Power Point Of Photovoltaic System Research Articles

Forecasting Optimal Power Point of Photovoltaic System Using Reference Current Based Model Predictive Control Strategy Under Varying Climate Conditions

Forecasting Optimal Power Point of Photovoltaic System Using Reference Current Based Model Predictive Control Strategy Under Varying Climate Conditions

MPPT control of photovoltaic array based on improved marine predator algorithm under complex solar irradiance conditions

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness.

A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization

The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.

Intelligent Optimization Control Method for Photovoltaic Power Generation Systems Under Shadow Occlusion Conditions

<p>In the process of photovoltaic power generation, maximum power point tracking is an important method to improve the efficiency of photovoltaic power generation. Under the actual local shadow condition, the maximum power point of Photovoltaic system fluctuates. For this reason, this paper establishes the mathematical model and output characteristic equation of photovoltaic cells according to the actual application, and then uses the adaptive inertia weight Particle Swarm Optimization algorithm to solve the problem of slow search speed and low accuracy in the process of maximum power point tracking. After optimization, the method proposed in this paper can significantly improve the tracking effect efficiency, and the optimization results in real operation scenarios can improve the photovoltaic cell power generation efficiency by 21.3%, which proves the effectiveness of the algorithm.</p> <p> </p>

Investigate the maximum power point of photovoltaic system at different environmental conditions

<p style="margin: 0in 0in 0pt; text-align: justify;"><span lang="EN-US">The main objective of this work is to implement a circuit-based simulation model of a photovoltaic (PV) cell in order to investigate the electrical behavior of the practical cell with respect to some changes in weather parameters. The simulation model consists of three subsystems: photovoltaic cells, DC/DC converter and MPPT controller based logic fuzzy control. The maximum power control function is achieved with the appropriate power control of the power inverter. Fuzzy logic controller has been used to perform MPPT functions to get maximum power from the PV panel. The proposed circuit was implemented in MATLAB/Simulink. The obtained results show that the output sequence is non-linear and almost constant current to the open circuit voltage and the power has maximum motion to voltage for certain environmental conditions.</span></p>

Photovoltaic global maximum power point tracking method based on fuzzy PI control

At present, algorithms for finding the maximum power point of photovoltaic systems generally have some limitations, especially in cases of uneven light intensity, traditional algorithms often fall into local optima, with limited application space, and their tracking speed and stability are also not very good. To address this issue and improve tracking performance, the combination control of global scanning and fuzzy PI control is proposed. This combination control can avoid falling into local optima and achieve stable output of maximum power. This article first proves theoretically that the proposed method can achieve maximum power output; Then, an experimental platform was built for testing. Build control and sampling circuits and use a solar simulator to irradiate the photovoltaic array unevenly. In this environment, test the control effects of combination control, conductivity increment method, and disturbance observation method respectively. Through testing, it was found that compared to conductivity increment method and disturbance observation method, the proposed method in this article has faster tracking speed, higher output power, and better stability. The overall performance of combination control is better than other algorithms and has a relatively broad application space.

A Novel MPPT Algorithm for Photovoltaic Systems Based on Improved Sliding Mode Control

Due to the poor tracking performance and significant chattering of traditional sliding mode control in the maximum power point tracking (MPPT) algorithm, a novel MPPT algorithm based on sliding mode control for photovoltaic systems is proposed in this paper. The sliding mode control structure and new sliding mode surface of the multi-power reaching law are designed with the boost converter as the carrier of the photovoltaic system, and the sigmoid function is proposed to replace the symbolic function and saturation function in the power reaching law to improve the reaching rate and control quality of the traditional sliding mode control. Furthermore, the Liapunov function is employed to analyze the accessibility, existence and stability of the improved sliding mode control. Simulation results under dynamic and partial shading conditions show that compared with exponential sliding mode and constant speed sliding mode, the improved sliding mode control strategy can quickly track the maximum power point of photovoltaic systems under various atmospheric conditions. The proposed MPPT algorithm has stronger robustness and universality. Additionally, the efficiency of the proposed algorithm is improved by 2.3% and 5.6% as compared to the exponential sliding model control algorithm and constant velocity sliding model control algorithm. In addition, the experimental platform is constructed to further validate the feasibility and effectiveness of the proposed algorithm.

Fast Artificial Neural Network Based Method for Estimation of the Global Maximum Power Point in Photovoltaic Systems

In partially shaded photovoltaic (PV) arrays, the power-voltage curve displays multiple maxima, one of which is the global maximum power point (GMPP). One of the major concerns related to PV systems is to locate and track the GMPP in all circumstances to boost efficiency. Combining conventional hill climbing (HC) algorithm and artificial neural networks (ANNs), a new two-stage GMPP tracking method is introduced in this article that aims to be fast and accurate. Moreover, it does not require irradiance or temperature sensors. In the first stage, the current-voltage (I-V) curve is sampled at specific points determined based on the array I-V curve analysis with the objective to be the minimum samples possible that can reflect the changes of irradiance and temperature. Then a simple feedforward ANN is employed to estimate the neighborhood of the GMPP using these samples. In the second stage, the HC algorithm is adopted to ensure the GMPP is tracked precisely. The proposed method is validated by simulations in MATLAB/Simulink environment and experimental tests under uniform irradiance condition, partial shading conditions, and a wide range of temperatures.

Application of fuzzy logic technique to track maximum power point in photovoltaic systems

The use of photovoltaic (PV) systems for generating electric power is increasing in our everyday lives but since the generated voltage and current vary non linearly it has been very difficult to trace the maximum power point (MPP) of the PV systems so to overcome with this problem many power tracking methods were introduced out of which fuzzy logic technique was found to be one of the easy and efficient maximum power point tracking (MPPT) method. In this paper, various MPPT algorithms are observed how they help in improving the efficiency of PV systems by adjusting the duty ratio of the power interface, and also understand why the fuzzy logic control (FLC) technique is preferred over other algorithms. The system was established using MATLAB/Simulink.

A Novel MPPT Design for a Partially Shaded PV System Using Spotted Hyena Optimization Algorithm

Partial shading is a common problem in photovoltaic (PV) systems, known for its difficulty. Numerous attempts have been conducted to mitigate this problem. Some of these efforts deploy metaheuristic optimization with a view to tracking the multiple-peak P–V curve in a partial shading PV system. Hence, this paper proposes a novel metaheuristic algorithm to track the maximum power point of PV systems using the Spotted Hyena Optimization (SHO) algorithm. When evaluated, the SHO algorithm proved to be very fast, robust, and accurate in standard conditions, Partial Shading Conditions (PSCs), and irradiance variations. Also, the results reveal a remarkable improvement in the performance when we compare the SHO algorithm with the Grey Wolf Optimization (GWO) algorithm and the Perturb and Observe (P&O) algorithm.

Perturb and observe maximum power point tracking method for photovoltaic systems using duty-cycle modulation

This paper presents the implementation of perturb and observe maximum power point tracking algorithm with Duty Cycle Modulation instead of pulse width modulator for photovoltaic power generators. Like all the different methods used for tracking the maximum power point of photovoltaic system, perturb and observe method use a Pulse Width Modulation to control the DC-DC converter. Here, the Duty Cycle Modulation is used in the place of Pulse Width Modulation. A detailed analysis of the approach is proposed and simulations are performed using MATLAB/Simulink software. The results show the performance and speed at which this control reaches the maximum power point. The response times is very low and is between 0.0125s and 0.05s depending on the climatic variations. So, Duty Cycle Modulation can be used as an alternative to Pulse Width Modulation for the search of the maximum power point.

On-Site Traversal Fractional Open Circuit Voltage with Uninterrupted Output Power for Maximal Power Point Tracking of Photovoltaic Systems

The fractional open-circuit voltage (FOCV) method is commonly adopted to track maximal power point of photovoltaic systems due to easy implementation and cost-effectiveness. However, the FOCV method is confronted with unstable output power and limited tracking accuracy. This paper proposes a novel on-site traversal FOCV method with uninterrupted output power and increased tracking accuracy through simulation and experimental verifications. Each solar cell is connected with a bypass diode and switching circuitry, so that specific solar cell can be traced and measured consecutively for determining its maximal power point (MPP). MATLAB/Simulink simulation results show that, in the time-varying irradiance case, the proposed method achieves a low ripple factor of 0.13% in 11–13 h and 0.88% in 9–15 h, under the typical 24 h irradiance curve. In the spatial-varying irradiance case, the accuracy of the proposed method reaches 99.85%. Compared with other FOCV methods, like pilot cell and semi pilot cell methods, the proposed method is of higher accuracy with a limited ripple effect. Experimental results show that this method can effectively trace different output performance of specific solar cell while generating stable output voltage with a low ripple factor of 1.55%, proving its compatibility with distributed sensing and applicability in smart photovoltaic systems.

Integral sliding-mode controller for maximum power point tracking in the grid-connected photovoltaic systems

<p>The output power generated in the photovoltaic modules depends both on the solar radiation and the temperature of the solar cells. To maximize the efficiency of the system, it is required to monitor the maximum power point of the photovoltaic system. For this purpose, monitoring the maximum power point (MPPT) of photovoltaic systems should be as quick and accurately as possible for increasing energy production, which ultimately increases the cost-efficiency of the photovoltaic system. This paper proposes a new approach for MPPT) using the concept of the integral sliding mode controller (ISMC) to ensure fast and precise monitoring of the peak power. The performance of the ISMC is significantly influenced by the choice of the sliding surface. To assess the reliability ISMC control, the results have been compared with those of a PI controller. The results obtained are used to evaluate the performance of the ISMC strategy under different climatic conditions. Finally, the effectiveness of the proposed solution is confirmed using simulations in PSIM tools and experimental results were used to evaluate the effectiveness of the proposed approach.</p>

Novel Spline-MPPT Technique for Photovoltaic Systems Under Uniform Irradiance and Partial Shading Conditions

Different maximum power point tracking (MPPT) techniques used in photovoltaic (PV) systems are evaluated based on several criteria such as simplicity, speed, and accuracy. There are tradeoffs among these criteria, and generally higher accuracy is achieved at the expense of speed and simplicity. This article aims to introduce Spline-MPPT technique as a fast, accurate, and uncomplicated method to find the maximum power point of PV systems under uniform irradiance and partial shading condition (PSC) in which characteristics of the PV string are distorted. The proposed method is based on cubic spline interpolation that defines an approximate function for a few sample points. Several interpolation-based methods have been proposed in the literature to find MPP under uniform irradiance. They, however, are incapable of finding the global maximum power point (GMPP) under partial shading conditions. The Spline-MPPT technique only uses a small number of current and voltage samples to estimate the MPP of the system and maintain on this point as long as environmental conditions remain unchanged. Simulation results attest to the superiority of the proposed method.

Stopping the Drift Problem in the Tracking of Maximum Power Point for Photovoltaic System by Using Modified Variable Step Size Incremental Conductance Method

Stopping the Drift Problem in the Tracking of Maximum Power Point for Photovoltaic System by Using Modified Variable Step Size Incremental Conductance Method

PV SİSTEMLERDE KISMİ GÖLGELENME KOŞULLARINDA ESNEK HESAPLAMA TABANLI MAKSİMUM GÜÇ NOKTASI İZLEME TEKNİKLERİNİN KARŞILAŞTIRILMASI

Nowadays, solar or photovoltaic energy is the most commonly used renewable energy resources in the world. Despite its advantages such as freely available, low maintenance cost, pollution-free, inexhaustible, and reliable, its low conversion efficiency is a major drawback. To increase the efficiency of the photovoltaic system, all photovoltaic modules in the array must be operated at maximum power point. Therefore, maximum power point tracking technique is used for predicting and tracking the maximum power point. In the literature, maximum power point tracking techniques are generally classified as soft computing and conventional. Soft computing techniques are more preferred from both of them, because they can accurately track maximum power point of photovoltaic systems. In this study, an extensive review of soft computing based maximum power point tracking techniques under partial shading conditions until today is presented. The techniques are compared from the point of photovoltaic array dependency, sensors required, tracking efficiency, tracking speed, algorithm complexity, and oscillation around maximum power point.

Marginal Power-Based Maximum Power Point Tracking Control of Photovoltaic System Under Partially Shaded Condition

In this article, a two-stage maximum power point tracking algorithm is presented to track the global maximum power point of photovoltaic systems in partially shaded conditions. The proposed tracker uses an intelligent sampling procedure based on a fractional open-circuit voltage method and takes initial samples from the power-voltage characteristic of the photovoltaic array. Then, it determines the marginal array power around each sample. Using these marginal powers, the samples that definitely are not in the vicinity of global maximum power point are eliminated, and neighborhood of these samples are not searched in the global search stage. As a result, the search area gets smaller and global maximum power point is found accurately and rapidly. Furthermore, an algorithm is suggested to estimate module voltage at its maximum power point, which is used in the proposed maximum power point tracker. Eventually, several simulations are carried out through MATLAB/Simulink in order to evaluate the performance of the new tracking method and compare it with recently presented methods. Experimental results are also presented to verify the validity of the developed tracker.

Development of PSO for tracking Maximum Power Point of Photovoltaic Systems

For a photovoltaic system, the relationship of the output voltage and power is usually non-linear, so it is essential to equip a MPPT controller in PV systems. Furthermore, the hotspot problem is a common phenomenon, resulting from the PV system operating under PSC. Partial shading not only damages the PV cells, but also makes it difficult to find the global MPP in the characteristic curves of P-V. The paper proposes a novel version of PSO, namely PPSO in order to detect the global peak among the multiple peaks, known as the true maximum energy from PV panel. For this, the PPSO algorithm makes the velocity of each particle be perturbed once the particles are struck into a local minima state in order to find the best optimum solution in the MPPT problem. The perturbation in the velocity vector of each particle not only helps them tracking the MPP accurately under the changing environmental conditions, such as large fluctuations of insolation and temperature like PSC; but also removes the steady-state oscillation. The proposed approach has been tested on a MPPT system, which controls a dc-dc boost converter connected in series with a resistive load. Moreover, the obtained results are compared to those obtained without any MPPT controller to prove the efficiency of the suggested method. In addition, this novel version gives the highest accuracy of tracking the optimum power in the least iteration number as compared to the conventional PSO.

An Iterative Analytical Solution for Calculating Maximum Power Point in Photovoltaic Systems Under Partial Shading Conditions

Detection of maximum power point (MPP) is one of the most sought-after topics in the field of photovoltaic systems. There are many approaches to detecting MPP, amongst these are analytical methods. Analytical methods use mathematical functions to solve the given problem and therefore are one of the dominant strategies. However, their applications to detect MPP have been limited to study only uniform shading conditions. The use of analytical methods to detect MPP for more challenging cases like partial shading conditions is yet to be investigated. In this brief, an analytical solution to identify MPP under partial shading conditions is proposed. Equations describing photovoltaic panels and MPP conditions are derived by applying fundamental circuit laws. The derived equations are non-linear and can be solved using numerical techniques available in most of the simulation packages. The proposed model can theoretically detect the MPP amongst “N” peaks. The results from the simulation are verified by conducting experimentation with standard algorithms available in the literature. The results from simulation and experimentation are found to agree with each other.

A novel hybrid boost converter with extended duty cycles range for tracking the maximum power point in photovoltaic system applications

Nowadays, a large number of power conversion applications is commonly based on DC/DC converters with high voltage boost capability. Different voltage-boosting techniques have been reported in the literature. Each technique has its own merits and demerits depending on the application, cost, complexity, power density, reliability and efficiency. To meet the growing demand for such applications, new power converter topologies are continuously being proposed. This paper focuses on a novel hybrid boost converter, which combines the conventional boost (CB) and the quadratic boost (QB). This new topology allows the extension of the output voltage gain and the duty cycle range regarding to the original topologies. Thus, it ensures high conversion voltage ratio for almost duty cycle values. Consequently, it has two working modes, one as QB mode and the other one as CB mode. In order to verify the performance of the proposed topology, several simulations have been carried out under Matlab/Simulink environment for both QB and CB modes. The well-known P&O algorithm was implemented into a FPGA (Field Programmable Gate Array) board in order to verify experimentally the designed hybrid boost. Experimental results confirm the convenience of the proposed topology for tracking the maximum power point in photovoltaic systems.