With the depletion of fossil fuels and the worsening of environmental pollution, wind energy has garnered widespread attention as a renewable energy source. Direct-drive permanent magnet wind turbines offer advantages such as high efficiency and a gearbox-free design; however, their power converters are prone to failure under fluctuating wind speed conditions, making research into fault diagnosis particularly significant. This paper focuses on IGBT open-circuit faults in wind power converters. A simulation model of a direct-drive permanent magnet wind power system was established, employing grid voltage-oriented vector control to simulate three types of faults: single-tube, double-tube, and out-of-phase double-tube open circuits. Three-phase currents were used as feature signals to analyze waveform distortion patterns. By extracting quantitative indicators such as RMS value, THD, and three-phase asymmetry to compare fault characteristics, and using the Pearson correlation coefficient to analyze the correlation between wind speed and the amplitude of these characteristics, the results indicate that faults significantly increase current distortion and asymmetry, with distinct differences among the various fault types. Wind speed shows a weak correlation with the amplitude of these fault characteristics, and the characteristics demonstrate good stability and robustness. This study provides theoretical and data support for diagnosing converter open-circuit faults under fluctuating wind speeds.
Jia-Hui Hou, Yifan Wei, Yue Pan et al.· 2026 5th International Confe...· 0 citations
Accurate wind power forecasting is imperative for ensuring grid stability and facilitating the large-scale integration of renewable energy—both central pillars of the global energy transition and the Dual Carbon strategic goals. However, existing methods often fail to fully capture the spatial heterogeneity and interdependencies among individual turbines, limiting their effectiveness for sustainable grid operation. To address this gap, this paper proposes an ultra-short-term wind power forecasting framework that incorporates explicit multi-dimensional spatial features. At the feature level, a 12-dimensional spatial feature system is constructed to quantify the microscale topology of wind farms. These static spatial attributes are seamlessly fused with dynamic temporal data using a dimensionality-balance factor strategy. Finally, a hybrid deep learning network comprising a multi-scale CNN, a multi-layer BiLSTM, and a multi-head self-attention mechanism is developed to capture complex spatiotemporal patterns. Experimental results on three real-world datasets show that the proposed method significantly outperforms baseline models, reducing the Mean Absolute Percentage Error by up to 11.09% and improving the coefficient of determination R2 up to 0.9120. By improving forecast accuracy and robustness, the method directly supports more reliable grid dispatching, reduces curtailment of wind energy, and thus contributes to the sustainable utilization of renewable resources. These findings demonstrate that incorporating explicit spatial correlation effectively enhances the accuracy and robustness of ultra-short-term wind power forecasting, providing robust decision support for power grid dispatching and advancing the sustainability of modern power systems.
Yanxia Wang, Weilong Yu, Minghan Ma et al.· Sustainability· 0 citations
