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In the in‐service induction motors (IMs), friction and windage losses (FWL) value should be determined non‐intrusively. Hence, in the in‐service IMs, instead of measuring FWL, empirical equations are used to estimate FWL value. A novel technique is proposed for estimating the FWL value in low‐voltage three‐phase IMs based on obtained data from applying the no‐load test on 425 simulated IMs in the MATLAB software. The simulated IMs are 380 V, 50 Hz, with different numbers of poles in the power range of 0.37–400 kW. The FWL value for simulated IMs is calculated based on the IEEE 112 standard and by applying the no‐load test. Then, based on the dispersion of the obtained data from the no‐load test and using non‐linear regression, according to the number of IM poles for each number of poles, a third‐degree equation for FWL estimation is fitted to the test data. The proposed method to estimate the FWL value only needs the nominal output power listed on the IM nameplate. Also, unlike existing empirical relationships, the proposed approach estimates the FWL value for IMs with high accuracy and non‐intrusively. The effectiveness of the suggested technique is confirmed by simulation and practical results. [ABSTRACT FROM AUTHOR]

Soft magnetic lamination is produced with high width that usually is reduced by cutting. It yields permeability decreases Ɵ and loss increases r. So far, existing corresponding data is not consistent, since derived from strongly different lamination widths W. A consistent, ultra‐rapid test method is suggested, for international standardisation. It is based on a multi‐frequency single sheet tester. A 50 cm long, precisely prepared material sample of W = 17 cm is inserted into it, for an initial measurement in non‐cut state. Then, it is cut into two 8.5 cm wide strips, for a test in cut state. Changes Ɵ and r are evaluated for induction values B up to 1.8 T. They are rapidly measured for frequencies of 50, 400 and 1000 Hz. Results from steel are described for guillotine cutting, from amorphous ribbon also for scissors. As a general tendency, Ɵ exceeds r by an order of about 3, however, with low reproducibility. Thus, standardisation is suggested for loss‐rise functions r(B) only. Examples of r‐functions reveal strong individual differences, as foot‐prints of different products. With increasing f, r‐values tend to sink. With increasing B, they rise, except for high B of NO steel. Amorphous ribbon shows non‐clarified deviating tendencies. Standardisation of r‐tests promises comparable information on cutting effects, for a given material, through a given cutting tool—but also on the tool's state of wear. [ABSTRACT FROM AUTHOR]

Abstract Soft magnetic lamination is produced with high width that usually is reduced by cutting. It yields permeability decreases Ɵ and loss increases r. So far, existing corresponding data is not consistent, since derived from strongly different lamination widths W. A consistent, ultra‐rapid test method is suggested, for international standardisation. It is based on a multi‐frequency single sheet tester. A 50 cm long, precisely prepared material sample of W = 17 cm is inserted into it, for an initial measurement in non‐cut state. Then, it is cut into two 8.5 cm wide strips, for a test in cut state. Changes Ɵ and r are evaluated for induction values B up to 1.8 T. They are rapidly measured for frequencies of 50, 400 and 1000 Hz. Results from steel are described for guillotine cutting, from amorphous ribbon also for scissors. As a general tendency, Ɵ exceeds r by an order of about 3, however, with low reproducibility. Thus, standardisation is suggested for loss‐rise functions r(B) only. Examples of r‐functions reveal strong individual differences, as foot‐prints of different products. With increasing f, r‐values tend to sink. With increasing B, they rise, except for high B of NO steel. Amorphous ribbon shows non‐clarified deviating tendencies. Standardisation of r‐tests promises comparable information on cutting effects, for a given material, through a given cutting tool—but also on the tool's state of wear.

Abstract In the in‐service induction motors (IMs), friction and windage losses (FWL) value should be determined non‐intrusively. Hence, in the in‐service IMs, instead of measuring FWL, empirical equations are used to estimate FWL value. A novel technique is proposed for estimating the FWL value in low‐voltage three‐phase IMs based on obtained data from applying the no‐load test on 425 simulated IMs in the MATLAB software. The simulated IMs are 380 V, 50 Hz, with different numbers of poles in the power range of 0.37–400 kW. The FWL value for simulated IMs is calculated based on the IEEE 112 standard and by applying the no‐load test. Then, based on the dispersion of the obtained data from the no‐load test and using non‐linear regression, according to the number of IM poles for each number of poles, a third‐degree equation for FWL estimation is fitted to the test data. The proposed method to estimate the FWL value only needs the nominal output power listed on the IM nameplate. Also, unlike existing empirical relationships, the proposed approach estimates the FWL value for IMs with high accuracy and non‐intrusively. The effectiveness of the suggested technique is confirmed by simulation and practical results.

Abstract This paper introduces a novel application of the inverse modelling method, which is designed to estimate core losses in both the stator and rotor regions of an electrical machine. The technique focuses on the use of short‐term transient temperature measurements obtained from the stator core of a slotless induction machine, with a focus on validating the measured temperature rise through a forward model. The measurement setup involves two primary approaches: (i) the use of thermal sensors embedded in a printed circuit board inside the stator core and (ii) surface sensors embedded on the stator yoke. Through the use of this innovative approach, the results indicate that the inverse modelling technique is highly effective in predicting core losses based on short‐time transient temperature rise measurements.

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of the inverse modelling method, which is designed to estimate core losses in both the stator and rotor regions of an electrical machine. The technique focuses on the use of short‐term transient temperature measurements obtained from the stator core of a slotless induction machine, with a focus on validating the measured temperature rise through a forward model. The measurement setup involves two primary approaches: (i) the use of thermal sensors embedded in a printed circuit board inside the stator core and (ii) surface sensors embedded on the stator yoke. Through the use of this innovative approach, the results indicate that the inverse modelling technique is highly effective in predicting core losses based on short‐time transient temperature rise measurements. [ABSTRACT FROM AUTHOR]

The ultra-high voltage direct current (UHVDC) system is widely constructed due to its suitability for large-capacity long-distance transmission, and its losses have become an important part of the power grid losses. However, the overall and subcomponent losses of the power system are difficult to measure by measuring devices or accurately calculated by existing algorithms. In this paper, a convolutional neural network (CNN)-based digital twin model for UHVDC system loss measurement is proposed. The parameters and data of the physical system are fed into the digital space system through the data interaction. In the digital twin model of the digital space system, a CNN network with added topology information extraction layers and key equipment parameter adaptive perception layers excavates the deep multidimensional correlation features of the digital space mapping data. Then a multi-task joint processing layer fuses the extracted deep features and missing information to calculate the total and sub-component losses. The training and testing results based on actual UHVDC project data show that the mean absolute error (MAE) is 0.9, while R2 is 0.9999, which proves the accuracy of the proposed digital twin model is superior to mainstream deep learning models. This model is embedded in digital twin devices and applied in actual UHVDC converter stations.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]