A Novel Technique for &lt;italic&gt;In Situ&lt;/italic&gt; Efficiency Estimation of Three-Phase IM Operating With Unbalanced Voltages

Al-Badri, Maher; Pillay, Pragasen; Angers, Pierre

doi:10.1109/tia.2016.2533599

Cited by 27 publications

(11 citation statements)

References 29 publications

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“…In addition, the inaccuracy of the measured efficiency always was less than 1%, but the inaccuracy of the estimated efficiency always was less than 0.03%. The authors obtained similar results in Reference [23], where the difference between the measured and estimated efficiency was 0.4% for a 3 kW rated power. In Reference [27], for an 11-kW rated power induction motor, the Realistic Error Estimation (REE) was ±0.24%, which was lower than the Worst Case Estimation (WCE) of the method recommended by the IEEE 112-B standard for polyphase induction motors.…”

Section: Introductionsupporting

confidence: 73%

“…The increasing demand for the use of small AC motors has stimulated research related to the development and enhancement of techniques for the measurement of the motors' efficiency, and for the computation of the associated accuracy [18][19][20][21][22][23][24][25][26][27][28], where we provide a short review of the related research papers. In Reference [9], a comparative analysis of direct and indirect efficiency estimation techniques was presented for a 3 kW, three-phase induction motor, and it was concluded that the direct determination of efficiency should be used for all sizes of induction motors.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method

et al. 2018

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Brushless electric motors are used intensively in the industrial automation sector due to the motors low inertia and fast response. According to the International Electrotechnical Commission, IEC 60034-2-1, the efficiency of a three-phase electric machine (excluding machines for traction vehicles) can be determined by direct or indirect techniques. In the case of small traction motors (<10 kW), direct methods are used extensively by manufacturers, even if no standard has been published or scheduled by the IEC. In this paper, we evaluated the accuracy of the (direct) back-to-back method for the estimation of the energy performance of a 3 kW brushless AC electric motor used in a light electric vehicle. We measured the efficiencies of a pair of motors and inverters, as well as the overall efficiency of the entire power train. The results showed that the methodology was sufficiently accurate and comparable with other indirect methods available in existing literature. Moreover, we developed a Simulink model that used the powertrain efficiency map as the input to perform the simulation of a standard urban driving cycle. The simulation was run 500 times to calculate the probability density function associated with the total range of the vehicle, considering the uncertainty of the efficiency that was determined experimentally. The simulation results confirmed the low deviation of the distribution standard compared to the average value of the range of the vehicle.

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Section: Introductionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method

et al. 2018

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“…Especially in three phase situations, it's difficult to keep all phase voltages exactly the same. According to NEMA standards, when phase voltage fluctuates, the motor's performance degrades [9]. Recovering procedures are employed to defray drooping situations, however reward may only be around 50% [10].…”

Section: Technical Survey Of Current Industrial Drives -A Broad Overviewmentioning

confidence: 99%

Performance Assessment of Industrial Drives due to Power Quality Issues and its Potential Impacts on Electrical Drives

Vishnu¹,

Ashok²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Power quality difficulties have been predicted since electronically powered drives have been the main component of any manufacturing process. Evaluating the same's informative quality has proven to be a big challenge. Because of non-linearity on the load side and an over-reliance on semiconductor technology, a good power quality could be crucial. The capabilities of the drives section employed in the business are also attracting attention in the current circumstances. As a result of the concerns mentioned above, several literary scientific studies show disturbances in the load side and the efficiency of electric drives. Weakness resulting from power quality is simply too significant, making it difficult for even the most sophisticated engineering to function effectively. A deep insight examination of grid side characteristics was performed in conjunction with investigating the inverter drive section responsiveness to grid variation to confirm the grid side issues for the artificial intelligence technique. This study work emphasized and proposed an answer to the possible artificial wise approach for administering fault deterrence prediction algorithm after clear proof from analysis and accounting grid side quality concerns relevant to electronic powered drives. The result is an accurate forecast of abnormal grid side behavior to offer safe and trustworthy grid operation, reducing the likelihood of shutdown or failure to a greater extent. Improvements in forecast accuracy based on previous data have been viable feedback to business equipment manufacturers, in addition to the aforementioned technique.

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“…The imbalanced data is a common issue existing in the classification tasks where the distributions of data among classes are imbalance [25]- [27]. Practically, the imbalanced data is also common in the regression tasks, such as the imbalanced voltages of induction motors [28]. The imbalanced data sets always bring difficulties for the learning algorithms with the premise that the distribution of data is approximately balanced.…”

Section: B the Measurements Datamentioning

confidence: 99%

“…However, this premise is hardly tenable in many applications. The mismatch between leaning algorithms and the distributions of data exists in both classification and regression tasks [28]. In other words, the learning algorithms of balanced data are applied to learn imbalanced data.…”

Section: The Oline Mixture Model Of Mach Number a The Mixture Learning For The Imbalanced Datamentioning

confidence: 99%

The Regression Learning of the Imbalanced and Big Data by the Online Mixture Model for the Mach Number Forecasting

et al. 2019

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Extracting valuable information to enhance the performance of forecasting models from the imbalanced and big data requires the scalable implementation of advanced statistical learning methods. This paper proposes the online mixture model (OMM) and applies it to the Mach number forecasting. Treating the key variable (e.g., Mach number) forecasting under all working conditions as an entire task, and viewing that of each individual working condition as a subtask, the OMM separates the dense samples from the sparse ones on the basis of subtasks. The subtask models are independently learnt on the samples with reduced volume, and updated for the new working conditions without retaining samples from the old working conditions. Moreover, the tree-structure ensemble (TSE)-feature subsets ensembles (FSEs) algorithm is presented to fit the nonlinear function of a subtask model, where the FSE local models with low-dimensional input features are established on the non-overlapping sample subsets constructed by the TSE method. The TSE-FSEs not only reduce the volume of data but also perform distributed computing with parallel structure, and thus has the advantage of the learning of big data. Experiments carried out on the measurement data of wind tunnel indicate that the OMM with the TSE-FSEs outperforms other learning algorithms for the Mach number forecasting, and meets the precision and forecasting speed requirements in engineering.INDEX TERMS Big data, imbalanced data, mixture model, ensemble, online, regression, wind tunnel.

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A Novel Technique for <italic>In Situ</italic> Efficiency Estimation of Three-Phase IM Operating With Unbalanced Voltages

Cited by 27 publications

References 29 publications

Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method

Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method

Performance Assessment of Industrial Drives due to Power Quality Issues and its Potential Impacts on Electrical Drives

The Regression Learning of the Imbalanced and Big Data by the Online Mixture Model for the Mach Number Forecasting

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