Multiple Local Maximum Power Points Research Articles

Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm

Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.

Comparing MPPT Algorithms for Improved Partial-Shaded PV Power Generations

olar energy, accepted as an alternative energy source, is attracting commercial interest and scholars and researchers for improving efficiency and lowering the losses within the system. One of these significant losses is due to partial and complex shading. This study concentrates on reducing losses to enhance the efficiency of solar systems. Maximum Power Point Tracking (MPTT) uses several alternative algorithms for efficient operations. We have selected four algorithms supporting MPPT, namely P&O, PSO, Adaptive cuckoo, and Dragonfly. These algorithms are applied on photovoltaic (PV) systems in four different scenarios: uniform irradiance, partial shading, complex partial shading, and multiple local maximum power points. According to this study, results show that the algorithms' performance vary significantly based on these scenarios. It has been shown that PSO has the longest tracking time compared to other but tracks the maximum power best when exposed to uniform irradiance. In contrast, DFO takes the shortest tracking time and performs best in I-V curves but do not have a maximum power point at the knee. Both adaptive cuckoo and PSO perform well in tracking the global maximum power point, particularly in partial shadings. The study provides insights into the strengths and weaknesses of each algorithm in different scenarios and can guide the selection of an appropriate algorithm for a given PV system.

Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO.

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization.

Hill climbing MPPT performance analysis for photovoltaic system under partial shading

The HC approach is developed in this work with the intention of tracking maximum power from the PV array. Multiple local maximum power points are displayed on the power–voltage characteristic of photovoltaic (PV) arrays when all of the modules are not receiving uniform solar irradiation, which is another way of saying that they are operating under partial shading conditions (PSCs). Under conditions of consistent sun irradiation, it has been demonstrated that conventional maximum power point tracking (MPPT) approaches are effective. On the other hand, it is possible that they will miss the worldwide peak caused by PSCs. In this thesis, a new approach for maximum power point tracking (MPPT) of photovoltaic arrays is proposed for use under both PSCs and uniform circumstances. The proposed method locates all of the regional maximum power locations by doing an analysis of the sun irradiance pattern and then applying the well-known Hill Climbing method. Simulations run in a MATLAB/SIMULINK environment are used to assess how well the suggested approach performs.

Global Maximum Power Point Tracking in Dynamic Partial Shading Conditions Using Ripple Correlation Control

Under partial shading conditions (PSC), a photovoltaic (PV) system may produce multiple local maximum power points (LMPPs). Traditional maximum power point tracking (MPPT) techniques are not able to distinguish between LMPPs and the global maximum power point (GMPP), leading to sub-optimal PV array power outputs. This article proposes a two-level algorithm for tracking the GMPP. The first level is a discretized global search, allowing the system to hone in on the neighborhood containing the GMPP. In the second level, the well-known ripple correlation control (RCC) technique is used to swiftly converge to the GMPP. Using the proposed two-level algorithm, it can be guaranteed that the GMPP is successfully found and tracked. A benchmark analysis involving other state of the art algorithm reveals that the proposed method is the most superior algorithm in terms of accurately and swiftly tracking the global maximum power point in dynamic partial shading conditions. The algorithm is implemented with a simple inexpensive microcontroller, and therefore can be readily adopted in a myriad of dynamic partial shading applications.Therefore, this work allows the penetration of future photovoltaic power conversion systems to be more efficient.

Algorithm to extract model parameters of partially shaded photovoltaic modules

Uneven irradiation, due to partial shading, can produce hot spots in photovoltaic modules. A classical solution to avoid hot spot consists in using bypass diodes in antiparallel to series-connected cell groups. This solution brings a new problem: the presence of multiple local maximum power points. We present a simple algorithm for fast extraction of the model parameters of partially shaded photovoltaic panels with bypass diodes. An example of the application of the proposed algorithm is illustrated using the data from a real monocrystalline silicon technology photovoltaic module measured under uniform illumination and partial shading conditions. The possibility of using the algorithm as a practical approximate solution is also discussed. The simulations, using only four parameters, represent reasonably well the measured data.

Modeling and experimental validation of matrix structure photovoltaic array reconfiguration technique to harvest maximum power under continuous dynamic shading condition

This paper puts forth an analysis about the effects of continuous dynamic shading scenarios upon various photovoltaic (PV) array configurations. The detailed examination documents the extent of mismatch power losses (MPLs) that creep in PV arrays (PVAs) due to non-uniform solar irradiations. Along with MPLs, these partial shading conditions (PSCs) are also responsible for non-linearity and multiple local maximum power points on the P-V and the I-V characteristics. These examinations propose for an optimal PVA design to draw the maximum power from the PVA upon exposure to partial irradiance. A novel static reconfiguration-based matrix structure (MS) PVA and two new conventional PVAs such as alternative bridge link and short cross tied are proposed in this paper. Comparisons are also made between the proposed PVAs and conventional PVAs like bridge link, series parallel, total cross tied and honeycomb. These PVAs are examined under dynamic shading and the comparisons are drawn for MPLs, P-V curves and % fill factors (FFs). Experimental Test workbench and MATLAB/Simulink environment is adopted for validating all the PVA configurations (PVACs) under consideration. Among the analysed configurations, the MS PVAC have lesser MPLs and better % FF under continuous PSCs and, thus, found to be the superior configuration.

A Fast Parabolic-Assumption Algorithm for Global MPPT of Photovoltaic Systems Under Partial Shading Conditions

Photovoltaic (PV) arrays exhibit multiple local maximum power points (MPPs) in their I-V and P-V characteristic curves when different modules are subjected to different radiations at the same time, known as a partial shading condition (PSC). Hence, tracking the global MPP is crucial to increase the PV system efficiency. Conventional global MPP tracking (GMPPT) algorithms suffer from slow convergence, while some can fail to track the global MPP during PSCs. This article proposes a totally new approach to finding the global MPP during PSC using a single current sensor: the parabolic assumption GMPPT algorithm uses a fixed number of current scans (steps) equal to the number of solar modules connected in series to directly and immediately calculate the global MPP. The proposed algorithm uses simple parabolic equations to calculate the global MPP near-exactly during PSCs. The performance of the proposed algorithm is first evaluated by simulation in MATLAB/Simulink, and then by experimental verification. The results show very fast global MPP tracking and negligible tracking energy loss with an experimental tracking efficiency over 99.6% for the four PSC patterns tested.

Arithmetic optimization algorithm based MPPT technique for centralized TEG systems under different temperature gradients

Since centralized thermoelectric power generation (TEG) system presents multiple local maximum power points (LMPPs) at different temperature gradients (DTG), thus its optimal power harvesting is difficult to realize via conventional approaches. Therefore, an efficient arithmetic optimization algorithm (AOA) is applied to realize maximum power point tracking (MPPT) of centralized thermoelectric power generation system at different temperature gradients to improve the energy exploitation and utilization. AOA is utilized to efficiently and reliably identify unique global maximum power point (GMPP) in multiple LMPPs, which replicates the distribution mechanism of main arithmetic operators during mathematic calculation to find the best solution from a set of randomly generated candidate solutions. Compared with other well-known algorithms, AOA owns the distinctive superiorities of simple implementation structure, as well as few control parameters need to be tuning. Meanwhile, its own random and adaptive parameters selection principle greatly boost the AOA convergence performance. In addition, AOA shows strong ability to avoid falling into local optima thanks to its proper balancing between global exploration (diversification) and local exploitation (intensification). For further validation of the practicability of AOA based MPPT, two case studies are conducted, e.g., start-up test and step change of temperature. Experimental results show that AOA can achieve the optimal energy harvesting with minimal power fluctuations in comparison with the other three optimization algorithms, e.g., the energy produced by AOA is 34.44%, 6.03%, and 8.17% higher than that of perturb and observe (P&O), particle swarm optimization (PSO) and grey wolf optimization (GWO) respectively under the step change of temperature.

Efficient Global Maximum Power Point Tracking Strategy for PV Systems

Nowadays, research is focused on optimizing the efficiency of photovoltaic power generation, especially under partial shading or other disruptive events leading to mismatch losses. During most of these events, multiple local maximum power points may occur in the power-voltage characteristics of the PV array, and conventional Maximum Power Point Tracking (MPPT) methods may therefore fail to operate the PV array at the global maximum power point. On the other hand, scanning the entire voltage range requires much time, which results in PV power losses. In terms of reducing the scanning voltage range and improving the tracking performance, this paper proposes a novel Global MPPT (GMPPT) strategy based on a nonlinear controller. The characteristics of a PV array, under such disruptive events, are first analyzed and then the working principle of the proposed strategy elements is described. A dynamic model of the dc-dc converter, interfacing the PV array with the load, is considered in order to ensure better control accuracy and rapidity. The proposed scanning process and the design of the nonlinear controller are then described in detail. In order to validate the designed control strategy, a numerical simulation is carried out using a PV array powered battery charging system. Tracking performances, achieved using the proposed GMPPT method, are then compared with those of a previously proposed one. Simulation results have shown that the proposed strategy is significantly faster and more accurate, thus confirming its viability and efficiency.

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

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

Dynamic Surrogate Model Based Optimization for MPPT of Centralized Thermoelectric Generation Systems Under Heterogeneous Temperature Difference

A novel dynamic surrogate model based optimization (DSMO) for centralized thermoelectric generation (TEG) system affected by heterogeneous temperature difference (HeTD) is designed to achieve maximum power point tracking (MPPT) in this article. Since HeTD usually results in multiple local maximum power points (LMPPs), DSMO needs to rapidly approximate the global maximum power point (GMPP) instead of being trapped at a low quality LMPP. To avoid a blind search, a radial basis function network is adopted to construct the dynamic surrogate model of input/output feature according to the real-time data of centralized TEG system. Furthermore, a greedy search is adopted to accelerate the convergence based on dynamic surrogate model. Four case studies are undertaken to evaluate the practicability and superiority of the proposed method compared with that of a single LMPP based MPPT method and three common meta-heuristic algorithms. In addition, the implementation feasibility of DSMO is demonstrated by the hardware-in-the-loop (HIL) experiment based on dSpace platform.

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques.

Maximum photovoltaic array power point tracking algorithm based on modified particle swarm optimization under non-uniform irradiance

Under fast-changing non-uniform solar irradiance level the multiple local maximum power points are observed but only one global maximum power point exists which lead to a dynamic optimization problem. This paper presents a maximum photovoltaic array power point tracking algorithm based on the modified particle swarm optimization. The modification of particle swarm optimization employs re-randomization and the particles around a maximum power point initialization. The simulation results show that the proposed algorithm provides stability and fast maximum power point tracking of a photovoltaic array, as compared to a classical Perturb and Observe or particle swarm optimization algorithms under fast-changing non-uniform solar irradiance level.

Fast atom search optimization based MPPT design of centralized thermoelectric generation system under heterogeneous temperature difference

This paper designs a novel maximum power point tracking (MPPT) technique for centralized thermoelectric generation (TEG) system under heterogeneous temperature difference (HeTD). Since the HeTD can result in multiple local maximum power points (LMPPs) for centralized TEG system, a fast atom search optimization (FASO) is designed to approximate the global maximum power point (GMPP) from multiple LMPPs. In order to accelerate the convergence for a high quality solution, the Euclidian distance ratio of the original atom search optimization (ASO) is employed to adaptively update according to the dynamic optimization results, as well as for the number of neighbours for each atom. Through case studies such as start-up test, step variation of temperature, stochastic temperature change, and analyse of sensitivity are carried out, in which the practicability and superiority of FASO are compared with that of MPPT method based on a single LMPP and four meta-heuristic algorithms. Simulation results show that the energy output produced by FASO ranges from 101.64% to 451.49% to the alternatives. Finally, through hardware-in-the-loop (HIL) experiment based on a dSpace, the feasibility of the hardware realization is confirmed, in which the difference between simulation results and HIL experiment results is less than 2.3%.

An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading

Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P – V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.

A novel power-increment based GMPPT algorithm for PV arrays under partial shading conditions

Due to dust, structural interfering from surrounding buildings or trees, partial shading conditions (PSCs) are frequently occurred in photovoltaic (PV) arrays, which affects the generated power and system reliability significantly. Under PSCs, PV arrays exhibit multiple local maximum power points (LMPPs), which make the conventional maximum power point tracking (MPPT) algorithms difficult to quickly allocate the optimal operating point with the maximum output power. In order to solve this issue, a novel power-increment based global MPPT (GMPPT) algorithm is proposed by combining the voltage line, the load line, and the power line altogether in determining the tracking direction and the step size. The proposed algorithm retains the advantages of the conventional power incremental based GMPPT technique. Moreover, it can realize a successful convergence to the GMPP under any pattern of PSC, which is difficult to accomplish for some GMPPT algorithms. It simplifies the control implementation since it is not necessary to know exactly the internal connection of the PV array for the practical implementation of the algorithm. Furthermore, the proposed algorithm shows improved tracking speed and higher accuracy than other GMPPT techniques. It directly regulates the duty-cycle of the power interface rather than the output power command. Thus, the circuit design becomes easier. Finally, various partial shading scenarios are evaluated experimentally in order to validate the effectiveness of the proposed algorithm.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

Review of Maximum Power Point Tracking Control of Photovoltaic Systems in Case of Uniform & Non-uniform Irradiance Conditions

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. One of the approaches to increase the efficiency of PV power generation system is to operate the PV systems optimally at the maximum power point. However, the PV system can be optimally operated only at a specific output voltage; otherwise the output power fluctuates under intermittent weather conditions. In addition, it is very difficult to test the performance of PV systems controller under the same weather condition during the development process where the field testing is costly and time consuming. For these reasons, the presentation is about the state of the art techniques to track the maximum available output power of photovoltaic systems called maximum power point tracking (MPPT) control systems. This topic could be also one of the most challenges in photovoltaic systems application that has been receiving much more attention worldwide. The talks will cover the application of intelligent techniques by means the artificial neural network (ANN) and fuzzy logic controller scheme using polar information to develop a novel real-time simulation technique for MPPT control by using dSPACE real-time interface system. In this case, the three-layer feed-forward ANN is trained once for different scenarios to determine the global MPP voltage and power and the fuzzy logic with polar information controller takes the global maximum power point (MPP) voltage as a reference voltage to generate the required control signal for the power converter. This type of fuzzy logic rules is implemented for the first time in MPPT control application. The proposed method has been tested using different solar cell technologies such as monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies. In other cases, one of the main causes of reducing energy yield of photovoltaic systems is the partially shaded condition. Although the conventional MPPT control algorithms operate well in a uniform solar irradiance, they do not operate well in non-uniform solar irradiance conditions. The non-uniform conditions cause multiple local maximum power points on the power-voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global power point may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognize the global operating point under partially shaded conditions. From these reasons, the presentation will address the effectiveness of the proposed MPPT method to solve the partially shaded conditions under the experimental real-time simulation technique based dSPACE real-time interface system for different size of PV arrays, such as 3x3(0.5kW) and 20x3(3.3kW) and different interconnected PV arrays, for instance series-parallel (SP), bridge link (BL) and total cross tied (TCT) configurations.