利用电气量和时序信息的改进Petri网故障诊断模型

现有的电力系统故障诊断模型在利用多渠道、多方位的测控信息方面,大多采用决策级别的信息融合,在信息冲突或不完整时有可能导致误判;对时序信息的利用一般局限于对信息的初步筛选,未能充分利用时标偏差与信息准确程度之间的关联性。在此背景下,以现有模糊Petri网故障诊断模型为基础,对多源信息进行融合分析,充分利用设备遥信动作事件的顺序记录(SOE)、由广域测量系统得到的电气量信息以及这些信息所包含的时序特性,建立了一种考虑时序信息的多源Petri网故障诊断模型。所发展的故障诊断模型通过利用时序信息对保护设备动作事件进行置信度评价,利用多源信息的冗余度对所缺警报信息进行补充并校验所收到的警报信息的正确性,可以有效提高故障诊断的准确度和可靠性。最后,采用南方电网和江苏电力系统发生过的实际故障案例对所建立的故障诊断模型进行了验证。

An Improved Petri Net Model for Power System Fault Diagnosis Employing Electrical Data and Temporal Constraints

The existing power system fault diagnosis models employing multi-faceted monitoring and control information mostly implement information fusion in the decision-making level, and may lead to false diagnosis results when the received information is conflict or incomplete. The temporal information of alarms is only employed for preliminary screening, and the relevance between timestamps and information accuracy is not fully taken into consideration. Given this background, based on existing fuzzy Petri net models for power system fault diagnosis, an improved model for this purpose is presented employing electrical data and temporal constraints, and could accommodate the sequence of events (SOE) information from supervisory control and data acquisition (SCADA), electric parameters as well as their temporal features from the wide area measurement system (WAMS). In the proposed fault diagnosis model, a multi-source information fusion technique is employed to integrate, analyze and process information from multiple sources. The confidence levels of events are evaluated by the relevant temporal information. The redundancy of multiple sources of information is used to verify the correctness of the received information and to estimate some important but missed information, and hence the accuracy and reliability of fault diagnosis results could be significantly improved. Finally, actual fault scenarios from South China power system and Jiangsu power system are served for demonstrating the presented fault diagnosis model. This work is jointly supported by National Key Technologies R&D Program (No. 2011BAA07B02) and State Grid Jiangsu Electric Power Company (No. JS2014002).