Convolutional Adversarial Neural Network (CANN) for Fault Diagnosis within a Power System : Addressing the Challenge of Event Correlation for Diagnosis by Power Disturbance Monitoring Equipment in a Smart Grid

Chan, Steve; Oktavianti, Ika; Puspita, Verlly; Nopphawan, Parnmook

doi:10.1109/icoiact46704.2019.8938444

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…A classification algorithm using convolutional neural networks (CNNs) with different sampling frequencies is proposed in [17]. Wavelet transform has been used to extract fault harmonics for the input of CNNs, but data generalization issues impact the classification decisions and the accuracy of the results in [18]. In deep neural networks, CNNs become an effective method for image classification and are used as building blocks for ResNet [19] and VGG Net [20].…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning Based Fault Events Analysis in Power Systems

et al. 2022

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The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault types and locations under various conditions and reduce the fault analysis efforts.

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Section: Introductionmentioning

confidence: 99%

“…DLL-AB(6), DLL-AB(16), Double line to line (class index) DLL-BC(7), DLL-BC(17) , DLL-AC(8), DLL-AC(18),Triple line to (class index) TLG-ABC (9) TLG-ABC(19) …”

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confidence: 99%

Deep-Learning Based Fault Events Analysis in Power Systems

et al. 2022

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“…The study in [60] considers the analysis of disturbances that arose in the EPS Association of Southeast Asian Nations. To identify the type of disturbance, the authors used the CANN algorithm.…”

Section: Short Circuit Line Transformer Bus Trippingmentioning

confidence: 99%

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

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Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

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“…The simulation results are evaluated real-time measurements by the dataset from 2014 to 2020 that show the RNN accuracy of 94% and GRU and LSTM methods of 50%. In [91]- [92], the authors suggested the adversarial CNNs which combined the GANs and CNNs for Fault detection and isolation and repair. A CNN algorithm is proposed for fault classification of the power systems.…”

Section: Tp Precision = Tp +Fpmentioning

confidence: 99%

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

et al. 2022

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This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.

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Convolutional Adversarial Neural Network (CANN) for Fault Diagnosis within a Power System : Addressing the Challenge of Event Correlation for Diagnosis by Power Disturbance Monitoring Equipment in a Smart Grid

Cited by 8 publications

References 9 publications

Deep-Learning Based Fault Events Analysis in Power Systems

Deep-Learning Based Fault Events Analysis in Power Systems

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

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