Global Maximum Power Point Tracking Research Articles

Deep Reinforcement Learning using Deep-Q-Network for Global Maximum Power Point Tracking: Design and Experiments in Real Photovoltaic Systems

This paper presents a methodology for integrating Deep Reinforcement Learning (DRL) using a Deep-Q-Network (DQN) agent into real-time experiments to achieve the Global Maximum Power Point (GMPP) of Photovoltaic (PV) systems under various environmental conditions. Conventional methods, such as the Perturb and Observe (P&O) algorithm, often become stuck at the Local Maximum Power Point (LMPP) and fail to reach the GMPP under Partial Shading Conditions (PSC). The main contribution of this work is the experimental validation of the DQN agent’s implementation in a synchronous DC-DC Buck converter (step-down converter) un- der both uniform and PSC conditions. Additionally, we establish a testing pipeline for DRL models. The DQN agent’s performance is evaluated alongside the P&O algorithm. Results consistently indicate the DQN agent’s superiority over the P&O algorithm in all simulated scenarios. Although this trend does not entirely replicate in real-world test setups, significant results are observed. Specifically, in PSC sce- narios where the P&O algorithm becomes trapped at an LMPP, the DQN algorithm extracts up to 63.5% more power than the P&O algorithm. An open repository is available, containing PCB schematic designs and layouts, along with the code used for model training and deployment.

An enhanced global MPPT method to mitigate overheating in PV systems under partial shading conditions

The hot-spot phenomenon in photovoltaic modules is a reliability issue typically caused by many reasons, including partial shading conditions. In such situations, the P-V curve exhibits a unique global maximum power peak (GMPP) and several local maximum power peaks (LMPPs). However, all the existing tracking methods (GMPPT) track the GMPP, even for a slight difference between peaks. For this purpose, an experimental investigation of the relationship between the operating point of a PV module and the temperature of their shaded parts has been carried out. In the studied case, it was found that the shaded cells’ temperature exceeds 99 °C, and it is directly related to the GMPP position on the P-V curve, where it decreased as the operating point is moved from the short circuit current towards the open-circuit voltage. As the shaded cells' temperature may reach high levels, which can result in early degradation of PV modules, an enhanced GMPPT method is proposed in this paper to handle the overheating issue. The proposed method aims to track the secure peak when two peaks or more have closer power values. Consequently, it alleviates the overheating and hot-spotting risks. Furthermore, the scanning voltage interval of this technique is bounded by lower and upper voltage limits. The proposed technique is experimentally validated and compared to two existing methods: the 0.8 Voc and the full sweeping method. The results show that when the power peaks have approximately the same value, the proposed method tracks the peak with the upper voltage (automatically the lower current) to minimize the thermal stress within the shaded cells. Moreover, the proposed method exhibits a 40 % saving in tracking time compared to the full sweeping method thanks to the efficient tracking mechanism that allows skipping certain intervals in the search space.

Advanced particle swarm optimization for efficient and fast global maximum power point tracking under partial shading conditions

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading.

Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems

Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems

Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions

Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions

Global MPPT controllers for enhancing dynamic performance of photovoltaic systems under partial shading condition

This research focuses on the development and evaluation of global maximum power point tracking MPPT controllers for enhancing the dynamic performance of Photovoltaic (PV) systems under partial shading conditions. Three different controllers, namely Proportional-Integral (PI), Fractional-Order PI (FOPI), and Fuzzy Logic Controllers (FLC), are studied in conjunction with a modified Incremental Conductance (IC) and Fractional Open-Circuit Voltage (FOCV) algorithms. The proposed controllers with a novel optimization algorithm called Atom Search Optimization Algorithm (ASOA) are compared with classical control. The objective is to investigate the effectiveness of the global MPPT controllers in achieving higher tracking accuracy, faster convergence, and improved stability under varying shading conditions. The study involves extensive simulations using a representative PV system model subjected to different shading scenarios. The results demonstrate that the proposed global MPPT controllers with the ASOA algorithm, when integrated with the modified MPPT algorithms, outperform the classical control techniques in terms of dynamic performance and response to partial shading conditions. The suggested ASOA-based fuzzy IC MPPT controllers can reach the MPP more quickly with a shorter settling time and a better tracking response. Furthermore, with a reduced settling time of about 1.9 % and an average power harvesting efficiency of over 97.9 %, ASOA-based fuzzy IC MPPT controllers offer the best harvesting characteristics. The controller exhibits superior tracking accuracy and stability, mitigating the effects of shading-induced multiple peaks in the P-V curve.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses.

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions.

Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.

Fast indirect GMPPT method for PV systems under uniform and partial shading conditions

Under Partial Shading Conditions (PSCs), conventional MPPT techniques fail to locate the Global Maximum Power Point (GMPP) for PV generators, and when PSCs change suddenly and repetitively, several GMPP tracking techniques takes time to find or miss the target. To overcome these shortcomings, this paper proposes a new and fast technique that can identify and catch very quickly the GMPP. Due to the use of a PID controller, the PV system is improved in terms of response time and becomes very fast. On the other hand, the proposed algorithm is developed upon other known algorithms and enhanced in order to identify the occurrence of PSCs and to find the GMPP. The measured points during identification and searching process are reduced which increases the power efficiency of the PV system. The time required for the algorithm to catch the GMPP is minimized by 25% compared with other works. To examine the performance of the system a hard scenario, that contains several uniform and partial shading conditions, is used. The simulation is implemented in Matlab/Simulink. The obtained results show clearly the advantage of the proposed technique over others.

Improved Global Maximum Power Point Tracking Technique for a Partially Shaded Solar Photovoltaic Array Using Capacitor Transient Effect

Improved Global Maximum Power Point Tracking Technique for a Partially Shaded Solar Photovoltaic Array Using Capacitor Transient Effect

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions

In this paper, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.

New Global Maximum Power Point Tracking Technique Based on Indirect PV Array Voltage Control for Photovoltaic String Inverters With Reduced Number of Sensors

New Global Maximum Power Point Tracking Technique Based on Indirect PV Array Voltage Control for Photovoltaic String Inverters With Reduced Number of Sensors

Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

A communication-based method for PSC Detection and GMP tracking under PSC

This study proposes a novel communication-based method for tracking global maximum power point (GMP) under partial shading condition (PSC) for a rooftop photovoltaic (PV) systems. The proposed method uses communication between converters to detect the occurrence of PSC and finds the GMP. The method does not need to find all local maximum points (LMPs) to determine GMP. Consequently, the energy loss due to blind scan is avoided and the time of tracking GMP is reduced. In addition, the starting points for searching LMPs are defined which are very close to the LMPs. Furthermore, the system identifies repetitive shadow on the module and informs the operator to cope with the problem. The proposed method by using real-time measurements is immune to the change of module s parameters with respect to aging and environmental conditions(temperature and irradiance level). Simulation and experimental results demonstrate the effectiveness of the proposed method.

GMPP Tracking of Solar PV system using Spotted Hyena and Quadratic Approximation based Hybrid Algorithm under Partially shaded conditions

The demand for clean and renewable energy is increasing due to environmental concerns. Photovoltaic (PV) system is very popular from the renewable sources and its usage is increasing. The PV system power transfer efficiency depends on several factors such electrical characteristics of loads, solar irradiation, shading condition and panel temperature. The process of transferring maximum possible power to load from PV system by adjusting the system parameters is called maximum power point tracking (MPPT). Under the partially shaded conditions, multiple power peaks are created in the PV curves and makes the MPPT is a non-convex problem. It is a challenging problem to track the global maximum power point (GMPP) under partially shaded conditions. It is essential to recognize the GMPP and restart the string as early as possible to prevent the physical as well as economical damage. In this paper, a new hybrid algorithm for MPPT is proposed and called as QASHO, a combination of Spotted Hyena optimization (SHO) and Quadrature approximation (QA) technique. The QASHO is used to track the GMPP under different weather conditions such as partial shading. The simulation results are compared with Perturb and Observe (P&O) algorithm and Cuckoo Search optimization (CSO) algorithm and promised for better performance in tracking GMPP. The experimental results are provided to validate the proposed MPPT methodology.

Enhanced particle swarm optimization and P&O for MPPT of photovoltaic systems under partial shading conditions

Although Photovoltaic Technologies are largely deployed as a renewable energy source, several factors affect their performance. The major factors that affect PV performance are changes in irradiance and temperature. Maximum PowerPoint Tracking of PV output is essential in giving the maximum photovoltaic outputs at variable levels. Instantaneous variation in irradiance and temperature increases the complexity of tracking maximum power points. Partial shading conditions resulting from shade from trees, tall buildings, and Cloud formation amongst others greatly affect PV systems, especially in large Photovoltaic systems. Under the Partial shading condition, P-V curves become more complex as it is characterized by multiple peaks. The conventional PSO is associated with less accuracy in tracking the Global Maximum Power Point (Global MPP) and slow convergence time in obtaining the Global MPPT and oscillations. In this thesis, a modified Particle Swarm Optimization based Maximum Power Point Tracking technique is designed in MATLAB/Simulink to track the global Maximum Power Point of a Photovoltaic system under partial shading. The proposed modified PSO combines conventional PSO and P&O methods. The particle position in the PSO method is given as the duty cycle value d of the DC-DC converter. Conventional PSO equations are used to update the velocities and duty cycle. Thereafter, the maximum velocity and duty cycle are perturbed to reduce convergence time. The designed PV system was simulated in a MATLAB environment for 10 different irradiation levels and results were compared with results from related works. The average convergence time was 0.99 seconds and efficiency was up to 99.8% with the proposed model which performed better than conventional methods.

A new approach to solve the problem of partial shading in a photovoltaic system

<p>This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.</p>