Wind Farm Collector Line Fault Diagnosis and Location System Based on CNN-LSTM and ICEEMDAN-PE Combined with Wavelet Denoising

To enhance the accuracy and precision of fault diagnosis and location for the collector lines in wind farms under complex operating conditions, an intelligent combined method based on CNN-LSTM and ICEEMDAN-PE-improved wavelet threshold denoising is proposed. A wind power plant model is established using the PSCADV46/EMTDC software. In response to the issue of indistinct fault current signal characteristics under complex fault conditions, a hybrid fault diagnosis model is constructed using CNN-LSTM. The convolutional neural network is utilized to extract the local time-frequency features of the current signals, while the long short-term memory network is employed to capture the dynamic time series patterns of faults. Combined with the improved phase-mode transformation, various types of faults are intelligently classified, effectively resolving the problem of fault feature extraction and achieving a fault diagnosis accuracy rate of 96.5%. To resolve the problem of small fault current amplitudes, low fault traveling wave amplitudes, and difficulty in accurate location due to noise interference in actual wind farms with high-resistance grounding faults, a combined denoising algorithm based on ICEEMDAN-PE-improved wavelet threshold is proposed. This algorithm, through the collaborative optimization of modal decomposition and entropy threshold, significantly improves the signal-to-noise ratio and reduces the root mean square error under simulated conditions with injected Gaussian white noise, stabilizing the fault location error within 0.5%. Extensive simulation results demonstrate that the fault diagnosis and location method proposed in this paper can effectively meet engineering requirements and provide reliable technical support for the intelligent operation and maintenance system of a wind farm.