In light of the issues related to the low detection accuracy and inefficiency of existing anomaly detection methods when processing data from photovoltaic power plants, this paper presents an enhanced anomaly detection method for photovoltaic power plants using an improved isolation Forest algorithm. Firstly, the pearson feature selector is employed to choose high-correlation features as model inputs. Secondly, data is segmented through non-axis parallel cutting, and a cutting standard function is designed to select the optimal hyperplane. Subsequently, parallel training is conducted to construct multiple isolated trees, forming an isolated Forest. Finally, the isolated density method is utilized for data anomaly assessment, addressing the problem of overlooking local anomalies in anomaly detection. The improved isolation Forest algorithm is compared with eight other anomaly detection methods across seven datasets. After experimental analysis, this approach effectively enhances the accuracy and efficiency of photovoltaic data anomaly detection. The detection accuracy value in the photovoltaic dataset is found to be 10.36% higher than the original isolation Forest. Moreover, it exhibits robust detection capabilities in other standard datasets, providing reliable data support for photovoltaic power generation and grid connection research.