作者：迟福建¹, 刘聪¹, 申刚², 尚德华², 吕明琪³

作者单位：1. 国网天津市电力公司, 天津 300010;
2. 天津天大求实电力新技术股份有限公司, 天津 300384;
3. 杭州师范大学理学院, 浙江 杭州 310036

关键词：朴素Bayes;电网系统;故障诊断;防误合

摘要：

为提高电网故障诊断的有效性及稳定运行的可靠性，提出一种基于高阶朴素Bayes模型(higher order naive Bayes model，HONBM)的电网系统故障诊断与防误合策略。首先，对输电网模型结构进行分析，给出输电网模型中电气元件进行故障分析的Bayes模型结构；其次，为提高算法的性能，利用HONBM算法进行电网系统故障诊断与防误合策略的分析，并给出基于Bayes网络的电网系统故障诊断流程以及防误合策略；最后，通过在IEEE14算例上的仿真验证所提电网系统故障诊断与防误合策略的有效性。

Fault diagnosis of power network anti misoperation based on high-order simple Bayes algorithm
CHI Fujian¹, LIU Cong¹, SHEN Gang², SHANG Dehua², Lü Mingqi³
1. State Grid Tianjin Electric Power Company, Tianjin 300010, China;
2. Tianjin Tianda Qiushi Electric Power High Technology Co., Ltd,. Tianjin 300384, China;
3. School of science, Hangzhou Normal University, Hangzhou 310036, China
Abstract: In order to improve the effectiveness and the reliability of power grid stability operation, a fault diagnosis and anti misoperation strategy for power system based on higher order naive Bayes model is proposed. Firstly, the structure of the transmission network model is analyzed, and the Bayes model structure of the fault analysis of the electrical components in the transmission network model is given. Secondly, in order to improve the performance of the algorithm, the HONBM algorithm is used to analyze the fault diagnosis and anti maloperation strategy of the power grid system, and the fault diagnosis process of the power grid system based on the Bayes network and the error prevention strategy are given. Finally, the effectiveness of the fault diagnosis and anti maloperation strategy of the proposed power system is verified by the simulation on IEEE14.
Keywords: simple Bayes;power grid system;fault diagnosis;anti misoperation
2019, 45(12):132-137,164  收稿日期: 2018-05-15;收到修改稿日期: 2018-09-11
基金项目: 国家自然科学基金（61202282）
作者简介: 迟福建(1979-),男,内蒙古通辽市人,高级工程师,硕士,研究方向为配电网新技术、配电网规划管理
参考文献
[1] 孙明蔚, 童晓阳, 刘新宇, 等. 运用时序贝叶斯知识库的电网故障诊断方法[J]. 电网技术, 2014, 38(3):715-722
[2] 罗孝辉, 童晓阳. 计及可信度的变结构贝叶斯网络电网故障诊断[J]. 电网技术, 2015, 39(9):2658-2664
[3] SHI W W, ZHU Y X, HUANG T, et al. An integrated data preprocessing framework based on apache spark for fault diagnosis of power grid equipment[J]. Journal of Signal Processing Systems, 2017, 86(2):221-236
[4] ASLAN Y, YAĞAN Y E. Artificial neural-network-based fault location for power distribution lines using the frequency spectra of fault data[J]. Electrical Engineering, 2017, 99(1):301-311
[5] ZHANG K F, YUAN F, GUO J, et al. A novel neural network approach to transformer fault diagnosis based on momentum-embedded bp neural network optimized by genetic algorithm and fuzzy c-means[J]. Arabian Journal for Science and Engineering, 2016, 41(9):3451-3461
[6] REZVANI A, IZADBAKHSH M, GANDOMKAR M. Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine[J]. Energy Systems, 2015, 6(4):551-584
[7] MOHAMMAD-REZA M, AMIR T. Traveling-wave fault location techniques in power system based on wavelet analysis and neural network using GPS timing[J]. Wireless Personal Communications, 2016, 86(2):835-850
[8] HAMED H, HAMED R N, HOWARD I. Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control[J]. Frontiers of Mechanical Engineering, 2017, 12(3):377-388
[9] WANG Y M, ZHANG F J, CUI T, et al. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer[J]. Chinese Journal of Mechanical Engineering, 2016, 29(2):386-395
[10] RAKESH R S, PABITRA M K. Composite fault diagnosis in wireless sensor networks using neural networks[J]. Wireless Personal Communications, 2017, 95(3):2507-2548
[11] MARCELO P D S, JOSÉ R B D A M, FABBIO A S B, et al. Fault identification in doubly fed induction generator using FFT and neural networks[J]. Journal of Control, Automation and Electrical Systems, 2017, 28(2):228-237
[12] MARIAM J, OMAR K, HSAN H A. Optimizing PSS parameters for a multi-machine power system using genetic algorithm and neural network techniques[J]. The International Journal of Advanced Manufacturing Technology, 2017, 90(9):2669-2688
[13] SINGH M, ALLEN A J, MULJADI E. Interarea oscillation damping controls for wind power plants[J]. IEEE Transactions on Sustainable Energy, 2015, 6(3):967-975
[14] JAFARIAN M, RANJBAR A M. Interaction of the dynamics of doubly fed wind generators with power system electromechanical oscillations[J]. IET Renewable Power Generation, 2013, 7(2):89-97
[15] BEZA M, BONGIORNO M. An adaptive power oscillation damping controller by STATCOM with energy storage[J]. IEEE Transactions on Power Systems, 2014, 30(1):484-493