基于模型分层的配电网故障诊断方法

Model-based hierarchical diagnosis method for distribution network faults

针对配电网故障的基于模型诊断方法在发生多重多相故障时存在诊断速度慢、诊断准确率不高、容错能力低的情况,提出一种适用于配电网故障的基于模型分层诊断方法。在诊断算法上,利用新的适应度函数和特征学习搜索策略来提高诊断速度和诊断准确率。在诊断模型上,利用分层的方法,将单层单次高维度运算转变为2层多次低维度运算,进而再次提高诊断速度、诊断准确率;通过定义等效部件的约束关系式提高第1层诊断的容错能力,利用电压约束和电流约束的冗余关系提高第2层诊断的容错能力。算例表明,与其他模型相比,基于模型分层诊断方法的诊断速度有了较大的提高,诊断准确率始终维持在理想值附近,容错能力明显增强;在大规模配电网故障诊断中,其优势明显。

In order to solve the problems of model-based diagnosis method for distribution network faults,such as slow diagnosis speed,low accuracy and low fault tolerance in diagnosis of multiple multi-phase faults,a model-based hierarchical diagnosis method for distribution network faults is proposed.From the aspect of diagnosis algorithm,the new fitness function and the search strategy of feature learning are used to improve the diagnosis speed and accuracy.In the diagnosis model,the single-layered single high-dimensional operations is transformed into two-layered multiple low-dimensional operations by a hierarchical approach,which further improves the diagnosis speed and accuracy.The fault tolerance ability of the first layer diagnosis is improved by defining the constraint relation of the equivalent components,and that of the second layer diagnosis is improved by using the redundancy relation of voltage constraints and current constraints.Numerical examples show that compared with other diagnosis methods,the speed of model-based hierarchical diagnosis method is greatly improved,the accuracy is always maintained near the ideal value,and the fault tolerance ability is obviously enhanced.In the fault diagnosis of large-scale distribution network,the model-based hierarchical diagnosis method has obvious advantages.