Performance Engineering in Renewable Assets: Optimization, performance improvement, and energy efficiency

Here, you will understand how performance engineering can go far beyond theory, becoming a powerful ally to improve energy efficiency, reduce waste, and boost the productivity of your assets. 

We will explore together strategies that connect process automation, practical efficiency indicators, and a new way of viewing operations: with more clarity, confidence, and control.

In a sector where every megawatt counts, investing in applied technical knowledge is an essential step to stay ahead, safely meet targets, and achieve organizational performance that truly stands out.

What is performance engineering?

Performance engineering is the set of methodologies and technologies used to measure, analyze, and improve the performance of assets and industrial processes. It combines principles of process engineering with data analysis and efficiency indicators. Instead of relying solely on human effort, it focuses on intelligent systems and standardized processes. For example, energy efficiency indicators are measurement tools that identify where energy can be saved and consumption optimized without losing productivity. Similarly, performance engineering applies these indicators (such as availability, capacity factor, OEE) to anticipate failures and optimize the output of turbines or solar panels. Its goal is to create faster, safer, and more reliable processes by eliminating waste and automating decisions (e.g., automatic alerts when equipment begins to lose performance).

Why does it matter for renewable assets?

In renewable plants, even small performance gains have a huge financial and energy impact. Wind and solar assets require high investment and suffer from natural variability (wind, sun). Improving production by 1% means significant gains: 1% more generation (across thousands of turbines) can equate to 420 GWh of additional energy per year, enough to power thousands of homes.
Additionally, there are strong regulatory targets (EU, Paris Agreements, RenovaBio in Brazil) that push for more energy efficiency. As important as building new plants is extracting more energy from each existing installation. Performance engineering ensures this gain: it improves reliability (by detecting failures before they cause downtime) and automates maintenance processes. Organizations that adopt this approach see cost reduction and greater asset availability.

How does it work in practice?

In practice, performance engineering integrates data and automation at every operational stage. For example:

• Data collection and centralization: SCADA, IoT sensors, and maintenance history are integrated into a single platform. This provides a unified view of the state of each turbine, inverter, etc.
• Monitoring indicators: KPIs are defined such as energy efficiency, availability time, losses, or specific consumption per MWh produced. Automatic dashboards update these indicators in real time.
• Analysis and predictive models: Analysis and machine learning software assess historical performance patterns. Predictive models monitor equipment reliability and alert to possible failures. For example, algorithms detect vibrations or production deviations before a turbine breakdown.
• Automated actions: When a problem is identified, the system may suggest or even trigger corrective actions: dispatch maintenance teams, adjust operational parameters, or recalculate targets. This reduces time spent on data analysis and speeds up responses.
• Feedback and continuous improvement: After the intervention, results are measured and incorporated into new model adjustments. This PDCA cycle (Plan, Do, Check, Act) ensures continuous efficiency evolution.

In summary, it’s like giving engineers a “digital co-pilot.” Field data is transformed into insights and recommendations automatically, making the work more proactive.

When and how to apply it?

Performance engineering can be applied from project design to continuous operation:

• In the design phase: Define efficiency indicators and automation requirements from the start. This means designing systems with built-in monitoring (e.g., turbines with vibration meters, solar plants with advanced SCADA).
• In predictive maintenance: Before critical failures, use performance analysis to schedule planned repairs. For example, if a model shows a bearing is wearing out, schedule replacement before it breaks.
• In daily operations: During normal operation, automated routines monitor parameters and alert managers and technicians about deviations. This is useful whenever availability or performance targets must be ensured.
• After incidents or audits: If a plant shows below-expected performance, performance engineering helps identify operational bottlenecks (such as shading in solar plants or turbulence in wind farms) and correct the course.
• In upgrades and expansions: When incorporating new technologies (batteries, solar trackers, new software), performance engineering ensures integration without losses. It also serves as an investment criterion: regions that meet strict indicators tend to receive more funding.

What are the advantages?

Implementing performance strategies offers several concrete advantages:

• Increased energy generation: Less downtime and unplanned maintenance mean more productive hours. Even modest performance gains yield significant results.
• Reduced operational costs: By anticipating failures, costly emergency stops and reactive maintenance are avoided. Maintenance becomes data-driven rather than fixed-calendar-based. This reduces expenses on rework, parts, and external services
• Improved energy efficiency: Standardized and monitored processes consume fewer resources (like lubricants, cooling water, or internal energy). Efficiency indicators highlight bottlenecks and guide solutions that reduce losses and carbon footprint.
• Greater team productivity: Technicians no longer waste time digging through spreadsheets. With automatic alerts and clear dashboards, they focus on solving real problems. This increases technical productivity and professional pride – after all, they start leading measurable results, not just putting out fires.
• Organizational performance optimization: Standardized KPI reports (following international standards like IEC) provide a solid basis for management decisions. The organization gains clarity on where to invest and where to cut costs, increasing competitiveness.
• Appreciation and trust: Using performance engineering empowers the team. Technicians feel safer and prouder of anticipating problems and improving plant performance. This cultural shift is a market differentiator.

In short, performance engineering combines process automation, artificial intelligence, and indicator-based management to deliver leaner and more profitable operations.

Practical examples and case studies

Case 1 – V2i Energia: In partnership with Delfos, V2i reduced energy loss from recurring short stops by 18%. The platform’s intelligence integrated with the O&M team enabled more accurate diagnostics and more effective interventions. In just 4 months, production increased by 174 MWh.

Case 2 – Vestas V82 wind turbine: Power curve analysis revealed a turbine was limited to 1500 kW due to incorrect ambient temperature readings. After calibrating the sensor, performance was restored, recovering 256.68 MWh – equivalent to R$180,000 in financial return.

Case 3 – Solar plant: Significant morning generation losses were detected due to shading from terrain unevenness and backtracking failures. The correction recovered 1% of the affected inverter’s producible energy and reduced degradation impact on modules, improving system durability.

At Delfos, Performance Engineering is structured as a complementary service to the SaaS platform, focusing on technical analysis of low performance causes and applying practical, data-based recommendations. The service is results-oriented and based on real-time operational data extracted from the Delfos platform, enabling in-depth diagnostics, bottleneck identification, and prioritized recommendations for loss mitigation and performance optimization.

The work is led by specialist engineers who use tools like predictive analysis, smart monitoring, and failure modeling to identify operational deviations, propose control adjustments, and optimize energy efficiency of assets. With regular meetings and systematic KPI reviews, the service directly contributes to loss reduction, increased reliability, and support for availability and generation targets in solar and wind plants.

This model provides measurable gains in operational efficiency and availability, adding value both for teams with internal expertise and for operations seeking specialized support in renewable asset performance management.

Have questions and want to learn more about Delfos’ Performance Engineering service? Get in touch.

Conclusion

Performance engineering is essential to maximize returns from renewable assets. By integrating process automation, efficiency indicators, and advanced analytics, it is possible to produce more energy at lower cost and with greater safety. Engineers and technicians gain clarity to make decisions, feel proud of results, and deliver more efficient and competitive plants.

This content showed what performance engineering is and how to apply it in wind, solar, etc., presenting real examples and successful case studies. If you are a sector professional, reflect: how can you automate routines, better measure efficiency, and optimize processes in your plant? We hope the information here is useful and practical.

Share this post with colleagues facing similar challenges and leave a comment with your questions. To learn more about performance engineering and automation solutions, contact us. We want to help you turn these challenges into improvement opportunities!

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