A Novel Auto-Scaling MPPT Algorithm based on Perturb and Observe Method for Photovoltaic Modules under Partial Shading Conditions

Maximum Power Point Tracking (MPPT) controller is required in a solar photovoltaic (PV) system to deliver the maximum power to load from PV module. This paper proposes a novel stepped MPPT method to realize a simple MPPT controller, which can track the real maximum power point (RMPP) even under partial shading conditions. The proposed algorithm is started by scanning the characteristic curve of the PV modules to detect the global maximum power point and then the algorithm will be switched to the conventional P&O algorithm to track the true maximum power point. The obtained simulation results, using Power electronic simulation software (PSIM), are compared with those found using the P&O method to confirm the performance of our proposed MPPT method even under non-uniform solar irradiation.     

A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.     

A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach

The maximum power point tracking (MPPT) in the PV system has become complex due to the stochastic nature of the load, intermittency in solar irradiance and ambient temperature. To address this problem, a novel Grasshopper optimized fuzzy logic control (FLC) approach based MPPT technique is proposed in this paper. In this proposed MPPT, grasshopper optimization is used to tune the membership functions (MFs) of FLC to handle all uncertainties caused by variable irradiances and temperatures. The performance of the proposed grasshopper optimized FLC based MPPT is studied under rapidly changing irradiance and temperature. The proposed MPPT overcomes the limitations such as slow convergence speed, steady-state oscillations, lower tracking efficiency as encountered in conventional methods viz. perturb & observed (P&O) and FLC techniques. The feasibility of the proposed MPPT is validated through experimentation. The effectiveness of the proposed scheme is compared with P&O and also with FLC MPPT.     

A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems

Maximum power point tracking (MPPT) techniques are considered a crucial part in photovoltaic system design to maximise the output power of a photovoltaic array. Whilst several techniques have been designed, Perturb and Observe (P&O) is widely used for MPPT due to its low cost and simple implementation. Fuzzy logic (FL) is another common technique that achieves vastly improved performance for MPPT technique in terms of response speed and low fluctuation about the maximum power point. However, major issues of the conventional FL-MPPT are a drift problem associated with changing irradiance and complex implementation when compared with the P&O-MPPT. In this paper, a novel MPPT technique based on FL control and P&O algorithm is presented. The proposed method incorporates the advantages of the P&O-MPPT to account for slow and fast changes in solar irradiance and the reduced processing time for the FL-MPPT to address complex engineering problems when the membership functions are few. To evaluate the performance, the P&O-MPPT, FL-MPPT and the proposed method are simulated by a MATLAB-SIMULINK model for a grid-connected PV system. The EN 50530 standard test is used to calculate the efficiency of the proposed method under varying weather conditions. The simulation results demonstrate that the proposed technique accurately tracks the maximum power point and avoids the drift problem, whilst achieving efficiencies of greater than 99.6%.     

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P&O) algorithm dispositive.     

A maximum power point tracking control strategy with variable weather parameters for photovoltaic systems with DC bus

In photovoltaic (PV) system, the most commonly used DC/DC converter is the basic buck or boost circuit to implement the maximum point power tracking (MPPT) due to their simple structure and low cost while there are some MPPT constraint conditions. By contrast, the conventional buck/boost DC/DC converter without MPPT constraint condition is seldom used because of its high cost or poor performance. To keep the advantages of these three DC/DC converters while overcoming their shortcomings, in this paper, the constraint conditions of capturing the maximum power point (MPP) of PV systems with direct-current (DC) bus are found out. Then, on the basis of this work, a MPPT control strategy with variable weather parameters is proposed. In this strategy, a new buck/boost DC/DC converter is proposed, which not only avoids the MPPT constraint conditions of basic buck or boost DC/DC converter but also overcomes the shortcomings of conventional buck/boost DC/DC converter. Finally, lots of simulated experiments verify the accuracy of MPPT constraint conditions, test the feasibility and availability of proposed MPPT control strategy, analyze the MPPT performance of proposed PV system and compare the output transient-state performance with conventional perturb and observe (P&O) method.     

An improved maximum power point tracking method for photovoltaic systems

In most of the maximum power point tracking (MPPT) methods described currently in the literature, the optimal operation point of the photovoltaic (PV) systems is estimated by linear approximations. However these approximations can lead to less than optimal operating conditions and hence reduce considerably the performances of the PV system. This paper proposes a new approach to determine the maximum power point (MPP) based on measurements of the open-circuit voltage of the PV modules, and a nonlinear expression for the optimal operating voltage is developed based on this open-circuit voltage. The approach is thus a combination of the nonlinear and perturbation and observation (P&O) methods. The experimental results show that the approach improves clearly the tracking efficiency of the maximum power available at the output of the PV modules. The new method reduces the oscillations around the MPP, and increases the average efficiency of the MPPT obtained. The new MPPT method will deliver more power to any generic load or energy storage media.     

基于改进BA算法的光伏发电系统MPPT优化控制

为优化光伏阵列在部分遮蔽情况下的多峰值MPPT控制,保证光伏发电系统实时功率的最大输出,提出了基于改进BA算法的最大功率追踪控制方法,即在基本BA算法的基础上,融入了小生境技术的共享机制与排挤策略,减少相似个体数量,从而增加了BA算法在迭代过程中的种群多样性,提高了BA算法在MPPT控制中的全局搜索能力,增强了最大功率追踪的稳定性,并将该算法与PSO、PO算法在不同光照及温度条件下的MPPT控制效果进行了仿真试验对比。结果表明,与传统算法相比,改进的BA算法具有更好的追踪效果,不仅避免了光伏系统在遮蔽情况下输出功率陷入局部最大值的问题,且提高了发电效率。     

Optimization of fuzzy-based MPPT controller via metaheuristic techniques for stand-alone PV systems

Due to the alteration of power-voltage characteristics of solar module output under multiple environmental conditions such as solar irradiation and ambient temperature, these systems hardly function at maximum power point (MPP). However, maximum power point tracking (MPPT) plays a significant role in their efficiency. On the other hand, solar module characteristics are extremely nonlinear and their slope on either side of MPP is asymmetric. Thus using a nonlinear control method which has the potential of adapting the operating point of the system to MPP seems useful. This has motivated authors to present MPPT method which maximizes PV's output power by tracking MPP continuously. In the present study, a fuzzy logic controller (FLC) is presented for MPPT in photovoltaic systems. Four optimization algorithms are presented in this paper for optimizing fuzzy membership functions (MFs) and generating proper duty cycle for MPPT. The presented algorithms include: Teaching Learning Based Optimization (TLBO), Firefly Algorithm (FFA), Biogeography based optimization (BBO), and Particle Swarm Optimization (PSO), which are all described and simulated. Finally, to validate performance of the proposed optimized FLC, it is compared with other algorithms such as symmetrical fuzzy logic controller (SFLC) and conventional Perturbation and Observation (P&O). According to the simulation results, P&O algorithm shows significant oscillations, energy loss, and in some cases, it cannot obtain MPP. Simulation results also indicate that TLBO and FFA based asymmetric fuzzy MFs not only increase MPPT convergence speed but also enhance tracking accuracy in comparison with symmetric fuzzy MFs and asymmetric fuzzy MFs based on BBO and PSO.     

Perturbative methods for maximum power point tracking (MPPT) of photovoltaic (PV) systems: a review

Over the past few decades, the world demand for energy has risen steadily, forcing the world communities to look for alternative sources. Photovoltaic (PV) is seen as the most promising solution for this demand. However, the PV system is popularly known to suffer from low‐energy harvesting due to the change of environment conditions. An inexpensive and practical solution to extract the energy from the PV is by improving the maximum power point tracking (MPPT) controller technique. An ideal MPPT should be able to track the true maximum power operating point accurately under all circumstances and overcome all nonlinearities in the characteristic I‐V curves. This paper presents an updated review of the techniques based on the perturbative MPPT methods, both using the conventional and soft computing methods. The working principles of the techniques, parameter effects, and their limitations are discussed. The focus of this review is to direct the readers to the new direction of MPPT using the artificial intelligence and evolutionary computation techniques. Besides serving as a comprehensive source of information, the paper also provides a critical review on the relative performance of the selected MPPT methods. This includes the module dependency, tracking performance, and the ability to handle the partial shading conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

A new approach in MPPT for photovoltaic array based on Extremum Seeking Control under uniform and non-uniform irradiances

This work presents a Maximum Power Point Tracking (MPPT) based on analyzing the output characteristics of PV array under uniform irradiance and partial shading conditions. In order to carry out MPPT in PV panels, under partial shading conditions a method based on Extremum Seeking Control (ESC) is introduced. In contrast with classic ESC, in this method the double of dithering signal frequency is not used, consequently PV output power has a ripple of a lower frequency. Also the drop which occurs when MPPT system starts to operate in classic ESC method is minimized in this paper. The ESC approach for MPPT in this paper uses a series combination of a Low Pass Filter (LPF) and a High Pass Filter (HPF). These two filters act as a Band Pass Filter (BPF) and let a specific frequency of input power which includes the derivative of PV with respect to its voltage pass through. Finally, the system does not operate in local optimal points for efficient point will be global. The algorithm adds partial shadow judging conditions in ESC method. The system runs the variable step ESC method to realize MPPT when photovoltaic array is under uniform irradiance. Under Partial Shading Condition (PSC), the control method can eliminate the interference of local maximum power point (MPP) to make 23 the PV array running at global MPP. In addition, unlike other methods, the proposed MPPT operates on the global MPPs. The proposed MPP tracker does not add any extra complexity compared to the classical ones. However, it increases significantly the efficiency of the PV installation under PSC. We will show that under uniform irradiance, the proposed MPPT leads to faster performances than classical approaches.     

Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power?voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.     

基于并联光伏组件外特性的最大功率点跟踪方法

当阴影条件变化时,并联光伏组件的全局最大功率点(MPP)会随之改变.为了实现太阳能发电最大化,要求最大功率点跟踪(MPPT)方法始终能实时而准确地锁定住并联光伏组件的全局MPP.不同阴影条件下并联光伏组件会呈现不同的外特性特征,如多阶梯的电流电压特性以及多峰值的功率电压特性.基于此现象,该文提出一种基于并联光伏组件外特...     

A study of a two stage maximum power point tracking control of a photovoltaic system under partially shaded insolation conditions

A photovoltaic (PV) array shows relatively low output power density, and has a greatly drooping current–voltage (I–V) characteristic. Therefore, maximum power point tracking (MPPT) control is used to maximize the output power of the PV array. Many papers have been reported in relation to MPPT. However, the current–power (I–P) curve sometimes shows multi-local maximum point mode under non-uniform insolation conditions. The operating point of the PV system tends to converge to a local maximum output point which is not the real maximal output point on the I–P curve. Some papers have been also reported, trying to avoid this difficulty. However, most of those control systems become rather complicated. Then, the two stage MPPT control method is proposed in this paper to realize a relatively simple control system which can track the real maximum power point even under non-uniform insolation conditions. The feasibility of this control concept is confirmed for steady insolation as well as for rapidly changing insolation by simulation study using software PSIM and LabVIEW.     

SMC-DPC based active and reactive power control of grid-tied three phase inverter for PV systems

In this paper, sliding mode control (SMC) – direct power controller (DPC) based active and reactive power controller for three-phase grid-tied photovoltaic (PV) system is proposed. The proposed system consists of two main controllers: the DC/DC boost converter to track the possible maximum power from the PV panels and the grid-tied three-phase inverter. The Perturb and Observe (P&O) algorithm is used to transfer the maximum power from the PV panels. Control of the active and reactive powers is performed using the SMC-DPC strategy without any rotating coordinate transformations or phase angle tracking of the grid voltage. In addition, extra current control cycles are not used to simplify the system design and to increase transient performance. The fixed switching frequency is obtained by using space vector modulation (SVM). The proposed system provides very good results both in transient and steady states with the simple algorithms of P&O and SMC-DPC methods. Moreover, the results are evaluated by comparing the SMC-DPC method developed for MPPT and the traditional PI control method. The proposed controller method is achieved with TMS320F28335 DSP processor and the system is experimentally tested for 12 kW PV generation systems.     

Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions

This paper explains the development of a new algorithm for maximum power point tracking (MPPT) in large PV systems under partial shading conditions (PSC). The new algorithm combines the use of particle swarm optimization (PSO) for MPPT during the initial stages of tracking and then employs the traditional perturb and observe (PO) method at the final stages. The methodology has been first simulated in two different PV configurations under varying shading patterns and experimentally verified using a microcontroller based experimental system. The integration of swarm intelligence with PO algorithm is shown to yield faster convergence to the global maximum power point (GMPP) than when the two methods are individually used. The oscillations in the output power, voltage and current of the PV system with the proposed method are the least when compared to the ones obtained during PSO based MPPT.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

Modeling and maximum power point tracking (MPPT) method for PV array under partial shade conditions

Influenced by partial shade, PV module aging or fault, there are multiple peaks on PV array's output power–voltage (P–V) characteristic curve. Conventional maximum power point tracking (MPPT) methods are effective for single peak P–V characteristic under uniform solar irradiation, but they may fail in global MPP tracking under multi-peak P–V characteristics. Existing methods in literature for this problem are still unsatisfactory in terms of effectiveness, complexity and speed. In this paper, we first analyze the mathematical model of PV array that is suitable for simulation of complex partial shade situation. Then an adaptive MPPT (AMPPT) method is proposed, which can find real global maximum power point (MPP) for different partial shade conditions. When output characteristic of PV array varies, AMPPT will adjust tracking strategies to search for global peak area (GPA). Then it is easy for conventional MPPT to track the global MPP in GPA. Simulation and experimental results verify that the proposed AMPPT method is able to find real global MPP accurately, quickly and smoothly for complex multi-peak P–V characteristics. Comparison analysis results demonstrate that AMPPT is more effective for most shade types.     

光伏发电的最大功率跟踪算法研究

太阳能光伏阵列的输出特性受外界环境因素的影响,为了跟踪太阳能光伏阵列输出功率最大点,实现光伏阵列和负载的匹配,常在系统中加入最大功率跟踪器。准确跟踪太阳能光伏阵列的最大输出功率点依赖于有效的搜索算法。分析了传统的扰动观察法和增量电导法的特点,并提出了一种新的变步长寻优算法。通过验证表明,这种算法能够快速准确地跟踪最大功率点。