Research on Detection Method for High Ratio Abnormal Data in Photovoltaic Power Generation Output Based on Gaussian Mixture Model

The high proportion abnormal data in photovoltaic power generation output has the characteristics of overlapping and multi Gaussian distribution. How to accurately remove the high proportion abnormal data in photovoltaic power generation output is a key issue in achieving accurate prediction of photovoltaic power. The article analyzes the characteristics of high proportion abnormal data in photovoltaic power generation output, and studies a detection method for high proportion abnormal data in photovoltaic power generation output based on Gaussian mixture model. We have established an algorithm model for the Gaussian mixture model, using the Expectation Maximization (EM) algorithm to estimate the parameters of the Gaussian mixture model, and using the algorithm model to detect and eliminate high proportion abnormal data in photovoltaic power generation output. Actual case analysis and comparative experiments show that the method proposed in the article can accurately detect high proportion abnormal data from multiple classifications, making it more suitable for detecting high proportion abnormal data in photovoltaic power generation output compared to traditional abnormal data detection methods.