A wavelet-based universal data compression method for different types of signals in power systems

Ji, Xinchi; Zhang, Fang; Cheng, Lin; Chen, Liang; He, Hongyu

doi:10.1109/pesgm.2017.8274576

Cited by 3 publications

(13 citation statements)

References 16 publications

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“…Our previous work [25] proposed a wavelet-based universal data compression method to solve these problems. The criterion of the minimum compression distortion composite index (CDCI) was proposed to select the best wavelet and decomposition scale.…”

Section: Introductionmentioning

confidence: 99%

“…CDCI was proven to be suitable for the compression of different types of signals including oscillations and disturbance signals, and the selected wavelet and scale can provide balanced compression performance and reconstruction accuracy. However, in [25], signals have to be compressed and reconstructed by all candidates of wavelets and scales. Only then can the CDCIs of all the candidates be calculated to select the best one.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an improved wavelet-based data compression technique for oscillations in power systems is proposed in this paper based on [25]. The wavelet function and decomposition scale can be selected directly according to the oscillation frequency, which is the most significant characteristic of oscillations.…”

Section: Introductionmentioning

confidence: 99%

“…The wavelet function and decomposition scale can be selected directly according to the oscillation frequency, which is the most significant characteristic of oscillations. The amount of calculation in the proposed method is much lower than that in [25]. Specifically, 1) The qualitative relationship between the oscillation frequency and the corresponding optimal wavelet function and decomposition scale is discussed.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, 1) The qualitative relationship between the oscillation frequency and the corresponding optimal wavelet function and decomposition scale is discussed. As samples, actual recorded oscillations and simulated oscillations are compressed and reconstructed by the wavelet-based data compression method to select the best wavelets and scales based on the criterion of the minimum CDCI in [25]. 2) A quantitative relationship is established by linear regression.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Wavelet-based data compression for wide-area measurement data of oscillations

Cheng

Zhang

et al. 2018

J. Mod. Power Syst. Clean Energy

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This paper proposes a wavelet-based data compression method to compress the recorded data of oscillations in power systems for wide-area measurement systems. Actual recorded oscillations and simulated oscillations are compressed and reconstructed by the waveletbased data compression method to select the best wavelet functions and decomposition scales according to the criterion of the minimum compression distortion composite index, for a balanced consideration of compression performance and reconstruction accuracy. Based on the selections, the relationship between the oscillation frequency and the corresponding optimal wavelet and scale is discussed, and a piecewise linear model of the base-2 logarithm of the frequency and the order of the wavelet is developed, in which different pieces represent different scales. As a result, the wavelet function and decomposition scale can be selected according to the oscillation frequency. Compared with the wavelet-based data compression method with a fixed wavelet scale for disturbance signals and the real-time data compression method based on exception compression and swing door trending for oscillations, the proposed method can provide high compression ratios and low distortion rates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wavelet-based data compression for wide-area measurement data of oscillations

Cheng

Zhang

et al. 2018

J. Mod. Power Syst. Clean Energy

Self Cite

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Photovoltaic Systems Aggregation using Wavelet Analysis: A Case Study

2020

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Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

Zhu

et al. 2022

Sensors

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Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.

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A wavelet-based universal data compression method for different types of signals in power systems

Cited by 3 publications

References 16 publications

Wavelet-based data compression for wide-area measurement data of oscillations

Wavelet-based data compression for wide-area measurement data of oscillations

Photovoltaic Systems Aggregation using Wavelet Analysis: A Case Study

Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

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