Condition Monitoring of 154 kV HTS Cable Systems via Temporal Sliding LSTM Networks

Lee, Geon Seok; Bang, Su Sik; Mantooth, H. Alan; Shin, Yong June

doi:10.1109/access.2020.3014227

Cited by 16 publications

(3 citation statements)

References 34 publications

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“…These modelling methods are known as white-box models. To have higher accuracy and lower computation time, AI techniques can be used to create black-box models (BBMs) [124,[194][195][196][197][198]. At the first step, these models are trained with the help of experimental and simulation input data.…”

Section: Faster Modelling Approaches Based On Data-driven Methods Usi...mentioning

confidence: 99%

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

Yazdani-Asrami

Seyyedbarzegar

Sadeghi

et al. 2022

Supercond. Sci. Technol.

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Along with advancements in superconducting technology, especially in high temperature superconductors (HTS), the use of these materials in power system applications is gaining outstanding attention. Due to the lower weight, higher current carrying capability, and the lower loss of HTS cables compared to conventional counterparts, they are among the most focused applications of superconductors in power systems. In near future, these cables will be installed as key elements not only in power systems but also in cryo-electrified transportation units, which take advantage of both cryogenics and superconducting technology simultaneously, e.g. hydrogen-powered aircraft. Given the sensitivity of the reliable and continuous performance of HTS cables, any failures, caused by faults, could be catastrophic, if they are not designed appropriately. Thus, fault analysis of superconducting cables is crucial for ensuring their safety, reliability, and stability, and also for characterising the behaviour of HTS cables under fault currents at the design stage. Many investigations were conducted on the fault characterisation and analysis of HTS cables in the last few years. This paper aims to provide a topical review on all of these conducted studies, It will discuss the current challenges of HTS cables and after that current developments of fault behaviour of HTS cables would be presented, and then we will discuss the future trends and future challenges of superconducting cables regarding their fault performance.

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Section: Faster Modelling Approaches Based On Data-driven Methods Usi...mentioning

confidence: 99%

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

Yazdani-Asrami

Seyyedbarzegar

Sadeghi

et al. 2022

Supercond. Sci. Technol.

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“…In this case, deep learning algorithms, such as RNN (recurrent neural network), have been adopted by researchers to analyze the high-dimensional load data (Greff et al, 2017;Lee et al, 2020). However, it is difficult for original RNNs to tackle the gradient disappearance and the long-term dependency issues.…”

Section: Introductionmentioning

confidence: 99%

An improved deep learning algorithm in enabling load data classification for power system

Ziyao

Li²,

Wu³

2022

Front. Energy Res.

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Load behaviors significantly impact the planning, dispatching, and operation of the modern power systems. Load classification has been proved as one of the most effective ways of analyzing the load behaviors. However, due to the issues of data collection, transmission, and storage in current power systems, data missing problems frequently occur, which prevents the load classification tasks from precisely identifying the load classes. Simultaneously, because of the diversities of the load categories, different loads contribute various amounts of data, which causes the class imbalance issue. The traditional load data classification algorithms lack the ability to solve the aforementioned issues, which may deteriorate the load classification accuracy. Therefore, this study proposed an improved deep learning algorithm based on the load classification approach in terms of raising the classification performances with solving the data missing and class imbalance issues. First, the LATC (low-rank autoregressive tensor completion) algorithm is used to solve the data missing issue to improve the quality of the training dataset. A Borderline-SMOTE algorithm is further adopted to improve the class distribution in the training dataset to improve the training performances of biGRU (bidirectional gated recurrent unit). Afterward, to improve the classification accuracy in the classification task, the biGRU algorithm, combined with the attention mechanism, is used as the underlying infrastructure. The experimental results show the effectiveness of the proposed approach.

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“…The main issues with CNN are the low training efficiency and manually adjusted parameters. Different from the aforementioned AI technologies, the long short-term memory (LSTM) is a special architecture of recurrent neural network (RNN) and has been proven effective for fault diagnosis of power systems [10]. On the other hand, the LSTM parameters are determined empirically based on previous knowledge and experience and thereby not capable of dealing with unexpected faults in volatile environments.…”

Section: Introductionmentioning

confidence: 99%

A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

Yan

2021

Proceedings of CECNet 2021

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Recently, long short-term memory (LSTM) networks have been widely adopted to help with fault diagnosis for power systems. However, the parameters of LSTM networks are determined by prior knowledge and experience and thereby not capable of dealing with unexpected faults in volatile environments. In this paper, we propose and apply an improved grey wolf optimization (IGWO) algorithm to optimize the parameters of LSTM networks, aiming to circumvent the drawback of empirical LSTM parameters and enhance the fault diagnosis accuracy for on-load tap changers (OLTCs). The composite multiscale weighted permutation entropy and energy entropy yielded by the grasshopper optimization algorithm and variational mode decomposition (GOA-VMD) method are used as the inputs of LSTM networks. The IGWO algorithm is applied in an iterative manner to optimize the relevant super arithmetic of the LSTM. In this way, an IGWO-LSTM combination model is constructed to classify different faults diagnosed in OLTCs. Experimental results verify the diagnosis performance superiority of the proposed method over several widely used comparison benchmarks

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Condition Monitoring of 154 kV HTS Cable Systems via Temporal Sliding LSTM Networks

Cited by 16 publications

References 34 publications

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

High temperature superconducting cables and their performance against short circuit faults: current development, challenges, solutions, and future trends

An improved deep learning algorithm in enabling load data classification for power system

A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

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