基于实时非侵入式负荷监测的纺织行业切割机同型号感应电机功率监控
Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry
AI总结：
针对纺织行业能源监控落后的问题，提出基于实时非侵入式负荷监测（NILM）的框架，使用MATNILM模型对同型号感应电机进行功率分解，验证了实时监控可行性并指出多台相同设备同时运行时的分解困难。
摘要：
孟加拉国的纺织行业是能源密集型行业之一，但其监控实践仍然大多过时，导致电力使用效率低下和运营成本高昂。为了解决这个问题，我们提出了一种基于实时非侵入式负荷监测（NILM）的框架，专为工业应用定制，重点关注代表纺织切割机的相同电机驱动负载。开发了一个包含电压和电流传感器、Arduino Mega和ESP8266的硬件装置，用于捕获总负荷和单个负荷数据，并在云平台上存储和处理。从三个相同的感应电机和辅助负载创建了一个新数据集，总计超过180,000个样本，以在具有挑战性的工业条件下评估最先进的MATNILM模型。结果表明，虽然总能量估计相当准确，但每个电器的分解面临困难，特别是当多台相同机器同时运行时。尽管存在这些挑战，集成系统通过Blynk应用程序展示了具有远程访问功能的实际实时监控。这项工作突出了NILM在工业环境中的潜力和局限性，为未来的改进提供了见解，例如更高频率的数据收集、更大规模的数据集以及用于处理相同负载的先进深度学习方法。
英文摘要：
The textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs. To address this, we propose a real-time Non-Intrusive Load Monitoring (NILM)-based framework tailored for industrial applications, with a focus on identical motor-driven loads representing textile cutting machines. A hardware setup comprising voltage and current sensors, Arduino Mega and ESP8266 was developed to capture aggregate and individual load data, which was stored and processed on cloud platforms. A new dataset was created from three identical induction motors and auxiliary loads, totaling over 180,000 samples, to evaluate the state-of-the-art MATNILM model under challenging industrial conditions. Results indicate that while aggregate energy estimation was reasonably accurate, per-appliance disaggregation faced difficulties, particularly when multiple identical machines operated simultaneously. Despite these challenges, the integrated system demonstrated practical real-time monitoring with remote accessibility through the Blynk application. This work highlights both the potential and limitations of NILM in industrial contexts, offering insights into future improvements such as higher-frequency data collection, larger-scale datasets and advanced deep learning approaches for handling identical loads.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
