DC Offset Removal in Power Systems Using Deep Neural Network

Kim, Sun-Bin; Lee, Sunwoo; Son, Dae-Hee; Nam, Soon-Ryul

doi:10.1109/isgt.2019.8791675

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…A DNN optimally handles nonlinear applications [26][27][28][29][30][31], and can remove DC offsets from various fault currents (featuring both noise and harmonics) by training 1 cycle data window (64 samples per cycles) [26,27]. From the previous study, it was determined that the DNN has 64 inputs and 64 outputs.…”

Section: Deep Neural Network-based Removal Of a Decaying DC Offsetmentioning

confidence: 99%

“…After training, the DNN signal served as an input to the least squares error (LSE) method; this determined the phasor. The results were compared to those of the partial sums (PS) method, DFT, and DNN in [26,27]. The PS is a powerful method used to determine the magnitude of the DC-offset waveform; DFT is commonly used to define the signal phasors.…”

Section: Dnn Architecturementioning

confidence: 99%

“…The line parameter in this simulation is presented in Table 4. The results of the DNN were compared to those of the PS method, DFT, and DNN proposed in [26] and [27]. In this case, the proposed DNN is referred to as DNN, and DNN3 is the DNN proposed in [26] and [27].…”

Section: Validation Of the 57-samples Scenariomentioning

confidence: 99%

“…The results of the DNN were compared to those of the PS method, DFT, and DNN proposed in [26] and [27]. In this case, the proposed DNN is referred to as DNN, and DNN3 is the DNN proposed in [26] and [27]. Table 5 summarizes the DNN results in terms of the converging times; the transient is ±5% of the steady state after the fault occurred.…”

Section: Validation Of the 57-samples Scenariomentioning

confidence: 99%

“…The use of a Deep Neural Network (DNN) has been suggested to eliminate dependence on a time constant and reduce fragility in the presence of high-frequency components and noise [26,27]. The DNN method performs better than a DC-offset filter; data from only a single cycle are required, and the method detects the fundamental component of the fault current with the DC offset, harmonics, and noise.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying

et al. 2022

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To make a correct decision during normal and transient states, the signal processing for relay protection must be completed and designated the correct task within the shortest given duration. This paper proposes to solve a dc offset fault current phasor with harmonics and noise based on a Deep Neural Network (DNN) autoencoder stack. The size of the data window was reduced to less than one cycle to ensure that the correct offset is rapidly computed. The effects of different numbers of the data samples per cycle are discussed. The simulations revealed that the DNN autoencoder stack reduced the size of the data window to approximately 90% of a cycle waveform, and that DNN performance accuracy depended on the number of samples per cycle (32, 64, or 128) and the training dataset used. The fewer the samples per cycle of the training dataset, the more training was required. After training using an adequate dataset, the delay in the correct magnitude prediction was better than that of the partial sums (PSs) method without an additional filter. Similarly, the proposed DNN outperformed the DNN-based full decay cycle dc offset in the case of converging time. Taking advantage of the smaller DNN size and rapid converging time, the proposed DNN could be launched for real-time relay protection and centralized backup protection.

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