RETRACTED ARTICLE: ANN-based
MPPT algorithm for solar PMSM drive system fed by direct-connected PV
array

Deniz, Erkan

doi:10.1007/s00521-016-2326-4

Cited by 27 publications

(10 citation statements)

References 22 publications

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“…is algorithm was commonly applied [26]. is was also shown in other articles in the literature [48][49][50][51][52][53][54]. e artificial neural networks could be used to solve the complicated civil engineering problems [55].…”

Section: Prediction Model Based On Artificial Neuralmentioning

confidence: 98%

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Tanyıldızı

2018

Advances in Civil Engineering

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e artificial neural network and support vector machine were used to estimate the compressive strength and flexural strength of carbon fiber-reinforced lightweight concrete with the silica fume exposed to the high temperature. Cement was replaced with three percentages of silica fumes (0%, 10%, and 20%). e carbon fibers were used in four different proportions (0, 2, 4, and 8 kg/m 3 ). e specimens of each concrete mixture were heated at 20°C, 400°C, 600°C, and 800°C. After this process, the specimens were subjected to the strength tests. e amount of cement, the amount of silica fumes, the amount of carbon fiber, the amount of aggregates, and temperature were selected as the input variables for the prediction models. e compressive and flexural strengths of the lightweight concrete were determined as the output variables. e model results were compared with the experimental results. e best results were achieved from the artificial neural network model. e accuracy of the artificial neural network model was found at 99.02% and 96.80%.

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Section: Prediction Model Based On Artificial Neuralmentioning

confidence: 98%

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Tanyıldızı

2018

Advances in Civil Engineering

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“…After substituting the expressions in Eqs. (11) and Eqs. ( 12) in Equation 10, if the expressions are divided into real and imaginary parts, the general expression of the dwell times for all sectors is obtained as in Eqs.…”

Section: Cosmentioning

confidence: 99%

“…There are two switching sequences for SVPWM. One of them is continuous and the other is discontinuous SVPWM (DSVPWM) [11]. In continuous SVPWM, half of a switching period starts with a zero vector (00000) and ends with another zero vector (11111) to obtain a symmetrical switching signal.…”

Section: Introductionmentioning

confidence: 99%

Medium and Large Vector-Based SVPWM Technique for Five-Phase Two-Level Inverter

Deniz¹

2021

European Journal of Technic

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In recent years, interest in the use of multi-phase (more than three-phase) dc/ac inverters in applications such as electric vehicles, electric traction, more electric aircraft, ship propulsion, aerospace applications, etc. has been increasing day by day. The paper presents the Discontinuous Space Vector Pulse Width Modulation (DSVPWM) technique for control of a five-phase two-level inverter. The five-phase DSVPWM algorithm uses zero, medium and large vectors combination. When calculating vectors dwell times, calculations are made by directing the Vref to Sector-1 regardless of which sector it falls into. Thus, complex calculations are reduced. DSVPWM controlled five-phase two-level inverter that feeds an RL load is simulated using MATLAB/Simulink-Simscape blocks. To demonstrate the effectiveness of the used DSVPWM algorithm, five-phase inverter's output voltages, current waveforms and their FFT analysis are given for different modulation indexes and switching frequencies.

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“…ANN has the advantage of implicitly detecting highly nonlinear relationships between input features and output variables, as encountered in the consequence modeling of jet and pool fires. ,,, In this study, three artificial neural network consequence models for jet fire, early pool fire, and late pool fire were developed with the MATLAB Neural Network Toolbox to correlate the safety-relevant chemical properties and release conditions to the designated radiation effect distance. The safety-related properties are heat of combustion, autoignition temperature (AIT), flash point, NFPA rating, lower flammable limit (LFL), UFL, and burning rate.…”

Section: Introductionmentioning

confidence: 99%

“…The training set was used to train and adjust the network to minimize errors, and then the validation set was used for network generalization. The test set was then used to quantify the accuracy of the developed model …”

Section: Introductionmentioning

confidence: 99%

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

Sun

Wang

Zhu

et al. 2019

Ind. Eng. Chem. Res.

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This paper demonstrates the successful implementation of an artificial neural network to accurately predict the designated thermal radiation distance for jet fire, early pool fire, and late pool fire hazard consequence analysis. Specifically, integrated feedforward neural network models employing the backpropagation Levenberg–Marquardt algorithm were trained using data sets obtained through separate PHAST software simulations of 450 leak scenarios of 35 common flammable chemicals. For each fire model (jet, early, late pool), there are 11 input parameters spanning both chemical parameters and release conditions. Simulation data was randomly divided into 70% training, 15% validation, and 15% test sets to conduct cross-validation and provide an independent measure of predictive accuracy for the neural network models. Statistical values, namely, coefficient of determination (R 2) and mean-square error (MSE), are calculated to evaluate model regression performance. All three neural network predictive models achieved considerably accurate predictions of the logarithm format of the designated radiation effect distance. The models give an overall R 2 of 0.9930 and an MSE of 0.0022 for jet fire, an overall R 2 of 0.9909 and an MSE of 0.0016 for early pool fire, and an overall R 2 of 0.9899 and an MSE of 0.0015 for late pool fire.

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RETRACTED ARTICLE: ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array

Cited by 27 publications

References 22 publications

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Medium and Large Vector-Based SVPWM Technique for Five-Phase Two-Level Inverter

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

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