Detection and Identification of Global Maximum Power Point Operation in Solar PV Applications Using a Hybrid ELPSO-P&amp;O Tracking Technique

Ram, J. Prasanth; Pillai, Dhanup S.; Rajasekar, N.; Strachan, Scott

doi:10.1109/jestpe.2019.2900999

Cited by 74 publications

(34 citation statements)

References 28 publications

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“…Li et al 33 applied the current sensorless (CS) to obtain the MPPT algorithm this is utilized different single‐input‐voltage sensor (SIVS) and entirely eliminates the costly current sensors; it reduces the cost of the system but introduces the drift problem. Ram et al 34 introduced enhanced leader particle swarm optimization (ELPSO) combined by a conventional P&O algorithm to recognize global MPPT places in the solar system. However, elevated oscillations are introduced in search of the best solutions.…”

Section: Review Of Related Workmentioning

confidence: 99%

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

Sundararaj

Anoop

Dixit

et al. 2020

Progress in Photovoltaics

176

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In general, the photovoltaic (PV) is considered as the best selection among renewable energy resources due to its nonpolluted operation and good flexibility condition. The PV system is affected because of the partial shading conditions (PSCs), which reduce the generated power. During steady-state operating conditions, there occurs a time delay in tracking the Global maximum power point (GMPP) and Local maximum power point (LMPP) under PSCs using the perturb and observe (P&O) method. In order to overcome such shortcomings, this paper proposed a hybrid algorithm with a P&O technique to improve the maximum power point tracking (MPPT) for the PV system under PSC. In addition to this, the P&O technique is utilized to achieve the LMPP in the first section, and the hybrid algorithm is utilized to achieve the GMPP in the second section. Here, the hybrid technique is the integration of Cauchy preferential crossover (CC) with the flower pollination algorithm (FPA). Furthermore, the exploitation ability of the FPA is enhanced by the CC, and the combined hybrid algorithm has the ability to produce the optimal duty cycle for the DC-DC boost converter for MPPT. Then the proposed method will be executed in MATLAB/Simulink model, and it is contrasted with the existing methods such as CC, current sensorless (CS), and FPA, respectively. The experimental results and analysis reveal that the proposed approach provides better performances when compared with several other metaheuristic algorithms. K E Y W O R D S crossover, DC to DC boost converter, global maximum power point, global pollination, local maximum power point, photovoltaic, PSC 1 | INTRODUCTION Due to the rapid growth and expansion in business as well as the consistently increasing refinement of existing ways of living, the world supply energy is exposed to a huge strain. This wonder creates uncertainties over energy security as well as environmental sustainability. 1,2 Besides, the problem of climatic changes and it is necessary to reduce carbon impressions has added to the solid force for organizations and countries to put resources into elective energy sources, especially renewable energy (RE). Solar energy is a standout among the most vital RE sources, rather than regular unrenewable assets, for example, fuel and coal. 3 Solar energy is spotless, limitless, and free. The primary utilization of photovoltaic (PV) frameworks is said to be a stand-alone (military, street and residential lighting, electric vehicles space applications, and water pumping). Despite these

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Section: Review Of Related Workmentioning

confidence: 99%

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

Sundararaj

Anoop

Dixit

et al. 2020

Progress in Photovoltaics

176

View full text Add to dashboard Cite

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“…These algorithms have been inspired by natural and biological processes and they differ on the operating principle used to produce the alternative operating points of the PV array, which are investigated during searching for the GMPP position. The particle swarm optimization (PSO) [11][12][13], grey wolf optimization (GWO) [14][15][16], flower pollination algorithm (FPA) [17,18], Jaya algorithm [19] and differential evolution (DE) algorithm [20] are the most frequently employed evolutionary algorithms. Genetic algorithms (GAs) inspired by the process of natural evolution are applied in [21].…”

Section: Introductionmentioning

confidence: 99%

Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions

2020

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A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm.

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“…Among all aforementioned meta-heuristic techniques, the PSO algorithm has been widely modified to address the problem of the MPPT under partial shading conditions [25][26][27][28][29][30][31]. In [25], a MPPT method based on a modified PSO algorithm is developed.…”

Section: Introductionmentioning

confidence: 99%

“…However, the conventional PSO algorithm is then used to identify accurate MPP as a second step. A hybrid enhanced leader PSO assisted by a conventional P&O algorithm is proposed in [28]. In this method, the region of the MPP is firstly located using enhanced leader PSO while the accurate MPP is identified using conventional P&O method.…”

Section: Introductionmentioning

confidence: 99%

“…In [29], the region of the MPP is firstly located using an improved PSO algorithm, and then the hill climbing (HC) method with a small disturbance is utilized to identify the accurate MPP. However, the previous two methods presented in [28] and [29] suffer from the oscillation around the peak point (MPP) due to the perturbation and observation processes utilized in P&O and HC methods, respectively. A hybrid method based on the combination of the PSO algorithm and the differential evaluation (DE) algorithm is presented in [30].…”

Section: Introductionmentioning

confidence: 99%

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Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition

Kamil¹,

Nasr²,

Alwash³

2020

IJIES

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The maximum power point tracking (MPPT) is an essential key to ensure that the photovoltaic (PV) system is operated at the highest possible power generation. This paper presents an efficient MPPT method for the PV system based on an enhanced particle swarm optimization algorithm to track the location of the global maximum power point, whatever its location changes in the search space under all environmental conditions, including the partial shading on strings. In this paper, the formulation of the conventional particle swarm optimization algorithm is enhanced to decrease the searching time and the oscillation of the generated output power as well as the power losses in the online tracking process. This enhancement can be achieved by utilizing a special time-varying weighting coefficient and removing the effect of some other coefficients in the conventional particle swarm optimization algorithm (PSO) that cause winding of the particles during the online tracking process. Test results verified the accuracy of the proposed method to track the global maximum power point with considering the effect of partial shading condition. The proposed method was also compared with other MPPT methods to verify the superiority of the proposed work. The obtained results reveal that the proposed method is effective to improve the tracking efficiency and reduce the tracking time and the number of iterations for the different irradiances and load conditions. The maximum number of iterations was 11 iteration and the highest tracking time was 0.273s with tracking efficiency of about 99.98%.

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Detection and Identification of Global Maximum Power Point Operation in Solar PV Applications Using a Hybrid ELPSO-P&O Tracking Technique

Cited by 74 publications

References 28 publications

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions

Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition

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