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

Ndiaye, El Hadji Mbaye; Ndiaye, Ababacar; Faye, M.; Gueye, Samba

doi:10.1155/2020/8649868

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…One study showed that effective battery power management is possible with an ANFIS controller in a hybrid standalone system [28]. Some researchers worked on battery power control of photovoltaic generators with the ANFIS controller [29]. Another work is done on electric power swap between PEV and electrical grid by using ANFIS controller in smart grid [30].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Performance Evaluation of ANFIS Controller-Based Bidirectional Power Management Scheme in Plug-In Electric Vehicles Integrated With Electric Grid

et al. 2021

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A bi-directional power exchange between the plug-in electric vehicle (PEV) and the AC electrical grid is necessary to perform the Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations. While performing these operations, different power converters and controllers play an important role as mediators between the PEV and electric grid. Various works have demonstrated the utilization of controllers for PEV's battery power management. However, the existing conventional controllers have technical shortcomings about vulnerability to controller gain, accurate mathematical modelling, poor adaptability, sluggish response to a sudden outburst and lengthy interval execution processing. Therefore, this paper develops an adaptive neuro-fuzzy inference system (ANFIS) control strategy based bidirectional power management scheme to ensure the optimal electrical power flow exchange between the AC electric grid and battery storage system in PEVs. This paper aims to reduce the stress on the grid power side and utilize the unused power properly. The performance of the ANFIS model is varied using two PEVs based on real-life power consumptions by different loads at home based on five operational modes. Besides, a comparative analysis between the ANFIS controller and the PI controller is carried out to demonstrate the effectiveness of the proposed control scheme. The results illustrate that the proposed ANFIS controller delivers a smoother power injection from the PEV to the AC power grid with the least harmonics as well as achieves a smoother battery profile and less distortion when power is absorbed by PEV battery.INDEX TERMS ANFIS controller, plug-in electric vehicle, bidirectional power, state of charge, PI controller.

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Section: Introductionmentioning

confidence: 99%

Modeling and Performance Evaluation of ANFIS Controller-Based Bidirectional Power Management Scheme in Plug-In Electric Vehicles Integrated With Electric Grid

et al. 2021

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“…Today's world sees rapid industry development and this makes us more energy dependent. Fossil sources occupy the largest share of electricity production [1], [2]. The use of conventional energies (around 87% of global energy consumption) has an undesirable impact on the environment in terms of greenhouse gas (GHG) emissions and safety (nuclear accidents).…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Between Three Methods for Optimizing the Power Produced from Photovoltaic Generator

Ndiaye¹,

Faye²,

Ndiaye³

2020

Adv. sci. technol. eng. syst. j.

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“…The use of fossil resources inevitably reduces the reserves and compromises the energy future of the next generations. As a result, the global challenge is focused on renewable energies (solar, wind, hydro, geothermal, biomass,...) [1].…”

Section: Introductionmentioning

confidence: 99%

“…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. HNF has a very low response time and fewer oscillations around the MPP than other conventional methods [1]. In this work, the InC method is used for maximum power point tracking (MPPT) controller.…”

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

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

Cited by 11 publications

References 20 publications

Modeling and Performance Evaluation of ANFIS Controller-Based Bidirectional Power Management Scheme in Plug-In Electric Vehicles Integrated With Electric Grid

Modeling and Performance Evaluation of ANFIS Controller-Based Bidirectional Power Management Scheme in Plug-In Electric Vehicles Integrated With Electric Grid

Comparative Study Between Three Methods for Optimizing the Power Produced from Photovoltaic Generator

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

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