Multi-mode control strategy for a stand-alone wind energy conversion system with battery energy storage

This work addresses the problem of controlling a stand-alone wind energy conversion system with battery energy storage. The study target consists of a series association of a permanent magnet synchronous aero-generator, an uncontrolled rectifier, a Zeta converter, a Li-ion battery, and a DC load. It is well-known that the intermittent nature of wind power, as well as the frequent variations in load demand, decreases battery lifetime and reduces its charging performance. To overcome these drawbacks, a battery charge controller is required to instantaneously balance the wind turbine power flow delivered to the DC load and the battery, so that the wind turbine power is properly utilized and the battery is suitably charged. Precisely, depending on the available wind power, the battery state of charge (SOC), and DC load demand, three charging modes are adapted, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Afterward, to ensure the safety of the battery a new battery energy management algorithm is designed to operate the system in one of the three aforementioned modes, taking into account the weather conditions and load demand variations. Interestingly, no wind speed or battery soc. sensors are required for the implementation of the control system. Therefore, a nonlinear adaptive is also developed to provide the online estimation of the battery SOC required for the management algorithm. The performances of the studied system are tested through a semi-experimental platform involving a Processor-in-the-Loop (PIL) under the embedded board eZdsp TMS320F28335. The obtained simulation results prove that the proposed controller meets its objectives with high performances.

Automated summary

This paper addresses the problem of controlling a stand-alone wind energy conversion system with battery storage. By designing a battery charge controller and a battery energy management algorithm, the balance between the power flow from the wind turbine to the DC load and the battery is achieved. A nonlinear adaptive algorithm is developed to estimate the battery SOC without the need for sensors. The proposed controller is tested on a semi-experimental platform, and the results show that it performs well.