James Paris

Diagnostic indicators for shipboard systems using non-intrusive load monitoring

Field studies have demonstrated that the non-intrusive load monitor (NILM) can effectively evaluate the state of many electromechanical systems by analyzing the electrical power that they draw. This paper discusses NILM applications in the marine environment. Machinery power data collected from USCGC SENECA (WMEC-906), a 270-foot U.S. Coast Guard cutter, indicates that the NILM can successfully diagnose the failure of flexible couplings and the presence of leaks in cycling systems. This paper discusses both of these shipboard problems, and it details the methodology used to develop the metrics that diagnose them.

NilmDB: The Non-Intrusive Load Monitor Database

This paper presents NilmDB, a comprehensive framework designed to solve the “big data” problem of non-intrusive load monitoring and diagnostics. It provides the central component of a flexible, distributed architecture for the storage, transfer, manipulation, and analysis of time-series data. NilmDB is network-transparent and facilitates remote viewing and management of large data sets by utilizing efficient data reduction and indexing techniques.

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.

Energy Applications for an Energy Box

Changes in the electric utility will necessitate new needs and opportunities for monitoring and controlling electric power consumption and generation. Technical solutions exploiting these opportunities and answering these needs would ideally preserve best practices like reliability, privacy, efficiency, and flexibility. A nonintrusive load monitor (NILM) can serve as an ideal platform for constructing an “energy box” capable of sophisticated monitoring and control. This paper introduces a data processing and analysis framework, NILM manager. NILM manager creates a business model for handling power data by minimizing network bandwidth and placing intelligence and feature expansion in easily transmitted “energy apps.”

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.

High-resolution physically-windowed sensors for power electronics applications

This paper presents a high-resolution, physically-windowed sensor architecture that is well-suited for energy scorekeeping and diagnostic applications. The sensor can track a large-scale main signal while capturing small-scale variations. The prototype system measures a small current signal using a closed-loop Hall sensor, and extends the range by driving a compensation current through an auxiliary winding. The system combines the compensation command and the sampled output of the residual sensor to reconstruct the input signal. Results show that the prototype can measure both dc and ac currents with 10 mA resolution over a 160 A current range.

Solid-state lamp with integral occupancy sensor

Previous work demonstrated a retrofit proximity detector for fluorescent lamps using the lamps own stray electric fields. This paper extends the retrofit sensor system to a solid-state (LED) lamp. The design and implementation of a suitable driver (“ballast”) for the LED lamp is presented. Design considerations for the ballast include those relevant to lighting (e.g. color cast and dimming levels) as well as those relevant to sensing of human occupants. Two electro-quasistatic modeling approaches for the lamp sensor are discussed. Experimental data from the LED lamp sensor are presented and compared to one of the proposed quasistatic models.