Simulation Study on Defect Detection of Penstocks Based on Kernel Principal Component Analysis

The pressure penstock of hydropower stations operates in harsh environments for extended periods, making the pipes susceptible to cracks, corrosion, and other defects. If these defects are not addressed in a timely manner, they may jeopardize the safe operation of the power station. Therefore, the efficient and accurate detection and identification of pipeline defects has become an urgent scientific problem to be solved. In response to this issue, this paper proposes a pipeline defect detection method that combines Kernel Principal Component Analysis (KPCA) with Support Vector Machine (SVM). Firstly, pulsed eddy current detection data is simulated using COMSOL software, and then this method is employed to judge the defects. Experimental results show that for the simulated data, the prediction accuracy of this method can reach 96.8%, enabling high-precision identification of the main penstock. This provides more reliable technical support for the safe operation of hydropower stations.