Monitoring and analysis of electronic current transformer’s field operating errors

Xu, Yun; Li, Yao; Xiao, Xun; Xu, Ziqiang; Hu, Haoliang

doi:10.1016/j.measurement.2017.08.015

Cited by 15 publications

(10 citation statements)

References 8 publications

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“…Therefore, after running, the transformation ratio cannot be switched adaptively according to the running situation [7][8] . When the load of the distribution network increases or decreases, the current transformer load current is too large or too small [9][10] . However, if the current transformer ratio is too large or too small, it will affect the accuracy of electric energy measurement and the vital economic interests of both power suppliers and consumers, and will affect the performance of the current transformer itself.…”

Section: Introductionmentioning

confidence: 99%

A method for improving the accuracy of wide-range current transformers based on deep belief networks

Ouyang

Feng

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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With the proposal of the national dual carbon strategic goal, the permeability of new energy power generation in the power grid continues growing. The inherent intermittency and uncertainty lead to fluctuations in line power, which widens the working current range of the metering current transformer. Therefore, it is difficult for traditional methods to effectively meet the measurement error requirements. To this end, this paper proposes an adaptive error compensation method for wide-range current transformers based on deep belief networks. With its unique advantages in feature extraction and pattern recognition, deep belief network has become an effective method for transformer error feature extraction in the context of big data. In this method, the original data signal measured by the transformer is sent to the deep belief network algorithm for training, and the error feature is automatically extracted. An adaptive calibration system for wide-range current transformers is developed, and an error compensation experiment is carried out. The experimental results show that the method proposed in this paper can simply and efficiently identify the error characteristics of the wide-range current transformers and self-adaptively identify them, which improves the accuracy of the measurement results of the wide-range current transformers.

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Section: Introductionmentioning

confidence: 99%

A method for improving the accuracy of wide-range current transformers based on deep belief networks

Ouyang

Feng

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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“…Hence, the digital intelligence of the electronic current transformer (ECT) as a measuring equipment will be indispensable. In recent years, with the development of smart grids, ECTs have been used in large-scale pilot applications [4]. ECTs, as key measurement equipment for power grids, must be calibrated regularly to ensure the accuracy of their measurement performance.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, with the development of smart grids, ECTs have been used in large‐scale pilot applications [4]. ECTs, as key measurement equipment for power grids, must be calibrated regularly to ensure the accuracy of their measurement performance.…”

Section: Introductionmentioning

confidence: 99%

Passive‐compensation clamp‐on two‐stage current transformer for online calibration

Xiong

et al. 2021

IET Science Measure & Tech

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Digital measurement equipment such as electronic transformers enable real-time functional control and monitoring systems to be built in power grids to accommodate the large-scale access of distributed renewable energy to the grid. However, the current offline calibration of electronic transformers is difficult to implement; therefore, an online calibration method for electronic transformers must be developed to ensure their accuracy. The design of a clamp-on two-stage current transformer (CTS-CT) with a passive compensation loop is presented herein. The compensation loop is utilised to reduce both the amplitude and phase errors, thereby achieving high accuracy. The formulas for calculating the resistance and capacitance of the passive compensation loop are presented. The expanded uncertainties (95% confidence level) of the proposed CTS-CT are estimated to be lower 0.03% for the amplitude error and 1′ for the phase error. Experimental results indicate that the proposed CTS-CT prototype is not affected by the external magnetic field nor the opening and closing times, thereby enabling good-quality online calibration for field applications. Moreover, the proposed CTS-CT adopts neither an active compensatory circuit nor software compensation; therefore, the manufacturing of the CTS-CT is steady and simple, and the calibration procedures are simplified.

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“…Many monitoring and diagnostic technologies have been developed to address the problems in transformers. Online monitoring technologies can provide real-time information of the power equipment's state [3]. Nowadays, the key operation parameters of power transformers such as terminal voltage, current, ampere turns, and temperature are usually monitored continuously.…”

Section: Introductionmentioning

confidence: 99%

Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

et al. 2018

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Frequency response analysis (FRA) demonstrates significant advantages in the diagnosis of transformer winding faults. The instrument market desires intelligent diagnostic functions to ensure that the FRA technique is more practically useful. In this paper, a hierarchical dimension reduction (HDR) classifier is proposed to identify types of typical incipient winding faults. The classifier procedure is hierarchical. First, measured frequency response (FR) curves are preprocessed using binarization and binary erosion to normalize FR data. Second, the pre-processed data are divided into groups according to the definition of dynamic frequency sub-bands. Then, hybrid algorithms comprised of two conventional and two novel quantitative indices are used to reduce the dimension of the FR data and extract the features for identifying typical types of transformer winding faults. The classifier provides an integration of a priori expertise and quantitative analysis in the furtherance of the automatic identification of FR data. Twenty-six sets of FR data from different types of power transformers with multiple types of winding faults were collected from an experimental simulation, literature, and real tests performed by a grid company. Finally, real case studies were conducted to verify the performance of the HDR classifier in the automatic identification of transformer winding faults.

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Monitoring and analysis of electronic current transformer’s field operating errors

Cited by 15 publications

References 8 publications

A method for improving the accuracy of wide-range current transformers based on deep belief networks

A method for improving the accuracy of wide-range current transformers based on deep belief networks

Passive‐compensation clamp‐on two‐stage current transformer for online calibration

Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

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