Special Project: Artificial Neural Networks for Load Flow Studies -- MSU-IIT

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

5. RESULTS AND DISCUSSIONS

8. RESULTS AND DISCUSSIONS

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

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 INTRODUCTION INTRODUCTION 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 INTRODUCTION V d P Q INTRODUCTION 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 INTRODUCTION INTRODUCTION 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 INTRODUCTION INTRODUCTION During 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 INTRODUCTION INTRODUCTION However, what are computer systems good at? and not so good at?

28. GENERAL INTRODUCTION INTRODUCTION If.. …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 INTRODUCTION In recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex problems. INTRODUCTION

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

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

33. GENERAL INTRODUCTION Why? INTRODUCTION

34. GENERAL INTRODUCTION In 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 INTRODUCTION With 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 INTRODUCTION Amongst 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 INTRODUCTION The 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 INTRODUCTION This special project shall utilize multi-layer perceptron model (MLP) based on Backpropagation (BP) Algorithm. A certain ANN software will be used. INTRODUCTION

40. GENERAL INTRODUCTION The 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

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

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 PROBLEM INTRODUCTION Although 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 PROBLEM This 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 PROBLEM Recently, 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 PROBLEM Hence, 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 PROBLEM In 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

61. General Introduction

62. Statement of the Problem

63. Objectives of the Study

64. Significance Study

65. Scopes and Limitation

66. Definition of TermsCONTENTS

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

75. OBJECTIVES 2.) To evaluate the MSU-IIT bus voltages for different loading conditions using a conventional power flow program. INTRODUCTION

76. OBJECTIVES 3.) To train an ANN network based on Backpropagation Algorithm by using the voltage calculations from the power flow software. INTRODUCTION

77. OBJECTIVES 4.) To validate and test the neural network and compare the results to the calculations from the power flow program. 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

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 STUDY Since 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 STUDY The 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 STUDY Numerical 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

96. General Introduction

97. Statement of the Problem

98. Objectives of the Study

99. Significance of the Study

100. Scopes and Limitations

101. Definition of TermsCONTENTS

103. General Introduction

104. Statement of the Problem

105. Objectives of the Study

106. Significance of the Study

107. Scopes and Limitations

108. Definition of TermsCONTENTS

109. SCOPES AND LIMITATIONS The study is centered only on MSU-IIT Electrical System. Some of the data were gathered from reliable sources and some the data were assumed. INTRODUCTION

110. SCOPES AND LIMITATIONS PowerWorld GSO 14 was the power flow simulator used. The whole MSU-IIT power system was lumped and reduced into a 5-bus system. INTRODUCTION

111. SCOPES AND LIMITATIONS Maximum load per bus was assumed based on transformer ratings total connected loads present in the bus. INTRODUCTION

112. SCOPES AND LIMITATIONS Although power factor between to different buses is different. The power factor of each bus was assumed to be constant. INTRODUCTION

113. SCOPES AND LIMITATIONS Two ANN programs/software were used to train, test and validate the calculations from the power flow program. These are: Neural Connection 2.1 and Neurosolutions 5. INTRODUCTION

115. General Introduction

116. Statement of the Problem

117. Objectives of the Study

118. Significance of the Study

119. Scopes and Limitations

120. Definition of TermsCONTENTS

122. General Introduction

123. Statement of the Problem

124. Objectives of the Study

125. Significance of the Study

126. Scopes and Limitations

127. Definition of TermsCONTENTS

128. DEFINITION OF TERMS Bus – a node or a common point of connection of elements; a conductor; or a group of conductors, that serves as a common connection for two circuits. INTRODUCTION

129. DEFINITION OF TERMS Bus – a node or a common point of connection of elements; a conductor; or a group of conductors, that serves as a common connection for two circuits. INTRODUCTION

130. DEFINITION OF TERMS Artificial Neural Network - is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. INTRODUCTION

131. DEFINITION OF TERMS Numerical Methods - is the study of algorithms that use numerical approximation for the problems of continuous mathematics. INTRODUCTION

132. DEFINITION OF TERMS Numerical Methods - is the study of algorithms that use numerical approximation for the problems of continuous mathematics. INTRODUCTION

135. RESULTS AND DISCUSSIONS

138. RESULTS AND DISCUSSIONS

143. Load flow studies Intro Load Flow Study is an important tool involving numerical analysis applied to a power system. REVIEW OF RELATED LITERATURE

144. Load flow studies Intro Unlike traditional circuit analysis, a power flow study usually uses simplified notation such as a one-line diagram and per-unit system, and focuses on various forms of AC power (ie: reactive, real, and apparent) rather than voltage and current. REVIEW OF RELATED LITERATURE

145. Load flow studies Intro Unlike traditional circuit analysis, a power flow study usually uses simplified notation such as a one-line diagram and per-unit system, and focuses on various forms of AC power (ie: reactive, real, and apparent) rather than voltage and current. REVIEW OF RELATED LITERATURE

146. Load flow studies Intro It analyses the power systems in normal steady-state operation. There exist a number of software implementations of power flow studies. REVIEW OF RELATED LITERATURE

147. Load flow studies Intro The great importance of power flow or load-flow studies is in the planning the future expansion of power systems as well as in determining the best operation of existing systems. REVIEW OF RELATED LITERATURE

148. Load flow studies Intro The principal information obtained from the power flow study is the magnitude and phase angle of the voltage at each bus and the real and reactive power flowing in each line. REVIEW OF RELATED LITERATURE

149. Load flow studies Intro Static Load Flow Equations (SLFE) REVIEW OF RELATED LITERATURE

150. Load flow studies Numerical Methods Newton Raphson Method – Newton's method can often converge remarkably quickly, especially if the iteration begins "sufficiently near" the desired root. REVIEW OF RELATED LITERATURE

151. Load flow studies Numerical Methods In NR method, the changes in real power (P) are very much influenced by the changes in load angle only and no influence due to the voltage magnitude changes. REVIEW OF RELATED LITERATURE

152. Load flow studies Numerical Methods Similarly the changes in reactive power are very much influenced by changes in voltage magnitudes and no change takes place due to load angle changes. REVIEW OF RELATED LITERATURE

153. Load flow studies Numerical Methods Gauss-Seidel Method - The iteration process begins with a flat voltage profile assumption to all the buses expect the slack bus. REVIEW OF RELATED LITERATURE

154. Load flow studies Numerical Methods The bus voltages are updated and the convergence check is made on updated voltages and the iteration process is continued till the tolerance value is reached. REVIEW OF RELATED LITERATURE

155. Load flow studies Fast-Decoupled Methods It is reliable and fastest method in obtaining convergence. This method with branches of high (R/X) ratios, could not solve problems with regard to non- convergence and long execution time. REVIEW OF RELATED LITERATURE

160. Artificial Neural Networks Intro Artificial Neural Networks (ANN) Connectionist computation Parallel distributed processing Computational models Biologically Inspired computational models Machine Learning Artificial intelligence REVIEW OF RELATED LITERATURE

161. Artificial Neural Networks Intro Artificial Neural network - information processing paradigm inspired by biological nervous systems, such as our brain REVIEW OF RELATED LITERATURE

162. Artificial Neural Networks Intro Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process In a biological system, learning involves adjustments to the synaptic connections between neurons REVIEW OF RELATED LITERATURE

163. Artificial Neural Networks When to use ANN’s? when we can't formulate an algorithmic solution. when we can get lots of examples of the behavior we require. ‘learning from experience’ when we need to pick out the structure from existing data. REVIEW OF RELATED LITERATURE

164. Artificial Neural Networks NeuroBiology A neuron: many-inputs / one-output unit output can be excited or not excited incoming signals from other neurons determine if the neuron shall excite ("fire") Output subject to attenuation in the synapses, which are junction parts of the neuron REVIEW OF RELATED LITERATURE

165. Artificial Neural Networks NeuroBiology REVIEW OF RELATED LITERATURE

166. Artificial Neural Networks Synapse Concept “The synapse resistance to the incoming signal can be changed during a "learning" process” [1949] REVIEW OF RELATED LITERATURE

167. Artificial Neural Networks Hebb’s Rule If an input of a neuron is repeatedly and persistently causing the neuron to fire, a metabolic change happens in the synapse of that particular input to reduce its resistance REVIEW OF RELATED LITERATURE

168. Artificial Neural Networks Mathematical Representation The neuron calculates a weighted sum of inputs and compares it to a threshold. If the sum is higher than the threshold, the output is set to 1, otherwise to -1. REVIEW OF RELATED LITERATURE

169. Artificial Neural Networks Mathematical Representation REVIEW OF RELATED LITERATURE

170. Artificial Neural Networks Mathematical Representation REVIEW OF RELATED LITERATURE Electro-chemical signals Threshold output firing

171. Artificial Neural Networks Mathematical Representation REVIEW OF RELATED LITERATURE Binary classifier functions Threshold activation function

172. Artificial Neural Networks Perceptron: Threshold Activation Function Binary classifier functions Threshold activation function REVIEW OF RELATED LITERATURE

173. Artificial Neural Networks Linear Activation Functions Output is scaled sum of inputs REVIEW OF RELATED LITERATURE

174. Artificial Neural Networks Nonlinear Activation Functions Sigmoid Neuron unit function REVIEW OF RELATED LITERATURE

175. Artificial Neural Networks Learning From experience: examples / training data Strength of connection between the neurons is stored as a weight-value for the specific connection Learning the solution to a problem = changing the connection weights REVIEW OF RELATED LITERATURE

177. No training data, no information available on the desired output

178. Learning by doing

179. Used to pick out structure in the input:

180. Clustering

181. Reduction of dimensionality  compression

182. Example: Kohonen’s Learning LawREVIEW OF RELATED LITERATURE

184. Error = difference between actual & desired output

185. Change weight relative to error size

186. Calculate output layer error , then propagate back to previous layer

187. Improved performance, very common!REVIEW OF RELATED LITERATURE

189. Error = difference between actual & desired output

190. Change weight relative to error size

191. Calculate output layer error , then propagate back to previous layer

192. Improved performance, very common!REVIEW OF RELATED LITERATURE