Zachary Remscrim

Smart Metering of Variable Power Loads

Nonintrusive load monitoring (NILM) seeks to determine the operation of individual loads in a building strictly from measurements made on an aggregate current signal serving a collection of loads. Great strides have been made in performing NILM for loads whose operating state can be represented by a finite-state machine, i.e., loads that consume discrete or distinct power levels for periods of time. It is much more difficult to track the operation of continuously variable loads that demand ever-changing power. These loads are becoming more prevalent as variable speed drives, daylight-responsive lighting, and other power electronic controlled loads emerge on the grid. This paper demonstrates a method for tracking the power consumption of variable demand loads nonintrusively. The method applies to any site where NILM might be of interest, including commercial and industrial buildings, residences, and transportation systems.

Fault detection and diagnostics for non-intrusive monitoring using motor harmonics

Harmonic analysis of motor current has been used to track the speed of motors for sensorless control. Algorithms exist that track the speed of a motor given a dedicated stator current measurement, for example [1–5]. Harmonic analysis has also been applied for diagnostic detection of electro-mechanical faults such as damaged bearings and rotor eccentricity [6–17]. This paper demonstrates the utility of harmonic analysis for fault detection and diagnostics in non-intrusive monitoring applications, where multiple loads are tracked by a sensor monitoring only the aggregate utility service. An optimization routine is implemented to maintain accuracy of speed estimation while using shorter lengths of data.

FPGA-based spectral envelope preprocessor for power monitoring and control

Smart Grid and Smart Meter initiatives seek to enable energy providers and consumers to intelligently manage their energy needs through real-time monitoring, analysis, and control. We have developed an inexpensive FPGA implementation of a spectral envelope preprocessor. This FPGA permits cost-effective and richly detailed power consumption monitoring for individual loads or collections of loads. It permits a flexible trade-off between data transmission, storage, and computation requirements in a power monitoring or control system. The information from the FPGA can be used to coordinate the operation of power electronic controls.

The Sinefit Spectral Envelope Preprocessor

This paper presents a new spectral envelope preprocessor based on sinusoid fitting and the discrete Fourier transform. This preprocessor is well-suited for nonintrusive condition monitoring and diagnostics due to its high noise resiliency and flexibility. It reduces data storage, transfer, and processing requirements by extracting only relevant harmonic signatures. This paper analyzes the resolution and accuracy benefits of spectral envelopes, including the effects of additive white Gaussian noise and presence of higher frequency spectral harmonics.

A waveform-based power estimator for variable power loads

This paper proposes a method to derive an estimator that predicts the power consumption of variable power loads from a subset of higher current harmonics without requiring a full analysis of the internal circuit of the load. The method exploits structural features of the current waveforms consumed by the load to develop the estimator. The computation involves Gaussian elimination of a cyclotomic field representation to compute the estimator coefficients, avoiding floating-point computational error. Experimental results have shown that the proposed algorithm can derive estimators that can extract the power consumption of variable speed drives, computers, or light dimmers from fixed power loads in aggregate measurements.