Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading

Abstract

<span>Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.</span>

Modernisation of DC-DC converter topologies for solar energy harvesting applications: A review

“…One of the interesting and potentially fruitful solutions to meet the ever-increasing energy demands of the globe is to collect energy from the sun. The total system for collecting solar energy is made up of a variety of different devices and components, including as PV arrays, MPPT controllers, DC-DC converters, batteries, loads (both AC and DC), and inverters [9], [11], [12], [16], [17], [58], [119], [120]. The comprehensive schematic block of the solar energy collecting system is shown in Figure 1.…”

“…Solar tracking is the traditional way that is utilized to get the most out of the energy that is collected [156] [157]. One other approach that may be taken to get maximum power is called MPPT [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245]. The MPP can be detected with the use of an MPPT algorithm that is placed on the microcontroller [246].…”

“…Numerous methods are utilized on a regular basis to accomplish the task of tracking maximum power point in PV systems [185]. These methods include hill-climbing, perturbation and observation (P&O), look-up table, incremental conductance (Inc-Cond), linearization-based, DC-link capacitor droop control, curve fitting, extremum seeking control, feedback voltage or current, feedback of power variation with voltage, feedback of power variation with current, firefly algorithm, fractional open-circuit voltage (FOCV), fractional short-circuit current (FSCI), artificial neural network (ANN), fuzzy logic, genetic algorithm, particle swarm optimization (PSO), ant colony based optimization, one cycle control (OCC), ripple correlation control (RCC), parasitic capacitance, and slidingmode [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245].…”

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

“…In the field of mathematics, the shooting swarm algorithm based on PSO and FA is introduced to solve boundary value problems, yielding accurate results and significantly reducing required iterations for problem solutions [36]. For PV system optimization, different algorithms are used to demonstrate quick and efficient maximum power point operation of PV system such as the hybrid FA-perturb and observe (P&O) algorithm [37], improved Seagull optimizer (SO) with modified variable step sizes perturb [38] and observe and evolutionary algorithms (genetic algorithm, FA, and fruit fly optimization) [39]. Moreover, the optimization also aims to optimize power systems by improving efficiency, reducing power loss, enhancing the stability index, controlling power flow and supporting voltage stability.…”

Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy

“…Harris hawk optimization (HHO) has been used to improve the quality of the kernel type in the support vector machine (SVM) technique [3]. Seagull optimization (SO) has been utilized to search the global maximum power point in the photovoltaic system [4]. The Archimedes optimizer algorithm (AOA) was used in the optimal power flow system by minimizing the emission, power loss, and fuel cost while maximizing the voltage profile [5].…”