A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
The paper presents analysis of optimisation results of power system stabilizer (PSS) parameters when taking into account the uncertainty of mathematical model parameters of the power system (PS) elements. The Pareto optimisation was used for optimisation of the system stabilizer parameters. Parameters of five stabilizers of PSS3B type were determined in optimisation process with use of a genetic algorithm with tournament selection. The results obtained were assessed from the point of view of selecting the criterion function. The analysis of influence of the parameter uncertainty on the quality of the results obtained was performed.