Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

The environmental and technical benefits of renewable energy sources make expanding their use essential in our lives. The main source of renewable energy used in this work is photovoltaic energy. Photovoltaic cells are a clean energy source dependent on solar irradiance to generate electricity from sunlight. The identification of solar cell variables is one of the main items in the simulation and modeling of photovoltaic models. The models used in this work are triple-diode, double-diode, and single-diode solar cells. A novel optimization method called weighted mean of vectors (INFO) is applied for estimating the solar cell variables in the three models. The fitness function of identification is to minimize the root-mean-square error (RMSE) between the measured data of current and the data of simulated current based on the parameters identified from the algorithms. The INFO technique is compared with another seven methods: Harris hawk optimization (HHO), tunicate swarm algorithm (TSA), sine—cosine algorithm (SCA), moth–flame optimizer (MFO), grey wolf optimization (GWO), chimp optimization algorithm (ChOA), and Runge–Kutta optimization (RUN).

AFPMSM is lighter, has a higher power-to-weight ratio, is shorter in length, is less expensive, and has a higher efficiency than the radial flux motor. Then AFPMSM is more suitable for driving the EV than radial flux motor. The proposed technique in this paper is the sensorless-based SVPWM-DTC of AFPMSM to drive electric vehicles. Sensorless research becomes more important in this circumstance since the axial motor can be placed inside the vehicle tire due to its condensed size and shape similar to the tires. DTC provides less fluctuation for the driver during driving for safety and comfort. SVPWM is preferred for its high performance. When measuring speed using a sensorless estimator, sensor inaccuracy is minimized, and the AFPMS motor can be mounted inside the tire. The control system is tested using two EVs driving cycles, and the results promise high performance. NEDC and HWFET driving cycles are used to test the proposed control scheme in 100 times less than the actual driving cycles’ time to test the coherence of the sensorless estimator. The results demonstrate that the proposed technique is valid for real-time applications with high-performance, minimum torque fluctuations, and minimum transient and steady-state errors.

<!--[if gte mso 9]> <![endif]--><!--[if gte mso 9]> Normal 0 false false false MS JA AR-SA <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 10]> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 9]> Normal 0 false false false MS JA AR-SA <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 10]> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} table.MsoTableGrid {mso-style-name:"Table Grid"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-priority:39; mso-style-unhide:no; border:solid windowtext 1.0pt; mso-border-alt:solid windowtext .5pt; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-border-insideh:.5pt solid windowtext; mso-border-insidev:.5pt solid windowtext; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} <![endif]-->Axial flux motors use less material and thus are inherently less expensive. They can also deliver a high-power density, which is four times that of a radial flux motor. That makes studying the control methods for this motor necessary. The purpose of this study is to introduce a new dynamic and steady-state response control technique for axial flux permanent magnet synchronous motors (AFPMSMs). Dynamic equations describe the control characteristics of axial flux permanent magnet motors. The AFPMSM model and the space vector pulse width modulation (SVPWM) inverter were created using MATLAB Simulink. For the AFPMSM motor with an SVPWM inverter, direct torque control (DTC) is provided. The results of the proposed control technique are simulated and analyzed, and it is found to provide good performance. According to the results, the proposed control method reveals advantages in reducing the ripples and pulsating of the torque while enhancing speed dynamic and steady-state response.

This paper studies the latest advances made in Deep Learning (DL) methods utilized for transformer inrush and fault currents classification. Inrush and fault currents at different operating conditions, initial flux and fault type are simulated. This paper presents a technique for the classification of power transformer faults which is based on a DL method called convolutional neural network (CNN) and compares it with traditional artificial neural network (ANN) and other techniques. The inrush and fault current signals of the transformer are simulated within MATLAB by using Fourier analyzers that provides the 2nd harmonic signal. The 2nd harmonic peak and variance statistic values of input signals of the three phases of transformer are used at different operating conditions. The resulted values are aggregated into a dataset to be used as an input for the CNN model, then training and testing the CNN model is performed. Consequently, it is obvious that the CNN algorithm achieves a better performance compared to other algorithms. This study helps with easy discrimination between normal signals and faulty signals and to determine the type of the fault to clear it easily.