This latest investment will enable Clir to continue its AI product development and add new features to its software platform.

“It’s exciting times here at Clir,” said Gareth Brown, Clir Renewables CEO. “We secured this bridge financing to put us in a sound financial position to continue to grow the company, globally, and develop our domain-specific AI. It’ll allow us to continue to lower the cost of renewable energy and give us time to find the right Series A investor later this year.”

Clir Renewables was previously awarded funding through Sustainable Development Technology Canada (SDTC), a Canadian government support for entrepreneurs accelerates the development and deployment of globally competitive clean technology solutions. The company also secured C$2.1m in a seed-stage financing round in 2018. This latest investment enables continued product development, strengthening existing features, and releasing new feature from the product roadmap.

“It’s fantastic to see so many of the previous investors reinvesting in the company and bringing in impact and renewable industry expertise investors from North America and Europe will align a lot of expertise with the company to facilitate a massive global impact,” added Brown. “With a bit of luck we’ll have over 10% of the world’s wind farm owners paying to use our platform by the end of June. We need to remain humble and focus on execution to drive our industry forward to lower cost of energy”

As Clir Renewables gains more market traction, it is considering sourcing larger investors to assist in capitalizing on this traction and increased interest in the software.

Mike Winterfield, Founder and Managing Partner of Active Impact Investments, commented: “We have been watching the success of the Clir Renewables team for over two years and are thrilled to get an opportunity to support them in accelerating their global expansion. Climate change requires an urgent shift from burning fossil fuels, and the insights provided by Clir’s software will continue to drive the costs of renewable energy down so it becomes the obvious choice in all markets.”

The post Clir secures investment to advance its renewable AI service appeared first on Windpower Engineering & Development.

Blade icing can have a major impact on wind turbines but has typically been challenging to assess. Clir Renewables’ software works to detect icing on turbine blades before it becomes a serious problem, reducing the associated production losses. Learn more at clir.eco

Reports show that turbine productivity losses because of icing events can range from a few percentage points to more than 40% throughout the winter season. What’s more is that icing occurrences are typically excluded from warranties or service contracts and the effects are difficult for owners to quantify. SCADA data analysis is generally insufficient at pinpointing exact instances and effects of icing on wind turbines.

Project owners require a reliable way to accurately quantify production losses and make an investment case for icing mitigation systems. But there is an answer.

“Clir has recognized this gap in information and developed a software system that automatically detects icing and quantifies the related losses,” shares Rebecka Klintström, Data Scientist at Clir Renewables. “An algorithm uses a probability analysis to flag deviations from turbine-specific power curves that are based on site-specific climatic conditions and historical icing events in the region.”

According to Klintström, software users are automatically notified of anticipated icing events and related production losses so they can proactively make an informed decision about how to proceed. “The method is based on IEA Task 19’s standardized and widely approved method for ice-loss calculations, which has been further refined within the Clir system,” she says. The International Energy Agency’s Task 19 is the IEA’s most recent recommended practices report for wind-power projects.

Clir’s system will also provide users with recommendations for wind-turbine optimization when a project is experiencing icing, based on the algorithms. Additionally, it evaluates the installed ice detection or mitigation system to ensure effectiveness.

“With all of this information, wind owners have the ability to take action and improve their project’s output,” says Klintström. “One owner saw an increase of almost 5% AEP after a manufacturer control update was implemented following assessment by Clir. While not all sites will see such an increase, it shows that icing is an issue that needs investigation.”

The post Detecting ice on wind-turbine blades appeared first on Windpower Engineering & Development.

Maintenance budgets for wind farms account for the majority of associated OPEX. These planned budgets can be shattered if unexpected repair is required due to a component failure. Increased expenditure is not the only cost involved with unexpected failures.

Replacing components in a wind turbine is a costly procedure, particularly if unexpected or required urgently. However, it can be avoided or planned for by on-going monitoring of temperatures, according to Clir Renewables.

When a failure occurs, the turbine can be out of operation anywhere from a few days to a few weeks, dependent on sourcing replacement parts or required machinery in a quick timeframe. This downtime can result in large quantities of lost energy generation.

The question is, can you predict and prevent component failure? The answer is yes. Clir AI can learn temperature behavior in the context of the real world operational environment anomalies or trends that could be used to identify when a component is operating at higher than expected temperatures under certain conditions like increased loads. Once identified this information allows owners and operators to assess components for signs of degradation which if ignored could lead to catastrophic failure.

Clir AI can remove some of the unknowns around unexpected failures by creating actions for the owner or operator to investigate the turbine further. The challenge for wind farm owners is the amount of data and its context. Simple peer-to-peer trending or other limited algorithms have shown time and time in our industry to frequently lead to false positives that cost owners money and impact confidence and trust in data analytics platforms.

This type of detection is difficult: turbine components heat up and cool down in different ways. Inconsistent measurement instruments between components and temperature are often driven by the conditions leading to a moment in time rather than specific live conditions.

“We really wanted to focus on building detection that has limited false positives, so the tool isn’t wasting peoples’ time while maximizing the benefit of early fault detection,” says Clir CEO Gareth Brown. “The approach maximizes the use of the data to drive improved performance, and crucially it can be scaled across all turbine technologies and as components are upgraded or replaced.”

Clir AI puts the data in context and takes into account a variety of factors including, but not limited to, service information, ambient temperature, rotor speed, ramping up and down, and seasonal variations. Based on all of this information, Clir AI learns a model of the behavior pattern for the turbine. If the temperature varies outside the probabilistic range, the system creates events and actions on the system. It reports multiple grades of severity, based on how much the trend deviates from the expected behaviour and learned failure models in the turbine.

As an independent system, Clir seamlessly integrates this detector with its other features, providing a multitude of information and actionable insights in one place.

“It’s exciting to see when we take deep domain expertise and apply the latest and greatest AI techniques what we achieve,” added Brown.

The post How artificial intelligence learns to quickly detect wind-turbine failures appeared first on Windpower Engineering & Development.