Disturbance Evaluation in Power System Based on Machine Learning

作     者：Emad M.Ahmed Mohamed A.Ahmed Ziad M.Ali Imran Khan 

作者机构：Department of Electrical EngineeringCollege of EngineeringJouf UniversitySakaka72388Al-JoufSaudi Arabia Department of Electrical EngineeringCollege of EngineeringPrince Sattam bin Abdulaziz UniversityWadi Addawaser11991Saudi Arabia Department of Electrical EngineeringAswan Faculty of EngineeringAswan UniversityAswan81542Egypt Department of Electrical EngineeringUniversity of Engineering and Technology PeshawarPakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：231-254页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at Jouf University for funding this work through research Grant No.(DSR-2021-02-0203) 

主　　题：Optimal power flow optimization algorithm deep learning power systems 

摘      要：The operation complexity of the distribution system increases as a large number of distributed generators(DG)and electric vehicles were introduced,resulting in higher demands for fast online reactive power *** a power system,the characteristic selection criteria for power quality disturbance classification are not *** classification effect and efficiency needs to be improved,as does the generalization *** order to categorize the quality in the power signal disturbance,this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality ***,a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality *** two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine,and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are *** proposed method can be used to classify the single and compound power quality disturbances with improved classification effect,reliability,robustness,and anti-noise performance,according to the experimental *** hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF,SVM and ML-KNN schemes have 0.28,0.23 and 0.22 respectively at a noise intensity of 20 *** average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF,SVM and ML-KNN schemes indicates 0.7,0.77 and 0.78 respectively.