Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.

Resilience-oriented intentional islanding of reconfigurable distribution power systems

The participation of distributed energy resources in the load restoration procedure,known as intentional islanding,can significantly improve the distribution system reliability.Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses.In addition,high-impact events such as hurricanes and earthquakes may complicate the procedure of load restoration,due to the disconnection of the distribution system from the upstream grid or concurrent component outages.This paper presents a two-level method for intentional islanding of a reconfigurable distribution system,considering high-impact events.In thefirst level,optimal islands are selected according to the graph model of the distribution system.In the second level,an optimal power flow(OPF)problem is solved to meet the operation constraints of the islands by reactive power control and demand side management.The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods.The OPF problem in the second level is solved in DIgSILENT software.The proposed method is implemented in the IEEE 69-bus test system,and the results show the validity and effectiveness of the proposed algorithm.

Simultaneous Placement of Tie-lines and Distributed Generations to Optimize Distribution System Post-outage Operations and Minimize Energy Losses

This study proposes a new method which aims to optimally install tie-lines and distributed generations simultaneously.This is done to optimize the post-outage reconfiguration and minimize energy losses and energy not supplied of distribution systems.The number and location of tie-lines,as well as the number,size,and location of DGs,are pinpointed through teaching the learning-based optimization(TLBO)method.The objective function in the current research is to minimize the costs pertaining to the investment,operation,energy losses,and energies not supplied.In addition to the normal operational condition,fault operational condition is also evaluated.Therefore,the optimal post-fault reconfigurations for fault occurrences in all lines are established.Moreover,the operational constraints such as the voltage and line current limits are taken into account in both normal and post-fault operational modes.Finally,the modified IEEE 33-bus and 69-bus distribution test systems are selected and tested to demonstrate the effectiveness of the simultaneous placement of DGs and tie-line technique proposed in this paper.