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A two switches non-isolated DC-DC novel buck-boost converter for charging the battery of electric vehicle is projected in this paper. The performance of the converter is compared with conventional buck-boost and transformer-less P/O buck-boost converter by Shan and Faqiang. The detail operation and performance analysis of the proposed converter is described both in continuous conduction mode and discontinuous conduction mode. A state space model and simulation model is designed in MATLAB. The PID controller parameters are tuned using Single-objective Salp swarm optimization algorithm using MATLAB. The controller is implemented using DSP board. The hardware and simulation results are projected in the paper to validate the effectiveness of the proposed buck-boost converter. A comparison analysis is projected among conventional converter and Shan & Faqiang converter.

The converter state space model is designed and simulation model is also developed in MATALAB. The controller is implemented using DSP board. The parameters are obtained using optimization technique using SSA algorithm. The hardware design is also implemented, and the result is compared with the Shan and Faqiang converter. The efficiency of the converter is also tested.

The converter is providing a higher efficiency. The inductor current is also positive in both buck and boost mode. The robustness of the controller is better for a wide range of variation of input voltage because the output voltage remains almost constant. Therefore, this is very suitable for battery charging and PV module application.

For battery charging from PV module where voltage fluctuation is frequent.

The authors can use household applications to charge the battery using PV module.

The converter design concept is new. Optimization is used to find the parameters of the controllers and is implemented in hardware design. The parameters obtained provide robustness in the converter performance.

Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.

A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.

Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.

The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

In this work, generation control of an isolated small hydro plant (SHP) is demonstrated by applying optimal controllers in speed governor and hydraulic turbine system. A comparative analysis of application of fuzzy PI (FPI) and PID controller is conferred for generation control (both power and terminal voltage) of an SHP. The controllers are designed optimally by using crow search algorithm (CSA) and novel hybrid differential evolution crow search algorithm (DECSA). The purpose of this paper is to settle the voltage and real power to improve the quality of the power.

In this work, the controllers (PID and FPI) are implemented in speed governor and excitation system of SHP to regulate power and terminal voltage. Differential evolution and CSA are hybridized to enhance the performance of controller to refurbish the power and terminal voltage of SHP.

The proposed DECSA algorithm is applied to solve ten benchmark functions, and the effectiveness of DECSA algorithm over CSA and DE is demonstrated in terms of best value, mean and standard deviation. CSA and DECSA algorithms optimized controllers (PID and FPI) are used to design SHP with the capability to contribute power and voltage of better quality. The comparative analysis to substantiate the competence of DECSA algorithm and FPI controller is demonstrated in terms of statistical measures of power and voltage of SHP. Robustness analysis is performed by varying all system parameters to prove the effectiveness of the proposed controller.

The proposed algorithm and FPI controller are applied individually to improve the quality of the power of SHP. DE, CSA and DECSA algorithms are implemented to solve benchmark equations. The solutions of all benchmark equations contributed by DECSA algorithm is converged rapidly and having minimum statistical measures as compared to DE and CSA algorithms. The DECSA algorithm and FPI controller are proposed with superior competence to enhance the generator performances by conceding undershoot, overshoot and settling time of power and terminal voltage. DECSA-based FPI controller contributes a noticeable improvement of the performances over other approaches.