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.

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.

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.

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.

Observer-Based Nonlinear Control of A Torque Motor with Perturbation Estimation

This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedbazk linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.

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.

局部阴影下基于GWO-P&O混合算法的光伏最大功率点跟踪

针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、后期收敛速度慢、稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&O)各自的优势,提出了基于GWO-P&O的混合优化最大功率点跟踪(Maximum power point tracking,MPPT)算法。首先,采用灰狼优化算法逐渐向光伏的全局最大功率点靠近。其次,在灰狼优化算法收敛后期引入P&O法,既保持了灰狼优化算法较高的稳态精度,又能以较快速度寻找到局部最大功率点。最后,在不同环境工况下,将所提出的GWO-P&O方法与传统GWO算法进行对比。结果表明,改进的GWO-P&O算法在保证良好稳态性能的同时,一定程度上提高了GWO算法后期跟踪最大功率时的收敛速度。

基于ICS-IP&O的光伏最大功率跟踪

针对光伏组件存在局部阴影条件(PSC)时,功率输出曲线呈现出的多峰现象,传统最大功率点追踪方法容易陷入局部最优情况。为了解决上述问题,提出一种将自适应布谷鸟搜索(ICS)算法和变步长扰动观察法(IP&O)相结合的复合算法(ICS-IP&O)。对布谷鸟搜索方法(CS)的切换概率、Lévy飞行步长系数进行非线性自适应优化,使其满足迭代前、后期不同的需求,加快收敛进程。在偏好随机游走部分,引入粒子群算法思想,对位置更新公式进行优化,提高其多样性,使算法具有较强的全局搜索随机性,降低陷入局部最优的可能性。算法后期切换成IP&O搜索,减小振荡。通过Simulink进行仿真测试,并与粒子群算法(PSO)、布谷鸟算法(CS)进行对比。结果表明,该算法在静态、局部遮阴、动态遮阴条件下均具有更好的收敛速度和精度。

基于LGPSO_P&O混合算法的光伏多峰MPPT研究

针对复杂遮阴环境下光伏阵列P-U曲线呈现多个峰值而导致传统最大功率点跟踪(MPPT)算法易陷入局部最优解的问题,提出一种新的控制算法模型——LGPSO_P&O模型。该模型结合Levy飞行、高斯变异策略和粒子群算法,并对粒子群算法惯性权重进行改进,构建LGPSO算法搜索全局最大功率点(MPP)。同时,改进扰动观察法(P&O)对MPP进行局部跟踪,进一步提高跟踪精度。在Matlab/Simulink中进行仿真实验,并与改进PSO、BPSHO和GA_ACO算法作对比,仿真结果表明该模型具有较高的鲁棒性,收敛速度快,跟踪精度高,最高可达99.8%。

An ILMI Approach to RFDF for Uncertain Linear Systems with Nonlinear Perturbations

The robust fault detection filter design for uncertain linear systems with nonlinear perturbations is formulated as a two-objective optimization problem. Solvable conditions for the existence of such a robust fault detection filter are given in terms of matrix inequalities (MIs), which can be solved by applying iterative linear matrix inequality (ILMI) techniques. Particularly, compared with two existing LMI methods, the developed algorithm is more generalized and less conservative.An illustrative example is given to show the effectiveness of the proposed method.

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.

改进BOA-PO算法下局部遮荫光伏最大功率跟踪控制

光伏阵列在局部遮荫的情况下,其输出特性呈现多峰值,在最大功率跟踪(maximum power point tracking,MPPT)过程中跟踪速度较慢,且跟踪精度不足。针对上述问题提出一种基于邻域搜索策略的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)和变步长扰动观察法(Perturbation Observation,PO)的融合改进算法。利用改进蝴蝶优化算法的全局搜索能力跟踪到最大功率点附近,通过变步长扰动观察法的局部搜索能力弥补前置算法跟踪精度不足的问题,使其快速准确的跟踪到最大功率点。光伏发电具有随机性和波动性,在应用中常与储能元件相结合,构成光储系统。为了结合实际应用,在MATLAB/Simulink中搭建光储系统模型分析,验证了上述算法在跟踪速度和精度上的优越性。

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.

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.

遮光条件下基于IPSO-FLC的光伏MPPT控制

光伏阵列在部分遮光条件下的P-U特性曲线会呈现多峰问题,致使跟踪算法变得更加复杂,而传统的MPPT算法可能会陷入局部最大功率点,导致对全局最大功率点的跟踪无法实现。为此,提出一种改进粒子群算法优化模糊控制器,来实现遮光条件下的光伏阵列最大功率点跟踪。在Matlab/Simulink环境下,对光伏系统和所提出的MPPT算法进行仿真,同时与扰动观测法等传统MPPT算法进行比较。仿真结果表明,所提方法能够有效地跟踪光伏阵列的最大功率点,并且具有较快的响应速度。

Optimal deterministic disturbances rejection for singularly perturbed linear systems

Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditions of the feedforward and feedback composite control （FFCC） laws for both infinite-time and finite-time are proposed, and the design approaches are given. A disturbance observer is introduced to make the FFCC laws realizable physically. Simulation results indicate that the FFCC laws are robust with respect to external disturbances.

黑寡妇算法与P&O相结合的光伏MPPT研究

目的 在局部遮阴条件下,传统最大功率点追踪(MPPT)算法通常会跟踪到局部最优值,单个群智能优化算法在多极值的情况下能有效跟踪最大功率点,但追踪时间相对较长,为此,提出一种基于黑寡妇(BWOA)与扰动观察法相结合的MPPT算法。方法 将boost电路中开关管的占空比定义为算法中的个体位置,通过对输出占空比的调整实现MPPT控制。结果 相比于原BWOA算法和PSO算法,基于BWOA与P&O结合算法的MPPT能够更加快速地跟踪到最大功率点。结论 与扰动观察法的结合可以有效加快BWOA算法收敛速度,并提高算法收敛精度。

Study and Optimization of a Photovoltaic Mill System Functioning on the Course of the Sun

This paper dealt with the optimization of the performance of a photovoltaic mill system operating on the sun race. Depending on the characteristics of the powered load which is a DC motor driving a grain mill and on the weather conditions (temperature and illumination), we noted a very big difference between the potential maximum power and that actually transferred to the load. In order to improve the overall efficiency of the system, we use an adaptation circuit consisting of a boost converter controlled by a numerical MPPT (Maximum Power Point Tracking) command. With the Perturb & Observe (P & O) algorithm, the MPPT control measure the photocurrent, the photo tension and the power released by the photovoltaic generator. From this result, the MPPT control adjusts the duty cyclic of the converter to bring the system to the optimum operating point. Hence, using MATLAB/Simulink software, we did the modeling and the simulation of the system which is composed by a PV generator, a boost converter, a Pulse Width Modulation and a DC motor.

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.

Observer for Linear Distributed-Parameter Systems with Application to Isothermal Plug-Flow Reactor

This paper presents a conception of an exponential observer for a class of linear distributed-parameter systems (DPSs), in which the dynamics are partially unknown. The given distributed-parameter observer ensures asymptotic state estimator with exponentially decay error, based on the theory of C0-semigroups in a Hilbert space. The theoretical observer developed is applied to a chemical tubular reactor, namely the isothermal Plug-Flow reactor basic dynamical model for which measurements are available at the reactor output only. The process is described by Partial differential equations with unknown initial states. For this application, performance issues are illustrated in a simulation study.