Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory


Service restoration is important in distribution networks following an outage. During the restoration process, the system operating conditions will fluctuate, together with variation of the load demand and also the output from distributed generators (DGs). These variations are laborious to be predicted and therefore the load demands are roughly estimated because of absence of real-time measurements, which can significantly affect the restoration strategy. In this paper, we tend to report a sturdy restoration call-making model based mostly on info gap decision theory, that takes into consideration the uncertainty within the load and output of the DGs. For a given bounded uncertain set of parameters, the solutions will guarantee feasibility which an objective will not fall below a given threshold. We describe the implementation of a strong optimization algorithm primarily based on a mixed integer quadratic constraint programming restoration model, the target of which is to revive maximal outage hundreds. Numerical tests on a changed Pacific Gas and Electric Company (PG&E) 69-node distribution network are mentioned to demonstrate the performance of the model.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Accurate and Robust Video Saliency Detection via Self-Paced Diffusion ABSTRACT: In order to estimate video saliency in the short term, traditional video saliency detection algorithms usually follow the common
PROJECT TITLE : Robust Lane Detection from Continuous Driving ScenesUsing Deep Neural Networks ABSTRACT: For autonomous vehicles and sophisticated driver assistance systems, lane recognition in driving scenes is a critical element.
PROJECT TITLE : Robust Unsupervised Multi-view Feature Learning with Dynamic Graph ABSTRACT: By modeling the affinity associations with a graph to lower the dimension, graph-based multi-view feature learning algorithms learn a
PROJECT TITLE : A Spatially Constrained Probabilistic Model for Robust Image Segmentation ABSTRACT: In probabilistic model based segmentation, the hidden Markov random field (HMRF) is used to describe the class label distribution
PROJECT TITLE : An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics ABSTRACT: One of the most important preprocessing steps for high-level tasks in the field of image analysis and computer vision is

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry