Analytic Model And Methods For Power System Fault Diagnosis With High Fault-tolerance Capability

When a fault occurs in a section or component of a given power system, the dispatching center will receive a flood of alarms from different monitoring systems, thus it is difficult for the dispatchers to process the information and judge the fault location accurately in a short period of time. Therefore, a fault diagnosis system is introduced to identify faults rapidly and accurately, in order to ensure the security and stability of the power system associated and improve the reliability of power supply. The main problems of the existing analytic models for fault diagnosis include:limited fault information source, low information redundancy, over reliance on the information of protection relays (PRs) and circuit breakers (CBs) after the fault. In an actual situation, PRs and CBs may fail to operate or refuse to act, and some alarms may be false or lost in the transmitting process, which have negative impacts on the accuracy of fault diagnosis result.Given this background, based on existing analytic model, uncertainties in the fault process are considered to improve the fault tolerance capability by developing a more reasonable and accurate model and increasing the information redundancy. Some research results are obtained as follows:1) The aim and functions of the fault diagnosis system are first analyzed, and the composition and characteristics of the fault information source are introduced. According to the layered characteristics of the fault information, a layered structure of power system fault diagnosis is constructed. The fault information is used reasonably according to the fault condition:on one hand, the diagnosis process is accelerated in a simple fault; on the other hand, the accuracy can be improved in a complex fault.2) The existing analytic model is introduced. First, the process of fault area identification is studied, the theory is analyzed and the steps are stated. Then, the objective function of the analytic model is introduced; the formulas of the expected states of PRs and CBs are put forward. Finally, the problems that need to solve in the automatic forming process of the objective function. 3) In order to handle uncertain factors, including the malfunctioning and other improper actions of PRs and CBs, in addition to the false and/or missing alarms, the authors have introduced a chance-constrained programming model into the application of power system fault diagnosis. This thesis presents such a novel type of fault diagnosis model with high fault tolerance. The Monte Carlo simulation based genetic algorithm is employed to solve the model. An actual complex fault scenario at a substation in Zhejiang province, China, is used to test the proposed method. As shown by the case study, the developed model produced a diagnosis result consistent with the real fault. Furthermore, the computation speed of the developed method meets the requirements of on-line fault diagnosis.4) The existing analytic model for power system fault diagnosis with a single data resource is insufficient in information redundancy and has difficulty in getting the correct diagnosis in a complex fault. The development of WAMS based on PMU makes real-time and accurate electrical data information of the system available. Therefore, electrical data information is introduced to improve the existing analytic model. Fault area is rapidly confirmed using states of circuit breakers combined with electrical data. Then, fault section is diagnosed using the developed analytic model. Finally, a fault scenario is served for demonstrating the feasibility and efficiency of the developed model. It is verified by simulation results that the developed model has high fault tolerance and can locate fault even with a large amount of malfunctions and lost alarms.Finally, several conclusions are obtained based on the research outcomes, and directions for future research indicated.