Weight Feedback Adaptive Multi Objective Cooperative Scheduling in Smart Grid Application

Abstract

Smart grid integration needs a highly accurate power scheduling to minimizes the losses and efficiently utilize the power supply to minimize the loss. Scheduling of a smart grid interface is monitored based on single or multiple objectives scheduler, where the smart grids are scheduled based on the measured parameters of power dispatch and the consumption model. Wherein, multi objective scheduling results in prominent result, the system is a linear monitoring model, where no previous observations are considered in making present decision. This constraint the accuracy of scheduling. In this paper, a new feedback scheduling operation based on feedback operation is proposed. the approach significantly feedbacks the past parameter variation and leads to an optimal power supply in smart grid interface. The experimental results obtained signifies a optimal improvement in the decision delay and power compensation.

Analysis and Comparison of Performance of Interline Power Flow Controller with Various Control Algorithms under Various Power Stability Problems

“…The connections are referred to as edges. The weight of inputs and edges varies as learning advances in most instances [10][11][12][13][14][15]. Depending on the weight, the signal intensity at a connection is raised or lowered.…”

A novel efficient adaptive-neuro fuzzy inference system control based smart grid to enhance power quality

“…The yield of a dynamic framework is a component of a past yield or previous information or both, thusly recognizable proof and control of dynamic framework are an inborn errand contrasted with static framework [16]- [21]. The feed forward neural fuzzy organizations have a significant downside so their application is restricted to static planning issues [22]- [27]. In this way, to recognize dynamic frameworks, repetitive neuro fuzzy organizations ought to be utilized.…”