Algorithm optimization based on intelligent management of computer electrical equipment: A comprehensive method for PT power monitoring and remote fault indicator.

The article focuses on optimizing power monitoring and fault diagnosis of electromechanical equipment by applying a long short-term memory (LSTM) network algorithm combined with potential transformer (PT) data acquisition and remote fault indicators. It details a process involving high-speed sampling, wavelet transform (WT) for noise reduction, fast Fourier transform (FFT) for feature extraction, and a multi-layer LSTM network trained on time series power data to detect anomalies and predict faults in real-time. The approach establishes a fault mode knowledge base for pattern matching and remote fault signaling, resulting in improved diagnostic accuracy (up to 93.4%) and reduced response times (e.g., voltage drop fault response time shortened by 3.3%). Experimental evaluations demonstrate that this integrated method enhances monitoring precision, fault prediction capability, and operational efficiency in electromechanical equipment management, while acknowledging challenges in processing large-scale data and adapting to complex environments.