Solar cells convert sun light into electricity,but have the major drawbacks of high initial cost,low photo-conversion efficiency and intermittency.The current-voltage characteristics of the solar cells depend on solar...Solar cells convert sun light into electricity,but have the major drawbacks of high initial cost,low photo-conversion efficiency and intermittency.The current-voltage characteristics of the solar cells depend on solar insolation level and temperature,which lead to the variation of the maximum power point(MPP).Herein,to improve photovoltaic(PV)system efficiency,and increase the lifetime of the battery,a microcontroller-based battery charge controller with maximum power point tracker(MPPT)is designed for harvesting the maximum power available from the PV system under given insolation and temperature conditions.Among different MPPT techniques,perturb and observe(P&O)technique gives excellent results and thus is used.This work involves the design of MPPT charge controller using DC/DC buck converter and microcontroller.A prototype MPPT charge controller is tested with a 200 W PV panel and lead acid battery.The results show that the designed MPPT controller improves the efficiency of the PV panel when compared to conventional charge controllers.展开更多
文章设计了一种光伏控制器,采用STM32F103RBT6单片机作为控制单元,采用降压式Buck变换电路作为控制主电路。控制器通过采集光伏板的输出电压和电流,计算输出功率,通过扰动观察算法保持充电功率的最大值,实现了最大功率点跟踪技术(Maximu...文章设计了一种光伏控制器,采用STM32F103RBT6单片机作为控制单元,采用降压式Buck变换电路作为控制主电路。控制器通过采集光伏板的输出电压和电流,计算输出功率,通过扰动观察算法保持充电功率的最大值,实现了最大功率点跟踪技术(Maximum Power Point Tracking,MPPT),提高光伏转换效率。文章加入温度检测,实现温度补偿,动态调整控制程序充放电阈值,防止蓄电池过充过放,提高蓄电池利用率。展开更多
Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the ...Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.展开更多
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...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.展开更多
基金2016 national key R&D program of China to support low-carbon Winter Olympics of integrated smart grid demonstration project(2016YFB0900501).
文摘Solar cells convert sun light into electricity,but have the major drawbacks of high initial cost,low photo-conversion efficiency and intermittency.The current-voltage characteristics of the solar cells depend on solar insolation level and temperature,which lead to the variation of the maximum power point(MPP).Herein,to improve photovoltaic(PV)system efficiency,and increase the lifetime of the battery,a microcontroller-based battery charge controller with maximum power point tracker(MPPT)is designed for harvesting the maximum power available from the PV system under given insolation and temperature conditions.Among different MPPT techniques,perturb and observe(P&O)technique gives excellent results and thus is used.This work involves the design of MPPT charge controller using DC/DC buck converter and microcontroller.A prototype MPPT charge controller is tested with a 200 W PV panel and lead acid battery.The results show that the designed MPPT controller improves the efficiency of the PV panel when compared to conventional charge controllers.
文摘文章设计了一种光伏控制器,采用STM32F103RBT6单片机作为控制单元,采用降压式Buck变换电路作为控制主电路。控制器通过采集光伏板的输出电压和电流,计算输出功率,通过扰动观察算法保持充电功率的最大值,实现了最大功率点跟踪技术(Maximum Power Point Tracking,MPPT),提高光伏转换效率。文章加入温度检测,实现温度补偿,动态调整控制程序充放电阈值,防止蓄电池过充过放,提高蓄电池利用率。
文摘Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.
基金Project (No. 20576071) supported by the National Natural Science Foundation of China
文摘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.
