This paper focuses on the energy management system and stability of DC bus in both grid-connected and islanded operations in microgrid system. The microgrid system consists of wind turbines, photovoltaic panels, batteries and supercapacitors, also includes both AC and DC zones. Voltage of DC bus must be kept stable especially in islanded operation. In gridconnected operation voltage of DC bus is controlled by inverter. Real power from renewable energy generations and storage system can be transferred to AC zone through DC bus. In islanded operation, inverter must be controlled to keep magnitude and frequency of AC bus stable, so storage system is used to regulate voltage of DC bus. Simulation results in the paper show that voltage of DC bus can be kept steady and power can be kept balance with the strategy in microgrid system.
Reducing the costs of wind power requires reasonable wind farm operation and maintenance strategies, and then to develop these strategies, the 24-hour ahead forecasting of wind speed is necessary. However, existing prediction work is mostly limited to 5 hours. This work developed a diurnal forecasting methodology for the regional wind farm according to real-life data of the supervisory control and data acquisition (SCADA) system of a wind farm from Jiangxi Province. The methodology used the variational mode decomposition (VMD) to extract wind characteristics, and then, the characteristics were put in the nonlinear autoregressive neural network (Narnet) and long short-term memory network (LSTM) for prediction; the forecast results of VMD-Narnet and VMD-LSTM are compared with the actual wind speed. The comparison results indicate that compared with the LSTM, the Narnet improves the accuracy by 61.90% in 24 hours on wind speed forecasting, and the predicted time horizon was improved by 6.8 hours. This work strongly supports the development of wind farm operation and maintenance strategies and provides a foundation for the reduction of wind power costs.
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