Data-Driven Event Diagnosis in Transmission Systems With Incomplete and Conflicting Alarms Given Sensor Malfunctions
Data-Driven Event Diagnosis in Transmission Systems With Incomplete and Conflicting Alarms Given Sensor Malfunctions
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DOI：
10.1109/tpwrd.2019.2947671
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发表时间：
2020-02
影响因子：
4.4
通讯作者：
Yazhou Jiang;A. Srivastava
中科院分区：
文献类型：
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作者：
Yazhou Jiang;A. Srivastava
在传感器故障情况下对不完整和冲突报警进行准确的故障事件诊断是电力系统运行人员面临的一个挑战问题。为解决这一问题，提出了一种基于混合整数线性规划(MILP)的数据驱动方法，用于快速确定具有不确定性的故障事件情景。不确定性包括继电器和断路器(CBS)的故障和故障，以及控制中心的传感器警报不完整/不正确。为提高故障事件诊断的准确性，综合运用相量测量单元(PMU)、监控与数据采集(SCADA)、事件顺序记录器(SERS)等多种来源的冗余报警。传感器报警的时间相关性被纳入到MILP模型的约束中。由此产生的数据驱动算法确定最可信的故障场景，该场景由控制中心的可用传感器警报很好地支持。IEEE 14节点系统、合成南卡罗来纳州500节点系统和实际复杂事件场景的仿真结果表明了该方法的有效性。
Accurate fault event diagnosis with incomplete and conflicting alarms given sensor malfunctions is a challenging problem for power system operators. To solve this problem, this study proposes a data-driven approach based on Mixed Integer Linear Programming (MILP) for fast determination of fault event scenarios with uncertainties. The uncertainties include failures and malfunction of relays and circuit breakers (CBs) as well as incomplete/incorrect sensor alarms at the control center. To improve the accuracy for fault event diagnosis, redundant alarms from multiple sources, i.e., Phasor Measurement Units (PMUs), Supervisory Control and Data Acquisition (SCADA), and Sequence of Events Recorders (SERs) are jointly used in this study. The temporal correlation of sensor alarms is incorporated in the constraints of the MILP model. The resulting data-driven algorithm determines the most credible fault scenario that is well supported by the available sensor alarms at the control center. Simulation results of the IEEE 14-bus system, the synthetic South Carolina 500-bus system, and a real-world complex event scenario demonstrate the effectiveness of the proposed approach for accurate and efficient fault event diagnosis.