Artificial Neural Network for Control and Grid Integration of Residential Solar Photovoltaic Systems

Sun, Yang; Li, Shuhui; Lin, Bo; Fu, Xingang; Ramezani, Malek; Jaithwa, Ishan

doi:10.1109/tste.2017.2691669

Cited by 118 publications

(43 citation statements)

References 28 publications

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“…In this sense, the estimation performed by the GA does not present this deviation making it a more reliable technique. On the other hand, the weights from Table 2 were substituted into (6) to obtain the mathematical model for the THD forecasting. Once again, the result of the estimation performed by the GA is compared with the estimation performed by an ANN.…”

Section: Experimentation Resultsmentioning

confidence: 99%

“…However, a more important fact is that the proposed approach can also describe the THD associated with the generation process. Having a priori knowledge of the quality of a generation process is important from the smart grid point of view in order to guarantee a On the other hand, the weights from Table 2 were substituted into (6) to obtain the mathematical model for the THD forecasting. Once again, the result of the estimation performed by the GA is compared with the estimation performed by an ANN.…”

Section: Experimentation Resultsmentioning

confidence: 99%

“…Notwithstanding, there are some challenges and disadvantages associated with photovoltaic generation, for instance, the generated power is unpredictable because it is highly dependent on environmental factors like the incident solar irradiance and the temperature [3,4]. Additionally, it has been reported that the inclusion of PV generation is associated with harmonic contamination [5,6], because it is necessary to use power inverters for converting the DC signals delivered by the PV panels into AC signals, which can be used by the final users. It is necessary to note that even small variations in the background conditions may lead to a severe harmonic contamination, making the constant detection of these variations essential [7].…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation

et al. 2020

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Renewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.

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Section: Experimentation Resultsmentioning

confidence: 99%

Section: Experimentation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation

et al. 2020

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“…The quantities of guidelines are [9] for the three membership function elements of the error and the adjustment in error. The proposed ANFIS based MPPT works faster and simple compared to other MPPT techniques [10][11][12]. The nine rules of ANFIS editor has shown in below table [1].…”

Section: Icm Mpptmentioning

confidence: 99%

ICM based ANFIS MPPT controller for grid connected photovoltaic system

Naidu

Meikandasivam²

2018

IJET

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In this paper, grid-connected photovoltaic (PV) system is presented. PV system consists of a photovoltaic module, a boost converter, and voltage source inverter. ANFIS based ICM (Incremental Conductance Method) MPPT (Maximum Power Point Tracking) controller is utilized to produce gate signal for DC-DC boost converter. This controller is used for optimizing the total performance of the Photovoltaic system in turn the errors were reduced in Voltage Source Inverter (VSI). The grid-connected PV system performance is evaluated and harmonics occurred in the system are decreased. The proposed methodology is implemented in MATLAB/Simulink.

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“…The design of the inverter will have different control methods and control targets according to the differences of the homologous characteristics. Besides V/f control and PQ control, droop control is another common control method (Sun et al, 2017). Figure 2 shows PQ control principle.…”

Section: Three-level Integrated Control Strategy Three-level Control mentioning

confidence: 99%

Integrated Control System for Three-level Inverters based on Artificial Neural Network

Deng

Zhang

2018

Neuroquantology

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In the actual operation of power grid, inverter is the access of source and power grid, and is an important control link affecting power quality. The three-level inverter has very strong nonlinearity, and the loading of nonlinear load produces large harmonic, which seriously affects the transmission quality. The artificial neural network has good fault tolerance and adaptive ability when dealing with the nonlinear system. Based on the artificial neural network algorithm, the three-level inverter control system is designed and simulated. The results show that the neural network control can deal with the nonlinear and uncertainty of the three level inverter control system. With constant voltage and constant frequency, the fluctuation of the output voltage and frequency is within controllable range. The three-level inverter controlled by artificial neural network shows smaller fluctuation and faster adjustment speed. The steady-state error of artificial neural network control is smaller than that under the control of PID. The use of artificial neural network control can greatly improve the dynamic and static capacity of the inverter system.

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Artificial Neural Network for Control and Grid Integration of Residential Solar Photovoltaic Systems

Cited by 118 publications

References 28 publications

Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation

Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation

ICM based ANFIS MPPT controller for grid connected photovoltaic system

Integrated Control System for Three-level Inverters based on Artificial Neural Network

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