A Fuzzy Inference Method for Assessing the Risk of Economic Losses of Voltage Transients for Sensitive Industrial Users

Voltage transients have become an important power quality event affecting quality power supply on the customer side, and accurate assessment of the economic loss of industrial users after the occurrence of sags is the key to carrying out the management work. For the temporary drop events in complex industrial processes, this paper firstly takes the voltage tolerance curve (VTC) of equipment combination and process immunity time (PIT) as the characteristic parameters, portrays the fuzzy relationship between the temporary drop characteristics and sub-process failure probability based on Mamdani algorithm, realizes the classification fuzzification of temporary drop events, and calculates the exact value of sub-process failure probability. Then, the actual disturbed devices are judged according to the topology of the customer ′s power supply system and the physical location where the transients occur, and the traditional fault tree is modified to obtain a modified process fault tree, based on which the mathematical relationship between the transient characteristic parameters and the economic loss of the customer is established. Finally, the accuracy and robustness of the proposed method are verified by simulating and analyzing the precision temperature control system of an optical center in Jiangsu Province. The conclusions obtained can provide important references for sensitive customers to effectively assess temporary economic losses in industrial processes and for power supply companies to optimize their power supply schemes.