An Efficient ANN-Based MPPT Optimal Controller of a DC/DC Boost Converter for Photovoltaic Systems

Makhloufi, Mohamed Tahar; Abdessemed, Y.; Khireddine, Mohamed Salah

doi:10.7305/automatika.2016.07.798

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…It mainly adjusts the connection weights of each neuron through network training, model the input data of the input layer, and store the learned knowledge in the adjusted connection weights. Finally, the network is able to solve practical problems [20].…”

Section: Photovoltaic System Optimized By Artificial Neural Networkmentioning

confidence: 99%

Optimization and Simulation of Photovoltaic MPPT Control Strategies

2022

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Power generation based on solar photovoltaic principle has developed rapidly in recent years due to its advantages of no pollution and inexhaustible resources. The output characteristics of solar photovoltaic are susceptible to external environmental factors, therefore, the maximum power point needs to be tracked in real time so that the PV system works continuously at the maximum power point. In order to improve the speed and stability of the PV system tracking and reduce the power loss in the steady state, the perturbation and observation (P&O) method which is used commonly in Maximum Power Point Tracking (MPPT) control method is selected for analysis in this paper. In view of the shortcomings of its existing control methods, this paper will analyze and compare the algorithms based on three perturbation and observation strategies: fixed-step, variable-step and artificial neural networks. The simulation results indicate that the perturbation and observation method of variable-step and neural network track strategy are relatively fast, and the neural network control strategy is the most stable after tracking to the maximum power point.

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Section: Photovoltaic System Optimized By Artificial Neural Networkmentioning

confidence: 99%

Optimization and Simulation of Photovoltaic MPPT Control Strategies

2022

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“…There are many machine learning techniques used in the literature for estimation purpose. The ANN is one of the mostly used methods, and it is a method with a high accuracy rate 23–30 …”

Section: Introductionmentioning

confidence: 99%

“…The ANN is one of the mostly used methods, and it is a method with a high accuracy rate. [23][24][25][26][27][28][29][30] The output voltage and current of photovoltaic (PV) systems, one of the alternative energy resources, have been estimated through ANN, and maximum power point tracking (MPPT) algorithm has been developed. [23][24][25] In another study carried out for PV systems, 26 the efficiency of the boost converter is increased for optimum MPPT control by developing an estimation model with ANN.…”

mentioning

confidence: 99%

“…[23][24][25][26][27][28][29][30] The output voltage and current of photovoltaic (PV) systems, one of the alternative energy resources, have been estimated through ANN, and maximum power point tracking (MPPT) algorithm has been developed. [23][24][25] In another study carried out for PV systems, 26 the efficiency of the boost converter is increased for optimum MPPT control by developing an estimation model with ANN. A control strategy is suggested by estimating an output voltage with ANN in hybrid-EV technology.…”

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confidence: 99%

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A novel zero voltage transition boost converter and artificial neural network‐based estimation of converter efficiency

Tınğ

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Şahin

2022

Circuit Theory & Apps

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In this paper, a novel zero voltage transition (ZVT) boost converter is proposed, and the overall efficiency of the converter is predicted with an artificial neural network (ANN) model. In the proposed converter, the main switch is turned on by ZVT and turned off by zero voltage switching (ZVS). Also, the other semiconductor elements operate by soft switching (SS). Besides, the proposed snubber cell has the bidirectional direct power transfer feature. The theoretical analyzes of the converter are verified by an prototype having 50 V DC input voltage, 100 V DC output voltage, 250 W output power, and 100 kHz switching frequency. The overall efficiency of the converter in hard switching (HS) condition is increased from 87.2% to 95.4% thanks to proposed snubber cell. Moreover, the efficiency of converter at HS operation is estimated with ANN. For this estimation, 110 efficiency values are obtained based on the different switching frequency and the output power values. When the actual efficiency measurements and the estimation results obtained with the ANN model are compared, it is seen that the results overlap and is obtained very close result to the truth by ANN. Thus, owing to the ANN model, the semiconductor power elements will not need to be operated at high frequencies and overheating, and the damaging to the elements will be prevented. Finally, the efficiency curve measurement of the converter takes long time in the experimental study when it takes highly short time as a few minutes in the estimation with ANN.

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“…in the literature for estimation purpose [8][9][10]. The ANN technique is one of the mostly used techniques and it is a technique with a high accuracy rate [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Estimation of Output Current Ripple in PFC-IBC Used in Battery Charger of Electrical Vehicles: A Comparison of LR, RF and ANN Techniques

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Tınğ

2022

IEEE Access

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In this study, an artificial neural network (ANN) model is developed for the purpose of estimating the output current ripple of a power factor correction (PFC) AC/DC interleaved boost converter (IBC) used in battery charger of electrical vehicles (EVs) based on the inductance current ripple, switching frequency and load changes. Besides, the improved ANN model is compared with some different machine learning (ML) techniques like linear regression (LR), random forest (RF). The PFC-IBC is simulated with the PSIM simulation program to estimate the output current ripple. As a result, 336 output current ripple values are obtained based on inductance current ripple, different switching frequency and load changes. Then, the value of output current ripple is estimated by training the input parameters with LR, RF and ANN machine learning techniques (MLTs) for controlling the current harmonics drawn from the grid and for reliable charging of batteries. It is seen that the estimation value obtained with MLTs is quite compatible with the actual value obtained with the simulation. In addition, in the study carried out with the simulation, it takes a period of several days to obtain the estimation results; whereas, the operation of estimation with MLTs can be completed in a short period such as a few minutes. This clearly reveals the advantage of the MLTs. Therefore, this value is estimated through the MLTs with a high accuracy before the design of the charging device in order to maintain at a secure level the output current ripple posing considerable importance in electrical vehicle battery charge. Also, in this estimation process, LR, RF and developed ANN techniques are examined and compared separately in the WEKA program and it is observed that the developed ANN model proposes better results than other techniques.INDEX TERMS Machine learning, artificial neural network, electrical vehicle, battery charging, power factor correction.

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An Efficient ANN-Based MPPT Optimal Controller of a DC/DC Boost Converter for Photovoltaic Systems

Cited by 7 publications

References 5 publications

Optimization and Simulation of Photovoltaic MPPT Control Strategies

Optimization and Simulation of Photovoltaic MPPT Control Strategies

A novel zero voltage transition boost converter and artificial neural network‐based estimation of converter efficiency

Machine Learning-Based Estimation of Output Current Ripple in PFC-IBC Used in Battery Charger of Electrical Vehicles: A Comparison of LR, RF and ANN Techniques

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