Ingeteam Wind Energy, the global specialist in electrical power conversion technology and control systems, has announced that it integrated its all-in-one SCADA software across 20 GW of renewable energy projects worldwide. Ingeteam has been at the forefront of asset monitoring and plant performance optimisation for over three decades.
In the past few years, the company has enjoyed increasingly strong demand for its INGESYS™ Smart SCADA suite across all renewable technology segments and recorded ongoing market share gains. It enjoys a leading market position in Spain, Mexico, the United States and Australia.
Its star product INGESYS™ Smart SCADA has been shown to deliver significant cost savings, enhanced security, and performance improvements for the integrated management of wind, solar and hydro assets. The INGESYS™ Smart SCADA platform, developed and fine-tuned over 30 years, harnesses Big Data and IoT into a single product capable of solving the most complex operational challenges.
“As the installation of renewable energy projects continues to grow worldwide, so too does the need for advanced SCADA systems to manage day-to-day operations and optimise performance. From monitoring the latest new-build projects to extending the lifetime of projects installed 15 to 20 years ago, our customers seek to optimise their operations using one central platform that is robust, scalable, and capable of integrating a wide mix of assets and systems,” comments Jorge Acedo, R&D Control Systems Director of Ingeteam Wind Energy.
Data-driven Digital Twin analytics is one of the most innovative tools to be integrated in the INGESYSTM Smart SCADA software. Created using both real-time and offline data collected from an extensive population in terms of location and technology through an integrated state-of-the-art historian tool suite, the models can be used to analyse performance and recommend corrective actions. Powerful algorithms, dashboards and reporting capabilities allow asset managers to gain valuable insights to support decisions that have a large impact on overall performance. Machine learning algorithms continuously track the behaviour of key components and raise alerts in the event of non-conformance. Visual analytics enable technicians to clearly identify issues and respond swiftly.
“Each operator faces particular challenges depending on their mix of assets and systems. The level of personalisation and integration of all our tools across diverse assets is what truly makes us stand out from our competitors. We can unify and value data on multi-technology assets spread over multiple countries and are able to continuously improve processes, leading to significant operational savings,” concludes Acedo.