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).