Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Abstract

The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

“…With the development of artificial intelligence, more and more researchers are exploring the use of machine learning methods to solve the problem of fault diagnosis for OWTs [79][80][81]. Relying on intelligent robots for data collection and intelligent algorithms for damage identification, the accuracy rate can reach up to 90% [13].…”

A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network

“…The training methodologies for these models were similar to those used for the EMD-CNN-TGNN model. These hybrid models were compared with traditional models employing autoregressive integrated moving average (ARIMA) and support vector regression (SVR) to highlight their performance differences and applicability in handling complex datasets with temporal and spatial dependencies [15,16,24,25].…”

“…To provide a comprehensive performance comparison framework, this research will also employ traditional machine learning algorithms such as the autoregressive integrated moving average (ARIMA) model and support vector regression (SVR) as baseline models [15]. The ARIMA model is widely used in electricity demand and price forecasting due to its ability to handle the non-stationarity of time series data [16].…”