Partial shading of PV systems generates many peaks in the P–V curve. These peaks have one global peak (GP), the remaining being local peaks (LPs). Metaheuristic techniques such as PSO have proven superiority in capturing the GP and avoiding entrapment in an LP in comparison to conventional techniques. In case of partial shading conditions (PSC), the GP may change its position and value in the P–V curve and the PSO is unable to capture the GP unless they reinitialize. Reinitialization of PSO particles spends a long time for convergence; and it may cause premature convergence. This paper proposes a novel strategy for scanning the new position of the GP in case of PSC changes without a need for reinitialization. The proposed strategy sends a particle to the anticipated places of peaks to search for any peak with power greater than the current GP and when it locates this new GP it will move the PSO particles directly to the new GP. This strategy reduced the reinitialization time by 650% as compared to the time required for the random reinitialization of the conventional PSO technique. Moreover; this proposed strategy completely avoids the premature convergence associated with conventional PSO techniques.

https://www.mdpi.com/2071-1050/12/3/1185/pdf

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

To enhance the production of solar photovoltaic (SPV)-based cleaner energy, the maximum power point (MPP) tracking (MPPT) schemes are utilized. To ensure a reliable and effective MPP extraction from SPV systems, the exploitation and implementation of different MPPT schemes are of great significance. This article intends to present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN). In recent years, modern optimization-based global MPP (GMPP) extraction schemes are gaining much attention from researchers. In this review article, thirteen modern optimizations and ANN-based GMPP tracking techniques are vividly described with their flowchart and detailed mathematical modeling. This work assesses all the schemes according to parameters like tracking efficacy, tracking time, application, sensed parameters, converter utilized, steady-state oscillations, experimental setup, and key notes. Based on the rigorous review, a novel GMPP extraction scheme based on a recently introduced meta-heuristic approach named artificial gorilla troops optimizer is proposed. This review work serves as a source of comprehensive information about applying these MPPT techniques to extract GMPP from the SPV system under PS conditions; furthermore, it can be considered a one-stop handbook for further study in this field.

A state-of-the-art review on shading mitigation techniques in solar photovoltaics via meta-heuristic approach

Microgrid systems began to be widely developed and researched as a future system using renewable energy capable of replacing systems with fossil energy sources. The energy source that is often used in microgrid systems is the solar energy converted into electrical energy using PV (Photovoltaic). Unfortunately, a system that uses PV is limited to time and requires a backup energy source to help power requirements on load when there is no sun. In addition to the problems that occur in PV is when the desired maximum output power required a maximum power point tracking using an algorithm. In addition to these problems are still a problem when the power on the PV is raised, automatically output voltage generated by the SEPIC converter will become variable and unstable while the DC bus requires a constant and stable voltage. Therefore, to solve all these problems this paper proposed Modeling and Simulation of MPPT Series SEPIC - BUCK Converter Using Flower Pollination Algorithm (FPA) - PI Controller in DC Microgrid Isolated System. Flower Pollination algorithm is used to find the maximum power point quickly and accurately. PI controller is used to adjust the output voltage Buck converter to obtain a constant voltage and stable. Both methods are applied through SEPIC and BUCK converter in order to generate maximum power from PV output and constant voltage to DC bus.

Modeling and Simulation of MPPT SEPIC - BUCK Converter Series Using Flower Pollination Algorithm (FPA) - PI Controller in DC Microgrid Isolated System

Significant increase in energy and reduction in carbon emissions are the biggest challenges in world energy demand. One of the best solutions is to use renewable energy. Solar energy is the energy that has advantages in terms of availability, eco-friendly, and cost-effectiveness. But the solar panel has a low energy conversion efficiency, so often the solar energy obtained by the solar panel cannot be utilized as a whole. Maximum Power Point Tracking (MPPT) is a technique that can improve the efficiency of energy conversion from the solar panel. In this paper, the type of MPPT used is MPPT hybrid, which is a combination of Artificial Neural Network based MPPT and Incremental Conductance algorithm. The value of short circuit current from the solar panel is used as an ANN reference to reach of maximum power from the solar panel. Incremental conductance is used to keep the maximum power value of the solar panel and improve the tracking results from MPPT ANN. The result of the proposed Hybrid MPPT method can improve the tracking speed of the Incremental Conductance algorithm and can reach the maximum power of the solar panel.

Hybrid Maximum Power Point Tracking Using Artificial Neural Network-Incremental Conduction With Short Circuit Current of Solar Panel

Under the partial shading effect, the photovoltaic modules are subjected to the non-uniform insolation. In this case, the Power-Voltage curve contains several maximum power points (global and local maximums). In fact, the MPPT algorithms such as P&O and IC, cannot distinguish the global maximum. Thus, a considerable drop in power is produced. To tackle this problem, this article proposes a SLG-Backstepping technic. This latter, Sweeps the Power-Voltage curve, Looks for the global maximum power point, and Generates the reference of optimal voltage. Then, the Backstepping control was designed to pursue the reference voltage by adjusting the duty cycle of the SEPIC converter. This algorithmÂ  wasÂ  simulatedÂ  on MATLAB/SIMULINKÂ  environment, evaluatedÂ  under uniform and non-uniform insolation and compared to the PSO with Backstepping control. InÂ  thisÂ  latter,Â  theÂ  PSOÂ  (ParticleÂ  Swarm Optimization)Â  servesÂ  to generateÂ  theÂ  voltage reference that corresponds to the Global Maximum Power Point, while the backstepping controller tracks this voltage reference. According to the results, the SLG-Backstepping algorithm is accurate and has a fast convergence time is about 40ms until 50ms depends on the shading pattern.

https://www.ijrer.com/index.php/ijrer/article/download/9111/pdf

Robust Control for Photovoltaic System Under Partial Shading Effect Using the SEPIC Converter

Solar energy source can be utilized into electric energy using Photovoltaic (PV) panel. Maximum power in PV can be obtained in sunny weather conditions, optimum temperature, and unshaded surface. When PV is blocked by an object, the resulting power will decrease depending on the size of the barrier. Barriers of the sun intensity are shadows of trees, buildings, peoples, and others. These conditions cause PV can't absorb the maximum intensity of sunlight. Unfortunately, an obstructed PV surface condition may affect the resulting characteristic curves. These conditions can produce different characteristic curves. The difference is the position of the maximum power point that should be only one that is GMPP (Global Maximum Power Point) but this is divided into two there are GMPP and LMPP (Local Maximum Power Point). These conditions make a simple MPPT trapped in LMPP or an unreal maximum power point. To solve this problem, in this paper proposed MPPT Partial Shading using Modified Particle Swarm Optimization (MPSO) method. The MPSO method is chosen to solve partial shading conditions so the MPPT can reach GMPP without being trapped in LMPP. The MPSO method is applied to the Sepic converter to generate maximum power. According to simulation result, the MPSO method has a good accuracy greater than 95% and a fast time convergence is about 0.5 until 1 second to obtain a MPP depend on the shading pattern.

Modeling and simulation of MPPT sepie converter using modified PSO to overcome partial shading impact on DC microgrid system

Solar energy can be utilized through photovoltaic (PV) into electrical energy. The output power of PV can reach maximum point when the irradiation of the sun is exposed to the surface without any object being obstructed. Unfortunately, In the actual implementation of irradiation received by PV it is often blocked by a moving object or a shadow of a non-moving object. This problem resulted in the emergence of more than one maximum power point on the P-V characteristic curve. The condition can't be solved by using the usual MPPT Algorithm so that MPPT Partial shading algorithm is required. Partial shading algorithm has been developed from conventional methods to artificial intelligence methods. Hence, in this paper proposed comparison method of MPSO, FPA, and GWO in MPPT SEPIC converter under dynamic partial shading condition. The level of accuracy of all three methods is validated through simulation. The simulation results show that the three methods are able to overcome the problem of partial shading very well with a high level of accuracy. The simulation results also show that the FPA method has advantages compared to other proposed methods. The simulation results also show that the MPSO method has more advantages compared to other proposed methods in tracking accuracy and the FPA method is faster to reach convergence situation by other proposed method.

Comparison method of MPSO, FPA, and GWO algorithm in MPPT SEPIC converter under dynamic partial shading condition

Renewable energy becomes very important because energy requirement can't always depend on fossil energy. Renewable energy such as solar energy is a great natural energy that can be utilized in the morning until the afternoon. Unfortunately, The problem of the output power in solar energy such as Photovoltaic (PV) is influenced by irradiation and temperature that can change suddenly. The output power released by PV depends on the value of irradiation and the temperature forming P-V and I-V characteristic curves. The maximum power output on the PV characteristic curve can be achieved using the MPPT algorithm. MPPT algorithm widely developed from conventional Algorithm up to AI algorithm. Hence, in this paper discussed the comparison of FPA (Flower Pollination Algorithm), MPSO (Modified Particle Swarm Optimization), and P&O (Perturb and Observe) methods in MPPT coupled inductor SEPIC converter on DC Microgrid Isolated System. Sepic converter used to reduce ripple current in part of input. Furthermore, simulated performance experiments show that all three methods were able to find the maximum power point with an accuracy greater than 95%. The simulation results show that the FPA method is superior compared to the MPSO and P&O methods.

Comparison method of flower pollination algorithm, modified particle swarm optimization and perturb & observe in MPPT coupled inductor sepic converter on DC microgrid isolated system

PV system (Photovoltaic) is a system that many discussed and researched by researchers in the world. Unfortunately, when converting solar energy into electrical energy, the Maximum Power Point Tracking (MPPT) is required to obtain maximum power output in PV. In addition, the problems that occur are when the PV is partially shaded resulting in the emergence of Local Maximum Power Point (LMPP) and Global Maximum Power Point (GMPP). The last problem is when the power of PV is maximized, the voltage changes rapidly at the output of the converter while the voltage required to enter the DC bus is a constant and stable voltage. Therefore, this paper proposes the modeling and simulation of MPPT-SEPIC converter using modified PSO series constant voltage Boost converter using FLC (Fuzzy Logic Controller) under dynamic partial shading condition in DC microgrid system. MPSO algorithm is used to maximize power on PV accurately through Single Ended Primary Inductance converter (SEPIC), while FLC is used to make the voltage to DC bus becomes constant and stable through Boost converter.

Modeling and Simulation of MPPT SEPIC-BOOST Using Modified Particle Swarm Optimization (MPSO)-FLC Under Dynamic Partial Shading Condition in DC Microgrid System

Solar panel is widely used as an alternative source to generate solar power plant. One of the ways to store solar panel power is by saving the power on a battery. Generally, the efficiency of solar panel is about 15% - 24.7%. It is quite small because the use of solar panel without control causes the power plant not maximum. In this paper, Modified Particle Swarm Optimization (Modified PSO) was proposed for MPPT control to optimize solar panel power. The algorithm of this method was used to increase efficiency of solar cell so that the power can reach maximum point. Furthermore, the power was used to optimize battery charging process. To verify the performance of Modified PSO algorithm, it was used SEPIC Converter as interfacing from solar panel to battery. Based on the experiment result on three shading patterns for 3 hours charging process, the increase of State of Charge (SOC) of the battery using Modified PSO algorithm was superior than uncontrolled and conventional method (Perturb and Observe).

Rangga Eka Setiawan

Papers

Modeling and simulation of MPPT sepie converter using modified PSO to overcome partial shading impact on DC microgrid system

Comparison method of MPSO, FPA, and GWO algorithm in MPPT SEPIC converter under dynamic partial shading condition

Comparison method of flower pollination algorithm, modified particle swarm optimization and perturb & observe in MPPT coupled inductor sepic converter on DC microgrid isolated system

Modeling and Simulation of MPPT SEPIC-BOOST Using Modified Particle Swarm Optimization (MPSO)-FLC Under Dynamic Partial Shading Condition in DC Microgrid System

Performance Evaluation of MPPT using Modified PSO Algorithm for Battery Charge Application