电能质量治理设备运行状态识别及其治理效果评价*

针对目前电网中电能质量干扰源复杂、用户侧治理设备管理困难的现状,文中提出了基于电网侧电能质量监测数据,对用户侧治理设备进行运行状态识别以及治理效果评价的算法,实现电网公司对用户侧电能质量治理设备的非侵入式监测。文章利用概率神经网络,将电压偏差、三相电压不平衡度、电压谐波总畸变率以及电压长时闪变作为网络的输入参数,对治理设备的工况进行分类,以实现治理设备的运行状态识别;通过对比治理设备投入前后的电能质量评估结果,结合模糊综合评价法来完成治理设备的治理效果评价。以安徽电网某变电站的监测数据为例,分析了所提电能质量治理设备运行状态识别方法和治理效果评价方法的实用性。

Recognition of Operating State of Power Quality Compensation Equipment and Evaluation of its Effect

In view of the complex power quality disturbance sources and the difficult management of user-side power quality compensation equipment in power grid, this paper presents an algorithm for identifying the operation status of user-side compensation equipment and evaluating its effect based on the monitoring data of power quality on power grid side. Thus, the non-intrusive monitoring of compensation equipment on the user side can be realized by using the monitoring data. Firstly, the probabilistic neural network is used to classify the working conditions of compensation equipment by taking the voltage deviation, three-phase voltage unbalance, total harmonic distortion rate and long-term voltage flicker as the input parameters, so as to realize the evaluation of the operation status of the compensation equipment. Secondly, by comparing the results of power quality evaluation before and after the operation of compensation equipment, this paper uses the fuzzy comprehensive evaluation method to achieve the effect evaluation of compensation equipment. Taking the monitoring data of a substation in Anhui Power Grid as an example, the practicability of the proposed state identification method and the effect evaluation method of the compensation equipment is analyzed.