Identification of critical nodes for cascade faults of grids based on electrical PageRank

In this paper,the electrical PageRank method is proposed to identify the critical nodes in a power grid considering cascading faults as well as directional weighting.This method can rapidly and accurately focus on the critical nodes in the power system.First,the proposed method simulates the scenario in a grid after a node is attacked by cascading faults.The load loss of the grid is calculated.Second,the electrical PageRank algorithm is proposed.The nodal importance of a grid is determined by considering cascading faults as well as directional weights.The electrical PageRank values of the system nodes are obtained based on the proposed electrical PageRank algorithm and ranked to identify the critical nodes in a grid.Finally,the effectiveness of the proposed method is verified using the IEEE39 node system.The proposed method is highly effective in preventing the occurrence of cascading faults in power systems.

A Vehicle Routing Problem Based on Intelligent Batteries Transfer Management for the EV Network

Batteries transfer management is one important aspect in electric vehicle(EV)network's intelligent operation management system.Batteries transfer is a special and much more complex VRP(Vehicle Routing Problem) which takes the multiple constraints such as dynamic multi-depots,time windows,simultaneous pickups and deliveries,distance minimization,etc.into account.We call it VRPEVB(VRP with EV Batteries).This paper,based on the intelligent management model of EV's battery power,puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization.By setting a reasonable tabv range,special update rules of the pheromone and path list memory functions,the algorithm can have a better convergence,and its feasibility is proved by the experiment in an EV's demonstration operation system.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

An Optimized Framework for Surgical Team Selection

In the healthcare system,a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery.Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient care.The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them.In this paper,we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient.The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient’s subgroups.For this end,two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed.The proposed algorithms were tested using different datasets and benchmark functions.Furthermore,the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India.The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.