Enhanced maximum power point tracking for photovoltaic systems: A Modified African Vulture Optimization (MAVO) algorithm approach

Partial shading is one of the important issues in maximum power point (MPP) tracking (MPPT) for photovoltaic (PV) systems. Multiple peaks on the power-voltage (P-V) curve under partial shading conditions can result in a conventional MPPT technique failing to track the global MPP, thus causing large power losses. Whereas, evolutionary optimization algorithms exhibit many advantages when applying them to MPPT, such as, the ability to track the global MPP, no requirement for irradiance or temperature sensors, system independence without knowledge of the PV system in advance, reduced current/voltage sensors compared to conventional methods when applied to PV systems with a distributed MPPT structure. This paper presents a uniform scheme for implementing evolutionary algorithms into the MPPT under various PV array structures. The effectiveness of the proposed method is verified both by simulations and experimental setup. The implementation of the ant colony optimization (ACO) based MPPT is conducted using this uniform scheme. In addition, a strategy to accelerate the convergence speed, which is important in systems with partial shading caused by rapid irradiance change, is also discussed.

For the optimal operation of photovoltaic system, The MPPT (Maximum Power Point Tracking) control unit is an essential part for the photovoltaic system. In addition to the protection function, this command ensures the continuation of the maximum power point (MPPT) and allows the PV generator to deliver its maximum power regardless of the variation in climatic conditions (sunshine and) temperature). This work intends to provide an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and the P&O algorithm. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB/ Simulink, we used in The proposed ANN two inputs which are irradiation and ambient temperature, and one output is the optimum voltage of the PV system. The proposed ANN was analyzed under different irradiation conditions. The response of the proposed ANN for MPPT controllers found to be lesser oscillation at MPP and faster tracking response compared with the P&O algorithm. Comparisons of MPPT with P&O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.

In order to obtain more power from photovoltaic (PV) modules under mismatching operating conditions, the submodule-based MPPT technique is an important solution. In this technique, since the power-voltage (P-V) curve of a submodule cannot be multi-peaked, the maximum power point (MPP) tracking (MPPT) is easily achieved through a DC-DC converter connected to each submodule. Since the P-V curve cannot be in a multi-peaked form, the maximum power can be obtained with a simple MPPT algorithm. For this reason, perturb & observe (P&O) algorithm can be chosen. In this study, the behaviour of a submodule-based MPPT with synchronous buck converter (SBC) is investigated for dynamic shading conditions. In addition, submodule-based MPPT and module-based MPPT technique were compared and the comparison was confirmed by simulation studies that submodule-based MPPT performed better. In this context, simulation studies were carried out for different shading conditions. According to the simulation results, the submodule-based MPPT approach achieves greater efficiencies to the module-based MPPT. In some simulations, when the module-based MPPT technique is used, the operation at the local MPP has been realized. In such cases, more advanced algorithms are needed. However, a simple algorithm is sufficient in submodule-based MPPT. The only disadvantage of this MPPT is the high hardware cost. However, the increase in efficiency obtained is at a level that can easily tolerate this cost.

In the future solar energy will become a very important green energy. Maximum power point (MPP) tracking (MPPT) technology is widely used in solar photovoltaic (PV) systems to generate peak power for PV arrays that depend on solar irradiation and ambient temperature. Literature[1] shows that the maximum power point tracking (MPPT) technology can improve the efficiency of photoelectric conversion by more than 20% and the economic cost is relatively low. Based on PROTUES, the output characteristics of PV arrays under different shadow conditions are simulated and the rule between local maximum power point and open circuit voltage is obtained. Among all the MPPT strategies, the incremental conductance (INC) algorithm and perturb and observe (P&O) algorithm are widely used due to the high tracking accuracy at steady state and easy implementation.In this paper, a novel adaptive variable step size INC MPPT method is proposed according to the above rule. This algorithm not only has the advantage of INC, but also can automatically adjust the step size to track the maximum power point of PV arrays. Compared with the adaptive perturb and observe (P&O) algorithm, the proposed approach can effectively improve the MPPT steady-state response speed and accuracy simultaneously. The theoretical analysis and design principle of the proposed algorithm are presented in this paper. The simulation results show that the algorithm can accurately track the maximum power point (MPP) with shadow. The average tracking time is only 0.13 seconds, and the power tracking efficiency reaches 98%.

Photovoltaic (PV) solar cells are essential in renewable energy generation because they can produce power directly. As one of the most practical and widely used methods for meeting global clean energy demands, PV systems are integral to modern energy strategies. However, these systems face significant challenges in maximizing power output, especially under shading conditions and fluctuating loads. To overcome these issues, a, practical Maximum Power Point Tracking (MPPT) function is crucial for optimizing power extraction in such dynamic environments. Solar panels typically produce electrical outputs that vary in DC voltage, requiring a well-designed DC link interfacing circuit to ensure efficient energy transfer from the PV source to the load. In response to these needs, this study introduces the Pelican Optimization Algorithm (POA), a novel nature-inspired stochastic optimization technique designed to track the Maximum Power Point (MPP) of solar sources with high precision. This innovative MPPT method is coupled with a PV-fed, energy-efficient high-power DC-to-DC converter, which enhances MPPT operation by providing substantial step-up voltage gain and improved overall efficiency. An ideal PV model technique is employed in this study to accurately approximate the system's mathematical parameters. The performance of the POA is benchmarked against three other Metaheuristics MPPT techniques: Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO),Gray Wolf Optimization (GWO), and Cuckoo Search (CS). These comparisons are conducted under uniform and partial shading conditions (PSCs) as well as varying load scenarios on a standalone PV system. The results reveal that the proposed MPPT technique excels in tracking the global maximum power point across diverse operating conditions. It offers rapid convergence, minimal MPP oscillation, quick response times (less than 0.2s), and higher efficiency (99%). MATLAB/Simulink simulations further validate POA's superior performance in MPP stability, tracking time, and effectiveness under PSCs.

Taking out the maximum obtainable power from solar photovoltaic (SPV) system under periodically varying atmospheric conditions is effectively facilitated through maximum power point tracking (MPPT) techniques. Among diverse MPPT techniques fuzzy logic (FL) based technique shows better response in context of maximum power point (MPP) tracking time and tracking efficacy under changing environmental conditions. This paper presents FL based proportional-integral-derivative (FL-PID) MPPT algorithm to track MPP. SPV system in combination with FL-PID MPPT algorithm drives DC-DC boost power converter which elevates array voltage besides effectively tracking MPP under changing environmental conditions with change in irradiance between 400W/m2-1000W/m2 and temperature between 25°C - 75°C. Compared with classical perturb and observe (P&O) and classical incremental conductance (IC) MPPT technique, the proposed technique can fruitfully enhance transient as well as the steady-state (SS) system response. With the help of MATLAB/Simulink environment a prototype of 200 W of an SPV array has been designed, simulated and investigated for the proposed system.

The use of a maximum power point (MPP) tracking (MPPT) controller is required for photovoltaic (PV) systems to extract maximum power from PV panels. However, under partial shading conditions, the PV cells/panels do not receive uniform insolation due to several power maxima appear on the PV array's P–V characteristic, a global MPP (GMPP) and two or more local MPPs (LMPPs). In this scenerio, conventional MPPT methods, including pertub and observe (P&O) and incremental conductance (INC), fail to differentiate between a GMPP and a LMPP, as they converge on the MPP that makes contact first, which in most cases is one of the LMPPs. This results in considerable energy loss. To address this issue, this paper introduces a new MPPT method based on the Seagull Optimization Algorithm (SOA) to operate PV systems at GMPP with high efficiency. The SOA is a new member of the bio-inspired algorithms. When compared to other evolutionary techniques, it uses fewer operators and modification parameters, which is advantageous when considering the rapid design process. In this paper, the SOA-based MPPT scheme is first proposed and then implemented for an 80 W PV system using the MATLAB/SIMULINK environment. The effectiveness of the SOA based MPPT method is verified by comparing its performance with P& O and PSO (particle swarm optimization) based MPPT methods under different shading scenarios. The results demonstrated that the SOA based MPPT method performs better in terms of tracking accuracy and efficiency.

Computational intelligence techniques for maximum power point tracking in PV systems: A review

This study introduces a quick, highly efficient and a single sensor based maximum power point (MPP) tracking (MPPT) for partially shaded solar photovoltaic (PV) system. For this purpose, a novel ‘human psychology optimisation’ (HPO) algorithm is proposed, which is based on psychological and mental states of an ambitious person. The main objective of the HPO algorithm is the maximum extraction of the power from PV panel and efficiently supplying it to the load (battery). In this study, a single (current) sensor based MPPT for battery charging, by using HPO and some recent state‐of‐the‐art MPPT algorithms, is tested on MATLAB simulation and verified on a developed prototype of the partially shaded solar PV system. The efficient battery charging and quickly reaching the MPP by HPO w.r.t. all other algorithms, in steady‐state as well as in dynamic conditions, show the superiority over all the recent state‐of‐the‐art control methods. Moreover, due to the single sensor, the cost of the MPPT system is reduced, as well as due to HPO the computational burden is very less, so it can be easily implemented on the low‐cost microcontroller.

Solar energy continues to be a viable renewable energy source owing to its eco-friendly nature and long-term economic prospect. The photovoltaic (PV) systems enjoy the trend of being commercialized in many countries due to their potential long-term benefits. Solar irradiance and temperature appear to be responsible for the nonlinear nature of I–V and P–V characteristics of the PV array. The nonlinear characteristics create an optimal point in the sense when the PV system is operated at the maximum power point; it plays a vital role in optimizing the PV power. One of the simplest methods that allow improving the efficiency of the PV systems relates to maximum power point tracking (MPPT). The MPPT aims to track the maximum power point when the weather conditions change, the variation of the solar irradiation, and the temperature leads to a change of the maximum power point. The extraction of the maximum power follows the matching of the power–voltage operating point of the PV modules with that of the corresponding power converter. However, owing to the nonlinear variation of the power output of the solar panel, the MPPT control method becomes an important part of any solar system. The maximum power point tracker (MPPT) ensures the optimal utilization of a large PV array when employed in conjunction with the power converter. The control becomes more complicated when the entire PV array does not receive uniform irradiance – a condition known as partial shading. Partial shading invites considerable interest due to its significance in influencing the energy yield of a PV system. It can be ascertained by the statistical measure of power loss due to partial shading that varies from 10 to 70% of the system yield. Though many conventional MPPT schemes remain in vogue, most of them remain suited when the irradiance varies very slowly and becomes ineffective when subjected to a sudden change in environmental conditions. The maximum power point tracking (MPPT) algorithm becomes crucial in attaining the maximal PV power, facilitating optimal PV cell performance. The MPPT algorithms demonstrate excellent tracking efficiency in uniform insolation conditions. However, under partially shaded conditions, when the entire array does not receive uniform insolation, the PV characteristics become more complex, displaying multiple peaks, of which one of them constitutes to be the global peak (GP) and the rest being local peaks. The occurrence of partially shaded conditions being quite common (e.g., due to clouds, trees, etc.) echoes a need to develop special MPPT schemes that can track the GP under these conditions. It becomes significant to propose advanced MPPT techniques for a large PV system with an array of PV panels. However, in the scenario of distributed PV system planning, decentralized MPP tracking schemes gather significance, and MPP tracking in a single PV panel under uniform and partial shading conditions needs attention. The main emphasis involves a two-stage approach to track the global peak, wherein it orients to explore the use of the maximum power from the solar PV system under the partially shaded environment. It augurs the role of the closed-loop controller to vary the duty cycle of the converter interface and arrive at the delivery of the maximum power to the load. The exercise relates to modeling the solar PV system under a partially shaded environment and analyzing the performance for different shading patterns in the solar panel. The focus incites improving the global peak tracking in all conditions through an algorithm that can operate in the vicinity of the global peak. It includes the identification of different possible combinations of partially shaded patterns and requires being tested for the effectiveness of the scheme. It further necessitates the implementation of the technique through a prototype model and therefrom demonstrates the effectiveness of the use of a simple controller in place of sophisticated MPPT methods.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Harris hawk optimization-based MPPT control for PV systems under partial shading conditions

An efficient implementation of three-level boost converter with capacitor voltage balancing for an advanced MPPT approach in PV Systems

An adaptive fuzzy logic (FL)-based new maximum power point (MPP) tracking (MPPT) methodology for controlling photovoltaic (PV) systems is proposed, designed, and implemented in this paper. The existing methods for implementing FL-based MPPTs lack for adaptivity with the operating point, which varies in wide range in practical PV systems with operating irradiance and ambient temperature. The new proposed adaptive FL-based MPPT (AFL-MPPT) algorithm is simple, accurate, and provides faster convergence to optimal operating point. The effectiveness and feasibility verifications of the proposed AFL-MPPT methodology are validated with considering various operating conditions at slow and fast change of solar radiation. In addition, the simplified implementation of the proposed algorithm is carried out using C-block in PSIM software environment, wherein the proposed algorithm and system are simulated. Additionally, experimental results are performed using a floating-point digital signal processing (DSP) controller (TMS320F28335) for verifying the feasibility of the proposed AFL-MPPT methodology. The results of simulations and experimental prototypes show great consistency and prove the capability of the new AFL-MPPT methodology to extract MPPT rapidly and precisely. The new proposed AFL-MPPT method achieves accurate output power of the PV system with smooth and low ripple. In addition, the new proposed AFL-MPPT method benefits fast dynamics and it reaches steady state within 0.01 s.

The fast advancement of photovoltaic (PV) power generation technologies led to the integration of solar-based generation systems into several modes of transportation, including aircraft, automobiles, and trains. In these PV systems, solar irradiation levels fluctuate significantly and invalidate specific maximum power point (MPP) tracking (MPPT) control strategies, lowering the energy conversion efficiency. It is evident that the new robust model reference adaptive control (MRAC) proposed in this paper alleviates these problems by reducing tracking direction loss and oscillations near MPP. To evaluate the proposed controller’s performance, MATLAB/Simulink is utilized and compared with well-known techniques (P&O, VSPO, INC, ANFIS, and swarm-based MPPT) under three different modes, i.e. stand-alone, grid-connected, and real-time mode. The proposed scheme has tracking efficacy lies between 99.02% and 99.96% and achieved MPP in just 4 msec under highly fluctuating radiation and temperature conditions. It takes only 0.08 sec to capture the global MPP, which is on average two times faster than swarm-based global MPPT methods. Furthermore, the effectiveness is tested in a 50 kW three-phase grid-connected mode with and without cloud effects in realistic weather situations. Finally, real-time validation on the OPAL-RT simulator demonstrates the proposed technique’s practicality in real-world applications.