 Simulation of the Classifier . . . . . . . . . . . . . . . 89
5.5 Results for the Event Detection . . . . . . . . . . . . . . . . . . . . . . 90
5.5.1 Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . 90
5.5.2 Best Signal for the Event Detection . . . . . . . . . . . . . . . . 91
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5.5.3 Results of the Event Detection Methods . . . . . . . . . . . . . 91
5.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
6 Classification 97
6.1 General Description of Neural Networks . . . . . . . . . . . . . . . . . 98
6.1.1 Biological Systems . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.1.2 Historical Overview . . . . . . . . . . . . . . . . . . . . . . . . 99
6.1.3 Applications for Artificial Neural Networks . . . . . . . . . . . 100
6.2 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.2.1 Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.2.2 Transfer Function . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.2.3 Structure of Networks . . . . . . . . . . . . . . . . . . . . . . . 102
6.2.4 Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 104
6.2.4.1 Learning Rule for the Perceptron . . . . . . . . . . . . 105
6.2.4.2 Learning Rule for the MLP . . . . . . . . . . . . . . . 105
6.2.5 Design of a MLP . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.2.6 ADALINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2.6.1 Structure of an ADALINE . . . . . . . . . . . . . . . 107
6.2.6.2 Learning Rule ADALINE . . . . . . . . . . . . . . . . 108
6.3 ADALINE for the Classification . . . . . . . . . . . . . . . . . . . . . . 109
6.3.1 ADALINE for Parameter Estimation . . . . . . . . . . . . . . . 109
6.3.2 Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
6.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.4 MLP for the Classification . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.4.1 Features for the Classification . . . . . . . . . . . . . . . . . . . 115
6.4.1.1 Train the MLP . . . . . . . . . . . . . . . . . . . . . . 116
6.4.2 Results of the Classification Method . . . . . . . . . . . . . . . 116
6.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7 Conclusion and Perspectives 121
A Measurements and Measurement System 127
A.1 Signals for NALM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
A.2 Measurements in the Laboratory . . . . . . . . . . . . . . . . . . . . . 129
A.3 Measurements in Residential Buildings . . . . . . . . . . . . . . . . . . 131
A.4 Harmonics of the Current . . . . . . . . . . . . . . . . . . . . . . . . . 132
A.5 Transient Responses of Appliances . . . . . . . . . . . . . . . . . . . . 134
List of Figures 137
List of Tables 143
B Bibliography 145
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