Smart battery controller using ANFIS for three phase grid connected PV array system

Ismail, Mohamed M.; Bendary, Ahmed F.

doi:10.1016/j.matcom.2018.04.008

Cited by 22 publications

(15 citation statements)

References 15 publications

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“…This explains its performance compared with other standard proofreaders. In reference [10] for example, it is compared with the PI and the PID. The results show that it is better with a response time of 0.4 s and an overshoot of 2.4%.…”

Section: Grid Currents Regulation With Mpidmentioning

confidence: 99%

“…The latter implements a Fuzzy Inference System (FIS) inspired by artificial neural networks. Its use offers the possibility of modeling prior knowledge and linguistic decision rules obtained by experts in the field [10]. Several studies in the literature show that the Adaptive Neuro-Fuzzy Inference System (HNF) method is much more effective than the techniques usually used for MPP research.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of a Hybrid Neuro-Fuzzy Corrector for DC Bus Voltage Regulation

Ndiaye¹,

Ndiaye²,

Faye³

2018

EAI Endorsed Transactions on Energy Web

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The pursuit of the MPP and the control of the inverter play a very important role in a PV system connected to the utility grid. In this paper, an intelligent method is used to regulate the DC bus voltage. The hybrid neuro-fuzzy algorithm HNF type is used. The latter is a combination of fuzzy logic and neural networks. The PV chain, dependent on climatic conditions needs mechanisms to optimize the power it delivers, but also the injection of good quality energy to the utility grid. Among these mechanisms, there is the control of the three-phase inverter: grid currents control and DC bus voltage regulation which is based on the HNF. The implementation of the model and its simulation under Matlab/Simulink indicates that only the active power is injected into the grid. They also reveal that the HNF has a response time (Rt) of 0.097 s, a rise time (rt) of 4.30610-3 s and a THD of 0.85% compared to the modified PID which has a response time of 0.659 s, a rise time of 0.156 s and a THD of 2.74%.

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Section: Grid Currents Regulation With Mpidmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Hybrid Neuro-Fuzzy Corrector for DC Bus Voltage Regulation

Ndiaye¹,

Ndiaye²,

Faye³

2018

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

show abstract

“…In reference [24], ANFIS is proposed for controlling the charging and discharging of a battery for a PV system connected to the three-phase network. The first model is used to control the charging process of the battery while the second manages the injection to the network.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

Ndiaye

Faye

et al. 2020

International Journal of Photoenergy

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This paper presents a method of intelligent control of a photovoltaic generator (PVG) connected to a load and a battery. The system consists of charging and discharging a battery. An intelligent algorithm based on adaptive neuro-fuzzy inference system (ANFIS) is presented in this work. It performs two separate tasks simultaneously. First, it is used as a PVG Maximum Power Point Tracking (MPPT) command. This same algorithm is used secondly for protecting the battery against deep charges and discharges. A regulation of the DC bus voltage is also carried out by means of a PI corrector for a good supply of the load. The simulation results under MATLAB/Simulink show that the method proposed in this work allows the PV system to function normally by charging and discharging the battery whatever the weather conditions.

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“…In [12], the optimal utilization of batteries in hybrid systems is studied and the effectiveness of FNNs is shown. In [13], FNNs are used to increase the power extraction in PV systems and energy saving plan for battery is investigated. In [14], a FNN is learned by bat algorithm and it is applied for PV/battery system and the effect of shading conditions on power extraction is studied.…”

Section: Introductionmentioning

confidence: 99%

Voltage Regulation for Photovoltaics-Battery-Fuel Systems Using Adaptive Group Method of Data Handling Neural Networks (GMDH-NN)

et al. 2020

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Smart battery controller using ANFIS for three phase grid connected PV array system

Cited by 22 publications

References 15 publications

Design and Implementation of a Hybrid Neuro-Fuzzy Corrector for DC Bus Voltage Regulation

Design and Implementation of a Hybrid Neuro-Fuzzy Corrector for DC Bus Voltage Regulation

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

Voltage Regulation for Photovoltaics-Battery-Fuel Systems Using Adaptive Group Method of Data Handling Neural Networks (GMDH-NN)

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