To address performance issues in state estimation caused by anomalous data in power systems,this paper proposes a multi-type data anomaly detection method for power system state estimation based on support vector machine (SVM). Firstly,extreme learning machine is proposed to enhance prediction accuracy in forecasting-aided state estimation(FASE).Then,SVM is utilized to detect various types of data anomalies,including bad data,load changes, and single-phase grounding,by incorporating prediction data,measurement data,and estimated values.To optimize SVM parameters and improve classification accuracy,the grey wolf algorithm is suggested to tackle the issue of penalty factor and kernel function parameters.Finally,simulations are conducted on real distribution systems,specifically the IEEE 33 and DTU 7K 47 systems,using data from different scenarios.The proposed method achieves accuracy improvement of 26.08%and 26.76%and calculating speed improvement of 46.05%under normal operating conditions, and a comprehensive enhancement of 32.04%and 29.27%in accuracy under data abnormality scenarios.The results demonstrate that the proposed method is highly generalizable and real-time,effectively detects various types of data anomalies,and enhances the performance and accuracy of state estimation.