Fault Diagnosis Of Power Transformer Based On Gas Analysis In Oil

With the rapid development of national economy,social power consumption increasing,increasingly large scale power system,power transformer,as one of the most key equipment in power grid,and is also one of the highest frequency of equipment accident,when the failure occurs,will pose a threat to the safe and stable operation of power grid,so in a timely manner to diagnose transformer faults so as to avoid or reduce the occurrence of failure is very importantTransformer after failure occurs,there will be a lot of and gases dissolved in transformer oil,and different gas content and there is a certain relationship between transformer fault type,use this relationship to judge the fault types,this is the oil dissolved gas analysis(DGA),aimed at the shortcoming of neural network,puts forward the longicorn must algorithm(BAS)combined with BP neural network algorithm of fault diagnosis methodsIn this paper,first using the BP neural network for transformer fault diagnosis,the content of dissolved gas in transformer oil as input,in the transformer fault types as output,training for the 100 groups of data collection and test for 25 sets of data,found that the BP neural network convergence rate,easily falling into the most superior,so must be based on the longicorn algorithm of BP neural network fault diagnosis methods,through the longicorn must algorithm to optimize initial weights and threshold of BP neural network,the optimized weight as initial weights,for transformer fault diagnosis A fault diagnosis model combining genetic algorithm and BP neural network is proposedMatlab is used for simulation,and it is found that the two models have some improvement in transformer fault diagnosis speed and fault diagnosis accuracy,and make up for the shortcomings of BP network such as slow convergence speed in transformer fault diagnosis,put forward a new idea,but also has a certain reference value...