In-depth Analysis And Evaluation Of Power Fitting Test Results

In power transmission and transformation equipment, components distribution line hardware serve functions of connection, support, and current transmission; their status information is crucial for assessing the overall health of the system. In scientific research, test data of overhead line connectors under different environmental conditions are collected by constructing an experimental platform, including phenomena such as corona discharge and partial discharge under external influencing factors such as temperature and sandstorms. These state indicators reflect the behavioral characteristics of overhead line hardware in actual operation.

Combining image processing and automatic detection technologies, detection results can be classified and feature extracted. By using deep learning and other methods to analyze the collected image data, it is possible to distinguish whether there are signs of discharge in overhead power line connectors and the severity of the discharge. Quantitative indicators, such as the correlation between discharge area and discharge quantity, provide a quantifiable basis for subsequent evaluation.

Evaluation of detection results is typically based on statistical analysis and model evaluation. By aggregating and comparing the detection results of multiple overhead power line hardware samples, the frequency and distribution characteristics of different types of defects can be analyzed, providing a theoretical basis for subsequent maintenance plans and condition monitoring strategies. For example, experiments using a multi-class classification model based on neural networks show that the classification accuracy of transmission line hardware defect identification can reach a high level, providing empirical support for the reliability evaluation of the detection results.