Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Abstract

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.