Grid-connected inverter for wind power generation system

In wind power generation system the grid-connected inverter is an important section for energy conversion and transmission, of which the performance has a direct influence on the entire wind power generation system. The mathematical model of the grid-connected inverter is deduced firstly. Then, the space vector pulse width modulation (SVPWM) is analyzed. The power factor can be controlled close to unity, leading or lagging, which is realized based on PI-type current controller and grid voltage vector-oriented control. The control strategy is verified by the simulation and experimental results with a good sinusoidal current, a small harmonic component and a fast dynamic response.     

光伏并网发电实验系统设计

设计开发了1套光伏并网发电实验系统。该系统由太阳能电池板及控制器、模拟负载、PC机和参数测量设备组成,系统可独立运行和模拟并网运行,控制器电路采用DC/DC/AC架构,实验装置可以测量系统的最大功率点跟踪能力、频率跟踪能力、逆变器的效率和逆变器的失真度等重要指标,具有二次开发功能,适合学生开展设计性实验。系统采用开放形式,直观性好,针对性强,结构原理清楚。     

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller (NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.     

A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.