Review of harmonic losses in HVDC converter transformer: Its causes and mitigation techniques

Londhe, Surabhi Sunil; Bachawad, Manjusha R.

doi:10.1109/ipact.2017.8245190

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…This means that when the energy reserve of an accession is below a certain value, the producer is more likely to forage elsewhere in order to obtain more energy. 3 Throughout the foraging process of the population, scroungers always search for the producer who finds the best food and then get food from it or forage around the producer. Meanwhile, some scroungers will monitor producers for a long time always be ready to compete for food in order to improve the success of their own predation.…”

Section: Sparrow Search Algorithm (Ssa) 421 Principle Of the Algorithmmentioning

confidence: 99%

“…In addition, research has consistently shown that there is an AC-DC complex electromagnetic field inside the converter transformer during its operation state. Operation state identification of converter transformers can be adversely affected by these problems, making it technically challenging [3,4]. Therefore, there is an urgent need to study an effective method for operation state identification of converter transformers.…”

Section: Introductionmentioning

confidence: 99%

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Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Zhang

Xiao

et al. 2021

Actuators

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The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state during its manufacture and processing to ensure the safety of its future connection to the grid. Numerous studies have shown that vibration signals in transformers can reflect their operating state. Therefore, in order to achieve an effective identification of the operation state of the converter transformer, this paper proposes a method for identifying the operation state of the converter transformer based on vibration detection technology and a deep belief network optimization algorithm. This paper firstly describes the background, principle and application of vibration detection technology, using vibration measurement systems with piezoelectric acceleration sensors, piezoelectric actuators and data acquisition instruments to collect vibration signals at different measurement points on the converter transformer in states of no-load and on-load. By analyzing the time-frequency characteristics of the vibration signals, fast Fourier transform (FFT), wavelet packet decomposition (WPD) and time domain indexes (TDI) are combined into a fused feature extraction method to extract the eigenvalues of the vibration signals, so that the fused eigenvectors of the signals can be constructed. Considering the excellent performance of deep learning in classification, the deep belief network is used to classify the signals’ eigenvectors. To effectively improve the network classification efficiency, the sparrow search algorithm was introduced to build a mathematical model based on the behavioral characteristics of sparrow populations and combine the model with a deep belief network, so as to achieve adaptive parameter optimization of the network and accurate classification of the signals’ eigenvectors. The proposed method is applied to a 500 kV converter transformer for experimental verification. The experimental results show that the fused feature extraction method was able to fully extract the features of the vibration signal, and the deep belief network optimization algorithm had higher classification accuracy and better operational efficiency, and was able to effectively achieve accurate identification of the operation state of the converter transformer. In addition, the method achieved a precision response to the detection results of the vibration sensors, contributing to future improvements in converter transformer manufacturing technology.

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Section: Sparrow Search Algorithm (Ssa) 421 Principle Of the Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%