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Application of NSGA-Ⅱ Algorithm for fault diagnosis in power system

机译：NSGA-Ⅱ算法在电力系统故障诊断中的应用

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Aiming at the problem of the artificial setting of weights faced by the existing mathematical models based on optimization theory for power system fault diagnosis, this paper presents use of a non-dominated sorting in genetic algorithms-II (NSGA-II) to the fault diagnosis problem. In this work, the fault diagnosis problem is transformed into a multi-objective optimization problem by constructing a multi-objective mathematical model. Then, Pareto approach is employed to settle the model, which eliminates errors resulted in the weight setting for fault diagnosis. Meanwhile, the constraints of the model are converted into an objective function, avoiding the subjective impact caused by adopting the penalty function method. Finally, NSGA-II is put forward to obtain the Pareto-optimal solutions, and technique for order performance by similarity to ideal solution (TOPSIS) is applied to determine the diagnostic result. Based on the cases of fault diagnosis, the feasibility and effectiveness of the developed method is demonstrated.
机译：针对电力系统故障诊断中基于优化理论的现有数学模型面临的人为设置权重的问题，本文提出了遗传算法-II（NSGA-II）中非支配排序在故障诊断中的应用问题。通过建立多目标数学模型，将故障诊断问题转化为多目标优化问题。然后，采用帕累托方法来建立模型，消除了因权重设置而导致的错误，以进行故障诊断。同时，将模型的约束条件转换为目标函数，避免了采用惩罚函数法带来的主观影响。最后，提出了NSGA-II求帕累托最优解，并采用与理想解相似的排序性能技术（TOPSIS）来确定诊断结果。基于故障诊断案例，论证了该方法的可行性和有效性。

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