This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis- tor (IGBT...This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis- tor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera- ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information sup- plied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lith- ium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.展开更多
A single-phase modular multilevel inverter based photovoltaic system for grid connection is proposed. This photovoltaic system utilizes two conversion stages： a boost converter for tracking the maximum power point an...A single-phase modular multilevel inverter based photovoltaic system for grid connection is proposed. This photovoltaic system utilizes two conversion stages： a boost converter for tracking the maximum power point and a modular multilevel inverter used as an interfacing unit. The maximum power point tracking is achieved with a fuzzy logic controller, and the modular multilevel inverter regulates the DC link voltage and synchronizes the grid voltage and current in order to achieve unity power factor operation. The proposed system provides high dynamic performance and power quality injected into the grid. The validity of the proposed system is confirmed by simulations.展开更多
This paper presents a PV （photovoltaic） powered RO （reverse osmosis） plant for brackish water without batteries and a self-regulating pressure valve. The aim is to extract the maximum power from the PV module usin...This paper presents a PV （photovoltaic） powered RO （reverse osmosis） plant for brackish water without batteries and a self-regulating pressure valve. The aim is to extract the maximum power from the PV module using an MPPT （maximum power point tracking） technique for powering a solar water pump and maintain constant the pressure in the RO membranes by using the self-operated valve. A Buck type converter using the InCond （incremental conductance） MPPT was developed for this application. The MPPT chosen was simulated, tested and validated, showing an efficiency of 86.8%. The technical feasibility of the RO plant was made by PLC （programmable logic controller） and was tested for two salinity levels （1,000 and 1,500 mg/L of TDS （total dissolved solids））. These salinity levels chosen are commonly found in most brackish water wells of the semi-arid region of Northeastern Brazil. The RO plant could permeate 175.3 L/day of drinking water with 120 mg/L of TDS and specific energy consumption of 2.56 kWh/m3.展开更多
This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy sto...This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.展开更多
Solar photovoltaic(SPV)modules have a low output voltage and are load-dependent.Therefore,it is critical that the SPV system has an adequate DC-DC converter to regulate and improve the output voltage to get maximum ou...Solar photovoltaic(SPV)modules have a low output voltage and are load-dependent.Therefore,it is critical that the SPV system has an adequate DC-DC converter to regulate and improve the output voltage to get maximum output voltage.To meet load requirements,the voltage must be increased,necessitating the use of energy-efficient power electronic converters.The performance of an SPV system coupled to a high-gain quadratic boost converter(HG-QBC)with a load is investigated in this paper.The suggested HG-QBC for the SPV system at a lower value of duty ratio provides high voltage gain with a boost factor of four times.An analytical comparison is carried out with the various existing boost converters in terms of the components and the boost factor.The issue of locating the maximum power generation point from the SPV system is crucial.As a result,choosing an appropriate maximum power point tracker(MPPT)-based technique to obtain the peak power output of the SPV system under the rapidly varying atmospheric conditions is vital.To determine the highest output power of an SPV system,a hybrid-based MPPT with a neural network assisted by a perturb and observe(P&O)technique is proposed.For the HG-QBC,a comparison of the proposed MPPT with a traditional P&O-based MPPT is illustrated.The comparative analysis takes into account rise time,settling time and voltage ripples.The output voltage and power characteristics of the proposed model are analysed under constant and varying irradiation conditions using MATLAB®/Simulink®.The results of a hybrid-based MPPT show that the oscillations are minimum at the maximum power point with fewer ripples of 0.20%and a settling time of 1.2 s in comparison with the other two techniques.展开更多
文摘This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis- tor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera- ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information sup- plied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lith- ium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.
基金supported by the National Natural Science Foundation of China(61203129,61174038,61473151,51507080)the Fundamental Research Funds for the Central Universities(30915011104,30920130121010,30920140112005)
文摘A single-phase modular multilevel inverter based photovoltaic system for grid connection is proposed. This photovoltaic system utilizes two conversion stages： a boost converter for tracking the maximum power point and a modular multilevel inverter used as an interfacing unit. The maximum power point tracking is achieved with a fuzzy logic controller, and the modular multilevel inverter regulates the DC link voltage and synchronizes the grid voltage and current in order to achieve unity power factor operation. The proposed system provides high dynamic performance and power quality injected into the grid. The validity of the proposed system is confirmed by simulations.
文摘This paper presents a PV （photovoltaic） powered RO （reverse osmosis） plant for brackish water without batteries and a self-regulating pressure valve. The aim is to extract the maximum power from the PV module using an MPPT （maximum power point tracking） technique for powering a solar water pump and maintain constant the pressure in the RO membranes by using the self-operated valve. A Buck type converter using the InCond （incremental conductance） MPPT was developed for this application. The MPPT chosen was simulated, tested and validated, showing an efficiency of 86.8%. The technical feasibility of the RO plant was made by PLC （programmable logic controller） and was tested for two salinity levels （1,000 and 1,500 mg/L of TDS （total dissolved solids））. These salinity levels chosen are commonly found in most brackish water wells of the semi-arid region of Northeastern Brazil. The RO plant could permeate 175.3 L/day of drinking water with 120 mg/L of TDS and specific energy consumption of 2.56 kWh/m3.
文摘This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.
文摘Solar photovoltaic(SPV)modules have a low output voltage and are load-dependent.Therefore,it is critical that the SPV system has an adequate DC-DC converter to regulate and improve the output voltage to get maximum output voltage.To meet load requirements,the voltage must be increased,necessitating the use of energy-efficient power electronic converters.The performance of an SPV system coupled to a high-gain quadratic boost converter(HG-QBC)with a load is investigated in this paper.The suggested HG-QBC for the SPV system at a lower value of duty ratio provides high voltage gain with a boost factor of four times.An analytical comparison is carried out with the various existing boost converters in terms of the components and the boost factor.The issue of locating the maximum power generation point from the SPV system is crucial.As a result,choosing an appropriate maximum power point tracker(MPPT)-based technique to obtain the peak power output of the SPV system under the rapidly varying atmospheric conditions is vital.To determine the highest output power of an SPV system,a hybrid-based MPPT with a neural network assisted by a perturb and observe(P&O)technique is proposed.For the HG-QBC,a comparison of the proposed MPPT with a traditional P&O-based MPPT is illustrated.The comparative analysis takes into account rise time,settling time and voltage ripples.The output voltage and power characteristics of the proposed model are analysed under constant and varying irradiation conditions using MATLAB®/Simulink®.The results of a hybrid-based MPPT show that the oscillations are minimum at the maximum power point with fewer ripples of 0.20%and a settling time of 1.2 s in comparison with the other two techniques.
