Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

Materials Selection Method Combined with Different MADM Methods

Materials selection is a multiple attribute decision making(MADM)problem.A lot of MADM methods are applicable to materials selection,and it may produce considerable differences between the results of materials selection.But it is unknown which MADM method is better.So it is desirable to decide reasonable final result of materials selection in consideration of the individual results from different MADM methods.In this paper,materials selection method combined with different MADM methods is proposed.The method is based on final ranks of alternative materials,where the final ranks are determined from the ranks of the alternative materials using different MADM methods.This method is applied to select optimal magnesium alloy material for automobile wheels.This method may be widely used to select optimal material in engineering practice.

Cloudy fuzzy inventory model under imperfect production process with demand dependent production rate

The aim of this article is an effort to initiate the cloudy fuzzy number in developing classical economic production lot-size model of an item produced in scrappy production process with fixed ordering cost and without shortages.Here,the market value of an item is cloudy fuzzy number and the production rate is demand dependent.In general,fuzziness of any parameter remains fixed over time,but in practice,fuzziness of parameter begins to reduce as time progresses because of collected experience and knowledge that motivates to take cloudy fuzzy number.The model is solved in a crisp,general fuzzy and cloudy fuzzy environment using Yager’s index method and De and Beg’s ranking index method and comparisons are made for all cases and better results obtained in the cloudy fuzzy model.The model is solved by dominance based Particle Swarm Optimization algorithm to obtain optimal decision and numerical examples and sensitivity analyses are presented to justify the notion.