Predictive Analysis can make the Renewable Energy Industry grow

Wind power has become one of the fastest-growing energy sources and one of the most cost-effective solutions for electricity generation, as a matter of fact 2020 was the best year in history for the global wind industry with nearly 100 GW of new capacity installed – a 59 per cent year-on-year increase, according to BloombergNEF.

Today, there is now 743 GW of wind power capacity worldwide and, in light of climate change, demand continues to grow. Yet, in order to be competitive and to move to a new era of wind energy availability, operators are looking for ways to further reduce costs and optimize performances.

Predicting failures is the key to improve performances

A good quality, modern wind turbine will generally last for 20 years, although this can be extended to 25 years or longer depending on environmental factors and the correct maintenance procedures being followed.

To optimize turbine performance, operators must conduct both planned and unplanned maintenance. Nonetheless, the more time technicians spend to fix failures, the more expenses are incurred.

Turbine maintenance is by far the most complex and costly aspect of wind energy production. Globally, wind O&M costs reached nearly $17bn in 2020.

So, the key is to predict failures before they occur.

Predictive Analysis to reduce costs

BaxEnergy recognizes the potential to apply predictive analysis to reduce the cost and complexity of operations and maintenance for operators in the wind energy industry.

In fact, in order to improve operationsreduce downtime and maximize revenues, predictive analysis is a fundamental solution for utilities, grid operators, transmission companies.

With all of the variables that contribute to wind-farm energy output, turbine maintenance is a data-intensive activity. Wind turbines generate a large amount of SCADA data, outputting values for up to 500 different metrics each second. Turbine SCADA metrics include energy output, weather conditions, temperatures, nacelle positions, blade angles, gearbox and generator accelerations, fault codes, and other system statuses and control values.

But how does Predictive Analysis work?

Our predictive analysis solution compares historical data of each asset to real-time operating data to detect subtle changes in turbines functioning. In this way, our software identifies changes before the failure reaches operational alarm levels, providing early warnings needed to take immediate corrective actions.

In fact, by comparing measured data with expected turbines behavior and by relying on a large database of failure events, Energy Studio Pro® can automatically detect the slightest technical fault and recommend corrective measures. Based on our experience, predictive analysis increases 0.5% of energy yield, which makes up to 2 mln euros annually.

Power curves and power loss detection

Real power curves are often different from warranted power curves. For example, a small power deterioration of 5% in the power curve could lead to loss of 5,000 Euro per month on a 3 MW turbine with a capacity factor of 40%.

Energy Studio Pro® contains a unique module that allows to analyze power curves and is capable to detect power loss due to alteration of the internal parameters of the wind turbine, or to external power regulations. Our solution is capable to compare the actual power curve of the wind turbine with the warranted power curve to produce exact reports on loss of yields due to mechanical and electrical regulation. The system can also detect deterioration of the power curve over time due to aging of the equipment.

Fine-tuning new turbine models

Energy Studio Pro® is especially suitable to analyze data from new or little-known turbines, to detect internal defects and fine tunings of parameters performed by the manufacturer during and after commissioning. This is very important when dealing with new turbine models that are still under development or wind turbines that have been running only for a few years.

In fact, new turbines models are often fine-tuned during and after commissioning, and the internal parameters are often altered remotely, affecting the overall performance and efficiency. Energy utilities are usually not aware of these activities because they are typically not displayed in the manufacturer SCADA system.

The energy industry can enormously benefit from predictive analysis and supercharged data processing. By leveraging those solutions, energy utilities can really establish a more affordablereliable, and sustainable infrastructure.

Interested in predictive analysis for your assetsPlease get in touch, we’ll be pleased to answer any question.