This document describes a special project on using an artificial neural network (ANN) for load flow studies of the MSU-IIT electrical system. The objectives are to model the power system as a 5-bus system, evaluate bus voltages using a power flow program under different loads, train an ANN using the power flow results, and validate the ANN's accuracy by comparing its results to the power flow program. The document reviews literature on load flow studies, numerical methods, ANNs, and discusses how ANNs could provide faster and more accurate solutions to complex load flow problems compared to numerical methods.

2. An Electrical Engineering Special ProjectANN-Based Load-flow studies for MSU-IIT Electrical System

3. ABSTRACTINTRODUCTION

4. REVIEW OF RELATED LITERATURECONTENTSMETHODOLOGY

5. RESULTS AND DISCUSSIONS

6. CONCLUSION AND RECOMMENDATIONSABSTRACTINTRODUCTION

7. REVIEW OF RELATED LITERATURECONTENTSMETHODOLOGY

8. RESULTS AND DISCUSSIONS

9. CONCLUSION AND RECOMMENDATIONSINTRODUCTION

10. General Introduction

11. Statement of the Problem

12. Objectives of the Study

13. Significance of the Study

14. Scopes and Limitations

15. Definition of TermsCONTENTS

16. INTRODUCTION

17. General Introduction

18. Statement of the Problem

19. Objectives of the Study

20. Significance of the Study

21. Scopes and Limitations

22. Definition of TermsCONTENTS

23. GENERAL INTRODUCTIONINTRODUCTION Load Flow studies is performed to solve the steady state operating condition of a power system, by solving the static load flow equations (SLFE) for a given network.

24. GENERAL INTRODUCTIONVdPQINTRODUCTION The main objective of power flow studies is to determine the bus voltagemagnitude with its angle at all the buses, real and reactivepowerflows (line flows) in different lines and the transmission losses occurring in a power system.

25. GENERAL INTRODUCTIONINTRODUCTION Power flow study is the most frequently carried out study performed by power utilities and it is required to be performed at almost all the stages of power system planning, optimization, operation and control.

26. GENERAL INTRODUCTIONINTRODUCTIONDuring last four decades, almost all the known methods of numerical analysis for solving a set of non-linear algebraic equations have been applied in solving power flow problems.

27. GENERAL INTRODUCTIONINTRODUCTION However, what are computer systems good at? and not so good at?

28. GENERAL INTRODUCTIONINTRODUCTIONIf..…then

29. GENERAL INTRODUCTION In other words, numerical methods cannot solve highly complex problems or it may require tedious mathematical iterations that will utilize high computational time and computer memory.INTRODUCTION

30. GENERAL INTRODUCTIONIn recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex problems.INTRODUCTION

31. GENERAL INTRODUCTIONArtificial Neural Networks (ANN) is one of the AI methods..Load Flow Studies itself is a highly complex problem.INTRODUCTION

32. GENERAL INTRODUCTIONThus, Artificial Neural Network would be a very good method for Load Flow Studies. INTRODUCTION

33. GENERAL INTRODUCTIONWhy?INTRODUCTION

34. GENERAL INTRODUCTIONIn Artificial Neural Networks, power flow problems can be solved not by giving the computer a set of rules or instructions but by letting the system learn by experience (like humans).INTRODUCTION

35. GENERAL INTRODUCTION Numerical power flow methods are accurate but become unacceptable for on-line implementation due to high computational time requirements. INTRODUCTION

36. GENERAL INTRODUCTIONWith the advent of artificial intelligence, in recent years, expert systems, pattern recognition, decision tree, neural networks and fuzzy logic methodologies have been applied to complex problems.INTRODUCTION

37. GENERAL INTRODUCTIONAmongst these approaches, the applications of artificial neural networks (ANNs) have shown great promise in power system engineering due to their ability to synthesize complex mappings accurately and rapidly. INTRODUCTION

38. GENERAL INTRODUCTIONThe composition of the input variables for the proposed neural network has been selected to emulate the solution process of a conventional power flow program.INTRODUCTION

39. GENERAL INTRODUCTIONThis special project shall utilize multi-layer perceptron model (MLP) based on Backpropagation (BP) Algorithm. A certain ANN software will be used. INTRODUCTION

40. GENERAL INTRODUCTIONThe effectiveness of the proposed ANN based approach for solving power flow is demonstrated by computation of bus voltage magnitudes in a lumped, 5-bus power system in MSU-IIT.INTRODUCTION

41. INTRODUCTION

42. General Introduction

43. Statement of the Problem

44. Objectives of the Study

45. Significance of the Study

46. Scopes and Limitations

47. Definition of TermsCONTENTS

48. INTRODUCTION

49. General Introduction

50. Statement of the Problem

51. Objectives of the Study

52. Significance of the Study

53. Scopes and Limitation

54. Definition of TermsCONTENTS

55. STATEMENT OF THE PROBLEMINTRODUCTIONAlthough Numerical Methods proven to be robust and reliable for Load flow studies, speed of solution is more important especially for online applications.

56. STATEMENT OF THE PROBLEMThis is why decoupled power flow methods are used over full AC numerical solutions because of its speed of solution. However, decoupled power flow methods are known for its high inaccuracies.INTRODUCTION

57. STATEMENT OF THE PROBLEMRecently, Artificial Intelligence (AI) methods have been used to solve complex problems in medicine, business, sciences and engineering because of its speed and accuracy.INTRODUCTION

58. STATEMENT OF THE PROBLEMHence, the methods of AI, like Artificial Neural Network (ANN), shall be a great importance for load flow studies. The study shall evaluate the possibility of using ANN for Load flow studies and its accuracy compared to the numerical solution. INTRODUCTION

59. STATEMENT OF THE PROBLEMIn connection to this, a small power system shall be used for us to conduct the load flow calculation.This will be the MSU-IIT power system lumped into a 5-bus system.INTRODUCTION

60. INTRODUCTION

61. General Introduction

62. Statement of the Problem

63. Objectives of the Study

64. Significance Study

65. Scopes and Limitation

66. Definition of TermsCONTENTS

67. INTRODUCTION

68. General Introduction

69. Statement of the Problem

70. Objectives of the Study

71. Significance of the Study

72. Scopes and Limitations

73. Definition of TermsCONTENTS

74. OBJECTIVESThe Study has the following main objectives:1.) To model MSU-IIT’s power system into a 5-bus system.INTRODUCTION

75. OBJECTIVES2.) To evaluate the MSU-IIT bus voltages for different loading conditions using a conventional power flow program.INTRODUCTION

76. OBJECTIVES3.) To train an ANN network based on Backpropagation Algorithm by using the voltage calculations from the power flow software.INTRODUCTION

77. OBJECTIVES4.) To validate and test the neural network and compare the results to the calculations from the power flow program.INTRODUCTION

78. INTRODUCTION

79. General Introduction

80. Statement of the Problem

81. Objectives of the Study

82. Significance of the Study

83. Scopes and Limitations

84. Definition of TermsCONTENTS

85. INTRODUCTION

86. General Introduction

87. Statement of the Problem

88. Objectives of the Study

89. Significance of the Study

90. Scopes and Limitations

91. Definition of TermsCONTENTS

92. SIGNIFICANCE of the STUDYSince the study shall evaluate the possibility of ANN as a method for Load flow studies, the results shall be of a great use for planning, optimization, operation and control.INTRODUCTION

93. SIGNIFICANCE of the STUDYThe results of this study can be used for further hardware implementation for power control applications (i.e. OLTC and Shunt Capacitor control).INTRODUCTION

94. SIGNIFICANCE of the STUDYNumerical methods are accurate but requires high computation time and memory. ANN, as an AI method, shall outweigh numerical methods when used in on-line power system applications.INTRODUCTION