Weather variations present a major challenge for photovoltaic(PV)systems in obtaining the optimal output during maximum power point tracking(MPPT),particularly under partial shadowing conditions(PSCs).Bypass diodes ar...Weather variations present a major challenge for photovoltaic(PV)systems in obtaining the optimal output during maximum power point tracking(MPPT),particularly under partial shadowing conditions(PSCs).Bypass diodes are typically installed across the series-connected PV modules to avoid the occurrence of the hotspots.Consequently,the power curve exhibits several local peaks(LPs)and one global peak(GP).The conventional MPPTs typically become stuck in one of these LPs,presenting a significant decrease in both the power output and overall efficiency of the PV system.A major constraint of several optimization techniques is their inability to differ-entiate between the irradiancefluctuations and load alterations.In this study,we analyze seven different methods for MPPT.These include:the team game algorithm(TGA),social ki driver algorithm(SSD),differential evolution(DE),grey wolf optimization(GWO),particle swarm optimization(PSO),cuckoo search(CS),and the perturb and observe(P&O)method.These algorithms were applied in practice,and their effectiveness was experimentally demonstrated under different amounts of solar irradiation while maintain-ing a constant temperature.The results indicate that the CS and TGA approaches can accurately track the MPPT across various posi-tions on the P-V curve.These methods achieve average efficiencies of 99.59%and 99.54%,respectively.Additionally,the TGA achieves superior performance with the shortest average tracking time of 0.92 s,outperforming the existing MPPT algorithms.展开更多
This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mod...This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode control.The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter.An offshore wind power generation system model is presented to verify the algorithm effect.An offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/Simulink.Compared with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational efficiency.Finally,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation platform.The results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.展开更多
The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum ...The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum power harnessing using various maximum power point tracking techniques available. With the large number of MPPT techniques, each having some merits and demerits, confusion is always there for their proper selection. Discussion on various proposed procedures for maximum power point tracking of photovoltaic array has been done. Based on different parameters analysis of MPPT techniques is carried out. This assessment will serve as a suitable reference for selection, understanding different ways and means of MPPT.展开更多
The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally us...The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point(MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking(MPPT) algorithms like perturb and observe(P&O) to get stuck with local maxima instead of capturing global maxima.Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution(DE).The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.展开更多
In order to ensure that the photovoltaic(PV) array always works at the global maximum point of power to increase the system's overall efficiency, this paper leads the study on maximum power point tracking(MPPT) in...In order to ensure that the photovoltaic(PV) array always works at the global maximum point of power to increase the system's overall efficiency, this paper leads the study on maximum power point tracking(MPPT) in redundant load mode. A new control system is designed by combining the redundant electronic load module, embedded controller, supportive capacitor and boost circuit. The system adjusts duty ratio of boost circuit dynamically based on the maximum power point parameter provided by redundant load unit in order to realize MPPT. An experiment shows that no matter whether system is under an even illumination or partly perturbed by shadow, this method can find the exact maximum power point.展开更多
In order to improve the efficiency and precision of maximum power point tracking(MPPT)control,a new method is proposed.Based on original MPPT technology of photovoltaic cells,the fuzzy adaptive proportion-integral-dif...In order to improve the efficiency and precision of maximum power point tracking(MPPT)control,a new method is proposed.Based on original MPPT technology of photovoltaic cells,the fuzzy adaptive proportion-integral-differential(PID)control has less fluctuation and higher stability.The simulation circuit using Simulink is established,and output power curves under constant temperature or constant sunlight are obtained.The superiority of the fuzzy PID control method has been proved by means of the simulation results,and it makes the solar system approach maximum power point quickly and smoothly.展开更多
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...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.展开更多
Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV pane...Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.展开更多
Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading pose...Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation.Under partial shade conditions,the global maximum power point(GMPP)may be missed by most traditional maximum power point tracker.The flower pollination algorithm(FPA)and particle swarm optimization(PSO)are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP.This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems.The first issue is that the initial values of pollen are determined randomly at first,which can lead to premature convergence.To minimize the convergence time and enhance the possibility of detecting the GMPP,the initial pollen values were modified so that they were near the expected peak positions.Secondly,in the modified FPA,population fitness and switch probability values both influence swapping between two-mode optimization,which may improve the flower pollination algorithm’s tracking speed.The performance of the modified flower pollination algorithm(MFPA)is assessed through a comparison with the perturb and observe(P&O)method and the standard FPA method.The simulation results reveal that under different partial shading conditions,the tracking time for MFPA is 0.24,0.24,0.22,and 0.23 s,while for FPA,it is 0.4,0.35,0.45,and 0.37 s.Additionally,the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions,with values of 99.98%,99.90%,99.93%,and 99.26%,compared to FPA with MPPT efficiencies of 99.93%,99.88%,99.91%,and 99.18%.Based on the findings from simulations,the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions.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
In the early development of the wind energy, the majority of the wind turbines have been operated at constant speed. Subsequently, the number of variable-speed wind turbines installed in wind farms has increased. In t...In the early development of the wind energy, the majority of the wind turbines have been operated at constant speed. Subsequently, the number of variable-speed wind turbines installed in wind farms has increased. In this paper, a comparative performance of fixed and variable speed wind generators with Pitch angle control has been presented. The first is based on a squirrel cage Induction Generator （IG） of 315 kW rated power, connected directly to the grid. The second incorporated a Permanent Magnet Synchronous Generator （PMSG） of 750 kW rated power. The performances of each studied wind generator are evaluated by simulation works and variable speed operation is highlighted as preferred mode of operation.展开更多
A novel direct-drive type wind power generation system based on hybrid excitation synchronous machine（HESM）is introduced in this paper.The generator is connected to an uncontrollable rectifier,and a fully controlled...A novel direct-drive type wind power generation system based on hybrid excitation synchronous machine（HESM）is introduced in this paper.The generator is connected to an uncontrollable rectifier,and a fully controlled voltage-sourceinverter is used to connect the system to utility grid.An intermediate DC bus exists between the rectifier and inverter.A new control strategy is proposed which achieves the maximum power point tracking（MPPT） with the control of excitation current of HESM and stabilizes the DC link voltage with the control of inverter output current simultaneously.Specially-designed buck circuit is used to control the excitation current of HESM,and grid voltage-oriented vector control strategy is employed to realize the decoupling of the inverter output power.Simulation results and experiment in 3 kW lab prototype show an excellent static and dynamic performance of the proposed system.展开更多
In this paper, a mathematical model of the photovoltaic （PV） pumping system＇s main components is firstly established. Then, the design of maximum power point tracking （MPPT） stage that ensures battery charging is...In this paper, a mathematical model of the photovoltaic （PV） pumping system＇s main components is firstly established. Then, the design of maximum power point tracking （MPPT） stage that ensures battery charging is described. This work is motivated by the need of photovoltaic generator （PVG） that efficiently extracts maximum power. The PVG is a special source of energy which has nonlinear current-voltage characteristics depending on variations in temperature and solar irradiance. In order to achieve the MPPT operating goals, a special interest is focused on the variable structure sliding mode （SM） control strategy and the classic perturb and observe （P＆O） algorithm. The permanent magnet synchronous motor （PMSM） is selected as a pump driver. The field oriented control is performed as the motor drive strategy. Simulation results show a high level of efficiency, obtained with the proposed PV based pumping system. The performance comparison between SM controller and P＆O controller has been carried out to demonstrate the effectiveness of the former in drawing more energy and a fast response against irradiation disturbances.展开更多
A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DF...A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DFIG is found to be the best option in the Wind Energy Conversion Systems(WECS)to mitigate the issues caused by power converters.In this work,a new Artificial Neural Network(ANN)is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking(MPPT)along with Wind Energy Conversion System(WECS)to minimize electrical faults.The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault.To ensure optimal MPPT for the proposed WECS,ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG.The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents.The generated solutions are evaluated and on being successful,the agents test their hypothesis again to create a positive feedback mechanism.Simulations are carried out,and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.展开更多
Recently the concern about energy consumption across the globe has become more severe due to global warming. One essential way to address this problem is to maximize the efficiency of existing renewable energy resourc...Recently the concern about energy consumption across the globe has become more severe due to global warming. One essential way to address this problem is to maximize the efficiency of existing renewable energy resources and effectively eliminate their power losses. The previous studies on energy harvesting of photovoltaic (PV) modules try to cope with this problem using gradient-based control techniques and pay little attention to the significant loss of solar energy in the form of waste heat. To reconcile these waste-heat problems, this paper investigates hybrid photovoltaic-thermoelectric generation (PV-TEG) systems. We implement the generalized particle swarm optimization (GEPSO) technique to maximize the power of PV systems under dynamic conditions by utilizing the waste heat to produce electricity through embedding the thermoelectric generator (TEG) with the PV module. The removal of waste heat increases the efficiency of PV systems and also adds significant electrical power. As a control method, the proposed GEPSO can maximize the output power. Simulations confirm that GEPSO outperforms some state-of-the-art methods, e.g., the perturb and observe (PO), cuckoo search (CS), incremental conductance (INC), and particle swarm optimization (PSO), in terms of accuracy and tracking speed.展开更多
A novel control strategy is introduced for tracking the maximum power point of a wind turbine coupled with a permanent magnet synchronous generator. In contrast to other control methods, this approach does not rely on...A novel control strategy is introduced for tracking the maximum power point of a wind turbine coupled with a permanent magnet synchronous generator. In contrast to other control methods, this approach does not rely on an electrical actuator such as a rectifier or inverter. Instead, it uses a mechanical actuator-specifically, a speed multiplier-to ensure maximum power point(MPP) tracking. The selection of the optimal speed multiplication ratio enables the pursuit of the maximum power coefficient by adjusting the tip-speed ratio(λ) to its optimal value. This approach requires no learning time, only knowledge of the wind turbine and generator parameters, which are used for selecting or sizing the speed multiplier and for control purposes. The new approach is validated through simulation in Matlab/Simulink, incorporating a wind profile measured by our team over a 15-day period. The results demonstrate effective tracking of maximum power, achieving a power coefficient efficiency of 98.1%. Considering the measured variability of the wind, the overall energy efficiency over the 15 days reaches 99.16%. The approach is applied to a model of a low-power wind turbine designed for domestic applications.展开更多
基金supported by the Ministry of Higher Education(MOHE)of Malaysia through a research grant FRGS/1/2023/TK08/UNITEN/02/9.
文摘Weather variations present a major challenge for photovoltaic(PV)systems in obtaining the optimal output during maximum power point tracking(MPPT),particularly under partial shadowing conditions(PSCs).Bypass diodes are typically installed across the series-connected PV modules to avoid the occurrence of the hotspots.Consequently,the power curve exhibits several local peaks(LPs)and one global peak(GP).The conventional MPPTs typically become stuck in one of these LPs,presenting a significant decrease in both the power output and overall efficiency of the PV system.A major constraint of several optimization techniques is their inability to differ-entiate between the irradiancefluctuations and load alterations.In this study,we analyze seven different methods for MPPT.These include:the team game algorithm(TGA),social ki driver algorithm(SSD),differential evolution(DE),grey wolf optimization(GWO),particle swarm optimization(PSO),cuckoo search(CS),and the perturb and observe(P&O)method.These algorithms were applied in practice,and their effectiveness was experimentally demonstrated under different amounts of solar irradiation while maintain-ing a constant temperature.The results indicate that the CS and TGA approaches can accurately track the MPPT across various posi-tions on the P-V curve.These methods achieve average efficiencies of 99.59%and 99.54%,respectively.Additionally,the TGA achieves superior performance with the shortest average tracking time of 0.92 s,outperforming the existing MPPT algorithms.
基金supported by the 2022 Sanya Science and Technology Innovation Project,China(No.2022KJCX03)the Sanya Science and Education Innovation Park,Wuhan University of Technology,China(Grant No.2022KF0028)the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City,China(Grant No.2021JJLH0036).
文摘This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode control.The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter.An offshore wind power generation system model is presented to verify the algorithm effect.An offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/Simulink.Compared with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational efficiency.Finally,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation platform.The results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.
文摘The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum power harnessing using various maximum power point tracking techniques available. With the large number of MPPT techniques, each having some merits and demerits, confusion is always there for their proper selection. Discussion on various proposed procedures for maximum power point tracking of photovoltaic array has been done. Based on different parameters analysis of MPPT techniques is carried out. This assessment will serve as a suitable reference for selection, understanding different ways and means of MPPT.
文摘The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point(MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking(MPPT) algorithms like perturb and observe(P&O) to get stuck with local maxima instead of capturing global maxima.Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution(DE).The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.
基金the National Natural Science Foundation of China(No.61107064)the Leading Academic Discipline Project of Communication and Information System(No.XXKZD1605)
文摘In order to ensure that the photovoltaic(PV) array always works at the global maximum point of power to increase the system's overall efficiency, this paper leads the study on maximum power point tracking(MPPT) in redundant load mode. A new control system is designed by combining the redundant electronic load module, embedded controller, supportive capacitor and boost circuit. The system adjusts duty ratio of boost circuit dynamically based on the maximum power point parameter provided by redundant load unit in order to realize MPPT. An experiment shows that no matter whether system is under an even illumination or partly perturbed by shadow, this method can find the exact maximum power point.
文摘In order to improve the efficiency and precision of maximum power point tracking(MPPT)control,a new method is proposed.Based on original MPPT technology of photovoltaic cells,the fuzzy adaptive proportion-integral-differential(PID)control has less fluctuation and higher stability.The simulation circuit using Simulink is established,and output power curves under constant temperature or constant sunlight are obtained.The superiority of the fuzzy PID control method has been proved by means of the simulation results,and it makes the solar system approach maximum power point quickly and smoothly.
基金supported by the National Natural Science Foundation of China (Grant No.20576071)
文摘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.
文摘Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.
文摘Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation.Under partial shade conditions,the global maximum power point(GMPP)may be missed by most traditional maximum power point tracker.The flower pollination algorithm(FPA)and particle swarm optimization(PSO)are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP.This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems.The first issue is that the initial values of pollen are determined randomly at first,which can lead to premature convergence.To minimize the convergence time and enhance the possibility of detecting the GMPP,the initial pollen values were modified so that they were near the expected peak positions.Secondly,in the modified FPA,population fitness and switch probability values both influence swapping between two-mode optimization,which may improve the flower pollination algorithm’s tracking speed.The performance of the modified flower pollination algorithm(MFPA)is assessed through a comparison with the perturb and observe(P&O)method and the standard FPA method.The simulation results reveal that under different partial shading conditions,the tracking time for MFPA is 0.24,0.24,0.22,and 0.23 s,while for FPA,it is 0.4,0.35,0.45,and 0.37 s.Additionally,the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions,with values of 99.98%,99.90%,99.93%,and 99.26%,compared to FPA with MPPT efficiencies of 99.93%,99.88%,99.91%,and 99.18%.Based on the findings from simulations,the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions.
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.
文摘In the early development of the wind energy, the majority of the wind turbines have been operated at constant speed. Subsequently, the number of variable-speed wind turbines installed in wind farms has increased. In this paper, a comparative performance of fixed and variable speed wind generators with Pitch angle control has been presented. The first is based on a squirrel cage Induction Generator （IG） of 315 kW rated power, connected directly to the grid. The second incorporated a Permanent Magnet Synchronous Generator （PMSG） of 750 kW rated power. The performances of each studied wind generator are evaluated by simulation works and variable speed operation is highlighted as preferred mode of operation.
基金Project supported by Delta Power Electronic Science and Education Development (Grant No.DRES2007002)
文摘A novel direct-drive type wind power generation system based on hybrid excitation synchronous machine（HESM）is introduced in this paper.The generator is connected to an uncontrollable rectifier,and a fully controlled voltage-sourceinverter is used to connect the system to utility grid.An intermediate DC bus exists between the rectifier and inverter.A new control strategy is proposed which achieves the maximum power point tracking（MPPT） with the control of excitation current of HESM and stabilizes the DC link voltage with the control of inverter output current simultaneously.Specially-designed buck circuit is used to control the excitation current of HESM,and grid voltage-oriented vector control strategy is employed to realize the decoupling of the inverter output power.Simulation results and experiment in 3 kW lab prototype show an excellent static and dynamic performance of the proposed system.
文摘In this paper, a mathematical model of the photovoltaic （PV） pumping system＇s main components is firstly established. Then, the design of maximum power point tracking （MPPT） stage that ensures battery charging is described. This work is motivated by the need of photovoltaic generator （PVG） that efficiently extracts maximum power. The PVG is a special source of energy which has nonlinear current-voltage characteristics depending on variations in temperature and solar irradiance. In order to achieve the MPPT operating goals, a special interest is focused on the variable structure sliding mode （SM） control strategy and the classic perturb and observe （P＆O） algorithm. The permanent magnet synchronous motor （PMSM） is selected as a pump driver. The field oriented control is performed as the motor drive strategy. Simulation results show a high level of efficiency, obtained with the proposed PV based pumping system. The performance comparison between SM controller and P＆O controller has been carried out to demonstrate the effectiveness of the former in drawing more energy and a fast response against irradiation disturbances.
文摘A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DFIG is found to be the best option in the Wind Energy Conversion Systems(WECS)to mitigate the issues caused by power converters.In this work,a new Artificial Neural Network(ANN)is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking(MPPT)along with Wind Energy Conversion System(WECS)to minimize electrical faults.The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault.To ensure optimal MPPT for the proposed WECS,ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG.The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents.The generated solutions are evaluated and on being successful,the agents test their hypothesis again to create a positive feedback mechanism.Simulations are carried out,and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.
文摘Recently the concern about energy consumption across the globe has become more severe due to global warming. One essential way to address this problem is to maximize the efficiency of existing renewable energy resources and effectively eliminate their power losses. The previous studies on energy harvesting of photovoltaic (PV) modules try to cope with this problem using gradient-based control techniques and pay little attention to the significant loss of solar energy in the form of waste heat. To reconcile these waste-heat problems, this paper investigates hybrid photovoltaic-thermoelectric generation (PV-TEG) systems. We implement the generalized particle swarm optimization (GEPSO) technique to maximize the power of PV systems under dynamic conditions by utilizing the waste heat to produce electricity through embedding the thermoelectric generator (TEG) with the PV module. The removal of waste heat increases the efficiency of PV systems and also adds significant electrical power. As a control method, the proposed GEPSO can maximize the output power. Simulations confirm that GEPSO outperforms some state-of-the-art methods, e.g., the perturb and observe (PO), cuckoo search (CS), incremental conductance (INC), and particle swarm optimization (PSO), in terms of accuracy and tracking speed.
基金Supported by the Algerian Ministry of Scientific Research through a budget allocated to the LEPA Laboratory at the University of Science and Technology in Oran.
文摘A novel control strategy is introduced for tracking the maximum power point of a wind turbine coupled with a permanent magnet synchronous generator. In contrast to other control methods, this approach does not rely on an electrical actuator such as a rectifier or inverter. Instead, it uses a mechanical actuator-specifically, a speed multiplier-to ensure maximum power point(MPP) tracking. The selection of the optimal speed multiplication ratio enables the pursuit of the maximum power coefficient by adjusting the tip-speed ratio(λ) to its optimal value. This approach requires no learning time, only knowledge of the wind turbine and generator parameters, which are used for selecting or sizing the speed multiplier and for control purposes. The new approach is validated through simulation in Matlab/Simulink, incorporating a wind profile measured by our team over a 15-day period. The results demonstrate effective tracking of maximum power, achieving a power coefficient efficiency of 98.1%. Considering the measured variability of the wind, the overall energy efficiency over the 15 days reaches 99.16%. The approach is applied to a model of a low-power wind turbine designed for domestic applications.
