基于混合优化算法的电网故障诊断
谢瑞，张兴旺
（南昌工程学院 江西省精密驱动与控制重点实验室，江西 南昌 333000）
摘 要：电网故障过程中保护和断路器动作及告警信息存在不确定性，会使原有电网故障解析模型诊断出现错误。在现有解析模型基础上，通过电网结构、保护配置及断路器的动作规则进行解析，考虑各级保护之间的互相影响，针对可疑母线和线路分别建立目标函数，构建新的解析模型。采用混合优化算法对目标函数进行求解，将模拟植物生长算法（PGSA）与粒子群算法（PSO）结合，初始生长点的选取对于PGSA能否收敛于全局最优解起着决定作用，先通过PSO的高鲁棒性初选优秀的初始生长点，再基于PGSA的高效搜索能力得到最终的全局最优解。算例结果表明，改进的解析模型更加合理，混合优化算法搜索速度与收敛精度大幅度提高。
关键词：故障诊断；优化模型；模拟植物生长算法；粒子群算法；告警信息
中图分类号：TM711 文献标识码：A 文章编号：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
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