This paper develops a model for relating input current harmonic content to real power consumption for variable electronic loads, specifically for loads' actively controlled inverters energized by an uncontrolled rectification of the utility. This model serves as the basis for a method for estimating and disaggregating the power consumption of variable speed drives (VSDs) and rectifier loads from other constant power loads. This method can be used for nonintrusive power monitoring. The approach described in this paper uses the approximate switching function of the rectifier to derive the best estimating function for the fundamental current harmonic from a finite set of current harmonics uniquely associated with the operation of the drive. Experimental results show that the proposed VSD power and harmonic estimator can track VSD power consumption for monitoring given knowledge or an estimate of the input current harmonic content.

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Many proposals for future power systems for warships are extant. Anticipated improvements in capability, operating economy, and signature reduction may not be uniquely associated with these power systems. Alternatives are available for constructing variable speed drives and prime movers for ships with electric drives. These alternatives may open new design possibilities.

Abstract-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][2][3][4][5]. Harmonic analysis has also been applied for diagnostic detection of electro-mechanical faults such as damaged bearings and rotor eccentricity [6][7][8][9][10][11][12][13][14][15][16][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. I. SMART MONITORINGAt any point in the life of a system, mechanical and electrical equipment may be poorly operated. For example, as buildings age, both the electro-mechanical actuators and associated mechanical components wear, cease to function properly, and eventually fail, via myriad processes that are often undetected. Valves do not close fully, filters clog, airconditioning system dampers stick, refrigerant leaks, heating and cooling coils -from the smallest refrigerator to the largest building air-conditioning system -become fouled with dirt and debris, and belts slip. Energy waste and excessive plant wear are often exacerbated by closed-loop control. Under active control, damaged but still functioning equipment will operate by extending run times or operating points to meet user commands, leaving few obvious signs of compromised operation.For example, a number of surveys of airflow faults in buildings hint at the range and extent of these problems. One compendium of fault surveys [18], which examined 503 rooftop air-conditioning units in 181 buildings in five states in the Western U.S. from [2001][2002][2003][2004], found that the airflow was out of the specified range in approximately 42% of the units surveyed. A separate study [19] of 4, 168 commercial airconditioners in California reported that 44% of the surveyed units had airflow that was out of specifications. Studies of 29 new homes in Washington State [20] found that average duct leakage rates to the exterior ranged from 687 to 140 cubic feet per minute (CFM). Extrapolating from such fault surveys, one estimate for the total energy consumed by duct leakage is $5 billion/year [21].When "failure is not an option," the performance of important electro-mechanical loads on mission-critical systems like warships or power plants is often tracked by dedicated monitoring equipment [22]. An extensive sensing network can provide obvious advantages for fault detection, diagnosis, and prognosis. However, a large sensing network can be expensive and difficult to maintain.Smart Grid and Smart Meter initiatives hope to allow energy providers and consumers to intelligently manage their energy needs through real-time monitoring, analysis, and control of electrical power usage. The U.S. Department of Energy has i...

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This paper presents a numerical model of rubber composite using a COMSOL multiphysics program to simulate electrical properties of the rubber composite in the frequency range of 300 kHz to 30 MHz. The rubber composite was made of natural rubber vulcanized with carbon black and carbon nanotube. The chracterization was done by setting up a parallel plate capacitive structure in a shape of circular disk with a diameter of 38 mm and using the RF vector network analyzer to measure electrical properties in term of electrical impedance, specifically resistance (R) and reactance (X). Three different thinknesses of rubber composite sheets were used in the experiment, specifically 0.7 mm, 1.7 mm, and 2.9 mm. From the physical dimension of the test setup, capacitance (C), dissipation factor (D), relative permitivity (εr), and conductivity (σ) can be calculated. These extracted parameters together with the physical dimension of the test structure were used to create COMSOL multiphysics simulation models. The program can simulate non-linear modeling of the rubber composite under different electromagnetic constrains. The simulation results were compared to the measured results for all samples. Comparison results show that all electrical parameters were closly matched, indicating that the COMSOL multiphysics models were correctly generated. The results also indicate that the conductivity and the relative permittivity of the tested rubber composite change dramatically at the frequency above 10 MHz. The results indicate the physical limit of the tested rubber composite in the sensing application. The simulation model proposed in this paper can be used to design and possibly predict the geometical and electrical properties of the rubber composite in future applications.

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.