Fault prediction for offshore wind turbines using mathematical simulation

This paper addresses frequent faults and high maintenance costs in offshore wind turbines operating in complex marine environments. The proposal of a fault prediction method based on a hybrid deep learning model is presented . A mathematical model of the offshore wind turbine is first developed, with fault injection techniques used to generate simulation datasets. These datasets include multiple input features such as wind speed, pitch angle, and generator speed, with actual turbine powe r used as the output target. A hybrid deep learning architecture integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks is developed. The convolutional neural network component extracts spatial features from time-series data, while the long short-term memory component captures temporal dependencies. The training phase employs backpropagation with gradient-based optimization to minimize loss metrics. Experimental results confirm that the developed model delivers strong predictive performance across three representative fault scenarios: sensor faults, actuator faults, and system-level degradation. The model is particularly effective in identifying sensor and actuator faults with high accuracy. This approach provides a promising solution for early fault detection and intelligent maintenance in offshore wind power systems, helping to reduce operational costs, enhance power generation efficiency, and ensure grid reliability.