基于最大测点正常率与GPU并行加速的不良数据辨识方法

Bad Data Identification Method Based on Maximum Normal Measurement Rate and GPU Parallel Acceleration

基于测量不确定度的概念,以测点正常率最大(MNMR)为目标的电力系统抗差状态估计方法具有较好的不良数据辨识能力。然而,该模型求解困难,已有研究对该模型进行了近似等效,并采用现代内点法进行求解,但存在因近似而辨识效果降低的问题。为此,基于MNMR状态估计模型,采用杂交变异粒子群算法,提出一种基于图形处理器(GPU)并行加速的不良数据辨识算法。该算法不对MNMR模型进行近似等效,根据GPU并行计算架构特点,设计了粗粒度和细粒度结合的并行加速策略。算例结果表明,所提的算法对不良数据的误检率和漏检率较低,具有较好的不良数据辨识能力,且计算时间短,加速效率高,能够满足实际运行需求。

Based on the concept of measurement uncertainty,the robust state estimation method for power system with maximum normal measurement rate(MNMR)has good identification ability of bad data.However,the model is difficult to solve.In existing researches,the model is approximated and solved by the modern interior point method,but the problems such as lower identification effect due to approximation are existed.Therefore,based on the state estimation model of MNMR,a hybrid particle swarm optimization(PSO)algorithm with hybrid mutation is used to propose a bad data identification algorithm based on graphics processing unit(GPU)parallel acceleration.Without approximating the MNMR model and according to the architecture characteristics of the GPU parallel compution,the algorithm designs a parallel acceleration strategy combining coarse and fine granularity.The results of the case analysis show that the proposed algorithm has low false detection rate and missed detection rate for bad data,and has good identification ability of bad data,short calculation time and high acceleration efficiency,which could meet the actual operation requirements.