Distribution feeder fault diagnosis classifier using SVM with PSO optimization

國立高雄應用科技大學 電機工程系博士班 博士論文 以 PSO 最佳化為基礎之配電饋線故障診斷 SVM 分類器 Distribution Feeder Fault Diagnosis Classifier Using SVM with PSO Optimization 研 究 生： 黃 氏 香 (Thi Thom Hoang) 指導教授： 卓明遠 博士 (Dr Ming-Yuan Cho) 中華民國 一零七 年 一 月 國立高雄應用科技大學 電機工程系博士班 博士論文 以 PSO 最佳化為基礎之配電饋線故障診斷 SVM 分類器 Distribution Feeder Fault Diagnosis Classifier Using SVM with PSO Optimization 研 究 生： 黃 氏 香 指導教授： 卓明遠 博士 中華民國 一零七 年 一 月 以 PSO 最佳化為基礎之配電饋線故障診斷 SVM 分類器 Distribution Feeder Fault Diagnosis Classifier Using SVM with PSO Optimization 研 究 生：黃 氏 香 Student：Thi Thom Hoang 指導教授：卓 明 遠 Advisor：Ming-Yuan Cho 國立高雄應用科技大學 電機工程系博士班 博士論文 A Dissertation Submitted to Department of Electrical Engineering National Kaohsiung University of Applied Sciences in Partial Fulfilment of the Requirements for the Degree of Doctor of Engineering in Electrical Engineering January 2018 Kaohsiung, Taiwan, Republic of China 中華民國 一零七 年 一 月 以 PSO 最佳化為基礎之配電饋線故障診斷 SVM 分類器 博士研究生: 黃氏香 指導教授: 卓明遠 博士 國立高雄應用科技大學 電機工程系博士班 博士論文 摘要 本文針對電力配電系統中的十種短路故障進行分類，我們的目標是開發一種 增強型支援向量機，眾所周知支援向量機在分析非線性系統問題上是一套強而有 力的工具，其特性是將原始資料轉換到更高維度以及僅需少量訓練的樣本。在基 於結構風險最小化原則和統計機器學習理論相結合的理論基礎上。解決有關機器 學習過程中如何選取最佳特徵和核心參數的這個目標正是許多相關研究人員致力 解決的重要課題。 在此工作中，透過時域反射法 (TDR) 分析得到包括 10 種類型配電系統短路 故障之人工神經網絡/支援向量機 (ANN/SVM) 分類器的數據，提取 12 個特徵作 為輸入特徵進行分類。接下來，使用從 TDR 響應獲得的訓練和驗證數據組對 ANN/SVM 進行訓練和驗證。然後，粒子群優化演算法首次被用來提高人工神經 網絡和多層支援向量機的性能，藉由特徵和徑向基函數核參數選擇，從而診斷配 電網故障。粒子群優化 (PSO) 算法用於提高分類精度，它的能力在於同時去除可 能混淆 ANN /SVM 分類器及選擇最佳參數的無關聯的輸入特徵。最後，發展了 PSO 的一些新的變種，包含突變 PSO，差分 PSO 和擾動 PSO，幫助粒子逃逸局 部最小值以獲得更高品質之分類問題的解決方案。 透過使用 PSO 演算法，ANN 和 SVM 分類器的效能得到明顯提升兩者均超 過 93％以上。SVM 的分類結果比神經網絡的分類結果更佳。特別是使用 PSO 變 i 體（如 DPSO 和 PPSO）的 SVM 的效率上升到 97％以上。模擬結果顯示了我們 所提出的 PSO-SVM 方法在提高分類成功率和計算速度方面的優越性。 具有高效選取最佳特徵及核心參數之 PSO 演算法的神經網絡/支援向量機分 類器可被應用於配電系統的故障診斷。一些 PSO 的新型變體被開發來推動粒子使 其具備較好的局部搜索能力。以 PSO 為基礎的支援向量機可以用來有效地解決饋 線間負載平衡和電力系統復電等電力工程問題。 關鍵詞: 故障診斷, 粒子群優化, 配電系統, 時域反射法, 支援向量機 ii Distribution Feeder Fault Diagnosis Classifier Using SVM with PSO Optimization PhD student: Thi Thom Hoang Advisor: Prof Ming-Yuan Cho Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan ABSTRACT To classify ten types of short-circuit faults for an electric power distribution system, we aim to develop an enhanced support vector machine (SVM), which has been well known as a powerful tool for nonlinearly problems that have high dimensionalities with a small number of training samples It has a solid theoretical foundation based on a combination between the structural risk minimization (SRM) principle and statistical machine learning theory (SLT) Solving the problems related to select the optimal feature and kernel parameters in machine learning has been considered by many researchers In this work, the dataset of artificial neural network (ANN)/SVM classifier including ten types of short-circuit faults in a distribution system is obtained by time domain reflectometry (TDR) analysis, and 12 features are extracted as input features for classification Next, the ANN/SVM is trained and validated using the training set and validation set that are obtained from TDR responses Then, particle swarm optimization (PSO) algorithms has been investigated to improve the performance of an ANN and a multi-layer SVM by feature and radial basis function (RBF) kernel parameter selection in order for fault diagnosis of a distribution network for the first time The PSO algorithm is applied to increase the classification accuracy; which are capacity of iii removing unrelated input features that may be confusing the ANN/SVM classifiers and selecting the optimal parameters at the same time Finally, some novel variants of PSO, including Mutant PSO, differential PSO (DPSO) and perturbed PSO (PPSO) are developed to help particle escaping the local minima in order to obtain a higher quality solution in classification problems By using the PSO algorithm, the performance of ANN and SVM classifiers are improved significantly The success rates of both reach over 93%, respectively, in which classification results for SVM was better than those of ANN Especially, this rate increases to over 97% for SVM using variants of PSO, such as DPSO and PPSO Simulation results show the superiority of the proposed PSO-SVM approach in increasing the success rate of classification as well as computational speed An effective PSO algorithm in optimal feature and parameter selection of ANN/SVM classifier was established for fault diagnosis in a power distribution system Some novel variants of PSO have been developed to push particles to escape from the local minima Suggestions for PSO-based SVM can be used for effectively solving electrical engineering problems, such as load balance among feeders and power system restoration in the future research Keywords: fault diagnosis, particle swarm optimization, power distribution system, time domain reflectometry, support vector machine iv Contents 摘要 i ABSTRACT iii Contents v List of Figures x List of Abbreviations xii Chapter Introduction 1.1 Motivation 1.2 Literature review 1.3 Contributions of this study 1.4 Dissertation organization Chapter Distribution system operation 2.1 Introduction 2.2 Feeder automation system (FAS) 2.2.1 Structure and control functions of FDIR 2.2.2 The operation of FDIR 11 2.3 Types of fault 14 2.3.1 Single line-to-ground fault 15 2.3.2 Double line-to-ground fault 16 2.3.3 Line-line fault 17 2.3.4 Three phase-to-ground fault 18 2.4 Time Domain Reflectometry (TDR) and Pseudo Random Binary Sequence (PRBS) 20 2.5 Summary 23 Chapter Support Vector Machine 24 3.1 Introduction 24 3.2 The Optimal Hyperplane 24 v 3.3 The optimal hyperplane for inseparable case 31 3.4 Non-linear SVM 35 3.5 Examples of SVM using the different kernel functions 43 3.6 Summary 45 Chapter Particle Swarm Optimization 48 4.1 Introduction 48 4.2 Brief history 49 4.3 Concepts & Formulation 51 4.3.1 Basic Concepts 51 4.3.2 Particle Swarm Optimization in Real Number Space 52 4.3.3 Discrete Particle Swarm Optimization 58 4.4 The popular variants of PSO 60 4.5 The proposed variants of PSO 61 4.5.1 Mutant-PSO 61 4.5.2 DPSO 64 4.5.3 PPSO 66 4.6 Summary 69 Chapter The PSO-based SVM 71 5.1 Introduction 71 5.2 The proposed fault diagnosis methods 73 5.3 Summary 89 Chapter Simulation Results 91 6.1 Introduction 91 6.2 Testing Run for the PSO Algorithms on Benchmark Problems 94 6.3 Results of SVM classifiers using PSO algorithms 97 6.3.1 Results of PSO-based ANN/SVM classifier 98 6.3.2 Results of Mutant PSO-based SVM classifier 100 6.3.3 Results of DPSO-based SVM classifier 102 vi 6.3.4 Results of PPSO-based SVM classifier 109 6.4 Summary 112 Chapter Conclusion and Future Research 114 7.1 Conclusion 114 7.2 Future Research 116 REFERENCES 118 Publication List Since 2016 129 vii of approaches have been developed to balance load in electrical distribution networks, including network reconfigurations [123], fuzzy reasoning approach [124], and loop power flow controller [125] In the future research, we will use ANN and SVM to control the switch closing sequence of each load for the minimum power loss which will lead to the optimal phase balance Then, PSO technique is applied to improve the performance of ANN and SVM in order to obtain the best results Power system restoration Power system restoration has attracted more attention and made great progress recently The objectives of restoration are to enable the power system to return to normal conditions securely and rapidly, minimize losses and restoration time, and diminish adverse impacts on society Many non-structured methods and technologies and object-oriented expert system have been employed in making restoration schemes, including black-start [126], network reconfiguration [127] and load restoration [128] However, the establishment and maintenance of a knowledge base of past restorations remains a bottleneck Therefore, in the future research, we will apply PSO-based SVM for prediction of fault classification before restoration of the power system in order to achieve fast and reliable restoration when the system is facing increasing blackout risks Consequently, modern power systems have become complex cyber-physical systems due to significant integration of variable renewable energy, rapid development of smart grid and wide application of emerging techniques With the superiority of PSO and its variants, we hope will apply them for effectively solving electrical engineering problems, such as load balance among feeders and power system restoration 117 REFERENCES [1] Mora-Flórez, J., Cormane-Angarita, J., Carrillo-Caicedo, G., K-means algorithm and mixture distributions for locating faults in power systems, Electric Power System Research, vol 79, pp 714-721, 2009 [2] Mokhlis H., Mohamad, H., Li, H., Bakar, Ab H A., Voltage sags matching to locate faults for underground distribution networks, Advances Electrical and Computer Engineering, vol 11, No.2, pp 43-48, 2011 [3] Steiner, J.P., Weeks, W.L., H W Ng, An automated fault locating 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Hoang, 2017, Feature Selection and Parameters Optimization of SVM using Particle Swarm Optimization for Fault Classification in Power Distribution Systems, Computational Intelligence and Neuroscience, vol 2017, pp 1-9 (SCI, JCR 2017 Journal impact factor: 1.215) [3] Ming Yuan Cho, Thi Thom Hoang, 2017, A Differential Particle Swarm Optimization-based Support Vector Machine Classifier for Fault Diagnosis in Power Distribution Systems, Advances in Electrical and Computer Engineering, vol 17, no 3, pp 51-60 (SCI, JCR 2017 Journal impact factor: 0.595) [4] Thi Thom Hoang, Ming Yuan Cho, Quoc Tuan Vu, 2017, A novel Perturbed Particle Swarm Optimization-based Support Vector Machine for fault diagnosis in power distribution systems, Turkish Journal of Electrical Engineering & Computer Sciences, DOI: 10.3906/elk-1705-241, (SCI, JCR 2017 Journal impact factor: 0.578) [5] Thi Thom Hoang, Ming Yuan Cho, Mahamad Nabab Alam, A Newly Enhanced Support Vector Machine using Variants of Particle Swarm Optimization for Power Distribution System Fault Diagnosis, Swarm and Evolutionary Computation Revised version submitted (SCI, JCR 2017 Journal impact factor: 3.893) [6] Chien-Nan Chen, Thi Thom Hoang, Ming Yuan Cho, “An enhanced Support Vector Machine for Diagnosis of Metal-oxide Surge Arrester Conditions,” Computational Intelligence and Neuroscience, Under review (SCI, JCR 2017 Journal impact factor: 1.215) B ESCI papers: [1] Ming Yuan Cho, Thi Thom Hoang, 2017, Fault Diagnosis for Distribution Networks using enhanced Support Vector Machine classifier with Classical 130 Multidimensional Scaling, Journal of Electrical Systems, Vol 13, No 3, pp.415-428 C EI papers: [1] Ming Yuan Cho, Hoang Thi Thom, Jeng Feng Hsu, 2016, Fault Diagnosis for High Voltage Distribution Networks using Pseudorandom Binary Sequence and Cross Correlation Technique, International Conference on Green Technology and Sustainable Development (GTSD) 2016 DOI: 0.1109/GTSD.2016.51, pp.185-190 [2] Ming Yuan Cho, Hsin Yi Huang, Chien Nan Chen, Hoang Thi Thom, Pei Ru Wang, Wen Yao Chang, Chin Tun Wang, 2016, The implementation and Application of Low Voltage Distribution Line Theft Supervisory System," International Conference on Green Technology and Sustainable Development (GTSD) 2016 DOI: 10.1109/GTSD.2016.50, pp.178-184 D Conference Papers: [1] Hoang Thi Thom, Hung-Chang Hsu, Ming Yuan Cho, Mahamad Nabab Alam, "Mutant particle swarm optimization based on support vector machine for fault diagnosis in power distribution systems," International Conference on Smart Grid Technology and Data Processing (SGTDP) 2017, IET Proceedings (EI, INSPEC) E Book Chapter: [1] Mahamad Nabab Alam, Thi Thom Hoang, Chapter Application of Particle Swarm Optimization for solving Electrical Engineering Problems, pp 61-86, Book: Focus on Swarm Intelligence Research and Applications, Nova Science Publisher, Inc., 2017 131 ... 博士論文 以 PSO 最佳化為基礎之配電饋線故障診斷 SVM 分類器 Distribution Feeder Fault Diagnosis Classifier Using SVM with PSO Optimization 研 究 生： 黃 氏 香 指導教授： 卓明遠 博士 中華民國 一零七 年 一 月 以 PSO 最佳化為基礎之配電饋線故障診斷 SVM 分類器 Distribution. .. of SVM classifiers using PSO algorithms 97 6.3.1 Results of PSO- based ANN /SVM classifier 98 6.3.2 Results of Mutant PSO- based SVM classifier 100 6.3.3 Results of DPSO-based SVM. .. 分類器及選擇最佳參數的無關聯的輸入特徵。最後，發展了 PSO 的一些新的變種，包含突變 PSO 差分 PSO 和擾動 PSO 幫助粒子逃逸局 部最小值以獲得更高品質之分類問題的解決方案。 透過使用 PSO 演算法，ANN 和 SVM 分類器的效能得到明顯提升兩者均超 過 93％以上 SVM 的分類結果比神經網絡的分類結果更佳。特別是使用 PSO 變 i 體（如 DPSO 和 PPSO）的 SVM 的效率上升到