Abstract: The development of artificial intelligence and machine learning provides a new and efficient solution for the false data detection of supervisory control and data acquisition(SCADA)system.At present, using single classifier in machine learning to detect the false data in power grid has some problems, such as low accuracy, high false detection rate, poor model differentiation ability and so on. This paper proposes a detection method based on ensemble learning to binary classify the power grid data, such as gradient boosting decision tree,XGBoost,LightGBM, RF-LightGBM and so on. Bagging classifier is used as the base classifier. After Bayesian parameter adjustment, the voting strategy is used to integrate.Ensemble learning not only integrates the advantages of each classifier, but also reduces the false detection rate, and improves the detection accuracy and the stability of model distinguishing ability.The experimental results show that the algorithm has certain application and reference value in the field of data detection.

Key words: SCADA system, integrated learning, Bayesian parameter adjustment, intrusion detection