基于改进隐马尔可夫模型的金属氧化物避雷器劣化监测方法

An MOA Deterioration Monitoring Method Based on an Improved HMM

针对MOA(金属氧化物避雷器)劣化监测问题,提出一种基于改进HMM(隐马尔可夫模型)的监测方法。该方法通过获取变电站内MOA泄漏电流数据,建立应用于MOA劣化监测的改进HMM模型;根据变电站内连接于同一母线的MOA泄漏电流的谐波含量,应用D-S证据融合理论与粒子群算法得出HMM观测概率矩阵。通过MATLAB仿真分析了电网电压谐波对该模型的影响,结果表明,在电压谐波的干扰下,MOA非线性特性系数a与k的最大误差分别为0.21%与0.02%,证明了该模型可以有效消除电网电压谐波的影响,提升MOA劣化在线监测的准确性。

In view of MOA(metal-oxide arrester)deterioration monitoring,a monitoring method based on an improved hidden Markov model(HMM)is proposed. An improved HMM model used for MOA deterioration monitoring is established by obtaining MOA leakage current data in a substation. According to the MOA leakage current harmonics content of the same bus in the substation,the HMM observation probability matrix is obtained using D-S(Dempster-Shafter)evidence fusion theory and particle swarm optimization. The influence of grid voltage harmonics on the model is studied by MATLAB simulation. The simulation results show that the maximum errors of MOA nonlinear characteristic coefficients a and k are 0.21% and 0.02%,respectively,under the interference of voltage harmonics. It has been proved that the model can counteract the influence of power grid voltage harmonics and improve the online monitoring accuracy of MOA deterioration.