AC Motor Efficiency - A Guide to Energy Savings
What we have done, and what we are doing
Part 3: TESTING FOR EFFICIENCY | Efficiency Test Methods | Problems of Variance | Testing Error | Efficiency and Bell Curve | Sample Testing | Because electric motor users are becoming more conscious of efficiency as a criterion for selecting and applying motors, it is important that differences between test methods and their attendant accuracies be clearly understood. The problems of testing occur because loading devices, which can accurately measure motor shaft output, become less available as motor horsepower increases and the magnitude of electrical power can exceed instrument accuracy range. Consequently, alternative methods for measuring motor efficiency have been introduced into U.S.A. test standards. The standard for testing induction machines in the U.S.A. is Institute of Electrical and Electronics Engineers (IEEE) 112 which recognizes five methods for determining motor efficiency - each of which has certain advantages as to accuracy, cost, and ease of testing, depending primarily on motor size (Figure 5). Although choice of a specific test method is somewhat subjective and dependent upon the aforementioned factors, Reliance and other motor manufacturers believe lEEE 112, test method B, to be the most accurate method for integral horsepower motors and best duplicates motor performance under actual operating conditions. Because dynamometer testing based on lEEE 112 method B test standard provides consistent, representative, and verifiable motor performance data required for proper motor comparison, it is the basis for the preferred method for polyphase AC motors rated 1-125 HP according to NEMA Standard MG 1-12.58.1.(1) The recently revised NEMA Standard specifies that motors 1-125 HP be tested by IEEE Standard 112, method B in accordance with certain additional steps to maximize the accuracy of this test procedure. This specified method reduces random test measurement errors and specifically deals with smoothing the stray-load loss using data from at least six motor load points followed by a linear regression analysis. It should be noted that this standard describes efficiency testing and labeling for full load only. In addition to the USA test methods described, there is also the Japanese Test Code (JEC 37) and alternative methods allowed by the International Test Standard (lEC 34-2). Both standards generally provide a different value of efficiency than the IEEE 112, test method B (Figure 6). The various test methods for efficiency can be divided into two broad categories: those which determine efficiency by direct measurement; and those which determine efficiency indirectly, by calculation of motor losses from data obtained through load or no-load testing. lEEE 112, test methods A, B and C determine efficiency, directly from measurements of electrical input and mechanical output under full operating conditions. Efficiency is calculated as the ratio of output to input.
(1) NEMA (National Electrical Manufacturers Association) Standard MG-1 is the industry standard for motors
Figure 6 Motor Efficiency By Test Method
lEEE 112, methods E and F and the lEC and JEC standards use various techniques to determine either input or output, or both, when a direct measurement is not available. Rather than measure output, watts losses are measured and deducted from input. The principal difference between the various methods is the treatment of stray load loss. IEEE methods E and F require a separate test for stray load loss while lEC 34-2 uses an assumed value of stray load loss. The JEC standard utilizes the circle diagram as the principal method to calculate efficiency and does not include a direct measurement of stray load loss. And, since stray load loss represents 8-15% of all load loss, estimating stray load loss compromises the accuracy values calculated by these methods.
Whether the product is automobiles or electric motors, one thing is for certain: variances associated with materials and manufacturing methods exist. And so, no two "identical" model cars will perform the same and deliver the same gas mileage despite being made on the same assembly line. The same is true for electric motors. Product variances in materials, such as steel used for laminations in the stator and rotor core, will lead to variances in electromagnetic properties and ultimately affect watts losses and motor efficiency. An increase in iron loss due to an order of lower quality steel could, for example, increase motor watts losses and reduce efficiency. Using a 10 HP motor (Figure 7) as an example, a 10% increase in iron loss (220 to 242 watts) would increase total losses from 1170 to 1192 watts and reduce efficiency from 86.4 to 86.2%. Variances also occur as the result of manufacturing process limitations. Motor parts, such as shafts, can only be economically produced within a tolerance of several thousandths of an inch. Moreover, combinations of mating parts contribute to dimensional variations, such as the size of the air gap, (air gap is the space between the rotor and stator. See Figure 4A) which cause variations in stray load loss and hence motor efficiency.
Figure 7 10 HP - 1750 RPM, 215T Therefore, efficiencies of specific type and horsepower motors produced by the same manufacturer vary because of variations in materials, components, and manufacturing processes. In addition to variances introduced by materials and manufacturing methods, variances occur due to the introduction of error by both test personnel and test equipment. With dynamometer testing, as with all motor test methods, there are several potential sources for inaccuracy: instrument accuracy; proper dynamometer application; and load cell and instrument calibration all affect test results. For example, a wattmeter used to measure power input during motor testing will affect efficiency readings if not properly calibrated. The example in Figure 8 illustrates that using a wattmeter out of calibration by 3/4% could overstate the motor efficiency by .5%. To minimize test error, Reliance calibrates all of its measuring equipment on a regular basis to ensure the any instrumentation errors are held within accepted limits.
Recognizing that testing AC motors requires extreme care and skill to obtain accurate consistent results, Reliance combines accurate test instruments with well trained and experienced technicians to provide consistent and representative motor performance data.
Figure 8
It is a statistical fact of life that a single characteristic of a population of products is described according to a bell-shaped curve (Figure 9). Thus, the measured diameter of a large number of motor shafts of nominal 1.000 inch/diameter, manufactured to a tolerance of + .002 inch, will vary from .998 inch to 1.002 inch according to a bell curve. Likewise, testing motors of specific size and design will result in an array of efficiency numbers which also form a bell curve.
Figure 9 The Bell Curve Two significant items can be observed from the bell curve; the mean and standard deviation. The mean is that value around which a given population is centered, while 95% of the population should be contained within two standard deviations. In the case of the motor shaft, one inch will be the average diameter and 95% will measure between .998 and 1.002 inches in diameter. In terms of motor efficiency, it will indicate an average efficiency and a minimum efficiency which motors must satisfy. Because of the time and additional expense of testing each motor manufactured in the precise method specified by the NEMA Standard, motor manufacturers should verify published efficiency, statistically, by a sampling method assuring that materials and manufacturing processes are in control. Reliance believes that, consistent with the NEMA Standard, all polyphase motors should be tested on a statistically valid quality control schedule using the NEMA Standard test method to verify published efficiency data. To confirm and establish confidence in the defined nominal efficiency values, Reliance tests all XE motors for efficiency. Depending on volume and statistical analysis, the efficiency test verification is either:
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