基于专家知识与监测数据联合驱动的高压开关柜状态评估OA北大核心CSTPCD

High-voltage switchgear(HVS)is an essential component of the power system,and its state evaluation is of great significance for maintaining stability and safety of the system.In fact,HVS is usually served in such harsh environments with high temperatures and humidity.It is common knowledge that the state of HVS is lost randomly due to unavoidable sensor failure and uncontrollable human factors,which has limitations on the integrity and avail-ability of data.As a result,data driven methods which require high-quality data are hard to be directly applied for state evaluation of HVS.To solve the above problem,an integrated state evaluation approach is proposed by combi-ning domain-knowledge-driven methodology with monitored-data-driven methodology.First,the internal structure of the HVS is in-depth analyzed and it is categorized into three regions pursuant to the terms of the device utility,i.e.,the cable-room region,busbar-room region,and circuit breaker-room region.Then,a three-layer Bayesian network(BN)topology for state evaluation of HVS is conducted by the causality analysis of the relationship be-tween each pair of system states,regional states,and basic device states.While the expert domain knowledge is adopted and three kinds of constraint penalty functions matching the BN model are developed.Since the constrained optimization problem is solved,the parameter estimation performance of BN model under incomplete data sets is im-proved and the accurate state evaluation of the HVS is accomplished.Finally,comparative experiments are carried out on the self-designed 10 kV HVS prototype.The results show that the proposed approach is able to achieve the goal of accurate state evaluation and has superior performance in items of both accuracy and convergence compared with support vector machines(SVM)and back propagation(BP)neural network.