A direct adaptive neural control for maximum power point tracking of photovoltaic system

Dounis, Anastasios I.; Kofinas, Panagiotis; Papadakis, G.; Alafodimos, C.

doi:10.1016/j.solener.2015.02.004

Cited by 42 publications

(18 citation statements)

References 18 publications

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“…The SN-RBFN's inputs are: the instantaneous conductance (I/V), the incremental conductance (ΔI/ΔV), and the reference voltage error (ΔVpvref(k)=Vpvref(k)-Vpvref(k-1)). In this paper, a supervised learning rule based gradient descent method [11,23] is adopted for the online update of the SN-RBFN parameters. The objective function used for the weights adaptation is defined as:…”

Section: A Mppt Control Of Pv Generatormentioning

confidence: 99%

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Chettibi

Mellit

Sulligoi

et al. 2016

IEEE Trans. Smart Grid

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-In this paper, the behavior of a grid-connected hybrid AC/DC Microgrid has been investigated. Different Renewable Energy Sources -photovoltaics modules and a wind turbine generator -have been considered together with a Solid Oxide Fuel Cell and a Battery Energy Storage System. The main contribute of this work is the design and the validation of an innovative online-trained artificial neural network based control system for a hybrid microgrid. Adaptive Neural Networks are used to track the Maximum Power Point of renewable energy generators and to control the power exchanged between the Front-End Converter and the electrical grid. Moreover, a fuzzy logic based Power Management System is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the Matlab/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system. OWADAYS, the wide diffusion of distributed RES presents a new scenario for the regulation of distribution networks and the availability of new technologies for storage systems encourages their use in power systems [1]. In general, a hybrid AC/DC MG integrates different Distributed Generators (e.g. solar power sources, wind power generators, cogenerators, etc.), a energy storage system and a number of AC and DC loads. A FEC can interface the MG with the electric grid and can operate either in a grid-connected or islanded mode. The use of a PMS is crucial to optimize the power flow through the different components of the MG and the exchange of energy with the electric grid. Moreover, since the power produced by RESs depends on the climatic conditions, MPPT algorithms are needed in order to harvest the maximum available energy. The intermittent nature of RESs with the time-varying loads demand make the use of advanced control structures fundamental in order to make the operation of the MG reliable, economic, and secure under different operating conditions. The MG must also guarantee a high quality power supply to both local loads and electrical grid. Index TermsMany works have focused on hybrid microgrids and have proposed a number of control schemes for different mode of operations [2]- [8]. A multiagent-based energy management system to optimizes the economic operation of a MG is presented in [2]. A reactive power sharing algorithm in hierarchical droop control is developed in [3]. A novel coordinated voltage control scheme with islanding capability for a MG is proposed in [4]. adapt to uncertainties and they can be used also when the model of the system to be controlled is not available. Recently, the NNs with the learning capability are widely applied for the control of complex power systems. In [9], a back-propagation NN is applied for the real-time estimation of the wind speed. A novel discrete-time NN controller for the control of DC distribution system is designed in [10]. In [11], a...

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Section: A Mppt Control Of Pv Generatormentioning

confidence: 99%

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Chettibi

Mellit

Sulligoi

et al. 2016

IEEE Trans. Smart Grid

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“…Large photovoltaic power stations have been built in places with sufficient light, such as Xinjiang and Tibet [1]. Since the electric energy generated by solar cells always changes with the change of solar cell temperature and irradiance, it is necessary to track the maximum power point of photovoltaic system effectively [2].At present, there are many MPPT methods, such as disturbance and observation algorithm [3][4][5], conductance increment algorithm [6][7], neural network method [8][9][10] and fuzzy logic algorithm [11][12][13][14][15].This type of control mode may have a series of effects, including large scale delay, more inaccurate detection circuits and sensors, and power oscillations under low radiation conditions.According to the nonlinear output characteristics of photovoltaic cells, the MPPT control algorithm based on fuzzy variable step size is proposed, which enables the system to quickly track the maximum power point and improve the energy conversion efficiency of photovoltaic system. This paper designs a small photovoltaic power generation system, including the main circuit and control part of the system, and Proteus simulation software is used for simulation.…”

Section: Introductionmentioning

confidence: 99%

Design of Small Photovoltaic Power Generation System Based on Maximum Power Point Tracking

et al. 2020

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According to the nonlinear output characteristics of photovoltaic cells, combined with artificial intelligence algorithmthe the MPPT (Maximum Power Point Tracking) control algorithm based on fuzzy variable step size is proposed, which enables the system to quickly track the maximum power point and improve the energy conversion efficiency of photovoltaic system.This paper designs a small-scale photovoltaic power generation system. The main circuit of the system consists of Perovskite Solar Panels, DC voltage regulator circuit, storage battery and one-way full bridge inverter circuit. The control circuit consists of sun-seeking, inverter and maximum power tracking on constant voltage. Proteus simulation software is used to simulate the sun-seeking part, the inverting part, the general control unit, the keys and the display interface. The results indicate that the functions of the small-scale photovoltaic power generation system can be achieved very well.

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“…To overcome this problems, the FLC has introduced to track accurately the MPP by using three steps: fuzzification, fuzzy rule base table and defuzzification [10]. However, the tracking accuracy depends on the number of the member functions, so the ANN [6], [11] is proposed in order to solute this problem. In spite of the higher operating efficiency, the ANN should be regularly trained because the PV characteristic varies to its life.…”

Section: Introductionmentioning

confidence: 99%

Development of PSO for tracking Maximum Power Point of Photovoltaic Systems

Thanh

Nhu

Dong³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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For a photovoltaic system, the relationship of the output voltage and power is usually non-linear, so it is essential to equip a MPPT controller in PV systems. Furthermore, the hotspot problem is a common phenomenon, resulting from the PV system operating under PSC. Partial shading not only damages the PV cells, but also makes it difficult to find the global MPP in the characteristic curves of P-V. The paper proposes a novel version of PSO, namely PPSO in order to detect the global peak among the multiple peaks, known as the true maximum energy from PV panel. For this, the PPSO algorithm makes the velocity of each particle be perturbed once the particles are struck into a local minima state in order to find the best optimum solution in the MPPT problem. The perturbation in the velocity vector of each particle not only helps them tracking the MPP accurately under the changing environmental conditions, such as large fluctuations of insolation and temperature like PSC; but also removes the steady-state oscillation. The proposed approach has been tested on a MPPT system, which controls a dc-dc boost converter connected in series with a resistive load. Moreover, the obtained results are compared to those obtained without any MPPT controller to prove the efficiency of the suggested method. In addition, this novel version gives the highest accuracy of tracking the optimum power in the least iteration number as compared to the conventional PSO.

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A direct adaptive neural control for maximum power point tracking of photovoltaic system

Cited by 42 publications

References 18 publications

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Design of Small Photovoltaic Power Generation System Based on Maximum Power Point Tracking

Development of PSO for tracking Maximum Power Point of Photovoltaic Systems

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