Optimal Maximum Power Point Tracking Research Articles

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Hybrid photovoltaic-thermoelectric generator (PV-TEG) system combines two types of energy conversion which is an important innovation to advance the development of renewable energy technologies. Hybrid system in practice needs to track the best operating point in real-time with the help of maximum power point tracking (MPPT) technology to ensure the system outputs maximum energy and optimizes performance. To improve the energy conversion efficiency and utilization of hybrid PV-TEG systems, as well as to effectively combat the negative impacts under partial shading conditions (PSC) and non-uniform temperature distribution (NTD), this paper proposes a hybrid system MPPT technique based on the bi-directional long and short-term neural networks (Bi-LSTM). The Bi-LSTM can consider both past and future data information, which will enhance the training process of the network and the accuracy of the model, making the results more comprehensive. It approaches the global maximum power point by searching for the maximum power down in the forward layer and up in the backward layer. The algorithm can effectively prevent from falling into local optimum under PSC and NTD. The case study section tests and explores the feasibility of utilizing Bi-LSTM to obtain the maximum power from a hybrid PV-TEG system under five case study scenarios, namely, start-up test, step change in solar irradiance, stochastic variation, Hong Kong four-season real case, and uncertainty analysis. Comparative analysis with the other five algorithms are also conducted to thoroughly verify the superiority of the proposed technique for hybrid system MPPT applications. The simulation results indicate that the hybrid PV-TEG system with Bi-LSTM achieves the optimal MPPT performance stably and efficiently under different operating conditions. In particular, low irradiate condition in spring, the energy generated by Bi-LSTM exceeds the INC, SAO and P&O energy outputs by 80.51%, 61.22%, and 31.04%, respectively.

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.

On-grid optimal MPPT for fine-tuned inverter based PV system using golf optimizer considering partial shading effect

Improving photovoltaic systems to better withstand variations in temperature and irradiance, lessen steady-state ripples, maintain high efficiency, require less tracking time, and have minimal complexity is becoming increasingly important. The PV array's overall power output is negatively impacted by partial shading because the shaded cells produce less power and consume less power overall, which lowers efficiency and creates localized hotspots. Although bypass diodes may be used to counteract these impacts by rerouting current around cells that are shaded, they can result in several peaks, which complicates maximum power point tracking (MPPT). To manage several peaks and optimize power output, metaheuristic algorithms are compulsory. A recently developed game-based optimization methodology known as Golf Optimization Approach (GOA) is used in this study to find a way to MPPT in a grid-connected photovoltaic (PV) system under partial shading conditions (PSCs). Additionally, the same suggested approach is applied to get optimal parameters for the inverter with the goal of optimizing its voltage and current regulators. It aims to evaluate a plan to lower the output voltage and current's total harmonic distortion and DC voltage error. The proposed technique is contrasted to other tracking approaches such as the traditional Perturb & Observe (P&O), Gray Wolf Optimization (GWO), and Sea-gull Optimization Approach (SOA). Simulation is performed in MATLAB R2022b/SIMULINK environment. The obtained outcomes demonstrate the ability of the suggested technique to enhance the utility grid's power quality and improve the tracking system under various partial shading effects and temperature alterations. The proposed technique achieves a tracking efficiency of 99.8 %, surpassing the effectiveness of both of the previous methods which are at 97.2 % and 96.7 %.

Comparative Analysis of Mppt Techniques for Grid Tie Solar Pv Applications

For effective energy harvesting, photovoltaic (PV) systems must produce their maximum amount of power. This is especially true for grid-tie applications, where the objective is to feed generated power into the grid. The different Maximum Power Point Tracking (MPPT) methods used in grid-tie solar PV systems are compared in this paper. In order to guarantee that PV arrays function at their maximum power point in a variety of environmental circumstances, MPPT algorithms are essential. A DC-DC boost converter is also used for interfacing the load and photovoltaic module. Result is simulated in MATLAB/Simulink environment, and all the condition is kept same for both techniques in simulation for the best outcomes of this comparative study. The efficiency, accuracy, convergence speed, and implementation complexity of several MPPT techniques—Perturb and Observe (P&O), Incremental Conductance (INC), Fractional Open Circuit Voltage (FOCV), and Particle Swarm Optimization (PSO)—are assessed and contrasted in this study. The performance of each technique under various operating conditions is demonstrated through MATLAB/Simulink simulation results, which help in the selection of the optimal MPPT method for grid-tie solar PV applications.

Optimizing Photovoltaic Systems: A Decision Matrix Approach for Selecting the Optimal Maximum Power Point Tracking Technique

Numerous strategies exist for improving maximum power point tracking (MPPT) techniques, which vary in terms of tracking speed, accuracy, sensor usage, complexity, and cost. Due to the importance of sustainable energy use and related challenges, it is essential to choose an appropriate algorithm that can reliably provide energy without exhausting resources. While much research has examined the benefits and drawbacks of different MPPT algorithms, guidance on selecting the best suited algorithm for a particular solar system application is lacking. For a fair and thorough comparison, this study analyzes four MPPT algorithms, including two artificial intelligence-based techniques, an adaptive neuro-fuzzy inference system, and a conventional technique. A decision matrix model and requirement analysis are used to determine their suitability for a standalone PV application, using particularly ranked evaluation criteria, which include tracking efficiency, implementation costs, rise time, settling time, tracking error and variance. Simulation results show that the perturb and observe technique has the fastest settling and rise times and high tracking efficiency for varying irradiance and temperature levels, with little implementation effort required for a stand-alone photovoltaic application.

PERFORMANCE ANALYSIS OF P&O AND PSO MPPT ALGORITHMS FOR PV SYSTEMS UNDER PARTIAL SHADING

Photovoltaic (PV) modules are devices that transform photon energy into electrical energy. The output power of the PV modules is influenced by the intensity of solar radiation and the ambient temperature. Non-uniform shading can cause variations in the extent of sunlight absorbed by PV modules, resulting in a decrease in power output. Maximum power point tracking (MPPT) techniques are employed to optimize the power output of PV modules by operating them at their maximum power point (MPP). The main objective of the study is to investigate the performance analysis of Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) MPPT strategies in uniform and partially shaded conditions with equally and unequally different irradiance differences. Simulation studies were conducted on the PV circuit model using Matlab/Simulink, and the results were evaluated. MPPT algorithms are compared based on their tracking efficiency and convergence speed when solar radiation conditions vary. The findings of the simulation indicate that the P&O is unable to determine global MPP and gets trapped in one of the local MPPs. However, the PSO is very effective in tracking MPP under different partial shading patterns with more than 96% tracking efficiency. In the first partial shading configuration where the sunlight intensity of the PV modules is uniformly distributed, the PSO technique has reduced steady-state oscillations around the MPP. However, the P&O technique demonstrates superior response time and convergence speed in comparison to the PSO technique.

Design of MPPT for Buck-boost Converter based on GA to Optimize Solar Power Generation

Maximizing power in the Solar Power Plant system was something that needs attention. Indonesia was a tropical country that has two seasons, where the changing weather or cloud movements are always erratic, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) is used. Currently the switching system in MPPT is still not optimal with a system efficiency of around 90%. In this study, the installation of MPPT was carried out which aims to maximize power in the solar photovoltaic (PV) system due to fluctuations or instability of sunlight at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this research, modeling of the solar PV system was made using MATLAB software, where the design of the solar PV system consists of a PV module with capacity 240W, DC to DC converter, battery and MPPT. The MPPT based on Genetic Algorithm (GA) has been tested and compared with MPPT based on Particle Swarm Optimization (PSO) and conventional MPPT, where MPPT based on GA works well in finding the maximum power point in the solar photovoltaic system. it was found that MPPT GA produces a maximum power point close to PV power with an efficiency of 92%, while MPPT PSO has an efficiency of 85% and for conventional MPPT it was 79%. In selecting the method for designing MPPT, a method is needed that has a wide range of sample data. This is due to the fluctuating characteristics of the irradiation value received by solar PV.

New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems

This research aimed to increase the power captured from photovoltaic (PV) systems by continuously adjusting the PV systems to work at the maximum power point under climate changes such as solar irradiance change and temperature change and by tracking the global maximum power under partial shading conditions (PSCs). Under the effect of partial shading (PS), the PV curve has many local maximum peaks (LMPs) and one global maximum peak (GMP) which is dynamic because it changes with time when the shading pattern (SP) changes. The traditional maximum power point tracking (MPPT) methods are unable to track the Dynamic GMP and may fall into one of the LMPs. Many modern MPPT methods have been introduced that can track the Dynamic GMP, but their effectiveness can be improved. In this respect, this work introduces a new optimal MPPT technique to enhance the performance of the maximum power point tracking of solar cells under environmental changes and partial shading conditions. The proposed technique combines three well-known and important MPPT techniques, which are the Artificial Neural Network (ANN), Variable Step Perturb and Observe (VSP&O), and Fuzzy Logic Controller (FLC). Artificial Neural Network gives a voltage near the optimum voltage, Variable Step Perturb and Observe updates the voltage to get close to the optimum voltage, and Fuzzy Logic Controller updates the step size of the (P&O) technique. The proposed hybrid ANN-VSP&O-FLC technique showed its ability to track the Dynamic GMP accurately and quickly under the variation in the shading patterns with time and its ability to follow maximum power efficiently and quickly under climate changes. The proposed hybrid ANN-VSP&O-FLC technique also showed very low distortions in waveforms and very low oscillations around the steady state. The proposed hybrid ANN-VSP&O-FLC technique was compared to the most recent and effective MPPT techniques in terms of steady-state behavior, tracking speed, tracking efficiency, and distortions in waveforms, and the comparison showed that it is superior to them, with lower distortions in waveforms, a faster tracking speed (less than 0.1 s), higher tracking efficiency (greater than 99.65%), and lower oscillations around the steady state (less than 2 Watts).

Design and performance analysis of hybrid optimization MPPT controller for proton exchange membrane fuel cell system with DC-DC converter

At present days, the hybrid Electric Vehicle (EV) utilizing the fuel stack power generation network. Most of the fuel cell output characteristics are nonlinear fashion. Also, its operating point fluctuates based on the input temperature of the fuel stack. So, the receiving of peak voltage from the fuel source is less. To enhance the voltage delivering capability of the fuel stack, an adaptive Cuckoo Search Controller (CSC) based fuzzy logic method is included in the Maximum Power Point Tracking (MPPT) block. The proposed controller is compared with the other basic hybrid controllers which are Perturb & Observe based Artificial Neural Network (P&O with ANN) controller, plus Hill Climb dependent ANN controller (HC with ANN). The major features of proposed power point finding controller are few steady state distortions, few voltage ripples, high tracking convergence speed, plus less iterations required for catching the MPP position. The fuel stack generation voltage is less. In order to increase the voltage of the proposed network, the boost converter is applied. The entire system is studied by the use of MATLAB/Simulink software.

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

Optimal maximum power point tracking of wind turbine doubly fed induction generator based on driving training algorithm

The operation of wind power system at optimum power point is a big challenge particularly under uncertainty of wind speed. As a result, it is necessary to install an effective maximum power point tracking (MPPT) controller for extracting the available maximal power from wind energy conversion system (WECS). Therefore, this paper aims to obtain the optimal values of injected rotor phase voltage for doubly fed induction generator (DFIG) to ensure the extraction of peak power from wind turbine under different wind speeds as well as to get the optimal performance of DFIG. A new metaheuristic optimization approach; Driving Training Algorithm (DTA) is used to crop the optimal DFIG rotor voltages. Three different scenarios are presented to have MPPT, the first one is the MPPT with unity stator power factor, the second one is the MPPT with minimum DFIG losses, and the third scenario is MPPT with minimum rotor current to reduce the rating of rotor inverter. The MATLAB environment is used to simulate and study the proposed controller with 2.4 MW wind turbine. The optimum power curve of wind turbine has been estimated to get the reference values of DFIG mechanical power. The results ensured the significance and robust of the proposed controller to have MPPT under different wind speeds. The DTA results are compared with other two well-known optimization algorithms; water cycle algorithm (WCA) and particle swarm optimizer (PSO) to verify the accuracy of results.

Design of high voltage gain converter for fuel cell based EV application with hybrid optimization MPPT controller

From the previously available articles, the fuel cell dependent power supply systems are increasing every year. Also, these systems are playing a major focus on the microgrid renewable energy networks. However, the fuel cell gives less output power extraction at different operating temperature values. In addition, the working point of the fuel cell vary with respect to the hydrogen supply, and water membrane content. In order to achieve the high output power of the fuel stack, a Maximum Power Point Tracking (MPPT) is used to track the peak power point of the proposed system. In this article, Artificial Neuro Fuzzy Inference System (ANFIS) controller is utilized in order to generate the smooth output voltage waveform of the fuel cell stack. The optimization of ANFIS controller have been done by using the Genetic Algorithm (GA). The advantages of ANFIS based MPPT controller are high output power extraction, and less distortions of converter voltages. Here, the Proton Exchange Membrane Fuel Cell (PEMFC) device is used as a source. The features of PEMFC are fast start-up, and high efficient. The PEMFC generates less voltage which is enhanced by using the interleaved non-isolated boost converter. The proposed fuel cell based interleaved system is analyzed by using the MATLAB/Simulink software.

Optimizing Step-Size of Perturb & Observe and Incremental Conductance MPPT Techniques Using PSO for Grid-Tied PV System

A maximum power point tracking (MPPT) technique plays an important role to ensure maximum photovoltaic (PV) output power is extracted under stochastic weather conditions. The research to date tends to focus on developing a standalone optimization MPPT algorithm rather than looking into a hybrid MPPT algorithm. This paper introduces particle swarm optimization (PSO) to optimize the maximum PV output power and to determine the best design variable for penalising the step size of the conventional methods namely the perturb and observe (PO) and the incremental conductance (IC). With the help of the hybrid MPPT algorithm (PSO+IC and PSO+PO), the step size is no longer fixed, and it is changing according to the solar irradiance. To evaluate the proposed hybrid algorithm, a single-stage grid connected PV system is designed for several different scenarios with various weather conditions. The performance of the hybrid MPPT algorithm and the conventional methods is compared. The results demonstrate that the hybrid MPPT algorithm is remarkably better than the conventional methods in terms of the output power ripple and the settling time.

A comparative study between deterministic and two meta-heuristic algorithms for solar PV MPPT control under partial shading conditions

• Under partial shading conditions, adequate tuning of firefly and particle swarm optimization maximum power point tracking algorithms converge to the global maximum power point irrespectively of the starting voltage value. • Under partial shading conditions, there is no guarantee that perturb and observe algorithm will converge to the global maximum power point even for different starting voltage values. • The sensitivities of the parameters of firefly algorithm in improving the performance of the algorithm are investigated to find the best possible control parameters. • Firefly algorithm based maximum power point tracking performed slightly well than particle swarm optimization based maximum power point tracking for most irradiance patterns investigated in this paper. During partial shading conditions (PSC), the PV modules in a solar PV array become reverse biased and act as a load, resulting in hotspot issues. This can significantly decrease the efficiency of the solar PV. The general way to deal with PSC is to connect bypass diodes across the non-shaded PV module. However, this will alter the uniform characteristics of the PV array, resulting in multiple power peaks i.e., a multi modal landscape. The conventional gradient-based Perturb and Observe (PnO) algorithm generally used for maximum power point tracking (MPPT) in solar PV is ineffective in finding the maximum power during PSC because it is prone to converge to local optimum due to its nature of searching in a multimodal landscape. However, in this article an attempt is made to show how the PnO behaves in a multimodal landscape considering different starting points. Robust stochastic algorithms that are based on a population of search agents are used to guarantee convergence to the global MPP. In this paper, the maximum power point under PSC was tracked using two meta-heuristic algorithms namely, particle swarm optimization (PSO) and the firefly algorithm (FA). The performances of these algorithms in tracking the maximum power point are evaluated. The efficiency, standard deviation (STD), and root mean square error (RMSE) of the PSO and FA algorithms are compared to those of the PnO algorithm. The PSO was found to have the lowest RMSE, and the FA had the lowest STD. The efficiency of the PSO and FA were relatively the same. Simulation results show that PSO and FA-based MPPT algorithms can efficiently track the global maximum power point (GMPP) irrespectively of the starting points. On the other hand, PnO is shown to be unreliable under PSC even with different starting points.

MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.

Power enhancement using improved maximum power point tracking for solar photovoltaic systems under partial shading

Abstract One of the key reasons contributing to a reduction in the performance of a solar photovoltaic (SPV) system is the presence of partial shade on the solar panels. It is necessary to use maximum power point tracking (MPPT) in SPV systems to get around the non-linear behaviour of photovoltaic panels. This research analyses the reduction in power output of SPV systems caused by partial shading. In the latter part of the paper, the improved dynamic behaviour-based ant colony optimization MPPT approach under the effect of module-level partial shading is implemented in MATLAB®/Simulink®. Various partial shading levels were applied to SPV modules to evaluate the system performance. The proposed system follows the maximum power point with 99.9% accuracy and a response time of 2 ms. The addition of a weight coefficient accelerates the convergence of the algorithm.

Comparative Study Of P&O and PSO Particle Swarm Optimization MPPT Controllers Under Partial Shading

Maximum power point tracking (MPPT) is a vital and essential requirement for photovoltaic (PV) systems under normal irradiance and partial shading conditions (PSCs). Although, perturb and observe (P&O) algorithm does not map the global maximum power point (GMPP), whereas the algorithm for particle swarm optimization (PSO) tracks it efficiently. This paper explores the rapid determination of GMPP under PSCs using a proposed particle swarm optimization MPPT technique which operates in conjunction with a boost converter. To achieve this analysis, MATLAB/SIMIULINK is used. The results of the simulation illustrate the high tracking performance of the proposed technique under various irradiance patterns.

Hierarchical Pigeon-Inspired Optimization-Based MPPT Method for Photovoltaic Systems Under Complex Partial Shading Conditions

This article proposes a novel maximum power point tracking (MPPT) method based on the variant of the pigeon-inspired optimization (PIO) algorithm for photovoltaic systems under partial shading conditions (PSCs). The proposed method integrates the hierarchical network behavior of pigeon flock and revises the map and compass operator of the original PIO algorithm to improve optimization efficiency. In addition, the landmark operator is used to perform a small-scale search to achieve fast tracking. Based on the combination of these mechanisms and dual-mode dynamic tracking scheme, the proposed hierarchical pigeon-inspired optimization (HPIO) MPPT method has a powerful search ability to deal with PSCs. To verify the superiority of the proposed HPIO MPPT method, it is compared with other existing advanced MPPT methods in simulation and experiments. Compared with traditional MPPT techniques based on artificial intelligence, the proposed HPIO MPPT method has a higher success rate in tracking GMPP and excellent tracking speed under PSCs. And the HPIO method also shows excellent performance under complex PSC with multiple clusters and load-variation conditions.

Optimized servo-speed control of wind turbine coupled to doubly fed induction generator

<p>Optimal control of any variable speed wind turbine needs maximum power point tracking (MPPT) coupled to doubly fed induction generator (DFIG) for better power generation. This paper offers a novel direct power servo-speed control of wind turbine. This latter is based on DFIG optimal hysteresis MPPT inverter current control combined with space voltage modulation (SVM) inverter voltage technique, thus providing a stable and continuous energy flow to power grid. In this design, the asynchronous machine stator is directly connected to the grid. Bidirectional power converter, acting as frequency converter, is rotor circuit located. Rectifier supplies rotor windings with voltages and reference frequency resulting from control procedure of the power exchange between the stator and grid. Inverter is directly controlled by means of SVM technique to maintain direct current (DC) bus voltage constant. Simulation results show that the proposed configuration improves power converters efficiency due that rotor circuit needs less power than stator circuit which is injected into the grid.</p>

Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System

The wind speed information measured by the wind speed sensor in the wind turbine generator may differ from sufficient wind speed. Therefore, this paper proposes a new effective wind speed calculation method and applies it to the optimal maximum power point tracking (MPPT) of wind energy. After estimating the turbine torque and its rotor speed, the process reverses the turbine’s aerodynamic model. The extended state observer (ESO) based on sliding mode control is used to estimate the aerodynamic torque, which solves complex and challenging tuning of the traditional ESO parameters, and replaces the conventional PI controller, thereby improving the anti-interference and robustness of the system. The sliding mode observer (SMO) is used to estimate the rotor speed. While satisfying the conditions of Lyapunov’s inequality, the design of the SMO is discussed in detail. The wind speed estimation method proposed for evaluating permanent magnet semidirect drive wind turbine and its application performance in maximum power tracking. The simulation was carried out in MATLAB/Simulink; the simulation confirmed that the estimated wind speed under different wind conditions is accurate. Compared with the traditional PI control, the utilization rate of wind energy is increased by 2%, which can be used for the MPPT control of the wind energy conversion system.