Research On The Fuzzy Petri Nets Based Fault Diagnosis For Power System

The reliability and power quality of the power network have been improved with continuous expansion of the power network and enhancement of the network connection. However, the potential risk of power system malfunction is also increased. To reduce or avoid the effect of power system malfunction, quick various power system faults detection and accurate different fault type classification are necessary. Meantime, as the development of the smart grid, quick detecting, accurate diagnosis and eliminating the potential fault are required to improve the system safety and reliability. Therefore, research and foundation of a high performance and intelligent power network fault diagnosis system is essential for artificial cognition and treatment of the power system malfunction.Currently, many research results have already issued at the related field. However, the greatest obstacle, which hinders the application of the fault diagnosis system, is the unavoidable on-site factors like losing the alarm information, false information and incomplete signal, etc. These factors will result in aberration of the fault characteristic and unassured fault diagnosis. Petri nets can clearly, visually and precisely depict and research the discrete dynamic event in fault state of each component. It has shown good fault tolerance capability. With wide application of Petri nets, fuzzy Petri nets which imitates human intelligent cognition was proposed by experts and researchers. Fuzzy Petri nets, adaptive fuzzy Petri nets, directional weighted fuzzy Petri nets, temporal order fuzzy Petri nets and hybrid fuzzy Petri nets are presented in this paper. Methodology of fuzzy Petri nets is applied in power system fault component diagnosis and fault type recognition.Adaptive fuzzy Petri nets and its application in power system fault diagnosis are studied. This method not only avoids errors caused by subjective factors in certain degree, but also fully considerate the influence caused by protective relays and circuit breakers. The diagnosis knowledge is adjusted by BP neural network. Diagnosis precision is improved by applying statistic relay correct action rate to represent the action value of protective relays and circuit breakers. Inference procedure is based on reality and more convinced. A220kV power system network model is used as verification. Results show that correct diagnosis can still be obtained even with protection malfunction, protection rejection and protection information loss.A new power system fault diagnosis method based on directional weighted fuzzy Petri nets is proposed. The traditional fuzzy Petri nets for power system fault diagnosis is unable to adapt to topology changes. In directional weighted fuzzy Petri nets fault diagnosis method, fuzzy Petri nets model is build on each fault spread direction. The method can adapt to topology changes automatically without remodeling and obtain good structure adaptability. A14nodes power network is chosen as an example for single fault and complex fault diagnosis under uncertain information. Simulation results show good structure adaptation and excellent fault tolerance capability. It provides new thoughts in power network fault diagnosis.Alarm information processing method with temporal order is proposed in this paper. Aiming at solving the problem of insufficient consideration of temporal order characteristic of the alarm information, time constraint route is presented without the algorithm structure adaptability. Temporal order characteristic is directly expressed and the distinguished algorithm for alarm error information is analyzed. Comparison of fault diagnosis results between two scenarios:with time constraint and without time constraint are made to a220kV power network as an example. After the consideration of alarm information's temporal order, time constraint relationship between alarm information is depicted in detail and more accurate results are obtained. Fault diagnosis method based on fuzzy Petri nets involing temporal order information is proposed taking the algorithm structure adaptability into account. A fast model revision algorithm for power network topology variation has been studied to improve the remodeling problem when the network topology changes. Some examples of a14-node power network are used as verification. Simulation results show that the novel method can obtain better diagnosis results and be adapted to the network topology change for large power system fault diagnosis with the consideration of temporal order characteristic.Because single Petri nets can not be used to recognize fault type, a new hybrid fuzzy Petri nets based method for recognizing fault type in high voltage transmission line is proposed. The feature extraction of wavelet transform and fuzzy logic are integrated as pre-processing for input vectors of fuzzy Petri nets. Through deduction of fuzzy Petri nets, statistic results of various fault happening reliability rate are given to staff for assisting decision-making. Take a500kV HVDC transmission line as an example, various simulation scenarios, like different fault types, initial angles of fault voltage, transition resistances and fault locations, etc. are chosen for simulation verification. Results show that this method has good adaptability in different wavelets, data windows, fault conditions, noises and line parameters. Successful experiment results are obtained with hybrid fuzzy Petri nets application in power system fault diagnosis. Moreover, this method can be easily migrated to other network.At last, an integrated fault diagnosis platform with power system fault component and type recognition function is developed based on Matlab graphical user interfaces (GUI). The platform has two subsystems:intelligent fault component diagnosis and intelligent fault type recognition.The thesis is supported by National Natural Science Foundation of China:'Information theory based power network fault diagnosis of multi-sourced signal'(No.50877068,2009-2011).