Fault Diagnosis For Power Transformer Based On Mind Evolution Algorithm Optimized Neural Network

Power transformer is one of the most important electrical equipment in the electric system. And it is also one of the equipments which lends to the most electric accidents. It is an important issue to find the potential faults of the transformer,to keep it operating safely,and to improve the reliability of power supply. Therefore, it has important practical significance to study the fault diagnosis technology of transformer in order to improve the level of operation and maintenance of the transformer.Dissolved Gases Analysis (DGA)is an important means to transformer internal fault diagnosis. And it offers an important basis to find the general incipient faults of the transformer indirectly. Firstly,this paper analyses the variety ruler of gases dissolved in transformer oil and the relationship between the faults of transformer and the gases dissolved in transformer oil. This paper compares the advantage and disadvantage among the methods of traditional transformer fault diagnosis. For example,three-ratio method is currently widely used in China. But there are two shortcomings to use the three-ratio method as a criterion of transformer fault diagnosis. The shortcomings are coding defect and threshold criterion defect. This paper analyzes deeply at the basic of predecessor's work about the essence, the main algorithm, characteristics of neural networks and mind evolutional algorithm.Then appliced in diagnosis for power transformer and acquired good diagnosis conclusion.Artificial Neural Networks has opened up new avenues for solving the shortage of traditional methods because of its advantages of distributed parallel processing, adaptive, self-learning, associative memory and non-linear mapping. However, due to its design feature of neural networks, its rapidity of convergence is slow, and its performance is often been impacted by the local minimum points. When the system demands many learning samples, high precision and complex input-output relationship, the rapidity of convergence of network is slow, the accuracy of convergence is not satisfactory, even no convergence. Mind Evolutionary Algorithm has the global optimization ability. It can effectively improve the convergence speed and convergence accuracy of neural networks, and improve the success rate of fault diagnosis. In order to compensate the lack of neural networks, this paper presents the method which is to optimize the weights of neural networks using of mind evolutionary algorithm according to characteristics of dissolved gas of transformer's oil and the characteristics of fault type. The method can avoid the neural network into a local minimum and increase the rapidity of convergence.After neural network model optimized by mind evolutional algorithm is applied to transformer fault diagnosis, training and diagnostic results show that: the system reach convergence. It is obvious improved the rapid of convergence than neural networks which reach convergence. After testing the system using the 75 groups of sample data, the result confirmed the accuracy of this fault diagnosis system was significantly higher than China's current implementation of standards DT/T722-2000 improved three-ratio method. This system greatly improves the reliability and accuracy of diagnosis.It's a good technical director for fault diagrosis for power transformer and the status of overhaul.