1.Electric Power Research Institute of State Grid Xinjiang Electric Power Co.， Ltd.；2.State Grid Xinjiang Electric Power Co.， Ltd.
Abstract： During the process of abnormal sampling value determination in the equipment status monitoring unit, if detection is only carried out by mining a single feature target, it is impossible to synchronously associate the detection objects within the interval, resulting in a decrease in the accuracy of the output results. For this purpose, an analysis and research on the abnormal detection technology of sampling values for the equipment condition monitoring unit of intelligent substations is proposed. The sampling values of the monitoring unit are quantified by using autoregressive fitting. After eliminating the non-uniform error values, the regression processing results are output. An abnormal response mechanism is established, using the autoregressive results as guidance, to mine the abnormal features of associated sampling within the normal response distribution interval, synchronously sample the detection objects, and determine the specific abnormal targets. Based on the abnormal targets output, alarm identification and location are carried out. Combined with the detection deviation, backtracking compensation and correction are conducted to ensure that the substation equipment is restored to the expected operating state. The experimental results show that the false detection rate of the proposed method is between 0.8% and 1.4%. It is the highest in the initial period, reaching 1.4%, and gradually stabilizes in the subsequent periods, controlled between 1.0% and 1.1%, without significant fluctuations. This indicates that the detection accuracy and effectiveness have improved, and the performance is superior and reliable.
