Woodward's modular frequency partial converter (DFIG). The CONCYCLE frequency converter technology has considerably enhanced the success of large wind turbines.
Wind
Woodward is one of the leading wind converter suppliers for renewable energy generation. More than 18,500 installed converters in onshore and offshore applications show Woodward’s competence and experience as a leader in the wind energy business. Precise and intelligent control algorithms of CONCYCLE® wind converters, in combination with variable-speed generators, create an optimized power-generation system with power plant quality. CONCYCLE® converters operating successfully throughout the world and are subject to varying conditions and requirements. We are there to support you in eight wind energy support centers from Fort Collins to Makuhari. With a service life of over 20 years and similar investment obligations, you need a strong partner who will be available when you need us-- whether it is now or in 20 years. We offer support during the commissioning phase and throughout the entire service life of your wind turbines, enabling you to operate cost-effectively and with maximum productivity.
CONCYCLE Full-Size Converter
Woodward's modular frequency full size converter (FSC). The CONCYCLE frequency converter technology has considerably enhanced the success of large wind turbines.
European Regional Development Fund
The European Regional Development Fund aims to strengthen economic and social cohesion in the European Union by correcting imbalances between its regions. Project CoBaMaS (Condition Based Maintenance System to enable predictive maintenance of frequency converters in wind applications) is funded under the NRW-climate protection competition „ErneuerbareEnergien.NRW“ by the state North Rhine-Westphalia (Germany) and aid of the European Regional Development Fund (EFRE) 2014-2020 “investments in growth and employment” from 03-23-2018 until 03-22-2021.
The objective within the funding is to develop a solution to reduce the power generation costs of a wind turbine and to increase the availability on basis of a self-learning predictive system to allow early identification of potential malfunction of components.