A new three phase multilevel inverter with reduced number of components count is proposed in this paper. This inverter is designed using a single DC source per phase to generate multiple level output voltage which makes it suitable for low and medium voltage applications, including ac-coupled renewables or energy storages. A generalized circuit configuration is shown in this paper following which the number of output voltage level can be increased as per expectation. Although, each element endures the voltage stress equivalent to the input DC voltage, the value of total standing voltage (TSV) is reduced by the utilization of minimized number of components with respect to the number of series connected capacitors. Further, staircase modulation scheme is used to generate the switching signals. Hence, the proposed inverter can be operated at low switching frequency with optimal output current harmonic distortion which decreases switching losses and suppresses power factor falling. In order to validate the theoretical explanations and practical performances of the proposed inverter, the hypothesis is simulated for 9, 13 and 39 output voltage level inverters for three phase with a line voltage total harmonic distortion (THD) of 6.06%, 4.16% and 2.10% respectively in MATLAB/Simulink and a 5-level single phase laboratory prototype is implemented in the laboratory.

Due to global warming and shortage of fossil fuels, the grid-connected solar photovoltaic (PV) system has gained significant popularity all over the world. The modular multilevel cascaded (MMC) inverter is the natural choice for step-up transformer and line filter less direct medium voltage grid integration of solar PV systems. However, power quality and loss are the important issues while connecting the PV system to the medium voltage grid through MMC inverter. Modulation technique is the key to maintain output power quality, e.g., total harmonic distortion (THD) and to ensure low switching and conduction losses. In this paper, an advanced modulation technique named “triangle saturated common mode pulse width modulation (TSCMPWM)” control is proposed for a 3-phase 5-level MMC inverter-based grid-tied PV system. Compared to traditional modulation techniques, the proposed TSCMPWM control offers the lowest voltage THD as well as lower inverter power losses. Performance of the proposed modulation technique is evaluated in MATLAB/Simulink environment and tested with a reduced scale prototype test platform. Both simulation and experimental results show the effectiveness of the proposed modulation technique.

This paper proposes a modified modulating signal-based pulse width modulation (PWM) scheme for a neutral point clamped (NPC) inverter-based induction motor drive (IMD) for enhancing the efficiency of solar photovoltaic (PV) fed water-pumping systems. During the previous several decades, the use of sustainable source-based power generation systems has benefited water pumps in household life, irrigation-based activities, and industrial sectors, especially in areas where the electric grid is less available. A critical concern has long been the power quality, speed, and torque of an induction motor used in solar PV-powered water pumping. This paper focuses on the improvement of power performance by minimizing total harmonic distortions (THDs), switching losses and conduction losses, and torque ripples in the induction motor driving the PV-fed water pump by using a modified PWM scheme that generates symmetrical and balanced gate driving signals for the NPC inverter employed in the IMD. The IMD with the suggested PWM switching strategy has been tested in all feasible induction motor operating situations. To demonstrate the effectiveness of the suggested PWM scheme, a performance comparison of the proposed PWM scheme with conventional PWM schemes is performed, and also the IMD with the suggested PWM switching strategy is tested in all feasible induction motor operating situations using the MATLAB/Simulink platform.

The use of different control techniques has become very popular for controlling the performance of grid-connected photovoltaic (PV) systems. Although the proportional-integral (PI) control technique is very popular, there are some difficulties such as less stability, slow dynamic response, low reference tracking capability, and lower output power quality in solar PV applications. In this paper, a robust, fast, and dynamic proportional-integral resonance controller with a harmonic and lead compensator (PIR + HC + LC) is proposed to control the current of a 15-level neutral-point-clamped (NPC) multilevel inverter. The proposed controlled is basically a proportional-integral resonance (PIR) controller with the feedback of a harmonic compensator and a lead compensator. The performance of the proposed controller is analyzed in a MATLAB/Simulink environment. The simulation result represents admirable performance in terms of stability, sudden load change response, fault handling capability, reference tracking capability, and total harmonic distortion (THD) than those of the existing controllers. The responses of the inverter and grid outlets under different conditions are also analyzed. The harmonic compensator decreases the lower order harmonics of grid voltage and current, and the lead compensator provides the phase lead. It is expected that the proposed controller is a dynamic aspirant in the grid-connected PV system.

Deep learning-based models such as deep neural network (DNN) and convolutional neural network (CNN) have recently been established as state-of-the-art for enumerating electric fields from transcranial magnetic stimulation coil. One of the main challenges related to this electric field enumeration is the prediction time and accuracy. Despite the low computational cost, the performance of the existing prediction models for electric field enumeration is quite inefficient. This study proposes a 1D CNN-based bi-directional long short-term memory (BiLSTM) model with an attention mechanism to predict electric field induced by a transcranial magnetic stimulation coil. The model employs three consecutive 1D CNN layers followed by the BiLSTM layer for extracting deep features. After that, the weights of the deep features are redistributed and integrated by the attention mechanism and a fully connected layer is utilized for the prediction. For the prediction purpose, six input features including coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil are mapped with the output of the electric field. The performance evaluation is conducted based on four verification metrics (e.g. R2, MSE, MAE, and RMSE) between the simulated data and predicted data. The results indicate that the proposed model outperforms existing DNN and CNN models in predicting the induced electrical field with R2 = 0.9992, MSE = 0.0005, MAE = 0.0188, and RMSE = 0.0228 in the testing stage.