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

Cheng, Lin; Ji, Xinchi; Zhang, Fang; Huang, He; Gao, Song Wei

doi:10.1007/s40565-018-0424-2

Cited by 14 publications

(7 citation statements)

References 27 publications

(58 reference statements)

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“…where * CR  is the normalized value of CR, denoted as CR 1/ due to its range of (0,1]; whereas * NMSE  is the normalized value of NMSE with its base value set as 2×10 -3 from [10]; a and b are the weights of CR and NMSE, respectively. The smaller the CDCI is, the better performance of the compression method will be.…”

Section: Performance Evaluation Of the Compression Methodsmentioning

confidence: 99%

“…Lossy compression techniques can significantly reduce the scale of PMU data, though at the cost of introducing limited errors between the reconstructed data and the originals. Signal feature analysis is a B well-studied lossy compression technique for PMU data, among which the most widely used include the principal component analysis (PCA)-based [9] and wavelet transform (WT)-based [10] methods. However, data buffering is required for PCA-based and WT-based methods, resulting in insufficient timeliness for real-time applications.…”

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confidence: 99%

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Curvature Quantified Douglas-Peucker-Based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Tan,

Zhang,

et al. 2024

Prot. Control Mod. Power Syst.

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Facing constraints imposed by storage and bandwidth limitations, the vast volume of phasor measurement unit (PMU) data collected by the wide-area measurement system (WAMS) for power systems cannot be fully utilized. This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications. In this work, an effective curvature quantified Douglas-Peucker (CQDP)-based PMU data compression method is proposed for situational awareness of power systems. First, a curvature integrated distance (CID) for measuring the local flection and fluctuation of PMU signals is developed. The Douglas-Peucker (DP) algorithm integrated with a quantile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals. This allows adaptive adjustment of the algorithm parameters, so as to maintain the desired compression ratio and reconstruction accuracy as much as possible, irrespective of the power system dynamics. Finally, case studies on the Western Electricity Coordinating Council (WECC) 179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method. The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system, and alleviates the compression performance degradation problem faced by existing compression methods. Index Terms-Curvature quantified Douglas-Peucker, data compression, phasor measurement unit, power system situational awareness.  Ⅰ. INTRODUCTION ecause of the increasing deployment of phasor measurement units (PMUs) and phasor data concentrators (PDCs), vast volumes of PMU data are being _____________________________________

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Section: Performance Evaluation Of the Compression Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Curvature Quantified Douglas-Peucker-Based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Tan,

Zhang,

et al. 2024

Prot. Control Mod. Power Syst.

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show abstract

“…In power systems, a wavelet-based data compression method can be used to compress the recorded data of oscillations [54]. This method selects the optimal wavelet function and decomposition scale according to the criterion of the minimum compression distortion composite index (CDCI).…”

Section: Wavelet Transformmentioning

confidence: 99%

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Prokop,

Bień,

Barczentewicz

2023

Energies

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Significant amounts of data need to be transferred in order to optimize the operation of power grids. The development of advanced metering and control infrastructure ensures a growth in the amount of data transferred within smart grids. Data compression is a strategy to reduce the burden. This paper presents current challenges in the field of time-series data compression. This paper’s novel contribution is the division of data in smart grids to real-time data used for control purposes and big data sets used for non-time-critical analysis of the system. Both of these two applications have different requirements for effective compression. Currently used algorithms are listed and described with their advantages and drawbacks for both of these applications. Details needed for the implementation of an algorithm were also provided. Comprehensive analysis and comparison are intended to facilitate the design of a data compression method tailored for a particular application. An important contribution is the description of the influence of data compression methods on cybersecurity, which is one of the major concerns in modern power grids. Future work includes the development of adaptive compression methods based on artificial intelligence, especially machine learning and quantum computing. This review will offer a solid foundation for the research and design of data compression methods.

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“…Ji [ 27 ] improved the existing wavelet transform and then proposed a general data compression method for different types of signals in the power system. Cheng [ 28 ] proposed a method for compressing oscillation wide-area measurement data based on wavelet transform, which selects the wavelet function and decomposition scale according to the oscillation frequency of the power system in the wide-area measurement system. Prathibha [ 29 ] proposed a dual-tree complex wavelet transform method for power quality monitoring and combined with run-length coding technology to compress the disturbing data.…”

Section: Related Workmentioning

confidence: 99%

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

Cited by 14 publications

References 27 publications

Curvature Quantified Douglas-Peucker-Based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Curvature Quantified Douglas-Peucker-Based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

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