Neural network MPPT control scheme with hysteresis current controlled inverter for photovoltaic system

Dubey, Rahul

doi:10.1109/raecs.2014.6799516

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Dubey [24] introduced the neural network-based MPPT system for the generation of high power from the PV system. For the improvement in the quality of the power hysteresis current controller-based inverter with 3 levels were employed.…”

Section: Related Workmentioning

confidence: 99%

Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid

Ghosh

2021

IEEE Access

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The Internet of Things (IoT) is commonly utilized for intelligent energy control, industrial automation, and a host of other applications. IoT sensors are installed in various stages of the smart grid (SG) to track and manage network statistics for safe and efficient power delivery. The challenges in the integration of IoT-SG must be overcome for the network to function efficiently. An IoT-based smart grid energy monitoring system depending on neuro-fuzzy is proposed in this paper. At the core of the operator, a wireless sensor network (WSN) is employed to calculate and transfer the necessary parameters for the prediction model. This project revolves around an IoT-based energy monitor, which can track and analyze electrical parameters, including current, voltage, active power, and load power consumption. For the collection of realtime electrical data from users, the IoT-based program is used. Based on this data, consumers and electric power companies in the SG model can better control their usage to minimize billing costs. The results obtained show that the performance of hybridized solar/wind power plants will be improved with the help of ANFIS controller to a great extent. Results indicate efficiency of 99.74% in the proposed ANFIS control system.

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Section: Related Workmentioning

confidence: 99%

Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid

Ghosh

2021

IEEE Access

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“…The complete system was established and examined in the MATLAB/SIMULINK environment and provided 94.8-99.4% efficiency. In an earlier work, Rahul Dubey [38] reported a 99% efficient simulation model of a neural network MPPT controller integrated with a hysteresis current controlled inverter (HCCI) for a PV system. Jubaer Ahmed et al [10] also reported an improved P&O MPPT algorithm for higher efficiency.…”

Section: Current Research Trends Of Mppt Solar Charge Controllermentioning

confidence: 99%

IoT-Enabled High Efficiency Smart Solar Charge Controller with Maximum Power Point Tracking—Design, Hardware Implementation and Performance Testing

et al. 2020

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Amid growing demand for solar photovoltaic (PV) energy, the output from PV panels/cells fails to deliver maximum power to the load, due to the intermittency of ambient conditions. Therefore, utilizing maximum power point tracking (MPPT) becomes essential for PV systems. In this paper, a novel internet of things (IoT)-equipped MPPT solar charge controller (SCC) is designed and implemented. The proposed circuit system utilizes IoT-based sensors to send vital data to the cloud for remote monitoring and controlling purposes. The IoT platform helps the system to be monitored remotely. The PIC16F877A is used as a main controller of the proposed MPPT-SCC besides implementing the perturb and observe (P&O) technique and a customized buck–boost converter. To validate the proposed system, both simulation and hardware implementation are carried out by the MATLAB/SIMULINK environment and laboratory set up, respectively. The proposed MPPT-SCC can handle the maximum current of 10 A at 12 V voltage. Results show that the efficiency of the proposed system reaches up to 99.74% during a month of performance testing duration.

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“…In Ref. [9], the MPPT control scheme is presented with the help of intelligent neural network algorithm based on a inverter controlled by hysteresis current for photovoltaic systems. These days, the photovoltaic panel is one of the most important sources of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

Zandi

Mazinan

2019

Complex Intell. Syst.

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The use of solar cells despite being free of contamination and unlimited in terms of the amount of energy is considered as a costly way to generate energy. Two main factors may be enumerated as follows. First of all, the amount of sunlight and ambient temperature affected the amount of energy received from sunlight by solar panels, as long as the amount of sunlight changes overnight in line with changing weather conditions and the second one is the low efficiency of the energy conversion. The main reason for the low electrical efficiency is the nonlinear variation of the output voltage and current along with the change of the amount of radiation, the change of the temperature of the operating environment and the change of the electric charge, respectively. To address this concern, the maximum point of the photovoltaic system can be tracked through an appropriate algorithm and pushes the system point to the optimal point. In a word, the key goal of the investigation presented here is to provide an approach that in the high speed and precision of convergence to the maximum power point is well considered. So far, a large number of available methods have been used to increase the efficiency of solar cells. Some of these are associated with problems in the tracking process or they respond slowly. It should be noted that a set of them are depended on the types and structures of solar cells and also their implementation is very complex and costly. Therefore, this study has focused on intelligence-based techniques such as artificial neural networks to solve all the problems mentioned. The investigated outcomes verify the effectiveness of the approach performance proposed.

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Neural network MPPT control scheme with hysteresis current controlled inverter for photovoltaic system

Cited by 9 publications

References 20 publications

Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid

Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid

IoT-Enabled High Efficiency Smart Solar Charge Controller with Maximum Power Point Tracking—Design, Hardware Implementation and Performance Testing

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

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