Maximum Power Point Tracking Technique Research Articles

Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Due to the extremely poor efficiency of solar energy, researchers created various maximum power point tracking (MPPT) techniques with the aim of enhancing the effectiveness of photovoltaic (PV) systems. Because of their propensity to address complex problems and their non-linear characteristics, Artificial Neural Network (ANN) algorithms are the most frequently used among these MPPT techniques. Nevertheless, the performance of the ANN-based MPP tracking algorithms is contingent upon various factors, including the choice of activation function, the quantity of hidden neurons, and the training algorithm employed. Shannon’s Information Criteria (SIC) is used to determine the optimal number of hidden neurons within a single hidden layer for the neuro-controller application. In this regard, a two-layer Feed-Forward Neural Network (FFNN) was trained using MATLAB/Simulink software, incorporating varying numbers of hidden neurons. The results indicate that the two-layer FFNN with five hidden neurons has the highest performance, as demonstrated by the lowest Mean Squared Error (MSE) of 4.03 × 10-9, which is statistically significant. The successful incorporation of Gaussian noise in the simulation of an 85 kW PV system demonstrates that the ANN-based MPPT algorithm is both theoretically robust and practically viable and reliable for enhancing the efficiency of solar PV systems in real-world scenarios.

A Feature Extraction of Photovoltaic Solar Panel monitoring system based on Internet of Things (IoT)

INTRODUCTION: The Internet of Things (IoT) is an modern technology that improves user experience and gives items more intelligence. A large number of applications have already embraced the IoT. Our lives were made significantly easier and more accessible by the development of the IoT. In this research a photovoltaic solar panel system has been monitored using IoT.
 OBJECTIVES: The feature extraction of a photovoltaic solar panel monitoring system based on the IoT working process is provided in this work. The implementation of maximum power point tracking (MPPT) algorithm also covered, along with a brief description of the pre-processing method, datasets and the PV system features are extracted.
 METHODS: The model develops a thorough grasp to increase the voltage and current efficiency, a maximum power point tracking technique (MPPT) is implemented in this research study.
 RESULTS: A safer solar panel monitoring system displays the result in LCD display screen it shows various readings, including the IP address, voltage and current rating, light intensity, temperature, and fault occur on the system receive warning message.
 CONCLUSION: The proposed solar panel monitoring system demonstrates high level voltage and current accuracy when compared to the existing method.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Design of a Novel Chaotic Horse Herd Optimizer and Application to MPPT for Optimal Performance of Stand-Alone Solar PV Water Pumping Systems

A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system’s accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions.

Implementation of Non-Isolated High Gain Interleaved DC-DC Converter for Fuel Cell Electric Vehicle Using ANN-Based MPPT Controller

A high conversion ratio DC-DC converter is crucial for fuel cell electric vehicles (FCEV). A fuel cell-based non-isolated high gain integrated DC-DC converter for electric vehicles is proposed in this paper. The system comprises an interleaved boost converter (IBC) at the source end, a switched capacitor cell, coupled inductors, a passive clamp circuit, and a voltage multiplier circuit (VMC). Its significance is to achieve the voltage conversion gain of 12.33 at a conversion ratio of 0.45. The idea is to use a proton exchange membrane fuel cell to power electric vehicles through a high-gain DC-DC converter. The use of an ineffective MPPT can result in lower energy conversion efficiency. Thus, this system incorporates a maximum power point tracking (MPPT) controller based on a neural network, which relies on the radial basis function network (RBFN) algorithm to track the maximum power point of the PEMFC accurately. The comparative study of the fuel cell electric vehicle (FCEV) structure with the RBFN-based MPPT technique was evaluated with that of the fuzzy logic technique using the MATLAB/Simulink platform (R2021b (MATLAB 9.11)). A 1.5 kW experimental prototype is designed with a switching frequency of 10 kHz to validate the design analysis, and its pursuance is compared between RBFN and FLC-based controllers. This manuscript will be a significant contribution towards evidencing a sustainable environment.

Design and development of photovoltaic solar system based single phase seven level inverter

For solar photovoltaic (PV) systems, an upgraded triple gain seven-level inverter that works both independently and while connected to the grid is proposed. The two-stage configuration of the system is boost cascaded. The first stage has a one switch improved gain converter (OSIGC) to increase and normalize the input direct current (DC) voltage, and the second stage includes a unique seven level alternating current (AC) is produced via a multilevel inverter (MLI) design with triple voltage gain. The proposed OSIGC is appropriate for a broad range of conversions. The voltage gain in MLI was achieved using switched capacitor techniques. The DC-DC converter can achieve a maximum voltage gain of twelve and the MLI can achieve a maximum voltage gain of three, resulting in a DC-DC-AC voltage that can reach 36. Maximum power point tracking (MPPT) technique based on modified perturb and observe (PO) is used in OSIGC to maximise PV module power utilisation, and MLI control utilises sinusoidal pulse width modulation (SPWM) realistically. For the purpose of analysing the suggested system, a 200 Watt prototype statel is created. With a total harmonic distortion (THD) of 0.181%, up to 92.12% of the converter system’s overall efficiency is possible.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Comparison of Solar P&O and FLC-based MPPT Controllers & Analysis under Dynamic Conditions

Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photovoltaic generation, due to the abundant availability. The maximum power generated by a PV module depends on the temperature and irradiance because the P-V and V-I natures are non-linear. Various DC-DC boost converters are used along with the MPPT techniques because the conversion efficiency of the PV system is low [1][2]. In this paper, comparative analysis between Perturb and Observe (P&O) and Fuzzy Logic-based Maximum Power Point Tracking (MPPT) systems along with modified SEPIC are done using MATLAB/ SIMULINK software. Simulations are done at different irradiations to observe its tracking speed towards MPP. From the obtained output (simulation), it is observed that the Fuzzy Logic Converter (FLC)-based MPPT controllers have good dynamic performance, reduced oscillation, high tracking speed, maximum power, etc...[3].

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%).

A novel control strategy of a variable-speed doubly-fed-induction-generator-based wind energy conversion system

Abstract This paper aims to address the issue of control of a variable-speed wind turbine based on doubly-fed induction generators. In this work, an effort is made to extract the maximum efficiency from a doubly-fed induction generator-based variable-speed wind turbine by controlling the rotor current. In the first step, a maximum power point tracking technique is used to extract the maximum power from the turbine. Then a stator-flux-oriented vector control strategy is employed to control the rotor-side current. Subsequently, a grid voltage vector-oriented control strategy is used to control the grid-side system of the grid-connected generator. Considering the nonlinearity and parameter uncertainty of the system, an active disturbance rejection controller with a sliding-mode-based extended-state observer is developed for the above-mentioned control strategies. Furthermore, the stability of the controller is tested and the performance of the controller is compared with the classical proportional–integral controller based on disturbance rejection, robustness and tracking capability in a highly non-linear wind speed variation scenario. Modelling, control and comparison are conducted in the MATLAB®/Simulink® environment. Finally, a real-time hardware set-up is presented using the dSPACE ds-1104 R&D processing board to validate the control scheme. From the result of the experiments, it is seen that the proposed controller takes 10–15 control cycles to settle to its steady-state values, depending on the control loop, whereas the conventional proportional–integral controller takes 60–75 control cycles. As a result, the settling time for the proposed control scheme is shorter than that of the proportional–integral controller.

Photovoltaic Maximum Power Point Grid Connected based on Power Conditioning Technique Employing Fuzzy Controller

Most of the Maximum Power Point Tracking (MPPT) techniques for Photovoltaic (PV) system utilize the PV voltage and current measurements. An MPPT technique for grid connected PV system, which does not require PV measurements, is proposed and implemented. This approach utilizes post-stage inverter current instead of calculating solar array power. This approach is called Power Conditioning System (PCS). PCS requires a searching engine to track the Maximum Power Point (MPP) of the PV system. Fuzzy logic is one of the most powerful MPPT engines, which has high performance and robustness. Therefore, Fuzzy Logic Control (FLC) method is implemented and compared with the other methods. Moreover a proposed method that combines FLC with PCS is designed and tested. In addition, the PCS employing an adaptive fuzzy controller is also designed in order to enhance system performance and robustness. To compare between classical MPPT techniques and the proposed techniques, simulations of overall system using different MPPT techniques are performed. The simulation results are analysed. Moreover, Practical implementation is carried out to validate the simulation results.

Fuzzy Logic Based Maximum Power Point Tracking Using Boost Converter for Solar Photovoltaic System in Kuwait

Currently, the production of the rapid increasing electrical energy in Kuwait depends mainly on oil and its derivatives, which result in environmental pollution. Moreover, the export of oil production represents a key income for Kuwait and any saving in its consumption represents an essential concern to land’s economy. Therefore, the power utility and economy of Kuwait will benefit from substituting fossil fuels by clean and renewable energy resources such as solar energy. For efficient utilization of solar energy, the PV panel should track the maximum power point (MPP) under various weather conditions in Kuwait. Most of existing MPP tracking techniques depend mainly on perturb & observe (P&O) or incremental conductance (IC) algorithm using conventional PID controllers. On the other side, fuzzy logic controller (FLC) provides an adaptive nature, fast response, good performance and ability to handle non-linear characteristics. In this paper, the proposed (FLC) substitutes PID controller in P&O and IC (MPPT) under different weather conditions in Kuwait. The obtained results of the proposed (FLC) are compared with conventional PID controller. The performance of the implemented PV panel is validated and the results indicated that FLC has successfully tracked the MPP with minimum voltage oscillations.

Low-cost MPPT for triple-junction solar cells used in nanosatellites: A comparative study between P&O and INC algorithms

This paper presents a study of solar panels composed of triple-junction solar cells used in CubeSats; in particular, it focuses on optimizing the power supplied by these panels using the maximum power point tracking technique (MPPT). Optimizing this power is a challenge that is not easily overcome, due to the constraints associated with CubeSats applications, especially in terms of weight, area, budget, as well as the low power generated by these panels which do not exceed 2.4 W at the maximum power point. For all these considerations, both P&O and INC techniques were chosen, since P&O is relatively easy to implement and does not require complex equipment, while INC is known for its high tracking accuracy in difficult environments under fast changing solar conditions, plus the low cost characterizing both techniques, which makes them suitable for CubeSats applications. To select the most appropriate technique for CubeSats applications, a comparative study between both P&O and INC algorithms is performed theoretically using a MATLAB/SIMULINK simulation, followed by a hardware implementation to transfer ideas from theory to practice for realizing the CubeSats power system electronic board since the present study is carried out in the context of a CubeSat application, it focuses on the nanosatellite power system, making it distinct compared to ordinary photovoltaic systems, which have been widely used and studied in previous research in the field of photovoltaics.

Maximum power point tracking techniques for low-cost solar photovoltaic applications – Part I: constant parameters and trial-and-error

Maximum power point tracking techniques for low-cost solar photovoltaic applications – Part I: constant parameters and trial-and-error

Maximum power point tracking techniques for low-cost solar photovoltaic applications – Part II: Mathematical Calculation and Measurement and Comparison, criteria on choices and suitable MPPT techniques

Maximum power point tracking techniques for low-cost solar photovoltaic applications – Part II: Mathematical Calculation and Measurement and Comparison, criteria on choices and suitable MPPT techniques

Design of a Level-3 electric vehicle charging station using a 1-MW solar system via the distributed maximum power point tracking technique

Abstract Solar power is mostly influenced by solar irradiation, weather conditions, solar array mismatches and partial shading conditions. Therefore, before installing solar arrays, it is necessary to simulate and determine the possible power generated. Maximum power point tracking is needed in order to make sure that, at any time, the maximum power will be extracted from the photovoltaic system. However, maximum power point tracking is not a suitable solution for mismatches and partial shading conditions. To overcome the drawbacks of maximum power point tracking due to mismatches and shadows, distributed maximum power point tracking is utilized in this paper. The solar farm can be distributed in different ways, including one DC–DC converter per group of modules or per module. In this paper, distributed maximum power point tracking per module is implemented, which has the highest efficiency. This technology is applied to electric vehicles (EVs) that can be charged with a Level 3 charging station in <1 hour. However, the problem is that charging an EV in <1 hour puts a lot of stress on the power grid, and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate. Therefore, a Level 3 (fast DC) EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue. Finally, the simulation result is reported using MATLAB®, LTSPICE and the System Advisor Model. Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day, which is enough to fully charge ~120 EVs each day. Additionally, the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants. For example, instead of supplying EVs with power from coal-fired power plants, 1989 pounds of CO2 will be eliminated from the air per hour.

Incorporating Incremental Conductance MPPT Techniques into Solar Power Extraction

Research into alternative, green energy sources such as solar power has been driven by concerns about environmental sustainability, escalating petroleum costs, and surging energy demand. Solar energy can power the entire world sustainably, since it is abundant and easy to access. Solar radiation, cell temperature, and load impedance all play a part in improving the efficiency of solar energy utilization. In order to maximize solar energy utilization, Maximum Power Point Tracking (MPPT) techniques are used. In order to address factors such as solar effectiveness, dynamic response, convergence speed, complexity, cost, and sensor requirements, different MPPT techniques have been developed. Using Incremental Conductance (INC) as an example, this paper provides a comprehensive overview of MPPT techniques. P&O’s drawback of oscillations around the Maximum Power Point (MPP) is overcome by INC, which minimizes them. The MPP voltage is maintained until the incremental conductance equals zero by comparing the instantaneous conductance of the panel with the incremental conductance. In addition to being easy to implement, INC-based methods offer rapid tracking and efficiency gains. Results from simulations demonstrate INC MPPT’s effectiveness in maximizing power extraction from photovoltaic systems, especially when environmental conditions change rapidly.

An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

Study and analysis of partial shading effect on power production of a photovoltaic string controlled by three different MPPT techniques: P&O, PSO and ANN

Partial shading occurs when some of the solar panels are exposed to reduced irradiation. Partial shading can lead to creating peaks and troughs in power production. The goal of this study is to compare the effect of partial shading on the capacity of maximum power point tracking (MPPT) methods, to find the global maximum power point. To this end, the study focuses on performance simulation and discussion of Perturb and Observe (P&O), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN) controls. Analysing the three MPPT controller's results, in terms of accuracy, the ANN and PSO controls showed high performance. On the other hand, the P\&O control showed lower accuracy, particularly under partial shading. For the speed of reaction, the P&O and ANN controls proved to be the fastest, while the PSO control showed a slightly longer response time. However, it is important to note that ANN approach presents added complexity in terms of conception.