scholarly journals A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Hichem Hamdi ◽  
Chiheb Ben Regaya ◽  
Abderrahmen Zaafouri
Keyword(s):  
Neural Network ◽  
Vector Control ◽  
Rural Areas ◽  
Sliding Mode ◽  
Optimization Techniques ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Renewable Sources ◽  
Pumping System ◽  
Power Point

AbstractEnergy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.

scholarly journals Robust Hybrid MPPT Controller for PV Pumping System

2019 ◽  
Vol 9 (2) ◽  
pp. 4012-4019
Keyword(s):  
Sliding Mode ◽  
Maximum Power ◽  
Duty Ratio ◽  
Maximum Speed ◽  
Hybrid Technique ◽  
Maximum Power Point ◽  
Power Circuit ◽  
Pumping System ◽  
Point Tracking ◽  
Power Point

This article suggests a maximum power point tracking (MPPT) method for a photovoltaic (PV) pumping system based on a nonlinear robust method combining the backstepping and the sliding mode techniques, which is referred to us as the BSMC controller. The system’s power circuit comprises of a solar panel, a step-up converter and a DC motor feeds a water pump. Two loops are in the control system: the first, provides the reference voltage, that is given by intelligent method based on artificial neural network (ANN), according with the maximum power point (MPP), to the BSMC in the second loop that regulates the PV array voltage in MPP and allows the converter to produce the required power to set the motor at the maximum speed. This is done through adjustment of the DC-DC boost converter duty ratio. The system is, on one side, able to predict the desired optimal voltage quickly by using the ANN, and also to prevent unnecessary calculation and research of the MPP. On the other side, the sliding mode and the backstepping controllers are used to provide good performance and robustness against rapid changes in the insolation and temperature. Also, the system’s asymptotic stability is proven by lyapunov’s functions. The proposed approach is compared to the method, P&O, IC and the hybrid technique ANN-integral sliding mode controller. Simulation results depict the proposed regulator effectiveness and robustness in relation to rapidly irradiance and temperature changes, using Maltab/Simulink

scholarly journals A Neural Network and Fuzzy Logic based MPPT Algorithm for Photovoltaic Pumping System

2018 ◽  
Vol 9 (4) ◽  
pp. 1823 ◽  
Author(s):  
Salwa Assahout ◽  
Hayat Elaissaoui ◽  
Abdelghani El Ougli ◽  
Belkassem Tidhaf ◽  
Hafida Zrouri
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fuzzy Controller ◽  
Boost Converter ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Pumping System ◽  
Point Tracking ◽  
Artificial Neural Network Ann ◽  
Power Point

<p><span lang="EN-US">The use of solar energy had gained a great attention last decades, as it is pollution-free. It is used in isolated areas for lighting, pumping, etc. However, the extraction of the maximum power generated by a PVG at any moment of the day is a big deal because the characteristic of a PVG in non-linear which makes the location of the Maximum Power Point (MPP) difficult. Therefore, a Maximum Power Point Tracking technique (MPPT) is required to maximize the output power.<strong> </strong>In this paper, a photovoltaic water pumping system has been studied. This system consists of three main parts: PVG, a DC-DC boost converter and a DC motor coupled with a centrifugal water pump. We have proposed a new MPPT algorithm based on Fuzzy logic and Artificial Neural Network (ANN) to improve the system performances. The ANN is used to predict the optimal voltage of the PVG, under different environmental conditions (temperature and solar irradiance) and the fuzzy controller is used to command the DC-DC boost converter. The proposed method is compared to P&amp;O technic, by simulation under Matlab/Simulink, to verify its effectiveness. </span></p>

scholarly journals A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3260
Author(s):  
Ming-Fa Tsai ◽  
Chung-Shi Tseng ◽  
Kuo-Tung Hung ◽  
Shih-Hua Lin
Keyword(s):  
Neural Network ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Irradiation Cell ◽  
Power Point ◽  
Neural Network Compensator

In this study, based on the slope of power versus voltage, a novel maximum-power-point tracking algorithm using a neural network compensator was proposed and implemented on a TI TMS320F28335 digital signal processing chip, which can easily process the input signals conversion and the complex floating-point computation on the neural network of the proposed control scheme. Because the output power of the photovoltaic system is a function of the solar irradiation, cell temperature, and characteristics of the photovoltaic array, the analytic solution for obtaining the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this work is to obtain the maximum power of the photovoltaic system using a neural network with the idea of transferring the maximum-power-point tracking problem into a proportional-integral current control problem despite the variation in solar irradiation, cell temperature, and the electrical load characteristics. The current controller parameters are determined via a genetic algorithm for finding the controller parameters by the minimization of a complicatedly nonlinear performance index function. The experimental result shows the output power of the photovoltaic system, which consists of the series connection of two 155-W TYN-155S5 modules, is 267.42 W at certain solar irradiation and ambient temperature. From the simulation and experimental results, the validity of the proposed controller was verified.

scholarly journals Recurrent Neural Network Based MPPT Control of Grid Connected DFIG for Wind Turbine

2020 ◽  
Vol 8 ◽  
pp. 1-10
Author(s):  
Mohsen Davoudi ◽  
Amin Kasiri Far
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Recurrent Neural Network ◽  
High Efficiency ◽  
Optimal Algorithm ◽  
Maximum Power ◽  
Rotor Speed ◽  
Maximum Power Point ◽  
Control Command ◽  
Power Point

This paper presents a new maximum-power-point-tracking (MPPT) controller in wind power generation using artificial neural networks (ANN) in order for making the wind turbine function in optimum working point and get high efficiency of wind energy conversion at different conditions. The algorithm uses fully connected recurrent neural network and is trained online using real-time recurrent learning (RTRL) algorithm in order to avoid the oscillation problem in wind-turbine generation systems. It generates control command for speed of the rotor side converter using optimal algorithm to enable the control system in order to track the maximum power point. The rotor speed and wind-turbine torque are the inputs of the networks, and the command signal for the rotor speed of wind turbine is the output. Simulation results verify the performance of the proposed algorithm.

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