Invention | Free Full Text | Novel MPPT-based Extended Gray Wolf Optimizer for Standalone Photovoltaic Systems: Performance Evaluation of Four Smart MPPT Technologies in Different Scenarios

As the demand for renewable energy continues to increase, researchers and engineers have been actively working on improving the output power of photovoltaic systems by improving MPPT algorithms and other control techniques. The following introduces the research progress in recent years. Traditional MPPT methods such as perturbation and observation (P&O) [11]Incremental conductance (INC) [12]Fractional open circuit voltage (FOCV) [13]Fractional short circuit current (FSCC) [14] and Hill Climbing (HL) [15], has been widely studied and adopted due to its simplicity and effectiveness. These methods use algorithms that continuously track and adjust the operating point of the PV system to maintain it at MPP, thereby optimizing its energy conversion efficiency.Although traditional MPPT technologies exhibit satisfactory performance under standard conditions, they face challenges from rapidly changing environmental factors (such as solar radiation, temperature, and shadow) in real-world scenarios. [16]. Therefore, researchers and engineers have been working to improve the adaptability, accuracy, and robustness of MPPT algorithms to ensure consistent performance under different conditions. One of the most commonly used optimization algorithms by researchers is swarm intelligence (SI) or intelligence techniques. These algorithms draw inspiration from the social behavior of different species and show promising results in finding MPPs in solar photovoltaic systems. Some well-known SI algorithms include Particle Swarm Optimization (PSO) [17]Artificial Bee Colony Algorithm (ABC) [18]Genetic Algorithm (GA) [19]Ant Colony Optimization (ACO) [20]Firefly Algorithm (FA) [21]Gray Wolf Optimizer (GWO) [22]Whale Optimization Algorithm (WOA) [23]cuckoo search (CS) algorithm [24]Artificial Fish School Algorithm (AFSA) [25] etc.These swarm MPPT techniques exploit the collective behavior and self-organization of agents in the swarm to efficiently explore the solution space and find the optimal solution present in the PV system operating point [26]. Their ability to adapt to changing environmental conditions and global optimization makes them promising tools for improving solar energy collection efficiency. In the existing study, Mohanty et al. [27] A comparative study is presented to evaluate the performance of different MPPT technologies currently used in solar photovoltaic systems. Their work provides valuable insights into the advantages and disadvantages of these techniques, helping to select the most appropriate MPPT method for specific application scenarios. Calvinho et al. [28] A PSO MPPT technique with variable step size is proposed to reduce unnecessary power oscillations. Therefore, the results show that the proposed technique effectively reduces the power oscillation around the MPP. Rajkumar et al. [29] A GWO MPPT controlled DC/DC converter connected to the photovoltaic system was implemented. The proposed method effectively solves certain shortcomings such as reduced tracking efficiency, sustained oscillations at steady state, and transient problems commonly encountered in P&O and PSO methods. Based on the results obtained from simulation and experimental tests, it is obvious that the proposed MPPT algorithm outperforms P&O and PSO-based MPPT systems. Sufyan et al. [30] A new MPPT-based ABC optimization technology is designed. This innovative algorithm not only overcomes the common limitations of traditional MPPT methods, but also provides a simple and powerful MPPT solution. The effectiveness of this method was verified using a joint simulation method, combining Matlab/Simulink and Cadence/Pspice to compare its performance with the PSO-based MPPT algorithm under dynamic weather conditions. Furthermore, experimental validation was performed using a laboratory setting. Both simulation and experimental results confirm the effectiveness of this method. Compared with the PSO-based MPPT algorithm, the new ABC shows superior tracking performance in locating the global MPP, especially under partial shadow and dynamic weather conditions. It is worth noting that ABC is insensitive to initial conditions and does not require knowledge of the PV array characteristics. Furthermore, the experimental results confirmed its ability to accurately track the MPP of the photovoltaic array under partial shading conditions. Priyadarsh ​​et al. [31] A hybrid solar-wind independent power system equipped with MPPT was implemented to generate electricity for residential applications in rural areas. In order to efficiently utilize the electricity from the wind energy system, the ACO algorithm is adopted. Compared with the classic proportional integral (PI) control, this study adopts fuzzy logic control (FLC) inverter control strategy. The MPPT function is performed using a single cuk converter as an impedance power adapter, eliminating the need for additional voltage and current circuitry, thereby increasing the converter’s conversion efficiency and maximizing the power output stage. According to the results, the proposed ACO facilitates fast battery charging and efficient power distribution within photovoltaic-wind energy hybrid systems. Notably, ACO converges seven times faster than PSO technology in terms of achieving MPP and tracking efficiency. Ahmed et al. [32] The concept of CS MPPT is outlined by emphasizing the importance of Lévy flights in affecting algorithm convergence. Finally, this explains the main equations that govern search behavior. To justify CS as a viable MPPT option, we conducted a comprehensive evaluation against two well-established methods (P&O and PSO). Assessments include: gradual changes in irradiance and temperature, step changes in irradiance, and rapid changes in irradiance and temperature. These tests are performed on large and medium-sized PV systems. Furthermore, the algorithm’s ability to handle partially shaded conditions is demonstrated. The results show that CS is able to track MPP within 100-250 ms under various types of environmental changes.Furthermore, the steady-state power loss due to MPP mismatch is only 0.000008 % . Additionally, it handles localized shadow situations very efficiently. Therefore, CS is superior to P&O and PSO in terms of tracking capability, transient behavior, and convergence. However, some MPPT systems combine multiple algorithms or employ adaptive techniques to switch between algorithms based on specific conditions. A hybrid approach can optimize MPPT performance under different weather and environmental conditions. To further improve the performance of traditional MPPT technology, researchers have explored innovative hybrid approaches.For example, the improved P&O algorithm ABC is given by [33], which combines the simplicity of the P&O method with the intelligent search capabilities of the ABC algorithm. This integration results in faster convergence speed and higher MPP tracking accuracy, overcoming some limitations of traditional P&O technology. Figueiredo et al. [34] The hybrid P&O-PSO MPPT technology is introduced and its performance is evaluated based on traditional methods including P&O and standard PSO.Simulation results show that the proposed hybrid algorithm can track the global MPP excellently under both uniform and partially shaded conditions, and the tracking time 50 % Shorter than standard PSO technology.Furthermore, the proposed method extracts 0.3 % The photovoltaic system provides more electricity compared to the P&O-PSO hybrid approach, highlighting its effectiveness in increasing power generation. Cao et al. [35] Two sequential convex methods (SCM), GA and ACO, are combined to enhance the robustness and speed of the MPPT technique.In simulations using Matlab, a GA-ACO MPPT controller was used, utilizing four SunPower SPR-305NE-WHT-D photovoltaic modules in series, each with a maximum power rating of 305.226 W. These tests were conducted under partial shade conditions to evaluate the performance of the newly proposed MPPT controller. The results were then analyzed and compared with those obtained with P&O MPPT and conventional ACO MPPT techniques. The proposed GA-ACO performs the fastest, occasionally approaching the global MPP in the first iteration. In comparison, both the P&O MPPT and ACO MPPT algorithms require more than 20 iterations to find a solution, and they often fail to fully achieve the global MPP. Furthermore, GA-ACO achieved its goals in only 10 iterations (sometimes even less).This translates into a speed advantage of at least 50 % Better than the other two algorithms. Furthermore, GA-ACO exhibits accuracy, stability, and robustness, consistently achieving global MPP quickly even under challenging conditions. To meet the requirements for enhanced performance and adaptability of renewable energy systems, the above-mentioned optimization techniques have demonstrated their potential to enhance the effectiveness of photovoltaic systems and microgrids. These advances mainly focus on improving control strategies, robustness, and frequency stability. However, given the increasing popularity of large-scale PV installations and high-resolution renewable energy penetration in microgrids, an equally important aspect to note is the scalability of these methods to accommodate different system sizes and configurations. The ability to seamlessly adapt to different scales is a fundamental requirement for these technologies to be effective in real-world applications, from small residential PV arrays to large utility-scale solar farms and complex microgrids. Therefore, Kerdphol et al. [36] H∞ robust control is introduced to enhance the frequency stability of island microgrids with high RES penetration and overcome the problem of system inertia weakening. Comparative analysis shows that the H∞-based virtual inertial controller has excellent frequency tracking and interference attenuation capabilities, making it a robust solution for such microgrids. Carly et al. [37] The energy dispatch problem in a network of users sharing renewable energy is solved. It combines social welfare optimization of energy distribution under time-varying pricing and cost optimization of user appliances. A decentralized optimization algorithm using Gauss-Seidel decomposition and competitive games is proposed. Case studies of various scenarios demonstrate that this approach leverages renewable energy sharing to reduce individual costs, manage peak loads and efficiently meet customers’ energy needs.

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