Online Control Using ANN-PI Controller for Single Stage Transformerless Grid Tied PV System

Chigane, Khalid; Ouassaid, Mohammed

doi:10.1109/isaect.2018.8618843

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Since solar is DC in nature whereas grid is AC in nature. So, we need to incorporate inverter control technology for converting DC generated from PV into AC to interface it with grid (K. Chigane and M. Ouassaid., 2018;Ritesh Dash, and Sarat Chandra Swain, 2018).…”

Section: Proposed Methods For Inverter Controlmentioning

confidence: 99%

Inverter Control Strategy for Grid Assembled Photovoltaic System Using Artificial Neural Network

Kumari,

Sahay,

Kumar

2022

INTERNATIONAL CONFERENCE ON POWER ELECTRONICS &Amp; SUSTAINABLE DEVELOPMENT (ICPESD)

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Renewable energy sources (RES) are grooming especially when we need a clean and green energy. It will reduce the dependency on fossil fuels and also reduces the environmental pollution. To improve efficiency in power system numerous techniques and optimization algorithms are available. But with each one some limitations and its complexities are there because they are time consuming due to heavy computation burden, large number of iterations, need heavy computation and slow convergence. So, a proficient method is required for tackling this issue. Therefore, we need to implement an efficient technique for solving this problem. This paper presents the inverter control strategy using Artificial neural network for grid assembled photovoltaic system. The three-phase inverter have been simulated by using ANN technique to mitigate the problems of voltage instability, harmonics distortion in case of constant irradiance, variable irradiance and faulty condition also. The obtained output in the proposed system reduces the harmonics to a large extent and it will also eliminate the power quality issues by inoculating the real or reactive power with no or very less distortions than the one in traditional system.

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Section: Proposed Methods For Inverter Controlmentioning

confidence: 99%

Inverter Control Strategy for Grid Assembled Photovoltaic System Using Artificial Neural Network

Kumari,

Sahay,

Kumar

2022

INTERNATIONAL CONFERENCE ON POWER ELECTRONICS &Amp; SUSTAINABLE DEVELOPMENT (ICPESD)

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“…In order to control the active and reactive powers, using Sliding mode control, the tracking error signals, including the reference variables I odref and I oqref are expressed as follows: e d = I odre f − I od e q = I oqre f − I oq (11) Using the same sliding surface formula as before, the derivative of ( 11) using (3), can be written as:…”

Section: Grid Side Control Designmentioning

confidence: 99%

“…Furthermore, numbers of nonlinear and intelligent control methods have been adopted for the grid tied inverter such as Backstepping control [7,8], Predictive Current control [9], Fuzzy Logic [10] and Artificial Neuron Network [11,12]. Regardless of their complexity and computational burden, these control strategies try to enhance the power quality and ensure a better dynamic response in addition to the benefits of the conventional controllers [6,13].…”

Section: Introductionmentioning

confidence: 99%

Voltage and Power Control for a Grid Tied Single Phase Single Stage Transformer-Less Photovoltaic System Using Sliding Mode Control

Chigane

Ouassaid

2021

Lecture Notes in Electrical Engineering

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This study presents a Sliding mode control (SMC) strategy applied to a single-phase grid connected photovoltaic plant. The proposed controller is designed in such a way to control the DC voltage, active and reactive power smoothly, and with insensitivity to system parameter variations and external perturbations. The global asymptotic stability of the developed control laws is guaranteed employing Lyapunov stability theory. So as to verify the effectiveness of the proposed controller, a simulation based comparative study has been carried out under a change in solar irradiance and ambient temperature. The results demonstrate that the SMC provides better dynamic performance, great decoupling of active and reactive power, good reference tracking and improved power quality even when facing operating point variation comparing with the conventional linear controller.

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“…Neural network PI control is used in many modern control industrial systems [9][10][11], such as high-voltage DC power transmission [12], new energy photovoltaic power generation [13], speed control of motors, permanent magnet synchronous motor drives [14], and some robot control systems with nonlinear multiple intelligence [15]. In the application of neural network PI control, many innovations have been made in combination with the advantages of other control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Current Control of EAST Fast Control Power Supply Based on BP Neuron Network Predictive PI

Chen

Huang

Wang

2022

Preprint

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The dynamic response speed of output current is one of the most important performance indexes of EAST fast control power supply. To further improve the response speed of output current, a predictive PI control algorithm of BP neural network is proposed to overcome the shortcomings of large computation and slow convergence in traditional BP neural network. A branch current prediction model of EAST fast control power supply is established. Based on linear PI control, the BP neural network structure is combined, and the current prediction model is used as the adaptive objective function. To simplify the calculation process of the processor and speed up the convergence rate, a piecewise linear activation function is adopted. The learning rate of BP neural network structure and the activation function factor of the output layer are adjusted in real time according to the error between the predicted current and the reference signal. The online predictive adaptive setting of PI control parameters is realized by the steepest gradient descent method. Simulation and experiments results show that the proposed control algorithm has faster dynamic response speed than the traditional PI control.

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Online Control Using ANN-PI Controller for Single Stage Transformerless Grid Tied PV System

Cited by 7 publications

References 22 publications

Inverter Control Strategy for Grid Assembled Photovoltaic System Using Artificial Neural Network

Inverter Control Strategy for Grid Assembled Photovoltaic System Using Artificial Neural Network

Voltage and Power Control for a Grid Tied Single Phase Single Stage Transformer-Less Photovoltaic System Using Sliding Mode Control

Current Control of EAST Fast Control Power Supply Based on BP Neuron Network Predictive PI

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