Novel Fractal-Based Features for Low-Power Appliances in Non-Intrusive Load Monitoring

Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually.Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances.Low-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as noise.This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances.Fractal dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for classification.Four classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features.The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.

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.

Exploring CNN Model with Inrush Current Pattern for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring(NILM)has gradually become a research focus in recent years to measure the power consumption in households for energy conservation.Most of the existing algorithms on NILM models independently measure when the total current load of appliances occurs,and NILM usually undergoes the problem of signatures of the appliance.This paper presents a distingue NILM design to measure and classify the appliances by investigating the inrush current pattern when the alliances begin.The proposed method is implemented while the five appliances operate simultaneously.The high sampling rate of field-programmable gate array(FPGA)is used to sample the inrush current,and then the current is converted to be image patterns using the kurtogram technique.These images are arranged to be four groups of data set depending on the number of appliances operating simultaneously.Furthermore,the five proposed modifications convolutional neural networks(CNN),which is based on very deep convolutional networks(VGGNet),are designed by adjusting the size to decrease the training time and increase faster operation.The proposed CNNs are then implement as a classification model to compare with the previous models.The F1 score and Recall are used to measure the accuracy classification.The results showed that the proposed system could be achieved at 99.06 accuracy classification.

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.

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.

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.

Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network

This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.

Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

Non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.

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.

Process Monitoring and Terminal Verification of Cable-Stayed Bridges with Corrugated Steel Webs under Contruction

In this paper,the construction process of a cable-stayed bridge with corrugated steel webs was monitored.Moreover,the end performance of the bridge was verified by load test.Owing to the consideration of the bridge structure safety,it is necessary to monitor the main girder deflection,stress,construction error and safety state during construction.Furthermore,to verify whether the bridge can meet the design requirements,the static and dynamic load tests are carried out after the completion of the bridge.The results of construction monitoring show that the stress state of the structure during construction is basically consistent with the theoretical calculation and design requirements,and both meet the design and specification requirements.The final measured stress state of the structure is within the allowable range of the cable-stayed bridge,and the stress state of the structure is normal and meets the specification requirements.The results of load tests show that the measured deflection values of the mid-span section of the main girder are less than the theoretical calculation values.The maximum deflection of the girder is−20.90 mm,which is less than−22.00 mm of the theoretical value,indicating that the girder has sufficient structural stiffness.The maximum impact coefficient under dynamic load test is 1.08,which is greater than 1.05 of theoretical value,indicating that the impact effect of heavy-duty truck on this type of bridge is larger.This study can provide important reference value for construction and maintenance of similar corrugated steel web cable-stayed bridges.

Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.

Field Load Test Based SHM System Safety Standard Determination for Rigid Frame Bridge

The deteriorated continuous rigid frame bridge is strengthened by external prestressing. Static loading tests wereconducted before and after the bridge rehabilitation to verify the effectiveness of the rehabilitation process. Thestiffness of the repaired bridge is improved, and the maximum deflection of the load test is reduced from 37.9 to27.6 mm. A bridge health monitoring system is installed after the bridge is reinforced. To achieve an easy assessmentof the bridge’s safety status by directly using transferred data, a real-time safety warning system is createdbased on a five-level safety standard. The threshold for each safety level will be determined by theoretical calculationsand the outcomes of static loading tests. The highest risk threshold will be set at the ultimate limit statevalue. The remaining levels, namely middle risk, low risk, and very low risk, will be determined usingreduction coefficients of 0.95, 0.9, and 0.8, respectively.

Solid Model Bridge Static Damage Monitoring Based on GBSAR

Bridge deformation monitoring usually adopts contact sensors,and the implementation process is often limited by the environment and observation conditions,resulting in unsatisfactory monitoring accuracy and effect.Ground-Based Synthetic Aperture Radar(GBSAR)combined with corner reflectors was used to perform static load-loaded deformation destruction experiments on solid model bridges in a non-contact manner.The semi parametric spline filtering and its optimization method were used to obtain the monitoring results of the GBSAR radar’s line of sight deformation,and the relative position of the corner reflector and the millimeter level deformation signals under different loading conditions were successfully extracted.The deformation transformation model from the radar line of sight direction to the vertical vibration direction was deduced.The transformation results of deformation monitoring and the measurement data such as the dial indicator were compared and analyzed.The occurrence and development process of bridge deformation and failure were successfully monitored,and the deformation characteristics of the bridge from continuous loading to eccentric loading until bridge failure were obtained.The experimental results show that GBSAR combined with corner reflector can be used for deformation feature acquisition,damage identification and health monitoring of bridges and other structures,and can provide a useful reference for design,construction and safety evaluation.

Health Monitoring-Based Assessment of Reinforcement with Prestressed Steel Strand for Cable-Stayed Bridge

This paper presents the method of reinforcing main girder of reinforced concrete cable-stayed bridge with prestressed steel strands.To verify the effectiveness of external prestressed strand reinforcement method.Static load tests and health monitoring-based assessment were carried out before and after reinforcement.Field load test shows that the deflection and stress values of the main girder are reduced by 10%~20%after reinforcement,and the flexural strength and stiffness of the strengthened beam are improved.The deflection and strain data of health monitoring of the specified section are collected.The deflection of the second span is 4 mm~10 mm,the strain range of the upper edge of the second span is-10με~-40με,and the strain range of the lower edge is 30με~75με.These values show the deflection and strain values fluctuate within a prescribed range,verifying the safety of the bridge.The reinforcement method of prestressed steel strand is feasible and effective.It can provide reference basis for the application of external prestressed strand reinforcement technology in similar projects.

A coal rib monitoring study in a room-and-pillar retreat mine

The National Institute for Occupational Safety and Health(NIOSH)conducted a comprehensive monitoring program in a room-and-pillar mine located in Southern Virginia.The deformation and the stress change in an instrumented pillar were monitored during the progress of pillar retreat mining at two sites of different geological conditions and depths of cover.The main objectives of the monitoring program were to better understand the stress transfer and load shedding on coal pillars and to quantify the rib deformation due to pillar retreat mining;and to examine the effect of rib geology and overburden depth on coal rib performance.The instrumentation at both sites included pull-out tests to measure the anchorage capacity of rib bolts,load cells mounted on rib bolts to monitor the induced loads in the bolts,borehole pressure cells(BPCs)installed at various depths in the study pillar to measure the change in vertical pressure within the pillar,and roof and rib extensometers installed to quantify the vertical displacement of the roof and the horizontal displacement of the rib that would occur during the retreat mining process.The outcome from the monitoring program provides insight into coal pillar rib support optimization at various depths and geological conditions.Also,this study contributes to the NIOSH rib support database in U.S coal mines and provides essential data for rib support design.