Conventional Maximum Power Point Tracking Methods Research Articles

Adaptive rapid neural optimization: A data-driven approach to MPPT for centralized TEG systems

Thermoelectric generation (TEG) system is designed to recover waste heat generated in industrial production and daily life with disadvantage of low power generation efficiency. Since non-uniform temperature distribution (NTD) easily gives rise to multiple local maximum power points (LMPPs) for centralized TEG systems, conventional maximum power point tracking (MPPT) methods is likely to lead to a low-quality LMPP. Hence, a novel adaptive rapid neural optimization (ARNO) approach is proposed to capture maximum power point (MPP). Particularly, generalized regression neural network (GRNN) is devoted to construct a proper mapping between control input of duty cycle and power output of TEG system, thus ARNO can implement an efficient search for MPP. In order to reduce possible instability caused by manual tuning, Bayesian optimization (BO) is adopted to adaptively select optimal parameters of GRNN. Compared to conventional MPPT methods, four simulations are carried out to verify the feasibility and advantages of ARNO, start-up experiment, step temperature variation, stochastic change of temperature, and sensitivity analysis included. Further, dSpace platform based hardware-in-the-loop experiments are performed to verify the feasibility of the proposed method.

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.

Improved hybrid intelligent controller design for MPPT of stand-alone PV System

Photovoltaic (PV) systems have low power conversion efficiency, so maximum power point tracking (MPPT) control methods are utilized to maximize the efficiency of PV systems. The present study proposes an improved hybrid intelligent controller design for the MPPT of stand-alone PV system. The hybrid intelligent control structure is integrated into Angle of Incremental Conductance (AIC) method and Interval Type-2 Takagi-Sugeno-Kang Fuzzy Logic Controller (IT2-TSKFLC). The proposed hybrid intelligent controller offers a superior performance in terms of dealing with uncertainties of sudden changes under different environmental conditions. A simulation model is created in Matlab/Simulink using daily data from a real solar PV plant to evaluate the performance of the proposed hybrid intelligent controller. The simulation findings demonstrated that the proposed hybrid intelligent controller displays a highly stable and robust performance in terms of tracking maximum power point compared to a conventional AIC MPPT method against various uncertainties stemming from disturbing inputs such as solar irradiance and panel temperature variations.

The global peak forecasting method for PV array based on the conservation of energy at uniform and partial shading

In order to extract as much power as possible from the costly PV power system, the maximum power point tracking (MPPT) is necessary. It is well known that the output characteristics of PV array are nonlinear, the MPPT control scheme at same irradiation is easy because of there is only one maximum power point (MPP). However, the P–V output curves of the PV array display the multiple peaks with several local peaks (LPs) and one global peak (GP) at partial shading. Rapid fluctuation of the shading and improper physical constraint make the MPPT method difficult. Generally, there will exist several peaks due to the passing cloud, shading of buildings, dust, and excreta of passing birds. In this paper, a novel forecasting technology using the law of conservation of energy is proposed to roughly track the GP of a PV array which located in the adjacent area of accurate GP. The proposed strategy is easy to combine with the conventional MPPT method and rapidly gain the actual GP. Study results and experiment tests demonstrate that the proposed algorithm can perform on a real PV power system under uniform or complex irradiation conditions.

Fast and improved PSO (FIPSO)‐based deterministic and adaptive MPPT technique under partial shading conditions

Partial shading in photovoltaic (PV) power generating systems is practically unavoidable and is a menace to the PV system. Non-uniform shading can be caused due to clouds, tree branches, buildings or other nearby items. The non-linear output characteristics of a PV source varies with temperature and solar insolation. During partial shading condition, the conventional hill-climbing methods of maximum power point tracking (MPPT) prove to be inefficient. In partial shading, the power–voltage curve exhibits multiple peaks. Conventional MPPT methods get stuck on one of the local maxima and fail to attain the global maximum power point (GMPP). Particle swarm optimisation (PSO) has proved to be a very accurate and powerful technique to find GMPP. In this study, the authors improved the conventional PSO by reducing swarm size, as well as, the number of iterations to achieve MPP at a fast pace with remarkable accuracy, thus making the system more efficient with less computational effort. Simulation and hardware implementation for performance evaluation of the proposed technique is carried out to verify various qualities under different partial shading conditions, such as flexibility, reliability, system-independence and high accuracy in tracking the GMPP under non-uniform insolations.

Maximum Power Point Tracking using Grey Wolf Technique Under Fast-Changing Irradiance

In this paper, maximum power point tracking (MPPT) using Grey wolf optimization (GWO) algorithm is presented using MATLAB/Simulink. As we know that meta-heuristic or nature-inspired algorithm has proven to be superior in performance compared to the conventional MPPT methods. Grey Wolf optimization algorithm is a meta-heuristic algorithm based on the hunting behaviour of grey wolves. The proposed system includes modelling of PV system under changing irradiance and the MPPT control is driven by GWO algorithm. Most of the conventional MPPTs are unable to track multiple peaks and also shows oscillations on the output side, for this reason proposed MPPT algorithm is used in this paper. For eliminating oscillations, this algorithm has proven to be better compared to perturb and observe (P&O) and particle swarm optimization (PSO). The results are compared in terms of output power.

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.

Implementation of MPPT for PV Isolated System by P&O, I_C and PSO Methods based on Arduino Technology

Maximum power point tracking (MPPT) algorithms are very important for the PV systems because they attract the points of maximum power at any conditions of the varying temperature and irradiation and subsequently decrease the PV array cost. This paper presents an improved MPPT technique for the PV system using particle swarm optimization (PSO) algorithm comparing with the conventional MPPT methods using perturb and observe (P&O) and incremental conductance (I_C). MATLAB simulations are carried out under the same irradiation and temperature and a variation in the load and comparing the performance of the three methods. In Protues 8.3 professional software, a design of the proposed model using Arduino UNO of the three algorithms is achieved. One of the important contributions of this paper is the proposed experimental work which is carried out by the combination of Arduino UNO which is coded by C language and coupled with MATLAB results which obtained by the simulation for the used different optimization methods in this paper. At the end of this paper, there is a comparison in tabular form between the presented 3 optimization methods with their experimental results. It is found that the results obtained by using PSO are more acceptable.
 The main advantages of the PSO are as follows: - it reduces the oscillations at the steady state (to approximately zero) once the MPP is located, and its algorithm is very fast comparing to the other two methods.

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

Solar energy and other renewables like geothermal, biomass, and wind energy can minimize the release of the CO2 and other harmful gases produced in case of fossil fuel. Low efficiency is the main drawback of the solar photovoltaic system specifically under partial shadowing condition (PSC). Commonly, with uniform solar radiation distribution, the power-voltage graph has single maximum power point (MPP). The single MPP can be definitely extracted by any traditional tracker like perturb and observe as an example. However, during PSC, the situation is completely different since the power-voltage curve has many MPPs (ie, multiple local points and single global point). The conventional MPP tracking methods cannot discriminate among local peaks and global peak; consequently, they can be easily trapped on the first local peak. Therefore, smart MPPTs based on modern optimization are required to track the global MPP. Most of MPPT tracking methods in the literature require both voltage and current sensors, and sometimes the control system needs an additional solar irradiance sensor and/or temperature sensor, which increase the system cost. In this paper, for the first time, a simple single-sensor–based global MPP tracking method for partially shaded photovoltaic battery chargers is proposed. A deterministic particle swarm optimizer is utilized to extract the global MPP. Several patterns of PSC are considered to test and evaluate the proposed strategy. The obtained results confirm the efficacy of a single-sensor–based global MPP tracking method to catch the global MPP accurately. Considering this research reduces the number of sensors, cost, and difficulty and consequently increases the power density of the MPP tracking methods under partial shadowing conditions.

Fast and precise global maximum power point tracking techniques for photovoltaic system

The multiple power peaks obtained in the power–voltage (P-V) curve of a photovoltaic string under partially shaded condition results in a complicated maximum power point tracking (MPPT) process. Under this condition, the conventional MPPT methods are not acknowledged as they result in false and slow tracking. In this study three novel global MPPT (GMPPT) methods have been proposed and validated. These are named as large and small duty step (LSDS), large and mutable duty step (LMDS) and fast and intelligent GMPPT (FI-GMPPT). The LSDS method sweeps almost the entire P-V curve using a combination of LSDS. Small duty steps are used in predefined areas near all local maximum power points of the P-V curve. LMDS is a further improved method, which uses a combination of LMDSs. The FI-GMPPT is an advance true GMPPT method which limits the area to be swept during the search process. This results in a further reduction in sweep time. In this method, the unnecessary area is skipped during the sweep process. The improved performance of the projected methods has been demonstrated and validated using MATLAB/SIMULINK and hardware implementation.

Cuckoo Search Based Approach Towards Maximum Power Point Tracking for Solar Photovoltaic System Under Partial Shading

Extracting the maximum power from as solar PV system is a critical task when high changes in light intensity or Partial Shading Condition (PSC) are experienced. The latter case is more difficult as it creates multiple maxima points on P–V curve. In this way, it is obligatory to thoroughly pick a precise Maximum Power Point Tracking (MPPT) method which recognizes adequately the Global Maximum Power Point (GMPP) and tracks it under partial shading. This paper first describes the modeling of PV module and PV characteristics under uniform irradiance as well as effect of PSC on PV characteristics. In the latter sections, a review of conventional and intelligent MPPT methods is done. To tackle the problem of MPPT under PSC, two metaheurisric algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are described briefly. A new optimization method called Cuckoo Search (CS) is implemented in MATLAB SIMULINK tool and tested under three different PSC patterns. A comparative analysis of different MPPT strategies is made after analyzing the results.

Reduced Sensor-Based PMSM Driven Autonomous Solar Water Pumping System

The nonlinear <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V–I</i> characteristic of solar cell, calls for the use of maximum power point tracking (MPPT) method in order to obtain optimum power from solar photovoltaic (PV) array. Conventional MPPT methods require one voltage and one current sensor. This paper proposes a novel single sensor-based MPPT technique for a PV array fed water pumping using a permanent magnet synchronous motor (PMSM) drive. As the use of encoders, is not preferred for submersible water pumping, this paper uses a sensorless speed and position estimation for speed control of PMSM. The estimation of rotor speed and position, is achieved through the estimation of stator flux from voltages and currents in stationary reference frame. As the conventional flux estimators suffer from the problems, such as saturation, dc drift, and distortion, this paper proposes a mix multi-resonant structure for flux estimation. A sensorless speed control decreases the cost and improves the reliability of the solar water pumping system. The energy extracted from the PV array, is used to drive the PMSM, which rotates the pump coupled to it. Suitability of the proposed system is demonstrated under varying insolation conditions through a prototype developed in the laboratory and utilizing a digital signal processor (dSPACE-1202) for controlling the SWP system.

Harness of maximum solar energy from solar PV strings using particle swarm optimisation technique

ABSTRACT Solar energy is a sustainable source of energy that is renewable and inexhaustible. It is clean and pollution free in nature; its usage is strongly influenced by environmental and technological aspects. This work mainly focuses on the application of novel optimisation technique for extracting maximum solar energy with the solar photovoltaic (PV) module. The solar radiation and temperature fluctuate throughout the day; hence the maximum power extraction using PV panel is always variable which results in decreasing the efficiency of PV module. Generally, Maximum Power Point Tracking (MPPT) method is widely used for extraction of optimum energy from the solar PV panels under certain conditions. Numerous conventional MPPT methods were developed by the earlier researchers such as Perturbation and Observation, Incremental Conductance and hill climbing for extracting maximum energy at a uniform irradiation level. Most of the conventional MPPT methods are generating errors under certain circumstances and this leads to further reduction in the efficiency of the PV systems. In this context, this research work explores an evolutionary technique referred as Particle Swarm optimisation (PSO) employed in MPPT detection and in harnessing maximum solar power from the solar PV module in order to enhance its efficiency. This work also depicts the superiority of PSO of the MPPT method over Perturbation and Observation approach.

Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance

This paper proposes an accurate global maximum power point tracking (MPPT) technique based on Wind Driven Optimization (WDO) algorithm for photovoltaics system (PVS) under non-uniform solar irradiance. Under non-uniform solar irradiance or partial shading conditions (PSC), the power-voltage curve of the PVS has several maximum power points (MPPs), one of which is assumed to be the global peak. This condition complicates the optimal MPP tracking and decreases of the PVS efficiency. Conventional MPPT methods, such as Perturb & Observe, Hill Climbing and Incremental Inductance are likely to be trapped at one of the local MPPs. The major disadvantage of these methods is their incompetence to specify the global MPP under the above mentioned situation. To overcome this problem, global MPPT techniques based on modern optimization algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Harmony Search Algorithm (HSA), Bat Algorithm (Bat), Sine-Cosine Algorithm (SCA), Cuckoo Search (CS), and Genetic Algorithm (GA) are previously developed. The main contribution of this research is to introduce a novel global MPPT based on WDO algorithm. A comprehensive statistical evaluation of WDO, PSO, DE, HSA, Bat, SCA, CS and GA is carried out under different scenarios of shadowing conditions. Seven statistical metrics including the relative error, root mean square error, mean absolute error, standard deviation, success rate, convergence time and efficiency are used to evaluate the powerfulness and the feasibility of the proposed compared to the others. Therefore, the proposed MPPT based on WDO algorithm is considered to be the most effective and superior optimization tool compared to the corresponding ones.

A Stand-Alone Solar PV System with MPPT Based on Fuzzy Logic Control for Direct Current Portable House Applications

Based on the fact of the difficulty of reaching grid electricity to the remote location, and to reduce the complexity of the solar PV system which delivers alternating electricity, this paper proposes a new design with analysis of stand-alone solar PV system which considers the direct current DC loads for small size portable house applications. This proposal reduces the complexity and the cost which are represented by applying the alternating current items such as inverter, filter and/or step up insulating transformer. The presented design considers 2.25 kW rated system power with the capability of Maximum Power Point Tracking (MPPT) based on an effective Fuzzy Logic Control FLC design. The proposed solution is evaluated through comparisons the conventional MPPT methods of the Perturb &amp; Observe (P&amp;O) and the Incremental Conductance (Inc. Cond.) using MATLAB/Simulink software and promises higher priority as a strong prototype for practical applications.

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.

A Novel Adaptive Neural MPPT Algorithm for Photovoltaic System

This paper presents a novel adaptive neural network (ANN) for maximum power point tracking (MPPT) in photovoltaic (PV) systems under variable working conditions. The ANN-based MPPT model includes two separate NNs for PV system identification and control. NNs are trained by using of a novel back propagation algorithm in pre/post control phases. Because of online optimal performance of NNs, the proposed method, not only overcome the common drawbacks of the conventional MPPT methods, but also gives a simple and a robust MPPT scheme. Simulation results, which carried on MATLAB, show that proposed controller is the most effective in comparison with conventional MPPT approaches.

A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application

In this paper, a novel beta parameter three-input one-output fuzzy-logic based maximum power point tracking (MPPT) algorithm is presented for the photovoltaic (PV) system application. The conventional fuzzy-logic controllers (FLCs) exhibit obvious limitations such as their dependence on the user's knowledge about the system and complicated rules. Furthermore, they show inherent dilemma between the rules number of FLC and the universality for various operating conditions, which is revealed and explained with details in this paper. Thus, a novel FLC is proposed by introducing a third input: an intermediate variable β. It can simplify the fuzzy rule membership functions and cover wider operating conditions. The dependence on the user's knowledge about the system is reduced. The converging speed for transients is improved and oscillations around the MPPs are completely eliminated compared with conventional MPPT methods. Typical operation conditions such as varying solar irradiation and load resistance are tested for fair comparison of various algorithms. An experimental prototype was designed and main experimental results were presented to verify the advantages of the proposed algorithm.

MPPT in PV systems under partial shading conditions using artificial vision

Maximum power point tracking (MPPT) algorithms should track and extract the maximum power from photovoltaic (PV) systems under any environmental conditions. Most of conventional MPPT methods are able to reach the maximum point when there is only one peak in the P–V characteristic curve but they fail when the solar cells are affected by partial shading conditions due to the fact that multiple peaks appear in the P–V curve. Thus, a local maximum may be reached instead of the global peak. In this work, a new method to accomplish the maximum power point (MPP) under partial shading conditions using artificial vision is presented. The artificial vision uses a webcam to identify in real time the shadow irradiance and provide the reference voltage that supplies the maximum power, regardless of the number of peaks that the P–V curve presents. Then, the reference voltage is used by a robust and non-linear control, the backstepping controller, to regulate the DC/DC converter input voltage and to guarantee the PV modules maximum energy extraction. Experimental tests carried out outdoor validate the proposed method, obtaining a MPP tracking efficiency that ranges from 98.1% to 99.6%.

MPPT algorithm for thermoelectric generators based on parabolic extrapolation

In this study, a maximum power point tracking (MPPT) technique based on parabolic extrapolation has been presented for thermoelectric generator systems. Conventional MPPT methods require a closed-loop controller and perturb and observe (P&O) method to provide fast-tracking response. However, they produce power loss due to small oscillations in the steady state. The proposed method excludes the use of closed-loop controller and steady-state oscillations by directly estimating the coordinates of MPP using three random operating points on the parabolic P–I curve. To substantiate the effectiveness of the parabolic extrapolation-based MPPT algorithm, different conditions of temperature gradient and load have been applied. The results demonstrate that the proposed algorithm takes <15% of the time taken by P&O method to track the MPP.