Conventional Maximum Power Point Tracking Techniques Research Articles

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

Performance optimization of photovoltaic system under real climatic conditions using a novel MPPT approach

ABSTRACT In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%).

PV Sistemlerde Kullanılan Maksimum Güç Noktası İzleme Tekniklerinin İncelenmesi ve Değerlendirilmesi

The issue of improving the efficiency and effectiveness of PV (Photovoltaic) systems remains a concern for researchers and manufacturers who aim to make these systems cost-effective, thereby encouraging their wider adoption. To achieve this goal, increasing the efficiency of the PV generation system by implementing the Maximum Power Point Tracking (MPPT) system has been proposed. Enhancing the energy output from the PV system is considered a crucial aspect of improving efficiency, as it will lead to increased revenue. Consequently, the cost of the generated energy is reduced, approaching that of energy produced by conventional systems based on fossil fuels. This review paper discusses conventional MPPT techniques designed to extract the maximum available power from PV panels operating under uniform environmental conditions. Subsequently, it highlights why these techniques often fail to perform adequately under partial shading conditions. Following this, modern MPPT techniques explicitly designed to operate under non-uniform and partial shading conditions are analyzed.

Research on Q-Table Design for Maximum Power Point Tracking-Based Reinforcement Learning in PV Systems

Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.

Modelling and performance analysis of improved incremental conductance MPPT technique for water pumping system

Utilization of Solar Photo Voltaic Cell (SPVC) has significantly increased in residential, commercial, and industrial due to freely available. From the power-voltage (P–V) curve, fluctuation has been revealed over an hour because of varying irradiance, and temperature of sun rays. Maximum power point tracking (MPPT) has been essential to track power and maximize the output from SPVC constantly. In this study, a novel Improved Incremental Conductance (IIC) MPPT technique is proposed, and integrated with a boost converter to take out the maximum power from SPVC. The output power has been provided to a brushless DC motor (BLDC). Pulse Width Modulation (PWM) commutation system has been employed to control the BLDC motor through the 3-level bridge converter. The proposed study has been implemented in MATLAB/Simulink. The complete analysis has been examined at different irradiation conditions. Power consumption, loss, and efficiency have been estimated. Simulation results have been compared with conventional incremental conductance (IC). The proposed IIC MPPT has an improved 5% of tracking efficiency than conventional MPPT techniques. The results show that the effectiveness of the proposed IIC technique has attained a steady state in varying irradiation levels. In spite of the low irradiation level of 250 W/m2, the system remains effective.

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions

The key objective of this paper is to develop a photovoltaic (PV) maximum power point tracking (MPPT) algorithm based on particle swarm optimization–butterfly optimization algorithm (PSO-BOA) that is adapted for partial shading conditions (PSCs). Generally, conventional MPPT techniques are often unable to accurately locate the global maximum power point (GMPP) generated by partial shading in PV systems. As a result, a significant decrease in power output occurs. The traditional particle swarm optimization (PSO) algorithm traps the local maxima point easily, while the butterfly optimization algorithm (BOA) has slow convergence speed and large oscillations during its use in research. To address the limitations of the aforementioned PSO and BOA algorithms, the MPPT strategy of PV systems combining PSO-BOA is presented, which can ameliorate the efficiency and accuracy in PSCs. In this paper, the control parameter of sensory modality in the BOA can be acquired based on logistic mapping, and the self-adaptive adjustment of the inertial weight of the PSO algorithm is designed. According to the simulation findings, the suggested method is more suitable than PSO and BOA with respect to intricate shading-induced variations in irradiance and changes in external temperatures. The average tracking time is less than 0.5 s, and the tracking accuracy is not less than 99.94%. Especially under sudden variations in irradiance and temperature conditions, the tracking time of the PSO-BOA algorithm is only 49.70% of that of the PSO algorithm and 55.63% of that of the BOA. Therefore, the MPPT method presented has the ability to improve the oscillations and result in less convergence speed, which in turn accurately tracks the GMPP.

Enhanced Hybrid PSO-CSA Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems: Modeling and Analysis

This research paper presents a mathematical model and analysis of an Improved Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) hybrid Maximum Power Point Tracking (MPPT) system for partially shaded solar PV systems. The proposed hybrid MPPT system aims to overcome challenges in tracking the global maximum power point (MPP) under partial shading conditions. The paper discusses the significance of MPPT techniques in solar PV systems, particularly under partial shading conditions, and the motivation for developing an improved MPPT algorithm using the hybrid PSO-CSA approach. The performance of the proposed hybrid MPPT system is assessed through simulation and experimentation, demonstrating its robustness and superior performance compared to conventional MPPT techniques. The results validate the efficiency and practicality of the proposed hybrid approach, emphasizing its potential for real-world implementation and advancement in renewable energy systems.
  

Fuzzy logic control based maximum power point tracking technique in standalone photovoltaic system

This study describes the development of a smart technique for tracking the highest power point on a standalone photovoltaic (SAPV) system when temperature and irradiance conditions are changing using fuzzy logic control (FLC). The PV systems comprises of a PV array, a boost converter, a controller for tracking the maximum power point, and an inverter to power the AC loads. The FLC-based maximum power point tracking (MPPT) is proposed because the technique is not complex and does not need a deep comprehension of the particular model of the system. Furthermore, the technique is efficient and fast response in tracking maximum power (MP) from the PV array. The technique overcame the limitation of the conventional MPPT technique that resulted in slow tracking of the MP and was not accurate on the optimal position of the PV array output power. The SAPV system is integrated with the sealed lead acid (SLA) battery bank as an alternative power source and makes up for power shortages by supplying energy to the load without interruption during power shortages. A system for managing the battery has been included in the system to preserve the battery's longevity by controlling the battery's charging process. The PV system is able to supply single-phase output AC voltage of 230 Vrms and has low total harmonic distortion (THD) that is suitable for home appliances. The achieved simulation results demonstrate the effectiveness of the suggested fuzzy logic controller in tracking the MPP.

A Self-Adaptive-Step-Size Incremental-Resistance-MPPT Technique for Reverse-Electrodialysis System

Salinity gradient power (SGP) is a kind of promising renewable energy source with huge potential, which can be derived through reverse electrodialysis (RED). Generally, RED stacks operate at either continuous mode or recycle mode, both of which possess respective advantages in different application scenes. Since SGP technology is still at an infant stage, there is a few research focusing on maximum-power-point-tracking (MPPT) techniques of RED stacks, which are crucial for the effective energy utilization of SGP. In this article, based on the generalized hybrid RED model, a self-adaptive-step-size incremental-resistance MPPT technique is proposed to extract maximum power from RED stacks. Not only is it simple and easy to implement but also it realizes self-adjustment of the step size to strike a balance between fast dynamic responses and small oscillations at a steady state. Moreover, according to the unique intrinsic features of RED stacks, the systematic design principles of the sampling interval and scaling factor are detailed, which help facilitate a fast and stable system. Various tests with the proposed technique under different parameter settings, as well as tests with other conventional MPPT techniques, are conducted on a RED stack prototype to validate the effectiveness of the proposed MPPT.

A Framework of Reduced Sensor Rooftop SPV System Using Parabolic Curve Fitting MPPT Technology for Household Consumers

In this paper, a novel Parabolic Curve-fitting based Hill Climbing (PCHC) MPPT (Maximum Power Point Tracking) algorithm is developed to extract maximum power from solar photovoltaic (PV) panels under dynamic environmental conditions for household consumers. Moreover, this developed PCHC algorithm is integrated with a novel reduced sensor-based approach, where only a single current sensor is used to complete the solar-powered battery charging process. The ease of implementation with a simple controller and reduced sensor approach makes it economical for residential rooftop SPV. In the proposed PCHC methodology, two consecutive points on the Power-Voltage (P-V) characteristic of PV are used to detect the Maximum point region quickly. Moreover, the parabolic nature of the P-V characteristic near the Maximum Power Point (MPP) zone is explored to calculate the approx value of voltage corresponding to the optimum power. Here in every iteration, the size of the perturbation in duty is reduced by fifty percent. It overcomes the trade-off between tracking speed and oscillations near the MPP of conventional MPPT techniques such as perturb and observe, incremental conductance. These oscillations affect the connected consumers’ load working efficiency and lifetime. Moreover, it also improves the dynamic performance by taking different step sizes for sudden and slow changes in irradiation levels. The boost converter has been used to realize the performance of the proposed PCHC MPPT technique. The performance of the PCHC MPPT algorithm has been validated on different types of irradiation patterns and obtained results that fulfill the motive of the work.

A critical evaluation of maximum power point tracking techniques for PV systems working under partial shading conditions

Photovoltaic (PV) energy is a promising source of renewable energy which is sturdy and environmentally friendly. PV generation systems, once installed, produce electricity from solar irradiance without emitting greenhouse gases. To maximize the output power of PV systems, the maximum power point tracking system has been employed (MPPT). The MPPT constitutes a fundamental part of PV systems. In recent years, a large number of MPPT techniques have been proposed. This paper is set up to critically review some of the proposed maximum power point tracking (MPPT) techniques to handle the emergence of multiple MPPs in PV panel characteristics due to the partial shading conditions (PSCs). To define the working principle and the pros and cons of the different proposed techniques clearly and sequentially, they are divided into three groups as follows: conventional MPPT techniques, improved MPPT techniques and artificial intelligence- based MPPT techniques to deal with PSCs. The paper also critically summarizes the findings in terms of their performance in capturing the global maximum power point (GMPP) for PV systems operating under PSCs.

Optimal control strategies-based maximum power point tracking for photovoltaic systems under variable environmental conditions

To increase the efficiency of photovoltaic (PV) array output under variable environmental conditions, maximum power point tracking (MPPT) of the solar arrays is needed. This paper proposes fuzzy logic controller (FLC)-based MPPT, artificial neural network (ANN)-based MPPT, neuro-fuzzy (NF)-based MPPT, particle swarm optimisation (PSO)-based MPPT, and cuckoo search (CS) algorithm-based MPPT to combine an adaptive controller and an optimisation, to guarantee global stability and a constant settling time for all operation conditions. This combination enables an increase in the power generated in comparison with conventional MPPT techniques. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions, and achieve good performance. It is also noticed that operating the photovoltaic array based on maximum power point tracking conditions gives about 43% extra power generation than in the case of normal operation.

Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic

<span lang="EN-US">The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.</span>

An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems

Recently, due to rapid growth in electric vehicle motors, used and power electronics have received a lot of concerns. 3ϕ induction motors and DC motors are two of the best and most researched electric vehicle (EV) motors. Developing countries have refined their solution with brushless DC (BLDC) motors for EVs. It is challenging to regulate the 3ϕ BLDC motor’s steady state, rising time, settling time, transient, overshoot, and other factors. The system may become unsteady, and the lifetime of the components may be shortened due to a break in control. The marine predator algorithm (MPA) is employed to propose an e-vehicle powered by the maximum power point tracking (MPPT) technique for photovoltaic (PV). The shortcomings of conventional MPPT techniques are addressed by the suggested approach of employing the MPA approach. As an outcome, the modeling would take less iteration to attain the initial stage, boosting the suggested system’s total performance. The PID (proportional integral derivative) is used to govern the speed of BLDC motors. The MPPT approach based on the MPA algorithm surpasses the variation in performance. In this research, the modeling of unique MPPT used in PV-based BLDC motor-driven electric vehicles is discussed. Various aspects, which are uneven sunlight, shade, and climate circumstances, play a part in the low performance in practical scenarios, highlighting the nonlinear properties of PV. The MPPT technique discussed in this paper can be used to increase total productivity and reduce the operating costs for e-vehicles based on the PV framework.

A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems

The present paper proposes a modified current sensorless approach to reduce the implementation cost of maximum power point tracking (MPPT) controller for photovoltaic (PV) systems under partial shading conditions (PSCs). The proposed scheme relies only on the input voltage signal without the need for a current sensor to track the global maximum power point (GMPP). This is performed using a predefined objective function derived from the mathematical model of the buck-boost converter with an adaptive step-size mechanism for quick convergence. In addition, a formula of lower and upper limit duty cycle values for every local peak is incorporated for fast detection of local power peaks. An extensive experimental study, using a hardware prototype composed of a buck-boost converter driven by a dSPACE DS1104, is carried out to validate the proposed control scheme. Experimental results show that the proposed controller is able to accurately track the GMPP with high speed under various PSCs scenarios. Additionally, the proposed scheme reduces the implementation cost by 27.95% compared to conventional MPPT techniques.

A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions

One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO–PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO–PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.

Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation

Considering the vast improvement of photovoltaics (PVs) efficiency, this paper proposes a robust modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm. The control strategy of the proposed MPO-MPPT algorithm is based on dividing the solar module/cell P-V curve into four operating areas depending on the open-circuit voltage estimation method. The two areas, which are located far from the maximum power point (MPP), utilize large voltage fixed step-size that improves the tracking speed of the PV system. Otherwise, a small step-size P&O MPPT algorithm is used to minimize the steady-state oscillations for the other two areas due to the close locations of these areas to the MPP. As well, the proposed tracking algorithm eliminates the necessity of wide small step-size iterations by concentrating small step-size search area to 15% of the whole P-V operation region. To prove the effectiveness of the proposed MPO algorithm compared with the conventional MPPT techniques; sinusoidal, ramp, and one-day (10 hr.) irradiance profiles are applied to the solar PV system. The proposed MPO-MPPT algorithm based solar PV system has been built using MATLAB/SIMULINK software. Obviously, the system results confirm the theoretical analysis of the proposed algorithm, which boosts the PV system tracking efficiency to 99.7%.

Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

Substantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.

Maximum Power Point in a Single Step: A Novel Method for PV Industry

The environmental concerns linked to traditional power generation methods have driven solar photovoltaic (SPV) systems to be established as a clean and viable alternative power solution [1]. However, SPV systems are associated with low energy conversion efficiency attributes. This highlights the need for an efficient power conversion system [2]. SPV sources showcase a nonlinear currentvoltage (I-V) characteristic that is subject to variation with concurring insolation and temperature conditions. Consequently, this alters the maximum power point (MPP) as well as the maximum power generation. As a result, the literature of this field reiterates the need for efficient maximum power point tracking (MPPT) techniques. Prior works have established a variety of solutions to track the MPP; each of them possesses its pros and cons [2]. Notably, a number of conventional MPPT techniques will fail to track the MPP under partial shading conditions [2]. Although some intelligent techniques can still achieve MPP, these methods oscillate around the MPP due and as a result, some of the available power is not utilized. The oscillating character of these MPPT techniques occurs under uniform conditions as well [3]. Hence there is a definite need to address this issue by determining.