Power transformer fault diagnosis method and system based on improved firefly algorithm optimization probabilistic neural network

Abstract

The invention discloses a power transformer fault diagnosis method based on an improved firefly algorithm (PFA) optimized probabilistic neural network (PNN). The power transformer fault diagnosis method comprises the following steps: firstly, collecting fault characteristic gas by using a gas chromatographic analysis method and carrying out pretreatment by using a fused DGA algorithm; initializinga PNN neural network, a firefly algorithm and a two-dimensional particle swarm; taking the PNN smoothing factor as a firefly individual, and calculating the position and brightness of the firefly; feeding the solving result of each firefly algorithm back to the particle swarm algorithm, carrying out fitness evaluation on each particle, and updating the positions and speeds of the particles; carrying out loop iteration, substituting the obtained optimal smoothing factor into the PNN to carry out fault prediction, and training a PNN model after PFA optimization; inputting a test sample, and outputting a fault type result, thereby achieving the fault diagnosis of the power transformer. The method is high in search speed, high in diagnosis precision, small in error, and obvious in classification effect.

Description

technical field [0001] The invention relates to a transformer fault diagnosis method, in particular to a power transformer fault diagnosis method based on an improved firefly algorithm (PFA) optimized probabilistic neural network (PNN), belonging to the field of power transformer fault diagnosis. Background technique [0002] The power transformer is one of the most important electrical equipment in the power system, and it is also one of the electrical equipment that causes the most accidents in the power system. Its operating status directly affects the safety level of the system. It is an important issue concerned by the power sector to discover latent faults of transformers in time, ensure the safe operation of transformers, and improve the reliability of power supply. Therefore, it is of great practical significance to study transformer fault diagnosis technology and improve the operation and maintenance level of transformers. [0003] Dissolved Gas Analysis (DGA) in t...

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Problems solved by technology

[0004] The existing technology is to use the firefly algorithm to optimize the probabilistic neural network, but the speed of the algorithm is slow in the initial stage of optimization, and oscillation will occur in the later stage of optimization, and the final result is not satisfactory

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