Comparative Study of Perturb-and-Observe and Fuzzy Logic MPPT for Stand-Alone PV System

Hichem, Louki; Leila, Merabet; Omeiri, Amar

doi:10.1007/978-3-030-92038-8_27

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…One of the most successful MPPT optimization strategies is FL. Numerous studies, including [31]- [42], have used FL to make the PV work around the MPP [36]. As shown above, climate is the primary effect on the non-linear features of the I-V and P-V characteristics.…”

Section: Fuzzy Logic Techniquementioning

confidence: 99%

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Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

2023

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Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.

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Section: Fuzzy Logic Techniquementioning

confidence: 99%

“…The FLC's primary steps can be divided into three categories: fuzzification, inference rules, and defuzzification. A FL controller composed of two inputs, one output, and 25 rules shown in Table 1 [36] is proposed. The synoptic scheme of the PV system using the FL MPPT technique is shown in Figure 5.…”

Section: Fuzzy Logic Techniquementioning

confidence: 99%

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

2023

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“…The current and voltage properties of a solar panel are nonlinear and depend on the solar radiation, and for a set amount of solar radiation, there is a maximum power point corresponding to the voltage at which the PV can provide its maximum energy. This study uses a disturb and observe method for MPPT control [30]. The general scheme of the proposed controller is shown in Figure 2.…”

Section: System Descriptionmentioning

confidence: 99%

Distributed Secondary Control for Battery Management in a DC Microgrid

et al. 2022

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This research presents the design and simulation of a distributed secondary control based on a consensus algorithm for the efficient management of an isolated DC microgrid (MG-DC) that secures the distribution of active power according to the capacities of each storage unit, reducing duty cycles and extending its life cycle. The balance of powers is fulfilled through a photovoltaic (PV) generation unit and an energy storage system (ESS) based on batteries. The PV Boost converter has a maximum power point tracking (MPPT) controller based on the perturb and observe (P & O) method. In contrast, a Buck–Boost converter is integrated into each battery, which is bidirectionally controlled through a local controller and a primary droop control that balances the required power at the loads. It produces a voltage deviation on the DC bus. To compensate for this deviation, a distributed secondary control strategy based on consensus is proposed to restore the voltage while managing the power sharing according to the capacities of each battery. It allows for the improvement of its life cycle, which is shown in the state of charge (SOC) index, thus extending its life cycle. The controllers are evaluated for solar re-source changes, load changes, and different storage capacities.

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Comparative Study of Perturb-and-Observe and Fuzzy Logic MPPT for Stand-Alone PV System

Cited by 2 publications

References 16 publications

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Distributed Secondary Control for Battery Management in a DC Microgrid

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