INTEGRATION OF AI-BASED PREDICTIVE MAINTENANCE INTO REAL-TIME MONITORING ARCHITECTURES FOR CENTRALIZED INVERTERS

ABSTRACT

This article presents the integration of AI-based predictive maintenance algorithms into real-time monitoring architectures for centralized inverters in renewable energy systems. Using LSTM and Random Forest models, the system predicts failures and estimates the remaining useful life of inverter components. Experimental results from a 10 MW solar power plant show a 1.8% forecasting error, 7-day failure lead time, and a 28% reduction in maintenance costs, demonstrating significant improvements in reliability and 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: AI-based predictive maintenance, centralized inverters, real-time monitoring, LSTM, Random Forest, renewable energy, fault prediction, inverter reliability, condition monitoring, remaining useful life.

Ключевые слова: Прогнозное обслуживание на основе ИИ, централизованные инверторы, мониторинг в реальном времени, LSTM, случайный лес, возобновляемая энергия, прогноз отказов, надежность инверторов, мониторинг состояния, остаточный ресурс.

Introduction

This study investigates the integration of AI-based predictive maintenance techniques into real-time monitoring architectures for centralized inverters in modern renewable energy systems. The objectives are to review current maintenance strategies, identify key failure modes, develop AI-driven models for fault detection and prognosis, design a real-time monitoring framework with embedded predictive algorithms, and validate its effectiveness through simulation and experimental analysis under realistic conditions [1,2,3]. The rapid growth of renewable energy infrastructure has driven large-scale deployment of centralized inverters, which are critical for converting DC to grid-compatible AC power. By 2024, global solar PV capacity exceeded 1.6 TW, with centralized inverter systems representing over 60% of utility-scale installations (IEA, 2024). These devices operate under variable environmental conditions and are prone to failures that can result in losses of up to $8,000 per MW annually [1,2]. Traditional scheduled or reactive maintenance is insufficient to address these risks, whereas AI-based predictive analytics leverage historical and real-time data to forecast failures, enabling proactive intervention, reducing O&M costs by up to 30%, and extending equipment life by 20–25% [3].

Methods

This article explores the integration of AI-based predictive maintenance algorithms into real-time monitoring architectures tailored for centralized inverters. It outlines the system architecture, discusses machine learning model selection and deployment, and evaluates effectiveness based on real-world implementation data from solar and wind energy sectors.

Figure 1. Real-time integration of ai-based predictive maintenance in centralized inverter monitoring systems

Figure 1 illustrates the integration of AI-based predictive maintenance into a real-time monitoring system for centralized inverters. Operational data, including voltage, current, switching frequency, and fault logs, are collected via high-frequency sensors and processed through monitoring platforms that visualize system performance and trends. These data streams are analyzed by AI algorithms to enable early fault detection, performance forecasting, and predictive maintenance scheduling, creating a closed-loop system that improves reliability and minimizes downtime [3-6]. To validate the approach, data from a 10 MW solar power plant were collected over one year, producing over 8 million time-stamped records. An LSTM network was trained on 70% of the dataset (15% validation, 15% testing) using the Adam optimizer and MSE loss, while a Random Forest model estimated Remaining Useful Life (RUL) of inverter components. Traditional models (e.g., ARIMA) were implemented as baselines, and performance was evaluated using MAPE and RMSE. This procedure ensured statistical rigor and enabled direct comparison of AI-based methods with conventional maintenance strategies. To calculate the Remaining Useful Life (RUL) of inverter components, a degradation-based approach was implemented using both time-series modeling and real-time condition monitoring data. The RUL was determined according to the following formula:

where,  is the maximum rated capacity of the component,  is its current capacity based on real-time monitoring data, and  is the observed degradation rate.

Results And Discussion

The models were trained to detect anomalies and predict failures using inverter temperature, DC voltage irregularities, and fault logs, demonstrating significant improvements over conventional maintenance methods. The LSTM model achieved a mean absolute percentage error (MAPE) below 1.8% in forecasting temperature anomalies, outperforming ARIMA and other statistical models. Training involved a labeled dataset of inverter parameters collected under real operating conditions, split into training, validation, and test sets, with hyperparameters optimized via validation performance. The results highlight LSTM’s ability to capture nonlinear temporal dependencies, enabling accurate early fault detection. The proposed RUL estimation method further provided actionable insights for maintenance scheduling, predicting 14 ± 2 months for IGBT modules, 8 ± 1.5 months for electrolytic capacitors under peak loads, and 18 ± 3 months for cooling fans, aligning with manufacturer guidelines and supporting proactive maintenance planning.

Table 1.

Predicted Remaining Useful Life (RUL) of Key Inverter Components Based on AI-Driven Monitoring

*Verification performed by comparing AI predictions with actual component inspection and replacement logs over a 12-month observation period at a 10 MW solar power plant.

The AI-driven predictive maintenance system was validated using a year-long dataset of over 8 million time-stamped records collected from a 10 MW solar power plant, including inverter temperature, voltage, current, switching frequency, and fault logs. An LSTM model trained on 70% of the dataset achieved a MAPE of 1.8% for temperature prediction, outperforming ARIMA’s 3.7% error, while a Random Forest model estimated the remaining useful life (RUL) of components. The framework predicted faults up to seven days in advance with ±2-day accuracy and correctly matched 90% of observed failures, enabling proactive maintenance scheduling. Economic analysis showed a 28% reduction in annual O&M costs and a 12.5% increase in energy yield due to optimized scheduling and fewer emergency interventions, confirming the system’s reliability through both statistical validation and real-world results. The reported 12.5% improvement in energy yield was derived by comparing annual energy production before and after implementing the AI-based predictive maintenance system. Historical SCADA and EMS logs from the 10 MW solar power plant indicated a baseline energy yield of 18,500 MWh/year, while post-integration data showed 20,800 MWh/year. The relative increase was computed using the formula:

resulting in a gain of approximately 12.5%. Similarly, the 28% reduction in maintenance costs was calculated based on financial records. Annual maintenance expenditure prior to predictive maintenance adoption was $250,000/year, whereas optimized maintenance scheduling and reduced emergency interventions lowered costs to $180,000/year. Using the formula:

the cost savings were determined to be 28%, confirming that predictive analytics improved both energy production efficiency and economic performance.

This approach allowed the operators to forecast component failure windows with a precision margin of ±2 days, offering ample time for intervention. The integrated system proved capable of detecting inverter anomalies up to seven days in advance of potential failure. This early warning capability reduced unplanned downtime and contributed to a 12,5% improvement in energy yield. Furthermore, maintenance costs were reduced by 28% annually due to the reduced need for emergency repairs and the efficient allocation of maintenance resources. Overall, the use of AI-based predictive maintenance in real-time monitoring architectures demonstrates a compelling case for adoption in large-scale solar and wind installations, significantly improving operational efficiency, reliability, and lifecycle cost management.

Conclusion. This study demonstrates the effectiveness of integrating AI-based predictive maintenance algorithms into real-time monitoring architectures for centralized inverters in large-scale renewable energy systems. By employing LSTM and Random Forest models, the proposed approach enables early fault detection, accurate forecasting of component degradation, and precise estimation of remaining useful life (RUL). Experimental validation at a 10 MW solar power plant confirmed that the system can predict failures seven days in advance, reduce unplanned downtime, and achieve a 28% reduction in annual maintenance costs, while improving energy yield by 12.5%. The integration of predictive analytics creates a closed-loop, data-driven maintenance strategy that enhances reliability, safety, and cost-effectiveness, offering a transformative solution for extending equipment life cycles, optimizing maintenance scheduling, and reducing operational risks in renewable energy infrastructure. Future research will focus on scaling this architecture to hybrid renewable systems, incorporating multi-source sensor data, and leveraging edge computing and advanced AI methods to further improve diagnostic precision and resilience in smart grid environments.

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