Abnormal operation detection in heat power plant using ensemble of binary classifiers

Abstract

The problem of abnormal operation detection is considered for prediction of malfunctions appearance and their progress in the equipment of power plant. Abnormal operation detection method based on multivariate state estimation technique (MSET) along with machine learning algorithms is proposed. The ensemble of linear regression models is used for feature construction. The ensembles of binary classifiers (logistic regressions) together with the multilayer neural network are used for the abnormal operation index calculation based on the constructed features. The method was applied to abnormal operation detection in turbo feed pump (TFP 1100-350-17-4) at Kashirskaya heat power plant (Moscow region, Kashira). It is shown that the abnormal operation index of the pump starts to increase a few days before accidents appear and stays close to zero during the normal operation periods. The obtained results demonstrate that the developed model can be used to detect and predict operation anomalies in the power plant equipment.