<p>This work compacts with the modeling, simulation, and application of a Fractional Order Proportional Integral Differential (FOP-I-D) controlled Cascaded Flyback Switched Mode Power Supply (CFSMPS) system. It recommends Parallel cascaded flyback converter for the production of essential DC voltage from the input supply voltage. The output from CFSMPS is regulated by using closed loop configuration. The simulation of Closed-loop Proportional-Integral (PI) and FOP-I-D controlled CFSMPS system has been done and the results of the systems are related. The outcomes signify that the FOP-I-D based system has presented an enhanced response to represent as similar to the PI controlled CFSMPS system. The FOP-I-D controlled CFSMPS system has benefits like decreased steady-state error and enhanced time-domain response.</p>

This paper mainly impacts on a bridgeless buck boost converter fed Brush Less Direct Current (BLDC) motor drive with Power Factor Correction (PFC) which is low cost and well suitable for low power applications. The speed of the BLDC motor is controlled by adjusting the Voltage Source Inverter's dc link voltage which is then employed along with a distinct voltage sensor. Thus the fundamental frequency switching of VSI operation becomes quite ease with BLDC motor's electronic turn off which provides minimal switching losses. An acceptable performance is attained for speed control having power quality indicators within the allowable limits. To end with the suggested drive's prototype model has been implemented to evaluate and confirm the suggested BLDC motor drive's performance for various speed controls with enhanced AC main's power quality.

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The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained. Correlation between Input and output variable using spearman’s correlation analysis using Heat maps. Followed by the Multiple Linear Regression based Training and testing of the Modified IEEE 14 and IEEE30 Bus systems for base load case, 10% and 20% load increment with the 5-fold cross validation is also presented. Comparative analysis is performed to find the best fit ML Model for our research.

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