Forecasting Active and Reactive Power at a Substation Transformer in Distribution Network

ABSTRACT This work addressed the problem of forecasting active and reactive power at a substation transformer in a distribution system. Accurate power forecast is of great importance in power distribution planning, reactive power support control and intelligent power management. Due to the complexity of the power system, an intelligent and adaptive forecast algorithm based on the Adaptive Neuro-fuzzy Inference System (ANFIS) was modeled for the power forecast. For the proposed ANFIS forecast model training and validation, historical data of active and reactive power from the Abakpa Enugu Nigeria distribution network was used. The case study power system is modeled in MATLAB SIMULINK with the proposed neuro-fuzzy forecast model integrated. Simulation is carried out to obtain the time series of one hour ahead and three hour ahead forecast of the active and reactive power. Graphical output shows that the forecasted active and reactive power time series follow the signal profile of the actual (measured) system active and reactive power. The evaluation of coefficient of multiple determination was used to determine the accuracy of the forecast model. Result evaluation carried out determined the coefficient of determination to be 0.98 and 0.72 for the one hour ahead and the three hour ahead active power forecast respectively. Similarly, the one hour ahead and three hour ahead reactive power forecast gave 0.82 and 0.71 respectively. For the one year ahead (long term) forecast obtained, the coefficients of multiple determination are 0.54 and 0.62 for active and reactive power respectively. The results indicate very strong degree of correlation between the actual power time series and the forecasted time series. However these values show that the near real-time forecast of one hour ahead and three hour ahead, are more accurate than the long term forecast. This shows the high degree of accuracy of the proposed neuro-fuzzy forecast model.

TABLE OF CONTENTS

TITLE PAGE i

CERTIFICATION ii

APPROVAL iii

DEDICATION iv

ACKNOWLEDGEMENTS v

ABSTRACT vi

TABLE OF CONTENTS vii

LIST OF TABLES ix

LIST OF FIGURES x

LIST OF ABBREVIATIONS xi

CHAPTER ONE: Introduction 1

1.0 BACKGROUND OF THE STUDY 1

1.2 STATEMENT OF THE PROBLEM 3

1.3 OBJECTIVES OF THE STUDY 4

1.4 SIGNIFICANCE OF THE STUDY

1.5 SCOPE OF THE STUDY

5

6

CHAPTER TWO: Literature Review 7

2.0. REVIEW OF RELATED LITERATURE 7

2.1. ELECTRICITY DEMAND FORECASTING 7

2.2. POWER FORECASTING IN THE PRESENCE OF ACTIVE DEMAND 8

2.3. ECONOMIC DISPATCH AND POWER FORECASTING 12

2.4. FORECASTING AND VAR PLANNING 14

CHAPTER THREE: Methodology And System Design 15

3.0. METHODOLOGY AND SYSTEM DESIGN 15

3.1. METHODOLOGY 15

3.2. DESIGN OF THE ACTIVE POWER FORECASTING MODEL 16

3.3. DESIGN OF THE REACTIVE POWER FORECASTING MODEL 23

3.4. THE POWER FORECAST PROCESSING FLOW

3.5. NEURAL NETWORK FOR LONG TERM NON REAL-TIME FORECAST

26

29

CHAPTER FOUR: Simulation And Result Evaluation 31

4.0. SIMULATION AND RESULT EVALUATION 31

7

4.1. MATLAB MODEL OF THE SUBSTATION 32

4.2. ACTIVE POWER FORECAST 34

4.3. REACTIVE POWER FORECAST 39

4.4. EVALUATION OF FORECAST ACCURACY 43

4.4.1. MEASURE OF ACCURACY FOR ACTIVE POWER FORECAST 45

4.4.2. MEASURE OF ACCURACY FOR ONE HOUR AHEAD ACTIVE POWER

FORECAST 45

4.4.3. MEASURE OF ACCURACY FOR THREE HOUR AHEAD ACTIVE

POWER FORECAST 46

4.4.4. MEASURE OF ACCURACY FOR REACTIVE POWER FORECAST 47

4.4.5. MEASURE OF ACCURACY FOR ONE HOUR AHEAD REACTIVE

POWER FORECAST

48

4.5. ONE YEAR AHEAD FORECAST

4.5.1. MEASURING OF ACCURACY FOR THE ONE YEAR AHEAD

FORECAST

50

53

CHAPTER FIVE: Conclusion And Recommendation 57

5.0. CONCLUSION AND RECOMMENDATION 57

5.1. CONCLUSION 57

5.2. RECOMMENDATION 59

REFERENCES

APPENDICES