A novel method to construct taxonomy electrical appliances based on load signatures

A load signature is an electrical expression that a load device or appliance distinctly possesses. Load signatures can be applied to produce many useful services and products, such as, determining the energy usage of individual appliances, monitoring the health of critical equipment, monitoring power quality, and developing facility management tools. Load signatures of typical yet extensive loads are needed to be collected before applying them to different services and products. As there are an enormous number of electrical appliances, it is beneficial to classify the appliances for building a well-organized load signature database. The objective of this study is to develop an effective method to classify the loads. A 2-dimensional form of load signatures, voltage-current (V-I) trajectory, is suggested for characterizing the typical household appliances. Hierarchical clustering method was employed to classify the appliances and construct the taxonomy of the appliances. The taxonomy based on V-I trajectory was compared to the taxonomies based on traditional power metrics and eigenvectors in the previous studies. It was found that the groups of appliances in the taxonomy based on V-I trajectory were well-separated and had engineering meanings. © 2007 IEEE.published_or_final_versio

Scalability of Non-intrusive Load Monitoring for Shipboard Applications

The non-intrusive load monitor has been demonstrated as an effective tool for evaluating and monitoring shipboard electro-mechanical systems through analysis of electrical power data. A key advantage of the non-intrusive approach is the ability to reduce sensor count by monitoring collections of loads. This paper reviews trade-offs that affect the likely performance of the NILM in a real world environment.Massachusetts Institute of Technology. Sea Grant College Program; Grainger Foundation; National Science Foundation (U.S.); United States. National Aeronautics and Space Administration; United States. Coast Guard; United States. Office of Naval Research. Electric Ship Research and Development Consortium; NAVSEA; University of North Carolin

Load Monitoring and fault detection on DC micro grids using STFT based feature vectors

This thesis explores load monitoring on dc micro-grids specifically applied to Naval shipboard power systems. More electronic loads are being placed on Naval ships and, increasingly, more of these loads are pulsed-power in nature; drawing pulsating current from the grid. This presents a challenge for conventional fault monitoring devices as many fault currents are also pulsating in nature and can be difficult to differentiate from a desirable pulse event. Short-time Fourier transform is the technique preferred in this work for spectral analysis of the current signals. In addition to event based monitoring, another unsupervised control is being employed to continuously check the frequency content of the current to look for arcing faults caused by loose electrical connections. The objective of the dissertation is to develop a load monitoring algorithm that records the current drawn by these loads and is able to detect events and differentiate between desirable events and faults using their frequency content in run-time. The algorithm is realized on an micro-controller unit and validated on a low-voltage dc test-bed

RJMCMC point process sampler for single sensor source separation : an application to electric load monitoring

This paper presents an original method to separate the residential electric load into its major components. The method is explained in the particular case of space-heating, which is the most consuming electric end-use in France1. This is a source separation problem from a single mixture. The components to be retrieved are square signals characterized by a periodic regulation and a slowly timevarying duty cycles. A point process is used to model the electric load as a configuration of possibly overlapping square signals, given the priors on magnitude, duty cycle variations and the regulation periodicity. This stochastic process is simulated using a Reversible Jump Markov Chain Monte Carlo procedure. A simulated annealing scheme is used to achieve the posterior density maximization. First results on real data provided by Electricité de France are quite encouraging

System identification techniques and modeling for nonintrusive load diagnostics

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 213-219).This thesis addresses the requirements of a system that can detect on/off transients and identify physical parameters of loads connected to a power distribution network. The thesis emphasizes three areas; a transient classifier that recognizes load transients using a pattern matching scheme, parameter estimation techniques suited for use with this classifier, and case studies of modeling and identification motivated by diagnostics and performance monitoring. Together, these areas support applications that can extract detailed load information from centralized, easily accessible parts of a distribution network. A new approach and implementation of pattern-based nonintrusive transient classification is presented. The classifier is nonintrusive in the sense that it uses aggregated measurements at a central location and does not require instrumentation of individual loads. The classifier implementation includes a framework that integrates preprocessors for AC and DC environments, programs that present results, and load-specific parameter identification modules that are executed as their associated transients are classified. An obstacle for these parameter identification programs is that a good initial guess is needed for the iterative optimization routines typically used to find parameter estimates. Two approaches are given to overcome this problem for certain systems. The first extends conventional optimization methods to identify model parameters given a poor initial guess. The second approach treats the identification as a modeling problem and suggests ways to construct "inverse" models that map observations to parameter estimates without iteration. The techniques presented in the thesis are demonstrated with simulation data and in real world scenarios including a dormitory, an automobile, and an experimental building.by Steven Robert Shaw.Ph.D

A multiprocessing platform for transient event detection

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references.by Umair A. Khan.M.S

Energy-Use Feedback Engineering - Technology and Information Design for Residential Users

The research presented in this study covers a first design iteration of energy feedback for residential users. This research contributes with a framework and new insights into the study of energy-use information for residential users, which exemplifies the challenges and potential of integrating information technology in this part of the energy system