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This paper aims to propose a new MLI topology with reduced number of switches for photovoltaic applications. Multilevel inverters (MLIs) have been found to be prospective for renewable energy applications like photovoltaic cell, as they produce output voltage from numerous separate DC sources or capacitor banks with reduced total harmonic distortion (THD) because of a staircase like waveform. However, they endure from serious setbacks including larger number of capacitors, isolated DC sources, associated gate drivers and increased control difficulty for higher number of voltage levels.

This paper proposes a new three-phase multilevel DC-link inverter topology overpowering the previously mentioned problems. The proposed topology is designed for five and seven levels in Matlab/Simulink with gating pulse using multicarrier pulse width modulation. The hardware results are shown for a five-level MLI to witness the viability of the proposed MLI for medium voltage applications.

The comparison of the proposed topology with other conventional and other topologies in terms of switch count, DC sources and power loss has been made in this paper. The reduction of switches in proposed topology results in reduced power loss. The simulation and hardware show that the output voltage yields a very close sinusoidal voltage and lesser THD.

The proposed topology can be extended for any level of output voltage which is helpful for sustainable source application.

Multilevel inverters become very popular in medium voltage applications owing to their inherent capability of reconciling stepped voltage waveform with reduced harmonic distortion and electromagnetic interference. They have several disadvantages like more number of switching devices required and devices with high voltage blocking and need additional dc sources count to engender particular voltage. So this paper aims to propose a novel tri-source symmetric cascaded multilevel inverter topology with reduced number of switching components and dc sources.

A novel multilevel inverter has been suggested in this study, offering minimal switch count in the conduction channel for the desired voltage level under symmetric and asymmetric configurations. This novel topology is optimized to prompt enormous output voltage levels by employing constant power switches count and/or dc sources of voltage. The topology claims its advantages in generating higher voltage levels with lesser number of voltage sources, gate drivers and dc voltage sources.

The consummation of the proposed arrangement is verified in Matlab/Simulink R2015b, and an experimental prototype for 7-level, 13-level, 21-level, 29-level, 25-level and 49-level operation modes is constructed to validate the simulation results.

The proposed topology operated with six new algorithms for asymmetrical configuration to propel increased number of voltage levels with reduced power components.

This paper presents a novel circuit topology based on a three-level neutral-point-clamped (NPC) inverter and a super-lift Luo-converter for minimizing torque ripple in a brushless DC motor (BLDCM) drive system. In the BLDCM, the stator winding inductance generates current ripple, distort the rectangular current shape, which produces the torque ripples. In addition, the torque ripple generates vibration, speed ripple and prevents the use of BLDCM in high-precision servo drive systems

Torque ripple can be mitigated by using the three-level NPC inverter, which applies half of dc-link voltage across the BLDCM terminals and this reduces the torque ripple during non-commutation period. The commutation torque ripple is reduced by employing the super-lift Luo-converter at the input of the three-level NPC inverter, which lifts the dc-link voltage to the desired value depending upon the BLDCM speed. Simulations and experimental results show that the proposed circuit topology is an attractive option to reduce the torque ripple significantly.

Experimental results show that the proposed topology can reduces the torque ripple significantly at higher speed, and operates with lower power losses than the two-level inverter-fed BLDCM drive system at higher switching frequency.

This paper has proposed a novel topology using a super-lift Luo-converter and a three-level NPC inverter to address the torque ripple issue in BLDCM drive system.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

Multilevel inverter (MLI) is a prevailing sensible alternative to two-level inverters that offer a high-quality output voltage waveform, wherein the multiple input direct current (DC) levels are established by using isolated DC sources, batteries and renewable energy sources. The purpose of this paper is to develop MLI to offer lower total harmonic distortion (THD), higher output voltage levels and reduced switching components for high power applications.

In this paper, a new tapped sources stack succored modified HX bridge MLI (TSSSMHXBMLI) topology is proposed which includes two modules, such as tapped sources stack (TSS) and modified HX bridge inverter, which perform their function in a single stage. Also, this paper outlines the formulaic implementation of the multicarrier/sub-harmonic pulse width modulation (MCPWM/SHPWM) in a Xilinx Spartan3E-500 field programmable gate array (FPGA) is suitable for the developed MLI.

The feasibility of the suggested topology is well proved by both simulation and experiment results.

This paper examines a new topology of TSSSMHXBMLI with a view to minimize total count of switching components against basic MLI topologies. The operating sequence of the suggested TSSSMHXBMLI topology is verified with the simulation study followed by an experimental investigation.

The simulation and experimental results of suggested MLI topology reveals to obtain lower THD, higher output voltage levels and reduced switching components for high power applications.

This study aims to present the experimental results for neural network (NN) based harmonic elimination technique for single-phase inverters.

Switching angles applied to power switches are determined using the NN technique based on the harmonics to be suppressed. Thus, besides controlling the output voltage, NN controller provides elimination of predetermined harmonics from output signal of single-phase inverter. Simulation and experimental results for the elimination of 15 and 20 low-order harmonics are presented. The switching angle values calculated by a NN , fuzzy logic and Newton–Raphson are compared for elimination of first 10 harmonics.

This paper provides the harmonic spectra showing that first 15 and 20 harmonics are suppressed from output signal. The NN is proved to give closest results to angle values calculated by Newton–Raphson’s numerical solution method.

The value of this paper is to verify the simulation results with the experimental result for the elimination of 15 and 20 low-order harmonics. Both the simulation and the experimental results demonstrate the success of the NN based selected harmonic elimination.

The aim of this research study is to mitigate shading impact on solar photovoltaic array. Photovoltaic (PV) array when getting shaded not only results in appreciable power loss but also exhibits multiple power peaks. Due to these multiple power peaks, the maximum power point tracking (MPPT) controllers’ performance will be affected, as most of the times it ends up in tracking the local maximum power peak and not the global power peak.

The PV panels in an PV array when getting shaded even partially would result in huge power loss. The pattern of shading also plays a crucial role, as it renders a cascaded impact on the overall power output because the cells/panels are connected in series and are parallel. Therefore, during shading, intelligent schemes are needed to appropriately connect and discard the unhealthy and healthy panels in right place with right combination. This research proposes one such scheme to mitigate the shading impact.

To mitigate the shading impact and also to have a smooth power-voltage (P-V) curve, a new series inducing switching scheme is introduced. The proposed scheme not only mitigates the shading impact and enhances the output power but also smoothens the P-V curve that facilitates the MPPTs to track the P-V appropriately.

The research findings are inventive in nature and not copied work. The reference works and the inspirations have been duly cited and credited.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results.

This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data.

MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models.

Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market.

This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.