Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于混合優化算法的電網故障診斷

來源:電工電氣發布時間:2020-11-19 14:19 瀏覽次數:102
基于混合優化算法的電網故障診斷
 
謝瑞,張興旺
(南昌工程學院 江西省精密驅動與控制重點實驗室,江西 南昌 333000)
 
    摘 要:電網故障過程中保護和斷路器動作及告警信息存在不確定性,會使原有電網故障解析模型診斷出現錯誤。在現有解析模型基礎上,通過電網結構、保護配置及斷路器的動作規則進行解析,考慮各級保護之間的互相影響,針對可疑母線和線路分別建立目標函數,構建新的解析模型。采用混合優化算法對目標函數進行求解,將模擬植物生長算法(PGSA)與粒子群算法(PSO)結合,初始生長點的選取對于PGSA能否收斂于全局最優解起著決定作用,先通過PSO的高魯棒性初選優秀的初始生長點,再基于PGSA的高效搜索能力得到最終的全局最優解。算例結果表明,改進的解析模型更加合理,混合優化算法搜索速度與收斂精度大幅度提高。
    關鍵詞:故障診斷;優化模型;模擬植物生長算法;粒子群算法;告警信息
    中圖分類號:TM711     文獻標識碼:A     文章編號:1007-3175(2020)11-0031-05
 
Power Grid Fault Diagnosis Based on Mixed Optimization Algorithm
 
XIE Rui, ZHANG Xing-wang
(Jiangxi Provincial Key Laboratory of Precision Drive and Control, Nanchang Institute of Technology, Nanchang 333000, China)
 
    Abstract: There are uncertainties in the protection and circuit breaker action and alarm information in the process of power grid failure, which will cause errors in the diagnosis of the original grid fault analysis model. On the basis of the existing analytical model, analyze the power grid structure, protection configuration and the action rules of the circuit breaker, consider the mutual influence between all levels of protection, establish objective functions for suspicious buses and lines, and build a new analytical model. A hybrid optimization algorithm is used to solve the objective function, and the simulated plant growth algorithm (PGSA) is combined with the particle swarm algorithm (PSO). The selection of the initial growth point determines whether the PGSA can converge to the global optimal solution. First pass the PSO The high robustness of PGSA initially selects excellent initial growth points, and then obtains the final global optimal solution based on the efficient search ability of PGSA. The results of calculation examples show that the improved analytical model is more reasonable, and the search speed and convergence accuracy of the hybrid optimization algorithm are greatly improved.
    Key words: fault diagnosis; optimization model; simulation plant growth algorithm; particle swarm algorithm; warning information
 
參考文獻
[1] 徐彪,尹項根,張哲,等. 電網故障診斷的分階段解析模型[J]. 電工技術學報,2018,33(17):4113-4122.
[2] ZHANG Yan, ZHANG Yong, WEN Fushuan, et al. A fuzzy Petri net based approach for fault diagnosis in power systems considering temporal constraints[J].International Journal of Electrical Power & Energy Systems,2016,78:215-224.
[3] LI Z, YIN X, ZHE Z, et al. Wide-Area Protection Fault Identification Algorithm Based on Multi-Information Fusion[J]. IEEE Transactions on Power Delivery,2013,28(3):1348-1355.
[4] YAN Z, CHI Y C, WEN F, et al. An Analytic Model for Fault Diagnosis in Power Systems Utilizing Redundancy and Temporal Information of Alarm Messages[J].IEEE Transactions on Power Systems,2016,31(6):4877-4886.
[5] 郭文鑫,文福拴,廖志偉,等. 計及保護和斷路器誤動與拒動的電力系統故障診斷解析模型[J]. 電力系統自動化,2009,33(24):6-10.
[6] 張巖,張勇,文福拴,等. 融合信息理論的電力系統故障診斷解析模型[J]. 電力自動化設備,2014,34(2):158-164.
[7] 翁漢琍,毛鵬,林湘寧. 一種改進的電網故障診斷優化模型[J]. 電力系統自動化,2007,31(7):66-70.
[8] 文福拴,韓禎祥,田磊,等. 基于遺傳算法的電力系統故障診斷的解析模型與方法——第一部分:模型與方法[J]. 電力系統及其自動化學報,1998,10(3):1-7.
[9] 李彤,王春峰,王文波,等. 求解整數規劃的一種仿生類全局優化算法——模擬植物生長算法[J].系統工程理論與實踐,2005,25(1):76-85.
[10] 胡年平,徐芳敏,謝寧,等. 改進小生境粒子群算法應用于電網故障診斷[J]. 電網與清潔能源,2018,34(2):9-16.
[11] 文福栓,韓禎祥. 基于遺傳算法和模擬退火算法電力系統的故障診斷[J]. 中國電機工程學報,1994,14(3):29-35.

 

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