Optimization of Perturb and Observe Based Fuzzy Logic MPPT Controller for Independent PV Solar System

Ramu, Arulmurugan

doi:10.37394/23202.2020.19.21

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…An inverter is required in the system to produce ac power for the loads; hence the inverter needs to make 220v ac at 50 Hz and deliver power up to 50Kw [7][8][9].…”

Section: Invertermentioning

confidence: 99%

Optimization of Solar System for Rural Electrification in as Suwadirah (Saudi Arabia)

Barnawi

2023

ETOAJ

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Rural electrification is essential as most villages worldwide are faced with the challenge of a lack of electricity. These villages use other sources of energy, such as petroleum and coal, which are fossil fuels. These fossil fuels lead to a lot of CO2 emissions that contribute to global warming. Environmentalists and governments have thus encouraged households to use renewable energy since it leads to less pollution and emissions, thus reducing global warming effects. In rural regions, solar energy has been marketed as a reliable source of power. In this paper, we proposed solar tracking devices that use microcontrollers, LDRs, and stepper motors effectively capture power maximally from sunlight. This leads to 35% in power generated annually compared to a fixed solar panel. For the sake of the charging battery, maximum power point tracking is also used to maintain a consistent voltage. An online simulator is used to simulate the dual-axis solar tracker, but the simulator lacks a plug adapter which necessitated the need to move to Arduino. This was required to power the motor through the adapter and connect 100 µf capacitors to protect the stepper motor driver from spikes.

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“…An inverter is required in the system to produce ac power for the loads; hence the inverter needs to make 220v ac at 50 Hz and deliver power up to 50Kw [7][8][9].…”

Section: Invertermentioning

confidence: 99%

Optimization of Solar System for Rural Electrification in as Suwadirah (Saudi Arabia)

Barnawi

2023

ETOAJ

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“…ANN is a computing model, which is interconnected by a large number of neurons. BPNN is a global approximation network, having the advantages of simplicity, self-learning and self-adaptation [27]. Figure 5 shows a typical three-layer ANN basic structure.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

et al. 2022

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The P–U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP voltage of PV cells. Finally, the fuzzy logical control (FLC) is used to eliminate local power oscillation and improve the robustness of the PV system. The proposed algorithm is applied and compared with GA-BPNN, simulated annealing-genetic (SA-GA), particle swarm optimization (PSO), grey wolf optimization (GWO) and FLC algorithm under the condition that both the irradiance and temperature change. Simulation results indicate that the proposed MPPT algorithm is superior to the above-mentioned algorithms with efficiency, steady-state oscillation rate, tracking time and stability accuracy, and they have a good universality and robustness.

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“…The most commonly used soft computing MPPT techniques are particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO), and the shuffled frog leap algorithm (SFLA) [7][8][9][10][11][12]. On the other hand, soft computing MPPT techniques are difficult to deploy under practical environmental conditions as the coding complexity and the embedded processor's deployment are major hurdles [13][14][15]. Therefore, research on dispersing the shading impact through reconfiguring the PV panels seems to be the pragmatic solution to mitigate the shading impact and to render a P-V curve without multiple power peaks [16].…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of a Partially Shaded Photovoltaic Array by Optimal Reconfiguration and Current Injection Schemes

et al. 2021

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The output of a photovoltaic array is reduced considerably when PV panels are shaded even partially. The impact of shading causes an appreciable loss in power delivery, since the PV panels are connected in series and parallel to contribute to the required voltage and power for the load. The prevailing research on mitigating the shading impact is mostly based on complex reconfiguration strategies where the PV panels are subjected to complex rewiring schemes. On the other hand, to disperse the shading many studies in the literature defend the physical rearrangement of the panels. The available intensive reconfiguration schemes, such as the series parallel (SP), bridge link (BL), honeycomb (HC), and total cross tied (TCT) schemes, try only to mitigate the shading impact and there is no scope for compensation; as a result, a loss of output power is inevitable. In the proposed research work, both the mitigation of and the compensation for the losses incurred due to shading are studied. In this work, an optimal reconfiguration scheme is adopted to reduce the shading impact and a power electronic circuit with a battery source is designed to compensate for the shading losses in all aspects. In the optimal reconfiguration scheme, a bifurcation strategy is adopted in each column and the electrical connections of the PV panels are interchanged such that the shading impact is dispersed. The power electronic circuit consists of a half-bridge buck converter with a battery source that injects the current required by a shaded column. This setup compensates for the shaded PV array’s power and improves the efficiency of the total system. The proposed scheme was implemented in a 3200 W system and subjected to various shading patterns, including single panel shading, corner shading, long and wide shading, and random shading. The proposed scheme was simulated in the MATLAB Simulink environment and compared with static 4 × 4 PV array configurations, including the series parallel (SP), bridge link (BL), honeycomb (HC), and total cross tied (TCT) configurations. The comparative performance was assessed in terms of mismatch power loss, fill factor, and efficiency. The proposed system is suitable for all shading patterns and was proved to be very efficient even in the worst shading, where 1353 W was saved.

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Optimization of Perturb and Observe Based Fuzzy Logic MPPT Controller for Independent PV Solar System

Cited by 8 publications

References 19 publications

Optimization of Solar System for Rural Electrification in as Suwadirah (Saudi Arabia)

Optimization of Solar System for Rural Electrification in as Suwadirah (Saudi Arabia)

Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

Performance Enhancement of a Partially Shaded Photovoltaic Array by Optimal Reconfiguration and Current Injection Schemes

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