University of Wisconsin Milwaukee UWM Digital Commons Theses and Dissertations May 2013 Neural Network Based Microgrid Voltage Control Chun-Ju Huang University of Wisconsin-Milwaukee Follow this and additional works at: https://dc.uwm.edu/etd Part of the Electrical and Electronics Commons Recommended Citation Huang, Chun-Ju, "Neural Network Based Microgrid Voltage Control" (2013). Theses and Dissertations. 118. https://dc.uwm.edu/etd/118 This Thesis is brought to you for free and open access by UWM Digital Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of UWM Digital Commons. For more information, please contact [email protected]. NEURAL NETWORK BASED MICROGRID VOLTAGE CONTROL by Chun-Ju Huang A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering at The University of Wisconsin-Milwaukee May 2013 ABSTRACT NEURAL NETWORK BASED MICROGRID VOLTAGE CONTROL by Chun-Ju Huang The University of Wisconsin-Milwaukee, 2013 Under the Supervision of Professor David Yu The primary purpose of this study is to improve the voltage profile of Microgrid using the neural network algorithm. Neural networks have been successfully used for character recognition, image compression, and stock market prediction, but there is no directly application related to controlling distributed generations of Microgrid. For this reason the author decided to investigate further applications, with the aim of controlling diesel generator outputs. Firstly, this thesis examines the neural network algorithm that can be utilized for alleviating voltage issues of Microgrid and presents the results. MATLAT and PSCAD are used for training neural network and simulating the Microgrid model respectively. The Feedforward Back-propagation algorithm is used in this study and the Microgrid consists of wind, solar, and diesel power generations, and battery storage. Neural network will indicate how much real and reactive power is needed from each generator. In the second stage, several scenarios are proposed to verify that the monitoring points are very ii important for training neural networks. Finally, the comparison of results is shown for further discussion of critical points. In conclusion, the results of the study show that neural network algorithm is well suited for the application, and it was effectively certified for the purpose of improving the voltage profile of Microgrid. iii © Copyright by Chun-Ju Huang, 2013 All Rights Reserved iv TABLE OF CONTENTS CHAPTER Page I. INTRODOUCTION ........................................................................................................ 1 Background ..................................................................................................................... 1 Literature review ............................................................................................................. 3 Objective of Study ........................................................................................................... 4 II. RENEWABLE ENERGY SYSTEMS INTEGRATION-MICROGRID ....................... 6 Overview ......................................................................................................................... 6 Components of Microgrid ............................................................................................... 7 Cost Optimization ......................................................................................................... 14 Microgrid Power System ............................................................................................... 14 III. NEURAL NETWORK BASED MICROGRID VOLTAGE CONTROL .................. 17 Microgrid Topology ...................................................................................................... 17 Architecture of Neural Networks .................................................................................. 21 System Optimization Scenarios .................................................................................... 26 Comparison of the Results ............................................................................................ 35 IV. CONCLUSIONS ........................................................................................................ 36 REFERENCES ................................................................................................................. 37 APPENDICES ................................................................................................................. 40 Appendix A: Training Cases ......................................................................................... 40 Appendix B: Output Targets ......................................................................................... 46 Appendix C: Test Cases ................................................................................................ 49 Appendix D: First Scenario Results .............................................................................. 50 Appendix E: Second Scenario Results .......................................................................... 51 Appendix F: Third Scenario Results ............................................................................. 52 Appendix G: Fourth Scenario Results ........................................................................... 53 Appendix H: Fifth Scenario Results ............................................................................. 54 Appendix I: Sixth Scenario Results .............................................................................. 55 Appendix J: Seventh Scenario Results .......................................................................... 56 v LIST OF FIGURES Figure Page 1.1 Years of production left for fossil fuels 3 2.1 Photovoltaic cell structure 8 2.2 World annual solar photovoltaic production 8 2.3 I-V curve 9 2.4 VAWT 10 2.5 HAWT 10 2.6 Wind turbine power output with steady wind speed 11 2.7 World annual wind energy production 12 2.8 Energy storage rated power versus discharge time 13 2.9 Diesel generator 14 2.10 Microgrid 15 2.11 Grid connected mode 16 2.12 Islanding mode 16 3.1 Microgrid topology 18 3.2 Diesel Generator Model 19 3.3 Inverter average model for wind and solar power 20 3.4 Single layer neural network 21 3.5 Multiple layers neural network 22 3.6 The back-propagation algorithm 22 3.7 Sigmoid transfer function 27 3.8 First scenario training information 26 3.9 Load voltages of case 5 27 3.10 Improved load voltages of case 5 27 3.11 Bus 10 and 11 voltages of test case 5 35 vi LIST OF TABLES Table Page 2.1 Energy use in U.S and World in 2011 6 3.1 Electricity generating capacity 18 3.2 Transmission ling parameters 18 3.3 Length of transmission line 19 3.4 5 of the 112 training cases 24 3.5 5 of the 112 output targets 25 3.6 5 of the 16 test cases 25 3.7 Neural network output of first network 27 3.8 Simulation results for the first scenario 28 3.9 Simulation results for the second scenario 29 3.10 Simulation results for the third scenario 30 3.11 Simulation results for the fourth scenario 31 3.12 Simulation results for the fifth scenario 32 3.13 Simulation results for the sixth scenario 33 3.14 Simulation results for the seventh scenario 34 vii LIST OF ABBREVIATIONS PV Photovoltaic CSP Concentrated solar power MPPT Maximum power point tracking HAWT Horizontal axis wind turbines VAWT Vertical axis wind turbines AC Alternating current DC Direct current MSE Mean Squared Error viii 1 CHAPTER I INTRODOUCTION Background New energy technologies and scientific discoveries are being made every day. These extraordinary technologies and discoveries have improved and changed humans living conditions. Most well known technologies are wind for sailing, insolation for grain drying and falling water for milling purposes. Electricity was the most important discovery of science. It is a basic feature of everything in the universe, and it brings convenience in many ways to humans’ life. Fossil fuels are very old. Oil deposits are around 150 million years old. Although humans probably used it in ancient times, it was the industrial revolution that led to their wide-scale extraction. The industrial revolution was a key turning point in Earth’s ecology and humans’ relationship with their environment and the era of fossil fuels was the product of it. The most obvious evidence of the industrial revolution’s impact on the world is human population growth that has rapidly increased energy consumption. Human beings have relied on finite sources over centuries and the CO2 production dramatically rising along with energy consumption. The results of major depletion of fossil fuel reserves cause global warming, financial instability, and political turmoil in the world. Scientists indicate that human will run out of fossil fuel reserves in the near future, world is facing an impending energy crisis. It is truly urgent to find energy alternatives that are reliable, sustainable and environment friendly. 2 Renewable energy sources will be a significant role to supply global energy demand in the future. Renewable energy could supply for almost 80% of the world’s energy demand within four decades and shift global dependence away from fossil fuels [1]. Renewable energy sources will also provide environmental benefits such as reduced pollution, land restoration, and slow down global warming. Renewable energy technologies development will excite global economy and it also offers more job opportunities. Energy