Optimizing Energy Availability: Preventive Maintenance and Analytics for Peak Production

As the global energy landscape shifts toward sustainable solutions, wind and solar energy stand at the forefront of the transition. For operators in these sectors, ensuring optimal energy availability is crucial—not only as a measure of efficiency but as a pillar of economic stability and environmental responsibility. During peak production seasons, where the balance between high output and operational reliability is paramount, leveraging preventive maintenance and predictive analytics can ensure consistent energy availability, minimize downtime, and maximize profitability.

Understanding Energy Availability Standards in Renewable Energy

In the renewable energy industry, energy availability is a critical metric representing the energy produced by an asset, such as a wind turbine or PV system, during the available periods divided by the potential production for both available and unavailable periods. Several industry standards guide energy availability calculations and maintenance protocols, notably IEC 63019 for photovoltaic systems (PVPS) and IEC 61400-26-2 for wind systems.

• IEC 63019 (PV Systems): This standard provides guidance on energy availability for PV systems, focusing on how effectively PV systems produce energy under ideal conditions.
• IEC 61400-26-2 (Wind Systems): This standard outlines benchmarks for wind turbine reliability and operational availability, offering a way of accounting for and classifying maintenance and other downtimes.

By adhering to these standards, renewable energy operators can ensure that maintenance and availability are aligned with industry best practices, providing a robust framework to minimize downtime and improve reliability.

Financial Implications of Downtime During Peak Seasons

During peak production seasons, typically sunny months for solar and windy periods for wind farms, the financial implications of downtime are intensified. Given the enhanced generation potential in these seasons, any loss in availability equates to a direct hit to revenue.

For instance, if a wind turbine goes offline for a few hours during a high-wind day, the loss could amount to thousands of kilowatt-hours (kWh) of missed generation. In monetary terms, this can translate to significant revenue loss, especially when multiplied across an entire fleet. This is why peak seasons demand heightened attention to asset availability, where effective maintenance planning and predictive analytics become indispensable tools in mitigating financial risk.

Preventive Maintenance (PM) in Wind and Solar Plants

Preventive Maintenance (PM) represents the proactive steps taken to sustain the health of wind and solar assets by identifying and addressing issues before they escalate. Here’s how PM supports high availability:

• Routine Inspections and Component Replacements: PM involves regular checks and scheduled replacements, which allow for timely identification of wear and tear. In wind turbines, this might mean lubricating gearbox components, inspecting slip rings, and greasing yaw and pitch bearings. For PV systems, routine cleaning and inspection of solar panels help maintain efficiency and prevent damage from debris.
• Compliance with IEC Standards: By following IEC 63019 and IEC 61400-26-2, operators can compare the same KPIs for different wind farms and solar fields, prioritizing the ones that require more attention and resources. This kind of decision is critical mainly during high-demand periods. This structured maintenance approach aligns with best practices and industry recommendations, ensuring the equipment is always prepared for peak conditions.
• Automation and Real-Time Monitoring: Platforms like Delfos provide real-time monitoring and automated alert systems, which allow maintenance teams to track operational health continuously. With automated alarms for component wear and performance drops, maintenance can be timely and data-driven, reducing the need for reactive repairs and prolonging asset life.

Predictive maintenance (PdM) – Revolutionizing Maintenance Strategy

Predictive maintenance, powered by predictive analytics, goes beyond routine scheduling by analyzing data to preemptively identify potential failures. Predictive analytics employs advanced algorithms to operational data to foresee equipment issues before they become failures, making it a revolutionary approach in renewable energy.

• Key Technologies in PdM: Predictive analytics in renewable energy relies on sensors, data collection, and machine learning. IoT sensors installed on turbines and panels collect data on parameters like vibration, temperature, and performance. This data feeds into predictive models that can identify patterns indicating potential failure, allowing asset managers to intervene before an issue arises.
• Delfos and Predictive Failure Detection: Delfos integrates machine learning algorithms to support PdM. Its failure prediction and behavior modeling capabilities offer precise insights, allowing operators to predict and mitigate failures effectively. With performance analytics tailored to renewable energy, Delfos flags high-risk equipment for early intervention, ensuring that assets are always available during peak production windows.

Integrating Preventive and Predictive Maintenance for Optimal Results

While PM and PdM are individually powerful, their combination offers a comprehensive approach to asset maintenance:

• PM as the Foundation, PdM as the Optimizer: Preventive maintenance establishes a baseline of reliability through regular, scheduled upkeep. Predictive analytics optimizes this approach by addressing the limitations of fixed schedules, allowing interventions precisely when they’re needed.
• Advantages of a Hybrid Approach: By combining PM and PdM, operators can extend equipment life, minimize repair costs, and improve availability during peak periods. PdM’s predictive capabilities allow for dynamic adjustments to PM schedules, ensuring that maintenance aligns with real-time asset needs and operational demands.

Delfos as a Hybrid Solution: Delfos seamlessly integrates preventive and predictive maintenance by consolidating both approaches on a single platform. Its data-driven maintenance planning, customized alerts, and predictive modeling make it possible for renewable energy operators to address issues proactively while benefiting from structured maintenance.

The Competitive Edge of Delfos in Renewable Energy Management

Delfos offers a suite of solutions designed specifically for renewable energy operators:

1. Centralized Monitoring and Alarm Management: Delfos consolidates alarms from multiple systems, creating a unified view of operational health. This centralized monitoring streamlines response times and aligns with industry reporting standards, making it easier to manage availability and compliance.
2. Customizable KPIs and Advanced Analytics: Delfos’ KPI customization allows engineers to track metrics that matter most to their operations. By offering in-depth analytics and customizable dashboards, Delfos empowers engineers to optimize availability effectively.
3. Prediction Module: In summary, the Prediction module empowers customers to:

• Detect and address potential failures early, reducing unplanned downtimes and maintenance costs.
• Analyze key variables with predictive models tailored to each asset.
• Investigate anomalies with comprehensive visualizations that combine real-time data, historical trends, and predictive alarms for better decision-making.
• Ensure the quality and accuracy of predictive models, improving long-term operational reliability.

4. Performance Engineering as a Service: Delfos’ Performance Engineering as a Service is designed to elevate your asset management with a tailored, hands-on approach. Through a dedicated customer success agent, personalized support, and seamless integration, we bring expertise directly to your team, ensuring you maximize your asset’s potential. Our service includes platform training, regular performance analysis, and actionable insights, coupled with strategic improvement suggestions and operational adjustments. Plus, with custom and investigative reports, you’ll have the data you need to make informed, impactful decisions. With Delfos, turn performance engineering from a challenge into a competitive advantage.

This predictive capability allows customers to move towards a more proactive and data-driven maintenance strategy, optimizing asset performance and extending equipment life.

Environmental and Operational Considerations

Maintenance strategies in renewable energy must be sustainable, both operationally and environmentally.

• Biodiversity and Land Use: The impact of solar farms on local ecosystems is a growing concern, especially regarding biodiversity and habitat disruption. Operators are adopting biodiversity-friendly practices, like preserving natural vegetation around solar arrays and using wildlife-friendly layouts in wind farms.
• Sustainability in Maintenance Practices: Maintenance schedules that prolong asset lifespans and reduce waste are more sustainable. By using predictive analytics to replace parts only when necessary, operators reduce the environmental impact associated with frequent replacements.

Future Trends in Sustainable Maintenance: Innovations like digital twins and machine learning models are set to revolutionize sustainability in renewable energy. Digital twins allow for virtual simulations, reducing the need for physical maintenance interventions, and improved forecasting helps operators plan resource use more sustainably.

Future Trends in Maintenance and Predictive Analytics for Renewables

The future of renewable energy maintenance is being shaped by advancements in AI, IoT, and big data. Here’s what lies ahead:

• Digital Twin Technology: Digital twins allow operators to create virtual replicas of their assets, providing a real-time view of operational health. By monitoring this digital twin, operators can make predictive adjustments, enhancing both availability and lifespan.
• Enhanced Forecasting Algorithms: Machine learning is enabling more accurate forecasts by analyzing historical and real-time data simultaneously. Future developments will improve predictive analytics and facilitate more robust hybrid approaches, combining traditional maintenance with advanced analytics.

Proactive Asset Management for Reliable Energy Availability

Ensuring optimal energy availability in wind and solar plants is essential, especially during peak seasons when demand and generation potential are high. By combining preventive and predictive maintenance strategies, operators can secure greater reliability, maximize profitability, and reduce operational costs. Delfos offers a comprehensive, data-driven approach that empowers renewable energy operators to stay proactive, maintaining high availability while supporting sustainable growth.

For those committed to operational excellence in renewable energy, Delfos provides the tools and insights necessary to navigate the challenges of peak production periods with confidence. With Delfos, energy managers can look forward to a resilient, reliable, and sustainable energy future.

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