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Various algorithms have been put forth for an efficiency estimation of an in-service induction motor with varying intrusion levels and accuracy. In the field, it is hard to reach the terminals of an in-service motor because of its location. This paper helps a field engineer to choose properly an efficiency estimation method for condition monitoring of an in-service induction motor. In this paper, the author put forth an approach based on symmetrical components to assess the efficiency of induction machine working on unbalanced power supplies at any loading condition. A comparison is done between two offline intelligence techniques like gravitational search (GS) algorithm and cuckoo search (CS) algorithm for estimating the efficiency of an induction machine by using limited measurements. The merit of these techniques is that the motor is not taken out from its place of installation for testing. Also, the highly intrusive no-load test is avoided. Simulations and experimental results support and validate the carried-out research. [ABSTRACT FROM AUTHOR]

The increasing use of induction motors (IMs) with variable frequency drives for industrial process control and automation has necessitated the need to evaluate their energy efficiency in the presence of voltage and current harmonics. As the determination of IM efficiency is becoming mandatory in many countries, there is a need to develop a testing approach that eliminates inaccuracies due to the operator. This paper presents an automated efficiency testing procedure developed to mitigate the adverse effect of varying technical skill and competence levels of testing personnel on efficiency test results. This is a particularly useful development considering the recently proposed IEC/TS 60034-2-3 methodology which specifies identical tests between sinusoidal supply and converter operation. A comparison of several manual and automated efficiency tests on a 55-kW IM highlighted improvements in repeatability, correlation, and temperature variation due to the latter. In addition, the advanced data acquisition and computation capability of the measurement device used in the developed test rig provides an excellent platform to investigate the dynamic operation of IM drives. [ABSTRACT FROM AUTHOR]

Temperature rise of an induction machine increases losses and decrease in the efficiency. The temperature rise depends on the machine design. It takes several hours after starting a machine to reach to thermal stability. In most of the in situ efficiency estimation methods, it is required to obtain the operating data of the machine at a thermally stable condition, which needs a long running time of the machine. In this paper, a method based on a particle swarm optimization (PSO) algorithm is proposed, which can estimate the machine efficiency at different loads with thermal stability. The machine operation data at the first 30 min after the start rather than data at a thermal stability condition are used in the method. The proposed algorithm utilizes two approaches to predict a full-load temperature at a thermally stable condition. The first approach is based on the insulation class of the machine and uses the equivalent circuit. The second approach is based on the trend of the temperature rise in the first 30 min of running the machine after the start. Furthermore, a method is proposed to narrow the parameters range, which helps the PSO to converge to the right answer. All results are validated by the experimental results. [ABSTRACT FROM AUTHOR]

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]

Induction motors are the most preferred engines in industry because of their simple but robust structure. The efficiency of the preferred motor is crucial for the limitations of the loads to be pulled by the locomotive and the locomotive's suitability for the geographic conditions. For this reason, determining the energy efficiency and operating conditions of induction motors is crucial. It is often not possible to realize the efficiency of induction motors experimentally because it is necessary for the motor to be stopped during the experiment. This prevents the analysis of the efffects of the experiment on the energy efficiency of the motor. The efficiency estimation of induction motors provides a significant contribution to operation and energy efficiency. There is a variety of studies in literature related to efficiency estimation. However, the future of this study is the realization of efficiency estimations of induction motors at 17 different power types with artificial neural networks and linear estimation by looking at the speed values, current and moment, listed in the manufacture's catalog in full load. Before obtaining the estimations, the statistical analysis of the correlations between efficiency and moment, efficiency and speed, efficiency and current of the motor were applied. [ABSTRACT FROM AUTHOR]

We propose a Laplace stochastic frontier model as an alternative to the traditional model with normal errors. An interesting feature of the Laplace model is that the distribution of inefficiency conditional on the composed error is constant for positive values of the composed error, but varies for negative values. A simulation study suggests that the model performs well relative to the normal-exponential model when the two-sided error is misspecified. An application to U.S. Airlines is provided. [ABSTRACT FROM AUTHOR]

An analytical three-phase induction machine model is proposed, derived, and validated in this paper. This model is capable for arbitrary qd0-frame analysis and core loss estimation at both line-fed and inverter-fed situations. Detailed model-based machine copper and core loss estimations are presented. A simulation verification of the model consistency is given under a changing load profile in MATLAB/Simulink. Then, the model is verified comprehensively using three induction machines (1.5 HP, 3 HP, and 10 HP), where the model is proved to be scalable, and to provide excellent machine loss estimation in line-fed situation and inverter-fed situation with machine input line filters, as well as in the flux-weakening region. Finally, a series of sensitivity tests of the model parameters are performed and the effects of the parameters on the machine losses are discussed. It is believed that the proposed model will be beneficial for various qd0-frame model-based research works of three-phase induction machines. [ABSTRACT FROM AUTHOR]

The issues of functional form, distributions of the error components, and endogeneity are for the most part still open in stochastic frontier models. The same is true when it comes to imposition of restrictions of monotonicity and curvature, making efficiency estimation an elusive goal. In this article, we attempt to consider these problems simultaneously and offer practical solutions to the problems raised by Stone and addressed by Badunenko, Henderson and Kumbhakar. We provide major extensions to smoothly mixing regressions and fractional polynomial approximations for both the functional form of the frontier and the structure of inefficiency. Endogeneity is handled, simultaneously, using copulas. We provide detailed computational experiments and an application to U.S. banks. To explore the posteriors of the new models we rely heavily on sequential Monte Carlo techniques. [ABSTRACT FROM AUTHOR]

International efficiency testing standards, such as the IEEE 112-B and IEC 34-2-1, can determine the efficiency of an induction machine (IM) accurately at the cost of hindering the machine's productivity. Alternatively, various methods used to determine a machine's efficiency in situ do so at the cost of accuracy. This paper proposes a method that determines an IM's efficiency over a range of load conditions from tests conducted and centered around one thermally stable load point in the least intrusive manner possible. The results are then compared to those of the IEEE 112-B and IEC 34-2-1 motor testing standards using segregated loss analysis. It was found that despite the proposed algorithm being unable to accurately determine core losses, the efficiency of a machine can be estimated to within 0.5%–2.1% and 1.1%–1.7% error, when compared to the IEEE 112-B and IEC 34-2-1 standards, respectively over a 25%–150% machine load profile (dependent upon the proposed method's implementations). [ABSTRACT FROM PUBLISHER]

This paper presents an air-gap torque (AGT)-based method for efficiency estimation of induction motors. A new concept of stator resistance that includes the mechanical losses effect is proposed. This new stator resistance is estimated through a particle swarm optimization approach based on the stator flux equations and minimization of torque error at the rated operation point. Then, the obtained stator resistance is used in the AGT equations to estimate the shaft torque and then the efficiency. Moreover, the rotor speed is estimated using induction motor current signature analysis. Thus, the proposed methodology for induction motor efficiency estimation relies only on line currents, line voltages, and nameplate data, being appropriate for in-service applications. Finally, the simulation and experimental results are presented to validate the proposed method at different load conditions. [ABSTRACT FROM PUBLISHER]

The monitoring of photovoltaic (PV) systems is important for the optimization of their efficiency. In this paper, a low-cost smart multisensor architecture equipped with voltage, current, irradiance, temperature, and inertial sensors, for the monitoring (at the panel level) of a PV system, is presented with the aim of detecting the causes of efficiency losses. The system is based on a Wireless Sensor Networks with sensing nodes installed on each PV panel. The acquired data are then transferred to a service center where dedicated paradigms continuously perform the assessment of electrical efficiency as well the estimation of correlated causes, at the single panel level. In this paper, the detection of critical faults (temporary and permanent shadowing, dirtying, and anomalous aging) is addressed. The methodology adopted to estimate efficiency losses and related causes is based on the comparison between the measured efficiency of each PV panel and the nominal one estimated in the real operating conditions. Moreover, the anomalous aging estimation is based on the five parameter model approach that exploits a dedicated minimization paradigm to analyze the mismatch between the nominal current–voltage model of the PV panel and the measured one. The main advantage of the proposed approach is the continuous monitoring of PV plants and the assessment of possible causes of power inefficiency at the PV panel level, allowing for the implementation of a really efficient distributed fault diagnosis system. The experimental results are presented along with the analysis of the uncertainty affecting the measurement system. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Using a bacterial foraging algorithm to estimate motor efficiency under unbalance. [•] The method was evaluated in a 7.5kW motor under high values of unbalance voltage. [•] Estimation of the mechanical and core losses considering load variation. [•] Estimation of additional losses applying IEC 60034-2-1 standard. [•] The method provides good accuracy for low charge factor. [Copyright &y& Elsevier]

The efficient operation of induction machines and methods to estimate their working efficiency have received increased attention in recent years due to the growing awareness of the demand side energy management programs. Various techniques have been proposed for efficiency estimation with different requirements. Numerous works have also been published in the literature about estimating the efficiency of a machine in situ, under the loaded condition without disturbing its operation. However, very little has been done on estimation of the efficiency of the machines after the refurbishment process in the workshops, which in fact can affect numerous machines in the industry. In this paper, a method is proposed for this purpose, which requires only the no-load test. The proposed method is validated by experimental results with seven different induction machines. [ABSTRACT FROM PUBLISHER]

Background: The casemix-based payment system has been adopted in many countries, although it often needs complementary adjustment taking account of each hospital's unique production structure such as teaching and research duties, and non-profit motives. It has been challenging to numerically evaluate the impact of such structural heterogeneity on production, separately of production inefficiency. The current study adopted stochastic frontier analysis and proposed a method to assess unique components of hospital production structures using a fixed-effect variable. Methods: There were two stages of analyses in this study. In the first stage, we estimated the efficiency score from the hospital production function using a true fixed-effect model (TFEM) in stochastic frontier analysis. The use of a TFEM allowed us to differentiate the unobserved heterogeneity of individual hospitals as hospital-specific fixed effects. In the second stage, we regressed the obtained fixed-effect variable for structural components of hospitals to test whether the variable was explicitly related to the characteristics and local disadvantages of the hospitals. Results: In the first analysis, the estimated efficiency score was approximately 0.6. The mean value of the fixed-effect estimator was 0.784, the standard deviation was 0.137, the range was between 0.437 and 1.212. The second-stage regression confirmed that the value of the fixed effect was significantly correlated with advanced technology and local conditions of the sample hospitals. Conclusion: The obtained fixed-effect estimator may reflect hospitals' unique structures of production, considering production inefficiency. The values of fixed-effect estimators can be used as evaluation tools to improve fairness in the reimbursement system for various functions of hospitals based on casemix classification. [ABSTRACT FROM AUTHOR]

Abstract: As the demand of electrical energy increases, it is vital to replace inefficient motors with new energy efficient ones. The first step towards achieving this goal is to estimate the existing motors efficiencies accurately to determine how much energy saving will be achieved by using energy efficient motors. This paper proposes an economical and accurate approach for motor field efficiency estimation using bacterial foraging algorithm. The approach relies on the measurement of the stator current, stator voltage, input power, stator resistance and rotor speed of the motor without conducting no-load and locked-rotor tests. The BF algorithm is used to determine the equivalent circuit parameters of the motor. The efficiency is then estimated using these parameters. The advantages of the proposed method over the existing methods are simple procedure, efficiency can be estimated accurately without conducting any invasive tests and inexpensive. The approach has been tested on a 5 HP motor and the results are compared with particle swarm optimization method, immune algorithm method, torque gauge method, equivalent circuit method, slip method, current method and segregated loss method. The results demonstrate that the proposed approach can accurately estimate the field efficiency of motor. Accordingly, it is suitable for conducting energy audits and management of the motor. [Copyright &y& Elsevier]

Energy usage evaluation and condition monitoring for electric machines are important in industry for overall energy savings. They are often expected to be implemented in an integrated product because of many common requirements such as data collection. Because of the uninterrupted characteristic of industrial processes, traditional methods defined in IEEE Standard 112 cannot be used for these in-service motors. This paper proposes a truly nonintrusive method for in-service motor-efficiency estimation based on air-gap torque using only motor terminal quantities and nameplate information, with special considerations of motor condition monitoring requirements. Rotor speed and stator resistance, the stumbling blocks of most in-service testing methods, are extracted from motor input currents instead of being measured. The no-load test, which is required for calculating the rotational loss and core loss, is eliminated by using empirical values. Stray-load loss is assumed according to the motor horsepower as suggested in IEEE Standard 112. Finally, the proposed method is validated by testing three induction motors with different configurations. Experimental results show that the proposed method can estimate motor efficiencies with less than 2% errors under normal load conditions. [ABSTRACT FROM AUTHOR]

Condition monitoring of electric motors avoids severe economical losses resulting from unexpected motor failures and greatly improves the system reliability and maintainability. Efficiency estimation, which shares many common requirements with condition monitoring in terms of data collections, is expected to be implemented in an integrated product. This brings more considerations into the selection of the efficiency-estimation methods. This paper presents the results of an up-to-date literature survey on efficiency-estimation methods of in-service motors, particularly with considerations of the motor-condition-monitoring requirements. More than 20 of the most commonly used methods are briefly described and classified into nine categories according to their physical properties. Six categories of these methods are more related to in-service testing and are compared in a table summarizing the required tests and measurements, intrusion level, and average accuracy. Estimation of the rotor speed and the stator resistance, the two stumbling blocks of various efficiency-estimation methods, is also carefully studied; commonly used methods are summarized. Based on the survey results, four efficiency-estimation methods are suggested as candidates for nonintrusive in-service motor-efficiency estimation and condition-monitoring applications. Another contribution of this paper is that a general approach for developing nonintrusive motor-efficiency-estimation methods is proposed, incorporating rotor speed, stator resistance, and no-load loss estimations. [ABSTRACT FROM AUTHOR]

Abstract: The paper provides an introduction to and overview of five papers which derive from a conference on efficiency estimation for utilities in UK price reviews held at the London Business School in June 2004. The introductory paper summarises the discussion on the role of efficiency estimation methods, firstly, where there are comparator companies–as in the water industry and electricity distribution; and, secondly, where there are no within-country comparator companies–as with railway networks and electricity transmission systems. The paper concludes with some personal conclusions both on the sensible uses and on the limitations of formal statistical efficiency estimation methods in utility regulation. [Copyright &y& Elsevier]

Induction motors consume a great portion of the generated electrical energy. Moreover, most of them work at underloaded conditions, so they have low efficiencies and waste a lot of energy. Because of this, the efficiency estimation of in-service induction motors is a matter of great importance. This efficiency estimation is usually performed through indirect methods, which do not require invasive measurements of torque or speed. One of these methods is the modified Air-Gap Torque (AGT) method, which only requires voltage and current data, the stator resistance value, and the mechanical losses. This paper approaches the computation of a modified stator resistance including the mechanical losses effect to be applied in the AGT method for torque and efficiency estimation of induction motors. Some improvements are proposed in the computation of this resistance by using a direct method, as well as the possibility to estimate this parameter directly from the nameplate data of the induction motor. The proposed methodology only relies on line voltages, currents, and nameplate data and is not intrusive. The proposed methodology is analyzed through simulation and validated through experimental results with three-phase induction motors. Also, a comparison of methods for in-service induction motors efficiency estimation is presented for the tested motors. [ABSTRACT FROM AUTHOR]