针对基本优化算法在电网发生复杂故障时求解的准确度不高、易陷入局部最优等问题，提出了一种粒子群混合灰狼算法（Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimization,PSO-GWO）优化电网解析模型的诊断方法。引入了一种计及误动、拒动等完备故障信息的完全解析模型，以应对复杂故障时产生的不确定问题。利用粒子群算法具有个体记忆性的特点，提出了一种与灰狼算法在结构上混合的优化算法。通过包含不确定信息的算例分析，发现混合算法具有平衡算法的局部搜索和全局开发的能力，并且诊断结果能够对故障情况作出解释，验证了混合算法比基本算法更适用于电网故障诊断。
英文摘要:
Aiming at the problem that the accuracy of the basic optimization algorithm is not high, and it is easy to fall into the local optimum when the complex faults occur in the power grid, a diagnosis method of hybrid algorithm of particle swarm optimization (PSO) and grey wolf optimization (GWO) is proposed to optimize the analytical model of power grid. Firstly, a complete analytical model that takes into account of complete fault information such as misoperation and refusal is introduced to cope with the uncertain problems generated by complex faults. Secondly, a hybrid optimization algorithm with GWO algorithm is proposed based on the characteristics of individual memory of PSO. Finally, through the analysis of the example with uncertain information, it is found that the hybrid algorithm has the ability of local search and global development of the balance algorithm, and the diagnosis results can explain the fault condition, which verifies that the hybrid algorithm is more suitable for fault diagnosis of power grid than the basic algorithm.