Overview

Modern energy providers are actively seeking to enhance the performance and productivity of renewable energy sources. ALTEN partnered with a leading European energy provider to design an intelligent management solution for their wind and photovoltaic farms. The project focused on developing advanced statistical and AI-driven models to detect equipment anomalies and facilitate a shift from reactive to predictive maintenance, thereby optimizing asset performance and operational efficiency.

Challenges

The client faced significant limitations with traditional, reactive maintenance approaches in managing distributed renewable energy assets. The primary challenges included:

• Designing effective statistical and AI methodologies capable of processing diverse, heterogeneous data streams from various sources.
• Moving beyond passive issue identification to proactively predict equipment failures and potential energy production losses.
• Integrating and analyzing contextual data (e.g., weather, equipment specs) with real-time IoT sensor data to accurately pinpoint the root causes of anomalies.

Solutions

ALTEN delivered an end-to-end data analytics solution built on a robust technical foundation:

• Data Foundation & Integration: Data from weather stations, PV panels, and IoT sensors were aggregated into a centralized data lake. The team performed comprehensive data cleansing and constructed key performance indicators to ensure high-quality input for analysis.
• Advanced Modeling: The core solution involved building sophisticated statistical models utilizing techniques like Markov chains, sequence analysis, and pattern recognition. Localized prediction models were developed for specific farm areas to estimate performance loss by comparing predictions with actual reported data.
• Validation & Deployment: The system"s performance was rigorously tested using historical data on past anomalies. The entire analytics pipeline was designed for operational reliability and collaboration.
• Technology Stack: The project leveraged Python and R for data analysis and modeling, Docker for containerized deployment, GitHub for version control, and various data visualization tools for monitoring and reporting.

Outcomes

The implementation of this predictive analytics framework delivered substantial benefits, enabling the client to:

• Gain deeper, proactive insights into asset health and farm performance, transforming their understanding of energy assets.
• Transition to a proactive maintenance regime, allowing for the anticipation of failures (e.g., blade breakage, bearing degradation) before they occur.
• Optimize energy performance and reduce unplanned downtime, leading to improved overall efficiency.
• Minimize operational costs and energy losses by reducing the frequency and urgency of physical interventions.
• Empower the organization with productivity-enhancing data-driven decision-making, contributing directly to the client"s renewable energy productivity goals.