Detection of District Heating Pipe Network Leakage Fault Using UCB Arm Selection Method

Abstract

District heating networks make up an important public energy service, in which leakage is the main problem affecting the safety of pipeline network operation. This paper proposes a Leakage Fault Detection (LFD) method based on the Linear Upper Confidence Bound (LinUCB) which is used for arm selection in the Contextual Bandit (CB) algorithm. With data collected from end-users’ pressure and flow information in the simulation model, the LinUCB method is adopted to locate the leakage faults. Firstly, we use a hydraulic simulation model to simulate all failure conditions that can occur in the network, and these change rate vectors of observed data form a dataset. Secondly, the LinUCB method is used to train an agent for the arm selection, and the outcome of arm selection is the leaking pipe label. Thirdly, the experiment results show that this method can detect the leaking pipe accurately and effectively. Furthermore, it allows operators to evaluate the system performance, supports troubleshooting of decision mechanisms, and provides guidance in the arrangement of maintenance.

Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach

“…• "Local heating transition-policymakers": Study (Busch et al 2017) Khalil and Fatmi (2022) uses household agents to represent the in-home and out-of-home activities during COVID 19 to analyze the energy demand for electricity and heat. In study (Shen et al 2021), the Linear Upper Confidence Bound (LinUCB) approach trains a single agent for branch selection to detect the leaky branch of the heating grid using home data. In Hall and Geissler (2020), the market coordinator and the agents represent certain buildings.…”

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Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities

“…Chen et al [74] present a leakage detection method for DH networks using reinforcement learning. The authors employ data from leakage simulations with ten-minute intervals, and state that leakage data is rare and does not cover all possible leakages.…”

Resistance Characteristic Parameters Estimation of Hydraulic Model in Heating Networks Based on Real-Time Operation Data

“…Reducing the energy consumption of heating systems is the key to achieving carbon peak and carbon neutrality in China. Heating networks (HNs) are the carrier of heat medium (hot water) in heating systems, and their major function is to transport and distribute heat medium on demand [2]. An HN needs to make the flow and thermal energy meet the requirements at the same time, that is, to achieve hydraulic balance and thermal balance.…”