In the quest to bolster the reliability of offshore wind energy, a team of researchers led by Rafael Noboro Tominaga from the University of São Paulo (USP) has developed a comprehensive dataset that could revolutionize the way we monitor and maintain permanent magnet synchronous generators (PMSGs). This dataset, published in the journal “Data in Brief,” offers high-resolution electrical measurements from a PMSG under both healthy and faulty conditions, providing a valuable resource for the energy sector.
Offshore wind power generation is increasingly turning to PMSGs due to their high efficiency and low maintenance requirements. However, internal short-circuit faults remain a significant challenge. “Inter-turn and inter-winding faults may not cause immediate damage, but they can evolve over time, leading to severe equipment failures,” explains Tominaga. These failures can result in generator shutdowns, causing substantial financial and operational losses.
The dataset collected by Tominaga and his team includes 225 .mat files covering 24 fault cases and one healthy case, each tested under three torque levels and three rotational speeds. The data was gathered from a laboratory test bench that allows controlled insertion of internal faults, such as short-circuits between turns and windings. The generator, connected to the grid via a power converter, was monitored using an Imperix B-Box RCP system loaded with control algorithms developed in Simulink. Signals were sampled at 20 kHz and recorded through the Imperix Cockpit, with each test lasting three seconds and capturing pre-fault, fault, and post-fault conditions.
This dataset is not just a collection of numbers; it’s a toolkit for innovation. “It enables users to study transient responses, steady-state behavior with faults, and system recovery,” says Tominaga. The dataset includes a Python interface to facilitate visualization, making it accessible for a wide range of users. It can support diverse applications, such as validating analytical models of PMSGs, benchmarking fault detection algorithms, and generating synthetic data for further testing. It may also serve as a practical tool in electrical engineering education, especially in courses focused on wind energy systems and fault analysis.
The implications of this research are far-reaching. By providing a benchmark dataset, Tominaga and his team are paving the way for more effective fault detection strategies. This could lead to significant improvements in the reliability and efficiency of offshore wind turbines, ultimately reducing financial and operational losses in the energy sector.
As the world continues to shift towards renewable energy sources, the need for robust and reliable monitoring systems becomes increasingly important. This dataset is a significant step forward in that direction, offering a valuable resource for researchers, engineers, and educators alike. It’s not just about understanding the past; it’s about shaping the future of offshore wind energy.