Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

Maximum Power Point Tracking in Variable Speed Wind Turbine Based on Permanent Magnet Synchronous Generator Using Maximum Torque Sliding Mode Control Strategy

The present study was carried out in order to track the maximum power point in a variable speed turbine by minimizing electromechanical torque changes using a sliding mode control strategy. In this strategy, first, the rotor speed is set at an optimal point for different wind speeds. As a result of which, the tip speed ratio reaches an optimal point, mechanical power coefficient is maximized, and wind turbine produces its maximum power and mechanical torque. Then, the maximum mechanical torque is tracked using electromechanical torque. In this technique, tracking error integral of maximum mechanical torque, the error, and the derivative of error are used as state variables. During changes in wind speed, sliding mode control is designed to absorb the maximum energy from the wind and minimize the response time of maximum power point tracking(MPPT). In this method, the actual control input signal is formed from a second order integral operation of the original sliding mode control input signal. The result of the second order integral in this model includes control signal integrity, full chattering attenuation, and prevention from large fluctuations in the power generator output. The simulation results, calculated by using MATLAB/m-file software, have shown the effectiveness of the proposed control strategy for wind energy systems based on the permanent magnet synchronous generator(PMSG).

Comparative assessment of maximum power point tracking procedures for photovoltaic systems

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.

Comprehensive review and performance evaluation of maximum power point tracking algorithms for photovoltaic system

A photovoltaic array is environmentally friendly and a source of unlimited energy generation.However,it is presently a costlier energy generation system than other non-renewable energy sources.The main reasons are seasonal variations and continuously changing weather conditions,which affect the amount of solar energy received by the solar panels.In addition,the non-linear characteristics of the voltage and current outputs along with the operating environment temperature and variation in the solar radiation decrease the energy conversion capability of the photovoltaic arrays.To address this problem,the global maxima of the PV arrays can be tracked using a maximum power point tracking algorithm(MPPT)and the operating point of the photovoltaic system can be forced to its optimum value.This technique increases the efficiency of the photovoltaic array and minimizes the cost of the system by reducing the number of solar modules required to obtain the desired power.However,the tracking algorithms are not equally effective in all areas of application.Therefore,selecting the correct MPPT is very critical.This paper presents a detailed review and comparison of the MPPT techniques for photovoltaic systems,with consideration of the following key parameters:photovoltaic array dependence,type of system(analog or digital),need for periodic tuning,convergence speed,complexity of the system,global maxima,implemented capacity,and sensed parameter(s).In addition,based on real meteorological data(irradiance and temperature at a site located in Addis Ababa,Ethiopia),a simulation is performed to evaluate the performance of tracking algorithms suitable for the application being studied.Finally,the study clearly validates the considerable energy saving achieved by using these algorithms.

On maximum power point tracking control strategy for variable speed constant frequency wind power generation

Based on the characteristic of AC-excited variable speed constant frequency(VSCF)wind power generation,the vector control technique was applied in a doubly fed induction generator(DFIG).Maximum wind energy or maximum output power point can be tracked by decoupling control of active power and reactive power.The research result shows that the net power of generation system delivered to grid in maximum wind energy tracking mode is not the most.We presented a novel maximum power point tracking(MPPT)control strategy by analyzing the DFIG mathematic model and power relations which delivered the maximum power to the grid.The maximum power point could be tracked automatically without measuring wind speed in the control strategy and the control was independent of optimal turbine power curve,which had excellent dynamic and static performances and robustness.Simulation and experimental results testify the accuracy and validity of the control strategy.

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.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

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.

A Fuzzy Logic Controller for Maximum Power Point Tracking with 8-Bit Microcontroller

This paper presents the implementation of maximum power point tracking （MPPT） with fuzzy logic controller. For cost consideration, an inexpensive 8-bit microcontroller, PIC 16F877A, is selected and programmed with C language and integer variables For evaluation, the implemented fuzzy logic controller （FLC） is compared with the MPPT controller of using perturbation and observation （P＆O）. Both types of MPPT controllers are tested on the same voltage source with a series-connected resistor. Experimental results show that the implemented FLC with appropriate design meets the control requirements of MPPT. The FLC based on linguistic fuzzy rules has more flexibility and intelligence than conventional P＆O controller, but the FLC spends more RAM and ROM spaces than the P＆O tracker does.

Tracking the maximum power point of solar panels through direct estimation of optimum voltage with temperature

Electricity production from photovoltaic(PV)panels is maximized when the operating point is located at the maximum power point thanks to dedicated controllers.These controllers are driven to track the maximum power by using various algorithms within distributed or centralized architectures accounting for factors such as partial irradiation and temperature changes.The effect of irradiance on the optimal panel voltage is weak or even negligible,while it is strong and quasi-linear-dependent on temperature.Based on this observation,this article introduces a straightforward method for tracking the maximum power of a PV panel by using an optimizer,focusing solely on its temperature response as an input variable.The proposed approach hinges on linearizing the relationship between panel temperature and operating voltage.This relationship enables the estimation of the maximum power point through temperature measurement alone.Thus,after determination of the linear temperature coefficient of the voltage requiring only the knowledge of two optimal voltages at different temperatures,for example from the datasheet of the panel,the power tracking involves only one temperature sensor placed on the panel alongside a voltage sensor for regulation.The principle,modelling,and validation post-panel ageing of the method are detailed in this paper.Simulation,conducted using real experimental irradiation and temperature data,attests to the effectiveness of the control.Results indicate an average effectiveness of the method of>99.1%in tracking the maximum power,with the panel generating 2.33 kWh out of a possible 2.35 kWh.This performance is comparable to that of tracking devices employing more complex algorithms.The simplicity and efficiency of the method make it a promising option for maximizing the power production at low cost from PV systems in small or residential,on-or off-grid connected applications.

Design of a Level-3 electric vehicle charging station using a 1-MW solar system via the distributed maximum power point tracking technique

Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading conditions.Therefore,before installing solar arrays,it is necessary to simulate and determine the possible power generated.Maximum power point tracking is needed in order to make sure that,at any time,the maximum power will be extracted from the photovoltaic system.However,maximum power point tracking is not a suitable solution for mismatches and partial shading conditions.To overcome the drawbacks of maximum power point tracking due to mismatches and shadows,distributed maximum power point tracking is util-ized in this paper.The solar farm can be distributed in different ways,including one DC-DC converter per group of modules or per module.In this paper,distributed maximum power point tracking per module is implemented,which has the highest efficiency.This technology is applied to electric vehicles(EVs)that can be charged with a Level 3 charging station in<1 hour.However,the problem is that charging an EV in<1 hour puts a lot of stress on the power grid,and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate.Therefore,a Level 3(fast DC)EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue.Finally,the simulation result is reported using MATLAB®,LTSPICE and the System Advisor Model.Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day,which is enough to fully charge~120 EVs each day.Additionally,the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants.For example,instead of supplying EVs with power from coal-fired power plants,1989 pounds of CO_(2) will be eliminated from the air per hour.

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

Partial shading conditions(PSCs)caused by uneven illumination become one of the most common problems in photovoltaic(PV)systems,which can make the PV power-voltage(P-V)characteristics curve show multi-peaks.Traditional maximum power point tracking(MPPT)methods have shortcomings in tracking to the global maximum power point(GMPP),resulting in a dramatic decrease in output power.In order to solve the above problems,intelligent algorithms are used in MPPT.However,the existing intelligent algorithms have some disadvantages,such as slow convergence speed and large search oscillation.Therefore,an improved whale algorithm(IWOA)combined with the P&O(IWOA-P&O)is proposed for the MPPT of PV power generation in this paper.Firstly,IWOA is used to track the range interval of the GMPP,and then P&O is used to accurately find the MPP in that interval.Compared with other algorithms,simulation results show that this method has an average tracking efficiency of 99.79%and an average tracking time of 0.16 s when tracking GMPP.Finally,experimental verification is conducted,and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization(PSO)and PSO-P&O algorithms.

Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

This research aims to enhance the performance of photovoltaic(PV)systems on a 2-fold basis.Firstly,it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking,ensuring optimal extraction of electrical power from PV arrays.Secondly,it proposes the use of 96-V,2.98-kW direct-current(DC)water pumps for farm irrigation,aiming to improve efficiency,reduce cost and complexity,and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid.In this study,it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers,power passive filters and inverters,therefore simplifying the architecture of the system.The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink®simulation results,whereby the proposed algorithm attains a mean squared error of 6.5705×10^(-5)and a system efficiency approaching 99.8%,ensuring a steady voltage under varying load conditions.

A Review of State-of-the-art Flexible Power Point Tracking Algorithms in Photovoltaic Systems for Grid Support:Classification and Application

To maximize conversion efficiency,photovoltaic(PV)systems generally operate in the maximum power point tracking(MPPT)mode.However,due to the increasing penetra tion level of PV systems,there is a need for more developed control functions in terms of frequency support services and voltage control to maintain the reliability and stability of the power grid.Therefore,flexible active power control is a manda tory task for grid-connected PV systems to meet part of the grid requirements.Hence,a significant number of flexible pow er point tracking(FPPT)algorithms have been introduced in the existing literature.The purpose of such algorithms is to real ize a cost-effective method to provide grid support functional ities while minimizing the reliance on energy storage systems.This paper provides a comprehensive overview of grid support functionalities that can be obtained with the FPPT control of PV systems such as frequency support and volt-var control.Each of these grid support functionalities necessitates PV sys tems to operate under one of the three control strategies,which can be provided with FPPT algorithms.The three control strate gies are classified as:①constant power generation control(CP GC),②power reserve control(PRC),and③power ramp rate control(PRRC).A detailed discussion on available FPPT algo rithms for each control strategy is also provided.This paper can serve as a comprehensive review of the state-of-the-art FPPT algorithms that can equip PV systems with various grid support functionalities.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

An Improved Perturb and Observe Maximum Power Point Tracking Algorithm for Photovoltaic Power Systems

This paper aims to improve the performance of the conventional perturb and observe(P&O)maximum power point tracking(MPPT)algorithm.As the oscillation around the maximum power point(MPP)is the main disadvantage of this technique,we introduce a modified P&O algorithm to conquer this handicap.The new algorithm recognizes approaching the peak of the photovoltaic(PV)array power curve and prevents the oscillation around the MPP.The key to achieve this goal is testing the change of output power in each cycle and comparing it with the change in array terminal power of the previous cycle.If a decrease in array terminal power is observed after an increase in the previous cycle or in the opposite direction,an increase in array terminal power is observed after a decrease in the previous cycle;it means we are at the peak of the power curve,so the duty cycle of the boost converter should remain the same as the previous cycle.Besides,an optimized duty cycle is introduced,which is adjusted based on the operating point of PV array.Furthermore,a DC-DC boost converter powered by a PV array simulator is used to test the proposed concept.When the irradiance changes,the proposed algorithm produces an averageηMPPT of nearly 3.1%greater than that of the conventional P&O algorithm and the incremental conductance(In C)algorithm.In addition,under strong partial shading conditions and drift avoidance tests,the proposed algorithm produces an averageηMPPT of nearly 9%and 8%greater than that of the conventional algorithms,respectively.

Spotted Hyena-Bat Optimized Extreme Learning Machine for Solar Power Extraction

Artificial intelligence,machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking(MPPT)in solar systems.In the traditional MPPT strategies,following of worldwide Global Maximum Power Point(GMPP)under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions.The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions(PSC).Still the selection of an efficient learning based MPPT is complex because each model has its advantages and drawbacks.Recently,Meta-heuristic algorithm based Learning techniques have provided better tracking efficiency but still exhibit dull performances under PSC.This work represents an excellent optimization based on Spotted Hyena Enabled Reliable BAT(SHERB)learning models,SHERB-MPPT integrated with powerful extreme learning machines to identify the GMPP with fast convergence,low steady-state oscillations,and good tracking efficiency.Extensive testing using MATLAB-SIMULINK,with 50000 data combinations gathered under partial shade and normal settings.As a result of simulations,the proposed approach offers 99.7%tracking efficiency with a slower convergence speed.To demonstrate the predominance of the proposed system,we have compared the performance of the system with other hybrid MPPT learning models.Results proved that the proposed cross breed MPPT model had beaten different techniques in recognizing GMPP viably under fractional concealing conditions.

Evaluation of series-parallel-cross-tied PV array configuration performance with maximum power point tracking techniques under partial shading conditions

Tracking the maximum power point is a critical issue with solar systems.The power output of the solar panel varies due to variations in irradiance and temperature.Nonuniform irradiation due to partial shading conditions has a direct impact on the characteristics of photovoltaic(PV)systems.To build a diversity of maximum power point tracking algorithms in solar PV systems,this work focuses on perturb and observe,incremental conductance,and fuzzy logic control methodologies.The suggested fuzzy logic control method outperformed the conventional incremental conductance and perturb and observe algorithms with a collection of 49 rules.This paper presents a novel series-parallel-cross-tied PV array configuration with a developed fuzzy methodology.To comment on the performance of a proposed system under various partial shading conditions,a series-parallel PV array configuration has been considered.The simulation result demonstrates that the fuzzy method has a percentage improvement in the global maximum power point tracking efficiency of 24.85%when compared to the perturb and observe method and a 65.5%improvement when compared to the incremental conductance method under long wide partial shading conditions.In the case of the middle partial shading condition,the fuzzy method has a percentage improvement in the global maximum power point tracking efficiency of 12.4%compared to the perturb and observe method and a 60.7%improvement compared to the incremental conductance method.

Maximum power point tracking of PV system under partial shading conditions through flower pollination algorithm

For maximum utilization of solar energy,photovoltaic(PV)power systems should be operated at the maximum power point(MPP)which can be achieved using maximum power point tracking(MPPT)methods.However,the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process.The global MPP cannot always be achieved by the conventional MPPT methods.Therefore a novel MPPT method for PV systems using flower pollination(FP)algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm.MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method,and the proposed method is compared with two well established MPPT methods.The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition.

PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions

A novel maximum power-point tracking approach is proposed based on studies investigating the output characteristics of photovoltaic(PV)systems under partial shading conditions.The existence of partially shaded conditions leads to the presence of several peaks on PV curves,which decrease the efficiency of conventional techniques.Hence,the proposed algorithm,which is based on the modified particle-swarm optimization(MPSO)technique,increases the output power of PV systems under such abnormal conditions and has a better performance compared to other methods.The proposed method is examined under several scenarios for partial shading condition and non-uniform irradiation levels using Matlab,and to investigate its effectiveness adequately,the results of the proposed method are compared with those of the neural network technique.The experimental results show that the proposed method can decrease the interference of the local maximum power-point to cause the PV system to operate at a global maximum power-point.The efficiency of the MPSO is achieved with the least number of steady-state oscillations under partial shading conditions compared with the neural network method.

Annual performance analysis of different maximum power point tracking techniques used in photovoltaic systems

This paper presents an annual performance evaluation of three maximum power point tracking (MPPT) methods. The used MPPT techniques (Perturb and Observe, Incremental Inductance and Sliding mode) are evaluated under an annual data of atmospheric conditions of the target site. The main contribution of this work is to consider real fluctuation conditions of solar irradiations, ambient temperatures and wind velocities. It was found that the Sliding mode provides higher energy yields independently of the period. Compared to the basic P&O and the IC techniques, sliding mode has the potential of generating up to 8.18% more electrical energy than other techniques.