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Estimation of induction machine parameters, which are commonly used in efficiency evaluation and control methods, can be effectively achieved by utilizing genetic algorithms (GAs). One of the difficulties of using GAs for efficiency estimation is the determination of variable (parameter) constraints (ranges). This paper focuses on the range determination of the parameters (variables) for GA applications. A wide range of variables can cause unstable outcomes. Hence, it is essential to determine an acceptable range for each variable prior to a GA run to produce stable and repeatable results. In this paper, relationships based on the nameplate information and a large database of tested induction motors provided by Hydro-Québec are proposed to determine reasonable induction motor parameter ranges. The proposed method is applied to three different cases. The results of the three cases are compared to each other and also against the corresponding experimental results, which validate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for in situ efficiency estimation for induction motors operating with unbalanced distorted voltages. The proposed technique utilizes the genetic algorithm, IEEE form F2-method F1 calculations, large motor test database, and a novel stray-load loss formula. The technique is evaluated by testing three small- and medium-sized induction motors with different combinations of voltage unbalance and total harmonic distortion. The results showed acceptable accuracy. The repeatability and usability of the technique with balanced sinusoidal voltages are also validated. The technique may be used as a potential industrial tool that can help derate induction motors in the presence of voltage unbalance and harmonics. [ABSTRACT FROM PUBLISHER]

The precise estimation of efficiency of induction motors is crucial in industries for energy savings, auditing, and management. This paper presents a novel method for in situ induction motors efficiency estimation by applying the genetic algorithm and utilizing IEEE Form F2-Method F1 calculations with pretested motor data. The method requires a dc test, full-load operating point rms voltages, currents, input power, and speed measurements. The proposed algorithm uses a sensorless technique to determine motor speed. The algorithm is not only an in situ tool; it can also be used as an on-site efficiency estimation tool that might replace the expensive dynamometer procedure. The method was validated by testing 30 induction motors. [ABSTRACT FROM AUTHOR]