Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies

• 作者: Amir GHOLAMI Anurag K.SRIVASTAVA Shikhar PANDEY
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: Failure diagnosis TRANSMISSION PROTECTION system PROTECTION mis-operation PHASOR measurement unit(PMU) DATA anomaly and cleaning Ensemble method
• 年，卷（期）: 2019,（4）
• 页码范围: 767-778
• 页数: 12页
• 分类号: TM933.313

摘要

To guarantee a reliable power supply,the expected operation of all the components in the power system is critical.Distance protection system is primarily responsible of isolating the faulty section from the healthy part for the grid.Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex event analysis to manually find the root cause of the observed system state.If not handled in time,it may lead to the propagation of the faults/failures to the adjacent transmission lines and components.With availability of the synchronized measurements from phasor measurement units(PMUs),real-time system monitoring and automated failure diagnosis are feasible.With multiple adverse events and possible data anomalies,the complexity of the problem will be escalated.In this paper,a PMUbased algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state,e.g.multiple line tripping andbreaker failures.The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool,which is tailored for the cases in which the PMU anomalies are present.In the developed algorithm,the validity of the PMU data is critical.However,such causes as communication errors or cyber-attacks might lead to the PMU data anomalies.This issue is welladdressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed.Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms.Additionally,the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes.Finally,both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator.The results show the accuracy of the enhanced tool and its advantages over the primary version of the