GLOBAL TRENDS IN MONITORING AND PREDICTIVE MAINTENANCE OF LARGE-SCALE WIND POWER PLANTS

ABSTRACT

This study examines global trends in monitoring and predictive maintenance of large-scale wind power plants using SCADA data and machine learning models. Data from five wind farms (1.2 GW total capacity) over three years were analyzed with ARIMA, Random Forest, and Long Short-Term Memory (LSTM) networks. LSTM achieved superior accuracy, reducing prediction errors by over 70%, while Health Index (HI) analysis identified critical components requiring preventive maintenance. Implementing predictive strategies reduced downtime by 40% and saved over $1.2 million annually. Findings emphasize AI-driven maintenance, digital twins, and IEC standards as key to enhancing wind energy reliability and cost efficiency.

АННОТАЦИЯ

В статье рассматривается интеграция алгоритмов прогнозного обслуживания на основе искусственного интеллекта в архитектуры реального времени мониторинга централизованных инверторов в системах возобновляемой энергетики. С использованием моделей LSTM и случайного леса обеспечивается прогнозирование отказов и оценка остаточного ресурса компонентов. Результаты испытаний на солнечной электростанции мощностью 10 МВт показали погрешность прогноза 1,8%, предупреждение об отказе за 7 суток и снижение затрат на обслуживание на 28%, что подтверждает повышение надежности и эффективности систем.

Keywords: Wind power plants; predictive maintenance; SCADA; machine learning; LSTM; digital twins; Health Index (HI); condition-based monitoring; IEC 61400-25; renewable energy

Ключевые слова: Ветроэнергетические установки; предиктивное техническое обслуживание; SCADA; машинное обучение; LSTM; цифровые двойники; индекс технического состояния (HI); обслуживание по техническому состоянию; IEC 61400-25; возобновляемая энергетика.

Introduction

The rapid growth of wind energy over the last decade has established it as one of the leading renewable energy sources worldwide. Global installed wind power capacity exceeded 1,000 GW in 2024, reflecting accelerated adoption of clean energy policies, declining equipment costs, and advancements in large-scale wind farm development. As wind turbines increase in size and complexity, operation and maintenance (O&M) strategies have become critical for ensuring reliability, reducing downtime, and optimizing performance. Modern wind farms often operate in remote or offshore environments, where equipment accessibility is limited and maintenance costs are high, emphasizing the importance of predictive analytics and smart monitoring systems (Figure 1) [1].

Figure 1. Steady increase in global installed wind power capacity over the past decade

Monitoring and predictive maintenance are now central to the global wind energy landscape, driven by advances in digital twin technology, machine learning algorithms, and high-frequency sensor networks. These tools enable early detection of failures, reduce operational costs, and extend turbine life cycles. The integration of SCADA systems, artificial intelligence (AI), and the Internet of Things (IoT) has enabled real-time analytics and health assessments, transforming traditional scheduled maintenance into data-driven, condition-based strategies [2,3]. This paper provides a comprehensive analysis of current trends, best practices, and research developments in monitoring and predictive maintenance of large-scale wind power plants, with a focus on regional advancements and industrial implementation challenges [6].

Methods

This study applied a multi-phase methodology involving data acquisition, preprocessing, modeling, and evaluation to analyze predictive maintenance trends in large-scale wind power plants. Data from five wind farms across Asia, Europe, and North America (1.2 GW total capacity) were collected over three years at 10-minute intervals using SCADA systems, capturing parameters such as rotor speed, generator temperature, gearbox vibration, and power output, supplemented by maintenance and environmental records. Preprocessing included outlier removal, sensor drift correction, and k-nearest neighbors imputation, followed by normalization and feature engineering to extract statistical, frequency, and trend-based indicators. These features were used to train ARIMA, Random Forest, and Long Short-Term Memory (LSTM) models, enabling accurate condition forecasting and early fault detection. To assess component-level reliability, a Health Index (HI) was computed for critical subsystems (gearbox, generator, blades, yaw system). The Health Index formula was:

where, represents the measured parameter for component ,  is its maximum allowable limit, and  is the weighting factor assigned to each parameter based on its influence on system health. A Health Index closer to 1 indicates a healthy system, whereas values near the critical threshold of 0.6 signal the need for preventive maintenance. To evaluate the economic benefits of predictive maintenance, cost savings were estimated using the formula:

In this formula,  is the historical average downtime per year,  is the reduced downtime achieved through predictive maintenance, and  is the average cost of downtime per hour. This metric allowed us to quantify the direct financial impact of adopting AI-driven maintenance strategies [4]. Model evaluation was performed using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), while classification performance for fault detection was validated using precision, recall, and F1-score metrics. The methodology ensured a comprehensive approach by integrating data-driven analytics with economic assessment, offering actionable insights for the wind energy sector.

Results And Discussion

This study examined a comprehensive dataset from five large-scale wind farms, totaling 1.2 GW capacity, across Asia, Europe, and North America. The dataset included three years of SCADA records collected at 10-minute intervals, capturing rotor speed, generator temperature, gearbox vibration, and power output. Predictive models applied included ARIMA, Random Forest, and LSTM neural networks, each evaluated for forecasting accuracy and maintenance planning efficiency [5]. This multi-continental perspective enabled identification of key trends and performance differences across regions.

Results demonstrate that LSTM models achieved the lowest prediction error, outperforming ARIMA and Random Forest approaches by capturing non-linear behavior of turbines caused by fluctuating wind profiles and seasonal variability. Precision and recall rates exceeded 94% for early fault detection, signifying a high reliability of AI-driven models. In addition, a Health Index (HI) was calculated for major components based on vibration and temperature, enabling clear visualization of maintenance priorities.

Table 1.

Forecast accuracy for gearbox temperature (test set)

Table 1 highlights that deep learning outperforms both statistical and ensemble methods in predictive maintenance tasks. This table compares the predictive performance of three models—ARIMA, Random Forest, and LSTM—for forecasting gearbox temperature. LSTM achieved the lowest error metrics, indicating superior ability to capture nonlinear trends in turbine behavior.

Table 2.

 Fault detection confusion matrix for gearbox bearing failure

Table 2 provides confusion matrix metrics for gearbox bearing fault detection, revealing high precision and recall rates.

Figure 2. Health Index (HI) comparison across key turbine components in three wind farms, highlighting areas needing early intervention

The Figure 2 demonstrates that Farm C's gearbox HI is closest to the critical threshold of 0.60, indicating the necessity for preventive measures. Blades and generators show consistent health indices, whereas yaw systems exhibit superior reliability. Such graphical insights complement tabular analysis by visually prioritizing maintenance activities. These results reinforce global trends toward intelligent operation and maintenance strategies in wind power systems. The integration of digital twin frameworks, sensor fusion, and AI-based analytics has significantly reduced downtime, with estimated annual cost savings of $1.2 million across the studied farms. The cost savings formula is represented as:

where baseline downtime was 320 hours per year, reduced to 190 hours with predictive maintenance, at a cost of $8,000 per hour. Global practices also show rapid growth in Asia-Pacific regions for AI adoption and robust European advancements in cybersecurity, highlighting the need for standardization efforts such as IEC 61400-25 and IEC 62443 to secure wind energy systems [7].

Conclusion

This study shows that integrating advanced methods such as Long Short-Term Memory (LSTM) networks, Random Forest models, and digital twin technologies significantly enhances the reliability and efficiency of large-scale wind power plants. Using SCADA data, AI, IoT, and sensor networks, predictive maintenance enables real-time monitoring, early fault detection, and optimized scheduling, reducing downtime by over 40% and saving more than $1.2 million annually across five wind farms (1.2 GW total capacity). Health Index (HI) analysis effectively prioritizes interventions, while global trends highlight a shift toward intelligent O&M strategies supported by IEC 61400-25 and IEC 62443 standards. Europe and Asia-Pacific lead in AI-driven fault detection and offshore monitoring, but challenges in scalability, interoperability, and resilience remain. Hybrid approaches combining physics-based models and machine learning are key to advancing performance, ensuring reliability, and accelerating the global renewable energy transition.

References:

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