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.

Maximum Power Point Tracking Based on Improved Kepler Optimization Algorithm and Optimized Perturb&Observe under Partial Shading Conditions

Under the partial shading conditions(PSC)of Photovoltaic(PV)modules in a PV hybrid system,the power output curve exhibits multiple peaks.This often causes traditional maximum power point tracking(MPPT)methods to fall into local optima and fail to find the global optimum.To address this issue,a composite MPPT algorithm is proposed.It combines the improved kepler optimization algorithm(IKOA)with the optimized variable-step perturb and observe(OIP&O).The update probabilities,planetary velocity and position step coefficients of IKOA are nonlinearly and adaptively optimized.This adaptation meets the varying needs of the initial and later stages of the iterative process and accelerates convergence.During stochastic exploration,the refined position update formulas enhance diversity and global search capability.The improvements in the algorithmreduces the likelihood of falling into local optima.In the later stages,the OIP&O algorithm decreases oscillation and increases accuracy.compared with cuckoo search(CS)and gray wolf optimization(GWO),simulation tests of the PV hybrid inverter demonstrate that the proposed IKOA-OIP&O algorithm achieves faster convergence and greater stability under static,local and dynamic shading conditions.These results can confirm the feasibility and effectiveness of the proposed PV MPPT algorithm for PV hybrid systems.

Robust Variable-Pitch Control Design of PMSG Via Perturbation Observer

Wind turbine employs pitch angle control to maintain captured power at its rated value when the wind speed is higher than rated value.This work adopts a perturbation observer based sliding-mode control(POSMC)strategy to realize robust variable-pitch control of permanent magnet synchronous generator(PMSG).POSMC combines system nonlinearities,parametric uncertainties,unmodelled dynamics,and time-varying external disturbances into a perturbation,which aims to estimate the perturbation via a perturbation observer without an accurate system model.Subsequently,sliding mode control(SMC)is designed to completely compensate perturbation estimation in real-time for the sake of achieving a global consistent control performance and improving system robustness under complicated environments.Simulation results indicate that,compared with vector control(VC),feedback linearization control(FLC),and nonlinear adaptive control(NAC),POSMC has the best control performance in ramp wind and random wind and the highest robustness in terms of parameter uncertainty.Specially,the integral absolute error index of!m of POSMC is only 11.69%,12.10%and 15.14%of that of VC,FLC and NAC in random wind speed.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

Implementation of FPGA Based MPPT Techniques for Grid-Connected PV System

Global energy demand is growing rapidly owing to industrial growth and urbanization.Alternative energy sources are driven by limited reserves and rapid depletion of conventional energy sources(e.g.,fossil fuels).Solar photovol-taic(PV),as a source of electricity,has grown in popularity over the last few dec-ades because of their clean,noise-free,low-maintenance,and abundant availability of solar energy.There are two types of maximum power point track-ing(MPPT)techniques:classical and evolutionary algorithm-based techniques.Precise and less complex perturb and observe(P&O)and incremental conduc-tance(INC)approaches are extensively employed among classical techniques.This study used afield-programmable gate array(FPGA)-based hardware arrange-ment for a grid-connected photovoltaic(PV)system.The PV panels,MPPT con-trollers,and battery management systems are all components of the proposed system.In the developed hardware prototype,various modes of operation of the grid-connected PV system were examined using P&O and incremental con-ductance MPPT approaches.

Enhanced Perturb and Observe Control Algorithm for a Standalone Domestic Renewable Energy System

The generation of electricity,considering environmental and eco-nomic factors is one of the most important challenges of recent years.In this article,a thermoelectric generator(TEG)is proposed to use the thermal energy of an electric water heater(EWH)to generate electricity independently.To improve the energy conversion efficiency of the TEG,a fuzzy logic con-troller(FLC)-based perturb&observe(P&O)type maximum power point tracking(MPPT)control algorithm is used in this study.An EWH is one of the major electricity consuming household appliances which causes a higher electricity price for consumers.Also,a significant amount of thermal energy generated by EWH is wasted every day,especially during the winter season.In recent years,TEGs have been widely developed to convert surplus or unused thermal energy into usable electricity.In this context,the proposed model is designed to use the thermal energy stored in the EWH to generate electricity.In addition,the generated electricity can be easily stored in a battery storage system to supply electricity to various household appliances with low-power-consumption.The proposed MPPT control algorithm helps the system to quickly reach the optimal point corresponding to the maximum power output and maintains the system operating point at the maximum power output level.To validate the usefulness of the proposed scheme,a study model was developed in the MATLAB Simulink environment and its performance was investigated by simulation under steady state and transient conditions.The results of the study confirmed that the system is capable of generating adequate power from the available thermal energy of EWH.It was also found that the output power and efficiency of the system can be improved by maintaining a higher temperature difference at the input terminals of the TEG.Moreover,the real-time temperature data of Abha city in Saudi Arabia is considered to analyze the feasibility of the proposed system for practical implementation.

Research on Cogging Force Suppression for Toroidal PMLSM Based on Disturbance Observer

For a new type of toroidal permanent magnet linear motor(TPMLSM), this paper analyzes the thrust fluctuation in the constant acceleration operation of the motor from the Angle of the cogging force of the linear motor. For the motor whose structure has been determined and processed, the structural parameters of the motor cannot be changed, and its performance cannot be improved from the perspective of the motor body.Therefore, this paper tries to consider the influence of the cogging force on the normal operation of the motor from the perspective of control. In this paper, starting from the body structure of motor, first on the annular linear motor of the cogging force characteristics were extracted, and its expression is obtained by Fourier decomposition, then investigated considering the cogging force and does not consider the cogging force control of motor model, it can be seen that the control performance deteriorates significantly after considering cogging force of the motor, and the acceleration fluctuation increases significantly during the operation of the motor. On this basis, disturbance observation algorithm is introduced, and feedforward compensation is carried out by extracting the characteristic values of the disturbance model. The results show that the disturbance observer can suppress the thrust fluctuation caused by the motor cogging force to a large extent, and it can reduce the peak-to-peak value of the thrust fluctuation by more than 85% during the motor acceleration operation.

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.

An Adaptive Single Neural Control for Variable Step-Size P&O MPPT of Marine Current Turbine System

Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine(MCT)system,the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’uncertainties.This paper proposes an adaptive single neural control(ASNC)strategy for variable step-size perturb and observe(P&O)maximum power point tracking(MPPT)control.Firstly,to automatically update the neuron weights of SNC for the nonlinear systems,an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients.Secondly,aiming to generate the exact reference speed for ASNC to extract the maximum power,a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT.The robust stability of the MCT control system is guaranteed by the Lyapunov theorem.Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions,and the MCT system operates at maximum power point steadily.

MPPT Design Using PSO Technique for Photovoltaic System Control Comparing to Fuzzy Logic and P&O Controllers

The Maximum Power Point Tracker (MPPT) is the optimum operating point of a photovoltaic module. It plays a very important role to obtain the maximum power of a solar panel as it allows an optimal use of a photovoltaic system, regardless of irradiation and temperature variations. In this research, we present a novel technique to improve the control’s performances optimization of the system consisting of a photovoltaic panel, a buck converter and a load. Simulations of different parts of the system are developed under Matlab/Simulink, thus allowing a comparison between the performances of the three studied controllers: “Fuzzy TS”, “P&O” and “PSO”. The three algorithms of MPPT associated with these techniques are tested in different meteorological conditions. The obtained results, in different operating conditions, reveal a clear improvement of controlling performances of MPPT of a photovoltaic system when the PSO tracking technique is used.

Neural Network and Fuzzy Control Based 11-Level Cascaded Inverter Operation

This paper presents a combined control and modulation technique to enhance the power quality(PQ)and power reliability(PR)of a hybrid energy system(HES)through a single-phase 11-level cascaded H-bridge inverter(11-CHBI).The controller and inverter specifically regulate the HES and meet the load demand.To track optimum power,a Modified Perturb and Observe(MP&O)technique is used for HES.Ultra-capacitor(UCAP)based energy storage device and a novel current control strategy are proposed to provide additional active power support during both voltage sag and swell conditions.For an improved PQ and PR,a two-way current control strategy such as the main controller(MC)and auxiliary controller(AC)is suggested for the 11-CHBI operation.MC is used to regulate the active current component through the fuzzy controller(FC),and AC is used to regulate the dc-link voltage of CHBI through a neural network-based PI controller(ANN-PI).By tracking the reference signals fromMC and AC,a novel hybrid pulse widthmodulation(HPWM)technique is proposed for the 11-CHBI operation.To justify and analyze the MATLAB/Simulink software-based designed model,the robust controller performance is tested through numerous steady-state and dynamic state case studies.

A Modified-Simplified MPPT Technique for Three-Phase Single-State Grid-Connected PV Systems

Nowadays,the single state inverter for the grid-connected photovoltaic(PV)systems is becoming more and more popular as they can reduce circuit complexity resulting in less power losses of the inverter.This paper focuses on the use of model predictive control(MPC)to control a 3-phase and 2-level single-state grid-connected inverter in order to regulate the PV maximum power point(MPP).The algorithm of MPC scheme was done to measure the simultaneous current signal including predicting the next sampling current flow.The reference current(Id∗)was used to control the distribution of electrical power from the solar cell to the grid.To be able to control the maximum power point tracking(MPPT)when the sunlight suddenly changes,so that a developing MPPT based on estimation current perturbation and observation(ECP&O-MPPT)technique was used to control the reference current.This concept was experimented by using MATLAB/Simulink software package.The proposed technique was tested and compared with the old technique.The simulation results showed that the developed MPPT technique can track the MPP faster when the light changes rapidly under 1,000W/m2,25℃ standard climatic conditions.The MPPT time was 0.015 s.The total harmonic distortion(THD)was 2.17%and the power factor was 1.

Adaptive Fractional-Order PID Control for VSC-HVDC Systems via Cooperative Beetle Antennae Search with Offshore Wind Integration

Since the voltage source converter based high voltage direct current(VSC-HVDC)systems owns the features of nonlinearity,strong coupling and multivariable,the classical proportional integral(PI)control is hard to obtain content control effect.Hence,a new perturbation observer based fractional-order PID(PoFoPID)control strategy is designed in this paper for(VSC-HVDC)systems with offshore wind integration,which can efficiently boost the robustness and control performance of entire system.Particularly,it employs a fractional-order PID(FoPID)fra-mework for the sake of compensating the perturbation estimate,which dramatically boost the dynamical responds of the closed-loop system,and the cooperative beetle antennae search(CBAS)algorithm is adopted to quickly and effi-ciently search its best control parameters.Besides,CBAS algorithm is able to efficiently escape a local optimum because of a suitable trade-off between global exploration and local exploitation can be realized.At last,comprehensive case studies are carried out,namely,active and reactive power tracking,5-cycle line-line-line-ground(LLLG)fault,and offshore wind farm integration.Simulation results validate superiorities and effectiveness of PoFoPID control in com-parison of that of PID control and feedback linearization sliding-mode control(FLSMC),respectively.

Simulation of Maximum Power Point Tracking for Photovoltaic Systems

A PV （photovoltaic） solar panels exhibit non-linear current--voltage characteristics, and according to the MPT （maximum power transform） theory, it can produce maximum power at only one particular OP （operating point）; namely, when the source impedance matches with the load impedance, a match which cannot be guaranteed spontaneously. Furthermore, the MPP （maximum power point） changes with temperature and light intensity variations. Therefore, different algorithms have been developed for finding MPPT （maximum power point tracking） based on offline and online methods. Evaluating the performance of these algorithms for various PV systems operating under highly dynamic environments are essentials to ensure producing reliable, efficient, cost-effective and high performance systems. One possible approach for system evaluation is to use computer simulation. This paper addresses the use of Matlab software as a simulation tool for evaluating the performance of PV solar systems and finding the MPPT.

A Correlative Study of Perturb and Observe Technique and GA-RBF-NN Method Supplying a Brushless DC Motor

A comparative study is done in regards to the performance of the popular Perturb and Observe algorithm and the Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) algorithm, both incorporating the Interleaved Boost converter. The Perturb and Observe method (P&O) is inarguably the most commonly used algorithm as its advantages pertaining to its ease in implementation and simplicity enable to track the Maximum Power Point (MPP). However, it is absolutely unreliable when subjected to rapidly fluctuating irradiation and temperature levels. More importantly, the system has the tendency to swing back and forth about the Maximum Power Point without reaching stability. At this juncture, the implementation of the Genetic-Assisted Radial Basis Function (GA-RBF) algorithm helps the system achieve MPP at a shorter time when compared to the Perturb and Observe technique. The ever reliable and robust Levenberg-Marquardt algorithm is included along with the MPPT controller that minimizes the Mean Square Error (MSE) and aids in faster training of the neural network. This PV system drives a brushless DC motor (BLDC), employing rotor position sensors.

Analysis of Maximum PowerPoint Tracking (MPPT) Adaptability in Inverters of the Three-Phase Photovoltaic Systems Integrated into the Electrical Grid of Congo-Brazzaville

This paper investigates the adaptability of Maximum Power Point Tracking (MPPT) algorithms in single-stage three-phase photovoltaic (PV) systems connected to the grid of Congo-Brazzaville and compares the attributes of various conventional, significance and novelty of controller system of the proposed of method and improved Incremental Conductance algorithms, Perturbation and Observation Techniques, and other Maximum Power Point Tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system although the control of this structure is relatively advanced technology but the conversion efficiency is difficult to improve due to increase in transformation series. The single stage topology has a simple topology with high reliability and efficiency because of high power consumption, but control algorithm is more complex because of its power convert main circuit a new strategy is being developed. This paper describes a method for maximum power point tracking (MPPT) in the single-stage and three single-phase PV grid-connected system. In the paper, the nonlinear output characteristics of the PV including I-V & P-V are obtained in changed solar insulations or temperature based on MATLAB, and the MPPT algorithm which is based on the P & O algorithm method, compared with Incremental Conductance, is also described, a dimensioning of the impedance adapter for better stabilization. A comparison SPWM and SVPWM control methods in the case of a grid connection applied to the electrical grid of Republic of Congo and their influences on the dynamic performance of the system and their impact in reducing the harmonic rate for better injection into the grid. The simulation model of three single-phase PV grid-connected system is built, and simulation results show the MPPT algorithm has excellent dynamic and static performances, which verifies the Incremental Conductance is effective for MPPT in the single-stage and three single-phase PV grid-connected system.

Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model

Using the conditional nonlinear optimal perturbation(CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport(UKT) were investigated in the Regional Ocean Modeling System(ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy(TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained:(1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable.(2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.

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.

Optimized Fuzzy Controller for MPPT of Grid-connected PV Systems in Rapidly Changing Atmospheric Conditions

Due to nonlinear behavior of power production of photovoltaic(PV)systems,it is necessary to apply the maximum power point tracking(MPPT)techniques to generate the maximum power.The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions.In this study,a fuzzy logic controller(FLC)optimized by a combination of particle swarm optimization(PSO)and genetic algorithm(GA)is proposed to obtain the maximum power point(MPP).The proposed FLC uses the ratio of power variations to voltage variations and the derivative of power variations to voltage variations as inputs and uses the duty cycle as the output.The range of changes in fuzzy membership functions and fuzzy rules are proposed as an optimization problem optimized by the PSO-GA.The proposed design is validated for MPPT of a PV system using MATLAB/Simulink software.The results indicate a better performance of the proposed FLC compared to the common methods.

Design of a P-&-O algorithm based MPPT charge controller for a stand-alone 200W PV system

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.