Event-Driven Non-Intrusive Load Monitoring Algorithm Based on Targeted Mining Multidimensional Load Characteristics

Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed features.This paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification performance.First,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original problem.Then,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load characteristics.The simulation results show the effectiveness and stability of the proposed algorithm.Compared with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.

Design, Implementation and Simulation of Non-Intrusive Sensor for On-Line Condition Monitoring of MV Electrical Components

Non-intrusive measurement technology is of great interest for the electrical utilities in order to avoid an interruption in the normal operation of the supply network during diagnostics measurements and inspections. Inductively coupled electromagnetic sensing provides a possibility of non-intrusive measurements for online condition monitoring of the electrical components in a Medium Voltage (MV) distribution network. This is accomplished by employing Partial Discharge (PD) activity monitoring, one of the successful methods to assess the working condition of MV components but often requires specialized equipment for carrying out the measurements. In this paper, Rogowski coil sensor is presented as a robust solution for non-intrusive measurements of PD signals. A high frequency prototype of Rogowski coil is designed in the laboratory. Step-by-step approach of constructing the sensor system is presented and performance of its components (coil head, damping component, integrator and data acquisition system) is evaluated using practical and simulated environments. Alternative Transient Program-Electromagnetic Transient Program (ATP-EMTP) is used to analyze the designed model of the Rogowski coil. Real and simulated models of the coil are used to investigate the behavior of Rogowski coil sensor at its different stages of development from a transducer coil to a complete measuring device. Both models are compared to evaluate their accuracy for PD applications. Due to simple design, flexible hardware, and low cost of Rogowski coil, it can be considered as an efficient current measuring device for integrated monitoring applications where a large number of sensors are required to develop an automated online condition monitoring system for a distribution network.

Partial Discharge Simulations Used for the Design of a Non-Intrusive Cable Condition Monitoring Technique

The purpose of this paper is to investigate the effect of PD （partial discharge） activity within medium voltage XLPE （cross-linked polyethylene） cables. The effect of partial discharge was studied by means of a number of simulations. The simulations were based on the well-known three capacitor model for partial discharge. An equivalent circuit was derived for partial discharge due to a single void in the insulation material of a power cable. The results obtained from the simulations will form the basis of the design proses of a non-intrusive condition monitoring technique. The technique is based on the classification of discharge activity according to five levels of PD. Future work will include the improvement of the simulation model by investigating the high frequency model of a power cable as well as the statistical nature of PD activity. This will improve the accuracy of the simulation results when compared to actual measurements. The work discussed in this paper will be used to construct and calibrate a practical model which will make use of PD measurements for non-intrusive condition monitoring of medium voltage electrical cables.

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.

Non-intrusive Load Monitoring Based on Graph Total Variation for Residential Appliances

Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.

Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques

Technological advances,innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems.The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models,generating inefficiency in the analysis and processing of informa-tion to validate the flexibility potential that large consumers can contribute to the network operator.In this sense,the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.

管道入侵报警和泄漏检测的智能化发展

第三方破坏、自然灾害破坏和管道的老化及腐蚀是天然气长输管道线路日常运行的安全隐患。为保障管道的安全运行,综合应用入侵报警、泄漏检测和其他安防技术,搭建综合智能安防监控平台,分析了几种常用的入侵报警和泄漏检测技术,明确了天然气长输管道行业以光纤传感技术作为入侵报警和泄漏检测系统的发展方向。以阿联酋天然气管网为例,分析了各种技术在特定环境中应用的情况,探讨我国输气管道项目实际应用的问题。入侵报警和泄漏检测在天然气管道上的应用越来越多,需要进一步开放平台接口,接入门禁系统、扩音对讲系统、巡更系统、动力环境监测系统,共同组成多系统融合的综合智能安防监控平台。依托5G网络、人工智能、云计算、大数据、物联网等智能技术的优势,推进管道运营体系改革,实现管道行业由传统运行模式逐步向数字化、智能化发展。

A Review of NILM Applications with Machine Learning Approaches

In recent years,Non-Intrusive LoadMonitoring (NILM) has become an emerging approach that provides affordableenergy management solutions using aggregated load obtained from a single smart meter in the power grid.Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less ofa burden for the energy monitoring process. However, conducted research works have limitations for real-timeimplementation due to the practical issues. This paper aims to identify the contribution of ML approaches todeveloping a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,along with the research works that have been conducted in the domain. Secondly, the contribution of machinelearning approaches in three aspects is discussed: Supervised learning, unsupervised learning, and hybridmodeling.It highlights the limitations in the applicability of ML approaches in the field. Then, the challenges in the realtimeimplementation are concerned with six use cases: Difficulty in recognizing multiple loads at a given time,cost of running the NILM system, lack of universal framework for appliance detection, anomaly detection andnew appliance identification, and complexity of the electricity loads and real-time demand side management.Furthermore, options for selecting an approach for an efficientNILMframework are suggested. Finally, suggestionsare provided for future research directions.

感应熔炼炉炉衬损耗监测系统设计

为进一步消除炼钢和铸造行业中机械生产和加工设备的安全隐患,基于感应接触式炉衬损耗监测技术,设计一种炉衬损耗监测系统。该系统在感应熔炼炉运行过程中实时采集感应圈回路与中间金属层回路的电流值,根据两回路电流值判断感应熔炼炉是否出现炉衬损耗,评估损耗状况,并在回路电流值达到设定阈值后及时发出对应的信号提醒工作人员。经实验验证表明,系统能够提升炉衬损耗监测的可靠性,同时提高了熔炼炉生产的安全性以及工作人员对炉衬损耗状况判断的便利性。

基于小波分析的热连轧设备液压系统泄漏的实时监测方法

针对液压系统的泄漏问题,提出了一种基于小波分析技术的热连轧设备液压系统油液泄漏监测方法,详细介绍了该方法的设计原理、实验验证以及在工业生产中的应用效果。该方法通过计算液压系统各液压元件油量变化,并结合小波变换分析方法对油量趋势曲线进行分析,实现对液压系统中的泄漏情况的实时监测,提前发现潜在的漏油问题,降低维修成本,提高设备可用性。

声音增强技术在煤矿瓦斯定点监测中的应用

深入分析声音增强技术的基本原理,重点探讨该技术在瓦斯泄漏定点监测中的应用。通过自适应滤波、谱减法、盲源分离等算法,对微弱泄漏声音进行降噪增强,结合梅尔倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)、支持向量机(Support Vector Machine,SVM)等模式识别方法,实现泄漏声音的可靠识别。工程应用表明,该方案可显著提高瓦斯泄漏监测的灵敏度和可靠性。

Machine Learning: An Overview

Machine learning is an Artificial Intelligence (or AI) application, an idea that came into being by giving machines access to data and letting them learn by themselves. AI has been making headlines, especially since ChatGPT was introduced. Malaysia has taken many significant steps to embrace and integrate the technology into various sectors. These include encouraging large companies to build AI infrastructure, creating AI training opportunities (for example, the local media reported Microsoft and Google plan to invest USD 2.2 billion and USD 2 billion, respectively, in the said activities), and, as part of AI Talent Roadmap 2024-2030, establishing AI faculty in one of its public universities (i.e., “Universiti Teknologi Malaysia”) leading the way in the integration and teaching of AI throughout the country. This article introduces several products developed by the author (for the energy and transportation industries) and recommends their improvement by incorporating Machine learning.

负压波与输量平衡法技术在气田水管道泄漏监测中的应用

气田水管道泄漏会造成严重的环境问题。为了掌握负压波与输量平衡法监测技术在气田水管道泄漏监测中的有效性及应用效果,采用7种不同直径的限流孔模拟管道泄漏,并开展现场试验,对比分析在消除空管段塞流、末站阀门全开测试及首站停泵末站关阀3种工况下的现场应用效果。测试实验结果表明:负压波与输量平衡法能对ϕ2 mm及以上孔径泄漏进行监测,平均定位误差在50 m以内,平均报警响应时间为60 s;ϕ2 mm孔径以下的微小泄漏,使用两端流量计并结合相关软件算法可以对泄漏进行报警,但监控效果差且无法定位;采用调节末站阀门开启程度、进站泵排量等手段使管道内空管段塞流消失,可提高系统对泄漏点的有效定位。

供热管网电预热安装无补偿敷设设计及施工要点浅析

本文介绍了某县城供暖热源由热电联产集中供热替代原燃煤锅炉热源,有效发挥经超低排放的热电联产集中供热的经济性、环保性、能源可靠性等优势。新建零次侧供热管网自电厂换热首站接至原热源厂内新建隔压泵站,敷设长度约2*10 km,管网管径为DN 800。供热管网敷设途中穿越高速、公路、泾河某支流、排洪沟等特殊地段,根据要求提出了相应的管网敷设解决方案和顶管用保温一体化滑动支架。根据团体标准《长输供热热水管网技术标准》,计算确定了电预热安装设计温度为56℃,供热管网设计和施工整体采用电预热安装无补偿敷设和分布式光纤泄漏监测系统,可以有效保障供热管网长期安全、可靠和经济运行。

注水复杂管网泄漏监测系统研究

注水复杂管网泄漏监测系统的研究目标是提高管网的运行效率,减少泄漏引起损失。研究主要涉及数据采集与处理、泄漏检测算法、人机交互综合管理等方面的工作。泄漏综合分析判断模型及管理平台研究,实现了2mm及以上孔径泄漏事件的有效判断监测。

红外成像技术在主变运维中的应用

从红外监测技术的原理出发,以主变渗漏油及套管发热2种常见缺陷为切入点,深入探讨了红外监测技术在主变运行过程中的重要作用。通过具体应用场景,结合现场实际,分析红外监测技术在主变运维过程中的优势,以保障主变运维工作的质量和安全。

A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation.Non-intrusive load monitoring(NILM)offers many promising applications in the context of energy efficiency and conservation.Load classification is a key component of NILM that relies on different artificial intelligence techniques,e.g.,machine learning.This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis.Moreover,this study also analyzes the role of input feature space dimensionality in the context of classification performance.For the above purposes,an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis,it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data.The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier.Furthermore,it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

Analysis of Dynamic Appliance Flexibility Considering User Behavior via Non-intrusive Load Monitoring and Deep User Modeling

The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.