Location monitoring approach of underground pipelines using time-sequential images

The location monitoring of underground pipelines is of utmost significance as it helps the effective management and maintenance of the pipelines,and facilitates the planning of nearby projects,preventing damage to the pipelines.However,currently there is a serious lack of data on the locations of underground pipelines.This paper proposes an image-based approach for monitoring the locations of underground pipelines by combing deep learning and visual-based reconstruction.The proposed approach can build the monitoring model for underground pipelines and characterize their locations through their centroid curve.Its advantages are:(1)simplicity:it only requires time-sequential images of the inner walls of underground pipelines;(2)clarity:the location model and the location curve of underground pipelines can be provided quickly;(3)robustness:it can cope with some existing problems in underground pipelines,such as light variations and small viewing angles.A lightweight approach for monitoring the locations of underground pipelines is achieved.The proposed approach’s effectiveness has been validated through laboratory simulation experiments,demonstrating accuracy at the millimeter level.

Confidence limits for the mean of exponential distribution in any time-sequential samples

We present the general results determining confidence limits for the mean of exponential distribution in any time-sequential samples, which are obtained in any sequential life tests with replacement or without replacement. Especially, we give the best lower confidence limits in the case of no failure data.

Reliability evaluation of integrated electricity–gas system utilizing network equivalent and integrated optimal power flow techniques

The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems.Therefore,it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration.In this paper,a novel reliability evaluation method for integrated electricity-gas systems(IEGSs)is proposed.First,reliability network equivalents are utilized to represent reliability models of gas-fired generating units,gas sources(GSs),power-to-gas facilities,and other conventional generating units in IEGS.A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique.Finally,the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents.The proposed method is capable to evaluate customers’reliabilities in IEGS,which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.

Real-time Energy Management of Low-carbon Ship Microgrid Based on Data-driven Stochastic Model Predictive Control

With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.