Application And Research On MBD For Fault Diagnosis Of Electric Distribution Network

In the modern society, how to guarantee the continuity and reliability of power supply is very important, but with continuous enlargement of the scale of power network, the probability of its failure is increasesing. In recent years, many scholars use the theory and method of artificial intelligence in fault diagnosis of the electric distribution network in order to find out the cause of the malfunction accurate and fast, such as the expert systems based on experiences. But the expert systems could not diagnose the faults beyond experiences and are very difficult for system transplanting and maintaining. Model-based diagnosis (MBD) diagnoses faulty components using measurement value of voltage and current, and it can locate the fault before protection device and breaker active. This method has a function of failure warning, which can get rid of fault before it turns worse and take measures. It is widely used in engineering, aerospace fields, circuit diagnostic and so on.Firstly, this paper analyzes the current studies on the fault diagnosis of the electric distribution network, the background and the significance of the subject. The basic ideas, the basic processes, the main problems existing in MBD were introduced.Secondly, for MBD (Model-Based Diagnosis), a key step is to compute the minimal hitting sets from the minimal conflict sets, however the search efficiency of commonly used minimal hitting set algorithms could not satisfy the real-time requirement of complex systems. The fast convergent and high efficiency characteristic of BPSO (Binary Particle Swarm Optimization) makes it used extensively. The minimal hitting set (that is, candidate diagnosis) could be calculated from the obtained minimal conflict set based on improved BPSO, so that the algorithm maps the minimal hitting sets problem to0/1integer programming problem. The results of modeling, programming and testing show that the search efficiency of the improved BPSO is much better than HS-Tree, Boolean Algebra, genetic algorithm and other commonly used minimal hitting set algorithms and could save the time of1/3-1/2. The improved BPSO algorithm avoid the problem of memory overflow in the case of large-scale problem, which showed excellent performances in fault diagnosis of distribution network based on MBD.Thirdly, the diagnosis results based on model-based diagnosis (MBD) were a group or several groups of fault components set involved uncertainty, so the practical application effect is not good. The Bayesian probability method is adopted to identify the diagnosis. We could get the possible system state according to the minimal diagnosis candidates, and the posteriori fault probabilities of each diagnosis candidates could be calculated by Bayesian probability method. So, we could quantize the measure standard of the diagnosis identification in the form of the posteriori fault probabilities. Finally, an example of actual electric distribution network was presented to verify the feasibility and validity of this method through modeling, programming and testing.In the end, considering the structure of the electric distribution network and the demands in the fault diagnosis of the electric distribution network, this paper presents a scheme in which the theories of MBD is applied to the distribution system for fault diagnosis. Taking two10kV distribution networks as diagnosis example, model building by the software of PSCAD, when fault occurs, the fault information of the distribution network could be accurately measured. The results of modeling, programming and testing show that the MBD method had excellent performances in fault diagnosis of distribution network, and verified the feasibility and effectiveness of the method.