Harmonics and Interharmonics Detection Based on Synchrosqueezing Adaptive S-Transform

Lou, Zhaoyu; Li, Pan; Ma, Keping; Teng, Feng-cheng

doi:10.3390/en15134539

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…This enhancement results in excellent time-frequency characteristics and reduced sensitivity to noise. Consequently, the S-transform is highly suitable for extracting dynamic signals [28]. The one-dimensional continuous S-change of the signal x(t) is defined as follows:…”

Section: S-transformmentioning

confidence: 99%

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Duan,

Cen,

et al. 2024

Energies

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To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues.

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Section: S-transformmentioning

confidence: 99%

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

Duan,

Cen,

et al. 2024

Energies

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“…The harmonic/resonance problem has undergone profound changes and has become an essential factor that affects the safe and reliable operation of power grids [1]. Harmonics cause distortions in voltage waveforms and resonance phenomena, reduce power quality, and exert adverse effects on electrical equipment, automatic devices, measuring instruments, and communication systems [2,3]. They are considered public hazards.…”

Section: Introductionmentioning

confidence: 99%

EhdNet: Efficient Harmonic Detection Network for All-Phase Processing with Channel Attention Mechanism

Deng,

Wang,

et al. 2024

Energies

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The core of harmonic detection is the recognition and extraction of each order harmonic in the signal. The current detection methods are seriously affected by the fence effect and spectrum aliasing, which brings great challenges to the detection of each order harmonic in the signal. This paper proposes an efficient harmonic detection neural network based on all-phase processing. It is based on three crucial designs. First, a harmonic signal-processing module is developed to ensure phase invariance and establish the foundation for subsequent modules. Then, we constructed the backbone network and utilized the feature-extraction module to extract deep abstract harmonic features of the target. Furthermore, a channel attention mechanism is also introduced in the weight-selection module to enhance the energy of the residual convolution stable spectrum feature, which facilitates the accurate and subtle expression of intrinsic characteristics of the target. We evaluate our method based on frequency, phase, and amplitude in two environments with and without noise. Experimental results demonstrate that the proposed EhdNet method can achieve 94% accuracy, which is higher than the compared methods. In comparison experiments with actual data, the RMSE of EhdNet is also lower than that of other recent methods. Moreover, the proposed method outperforms ResNet, BP, and other neural network approaches in data processing across diverse working conditions due to its incorporation of a channel attention mechanism.

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