Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system

Soufi, Youcef; Bechouat, Mohcene; Kahla, Sami

doi:10.1016/j.ijhydene.2016.07.212

Cited by 100 publications

(38 citation statements)

References 15 publications

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“…Numerous measurements have been made, in each of which the FLC has been modified considering the membership functions previously determined by the PSO in the offline phase through simulations in Matlab by varying the number of particles (5,10,20,40,50). In addition, three other cases have been taken into account, i.e., in the first considering the mobile board (node) without the FLC implemented in it, the second developing the FLC without the PSO and, finally, the third applying the approach of Castagnetti et al [21].…”

Section: Resultsmentioning

confidence: 99%

“…For instance, in [38], the PSO is introduced for wind turbine converters, in [39,40] for photovoltaic systems, in [41] for neural networks training, in [42] for DC/DC converter, in [43][44][45] for energy management, in [46] for Unmanned Aerial Vehicle (UAV) management and control, in [47] for clustering, and so on. The results shown in all these works confirm that an optimization through the PSO can lead to valuable advantages regarding the performance of approaches based on a fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

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Bluetooth 5 Energy Management through a Fuzzy-PSO Solution for Mobile Devices of Internet of Things

2017

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Abstract:Energy efficiency is a fundamental requirement for a wireless protocol to be suitable for use within the Internet of Things. New technologies are emerging aiming at an energy-efficient communication. Among them, Bluetooth Low Energy is an appealing solution. Recently, the specifications of Bluetooth 5 have been presented with the purpose to offer significant enhancements compared to the earlier versions of the protocol. Bluetooth 5 comes with new communication modes that differ in range, speed, and energy consumption. This paper proposes a fuzzy-based solution to cope with the selection of the communication mode, among those introduced with Bluetooth 5, that allows the best energy efficiency. This communication mode, used by mobile devices, is dynamically regulated by varying the transmission power, returned as the output of a Fuzzy Logic Controller (FLC). A Particle Swarm Optimization (PSO) algorithm is presented to achieve the optimal parameters of the proposed FLC, i.e., optimizing the triangular membership functions, by varying their range, to reach the best results concerning the battery life of mobile devices. The proposed FLC is based on triangular membership functions because they represent a good trade-off between computation cost and efficiency. The paper presents a detailed description of the FLC design, a logical analysis of the PSO algorithm for the derivation of best performance conditions values, and experimental assessments, obtained through testbed scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bluetooth 5 Energy Management through a Fuzzy-PSO Solution for Mobile Devices of Internet of Things

2017

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“…This is because it heavily depends on the good knowledge of a PV system, resulting in incorrect fuzzy rules and inaccurate membership functions. To address this issue, many modulations have been presented, e.g., an adaptive and optimized defined membership function of the traditional FLC-MPPT, as evidenced in [18][19][20]. The ANN is considered another powerful technique for a nonlinear system such as a PV array.…”

Section: Introductionmentioning

confidence: 99%

Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

2019

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Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

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“…This is because it heavily depends on the good knowledge of PV systems, resulting in inaccurate membership functions. To address this issue, many modifications have been proposed, for example, an adaptive and optimized membership function of the traditional FLC-MPPT, as evidence in [7,[15][16][17]. However, the implementation is become over complex.…”

Section: Introductionmentioning

confidence: 99%

Design of an intelligent MPPT based on ANN using a real photovoltaic system data

Al-Majidi

Abbod

Al-Raweshidy

2019

2019 54th International Universities Power Engineering Conference (UPEC)

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Maximum power point tracking (MPPT) methods are an essential part in photovoltaic (PV) system design for increasing the output power of a PV array. Whilst several methods have been introduced, the artificial neural network (ANN) is one of the most powerful method for MPPTs due to its less oscillation and fast response. However, accurate training data is a big challenge to design an optimized ANN-MPPT technique. In this paper, an ANN-MPPT technique based on a large experimental training data is proposed to avoid the system from having a high training error. Those data are collected during one year from experimental tests of a grid-connected PV system installed at Brunel University, London, United Kingdom. The irradiance and temperature of weather conditions are selected as the input, and the maximum power from the PV system as the output of the ANN model. To assess the performance, the Perturb and Observe (P&O) and the proposed ANN-MPPT methods are simulated using a MATLAB/Simulink model for a PV system. The results show that the proposed ANN method accurately tracks the optimal maximum power point and avoids the phenomenon of drift problem, whilst achieving a higher output power when compared with P&O-MPPT method.

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Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system

Cited by 100 publications

References 15 publications

Bluetooth 5 Energy Management through a Fuzzy-PSO Solution for Mobile Devices of Internet of Things

Bluetooth 5 Energy Management through a Fuzzy-PSO Solution for Mobile Devices of Internet of Things

Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Design of an intelligent MPPT based on ANN using a real photovoltaic system data

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