Research On Grid Fault Diagnosis Method Based On Electrical And Switching Information

With the deep integration of intelligent information technology and traditional power grid,the degree of intelligence and digitization of the power grid is increasing,and the utilization of intelligent technology and big data to realize the stable operation of the power grid has become a major mission to ensure national energy security.Currently,the power grid is more closely connected to the region,and once a fault occurs in the process of cross-regional,long-distance and large-capacity transmission,a large amount of alarm information will flood into the control center,which requires the staff to complete the analysis and processing of the information in a very short time,so as to ensure the stable operation of the power grid.The conventional fault diagnosis method only uses a large amount of switching information,but ignores the electrical information with better accuracy and real-time,so it is difficult to solve the problem of power grid fault diagnosis under the new development situation.With the continuous development of grid intelligence technology,the data sources and data volume collected by the control center are increased.By fully combining the accuracy of electrical information and the redundancy of switching information,this thesis proposes a grid fault diagnosis method based on electrical and switching information.First,several common power information acquisition systems are introduced and the characteristics of each type of information sources are analyzed,while the characteristics of each type of systems are analyzed and compared.By combining the principles of relay protection configuration of power systems with the characteristics of different information sources,a framework of grid fault diagnosis based on intelligent algorithm is planned.Second,a fault diagnosis model based on fault current and voltage information is established.The model uses wavelet transform decomposition to extract current amplitude distortion,current energy distortion,voltage amplitude distortion and voltage energy distortion values from the fault currents and voltages measured by simulation in PSCAD,and converts the characteristic values into probabilistic representation of current and voltage fault degrees,using the fault degrees as the source of electrical quantity evidence in information fusion.Then,a fault diagnosis model based on switch information was established.This model utilizes fuzzy theory to improve traditional Petri nets,while taking into account the action timing of relay protection devices,and establishes a hierarchical TFPN model corresponding to the line and bus.The hierarchical model can be quickly modified when the topology of the power grid changes.By building a TFPN simulation calculation model in MATLAB software,the fault degree based on switching value is obtained through simulation examples,and this fault degree is used as the source of switching value evidence in information synthesis.Finally,information fusion algorithms for different information sources were studied.By introducing the traditional D-S evidence theory synthesis algorithm and explaining its paradoxes when synthesizing strongly conflicting data,the D-S evidence theory based on evidence alliance was adopted;This method divides the evidence sources into different evidence alliances,and then synthesizes the fault degree obtained from the previous text based on the weighted values of each evidence alliance.Diagnosis decisions are made based on the difference threshold decision principle of the credibility function to determine the faulty component.This method can make correct diagnostic decisions for faults in the power system in the event of single or multiple faults,as well as incorrect or missing alarm information.It compensates for the one-sidedness of fault diagnosis methods based on single switch or electrical quantities,and verified the feasibility of the power grid fault diagnosis method based on electrical and switching information.This thesis contains a total of 37 figures,17 tables and 81 references.