Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller

Subiyanto, Subiyanto; Mohamed, Azah; Hannan, M. A.

doi:10.1016/j.enbuild.2012.04.012

Cited by 112 publications

(44 citation statements)

References 15 publications

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“…To deal with this issue, a variety of artificial intelligent (AI) methods have been proposed in the literatures to obtain the optimal configuration of the MFs. These AI methods include GA, PSO and Hopfield ANN [15][16][17]24]. According to the experimental results, these AI approaches can acquire the optimal configuration in most cases.…”

Section: Grid Search Methods To Determine the Uod Of ∆P Pvmentioning

confidence: 99%

“…Therefore, additional considerations are required during implementation. In contrast, [20][21][22][23][24] take power variation (ΔP pv ) and voltage or current variation (ΔV pv or ΔI pv ) as inputs, which avoids the precision loss and overflow problem when dealing with fixed-point division, thus simplifies the calculation. The inputs in [25] are dP PV /dI PV and e(t) (defined as P MPP − P PV ), while the inputs in [26] are error [e(t)] (defined as P MPP − P PV ) and error variation [Δe(t)].…”

Section: Introductionmentioning

confidence: 99%

“…In order to deal with this issue, genetic algorithm (GA), particle swarm optimization (PSO) and Hopfield artificial neural network (ANN) are proposed in the literatures to optimize the FLC MFs [15][16][17]24]. As for control schemes, typical dual-input fuzzy logic controller is adopted by [14][15][16][17][18][19][20][21][22][23][24][25][26]. These controllers employ nine to 49 rules in the rule table, depending on the numbers of linguistic variables in input MFs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems

et al. 2014

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Abstract:In this paper, a fuzzy-logic-control (FLC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is proposed. The power variation and output voltage variation are chosen as inputs of the proposed FLC, which simplifies the calculation. Compared with the conventional perturb and observe (P&O) method, the proposed FLC-based MPPT can simultaneously improve the dynamic and steady state performance of the PV system. To further improve the performance of the proposed method, an asymmetrical membership function (MF) concept is also proposed. Two design procedures are proposed to determine the universe of discourse (UOD) of the input MF. Comparing with the proposed symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are further improved by 42.8% and 0.06%, respectively.

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Section: Grid Search Methods To Determine the Uod Of ∆P Pvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems

et al. 2014

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“…Sugeno carried out further research in this area. The result of their work is the fuzzy logic controller (FLC), which has now become world famous [20][21][22]. The main advantage of the FLC is that it can be applied in situations where the controlled body is too complex or difficult to be mathematically modeled.…”

Section: Intelligent Fuzzy Logic Controllermentioning

confidence: 99%

Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System

2016

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This paper proposes the development of a two-axis sun tracking solar energy system using fuzzy logic as intelligent quality policy. To achieve maximum efficiency for solar panels, it is necessary to follow the sun's path in the sky. Therefore, the architecture for the two-axis sun tracking solar energy system uses software to control the hardware. The hardware comprises (i) solar cells; (ii) lead-acid batteries; (iii) a gear box; (iv) a stepping motor; and (v) a light detection circuit, while the software comprises (i) a detection system; (ii) a fuzzy tracking controller; and (iii) a database system. A fuzzy logic controller is designed as the software architecture of the system to decide the timing for tracking the sun. The nearest position that results in receiving direct sunlight is obtained from the database. Our system is fully automatic in a changing environment and takes into account meteorological changes and the effects of the external environment arising from a malfunction. This approach reduces the number of starting motors and results in smaller energy loss in cloudy, cloud mask, or unstable weather conditions.

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“…Such non-linear and non-minimum phase characteristics further confuse the MPPT of the boost converter [5]. To overcome these problems, different conventional and intelligent MPPT algorithms have been proposed such as Incremental Conductance (IC) [6][7][8], Open Circuit Voltage (OCV) [9], Short Circuit Current (SCC) [10], Perturb and Observe (P&O) [11], fuzzy logic [12][13][14][15], feedback linearization [16], neural network [17][18][19][20][21][22], neuro-fuzzy [23][24][25] and sliding mode [26,27]. Nevertheless, there still remains the concern of fast and accurately determining the locus of the MPP during high weather variations and external load changes occurring.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

Hassan

Kamal

et al. 2017

Energies

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An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

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Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller

Cited by 112 publications

References 15 publications

An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems

An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems

Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

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