Method of amplitude data recovery in PMU measurements that considers synchronisation errors

Xue, Ancheng; Kong, He; Lao, Yongzhao; Xu, Feiyang; Wang, Li; Wei, Guoen; Bonian, Shi; Leng, Shuang; Bi, Tianshu

doi:10.1049/iet-gtd.2020.0785

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Literature [22] introduces an optimized metadata recovery scheme that can retrieve tables faster than the contemporary scheme, which is corroborated by the simulation results, which confirms that the efficiency of the method is superior to other schemes. Literature [23] designed a method to recover missing or anomalous magnitude data (i.e., active power, reactive power, positive sequence current, and voltage magnitude) from PMU measurements based on historical PMU data obtained from both ends of the line. Simulation examples and measured data are used to demonstrate the feasibility and practicality of the method.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network-based Data Recovery Method for Power Systems

Yang,

Ji,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Facing the problem of power system data loss, this paper proposes a power system data recovery method based on a generative adversarial network. The power system clustering method utilizes aggregated hierarchical clustering and takes into consideration the similarity between different power system data. To transform the power system data recovery problem into a data generation problem, an improved GAN network data analysis method is proposed that utilizes LSTM as a generator and discriminator. Through experimental tests, the LSTM-GAN method is tested with the LSTM method, interpolation method and low-rank method to compare its effect on lost data recovery under different signals of power system data static and dynamic and four fault scenarios. The results show that the root-mean-square errors of the LSTM-GAN method for recovering data under static-dynamic fluctuations are less than 1.2%, and the difference between the errors under 55% and 15% missing data conditions is only 0.77%, with the highest data recovery error of 2.32% in the power system fault scenarios. Therefore, the GAN-based power system data recovery method can effectively realize the recovery of lost data.

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Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network-based Data Recovery Method for Power Systems

Yang,

Ji,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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“…For the methods of Category 1, as the voltage and current phasors can be provided by PMU [6], the linear equations based on least square estimation to identify ParTL can be established in [7–10], which work well even if there are measurement noise in PMU data. However, since bad data and synchronization errors may be introduced by the data loss [11, 12], gross error [13], manipulation attacks [14, 15], and time stamp shift [16, 17], the results with field data may contain errors [18].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the methods based on live measurements have attracted a lot of attention, they could be generally classified into three categories according to the data source, that is, Category 1: methods based on phasor measurement unit (PMU) measurements; Category 2: For the methods of Category 1, as the voltage and current phasors can be provided by PMU [6], the linear equations based on least square estimation to identify ParTL can be established in [7][8][9][10], which work well even if there are measurement noise in PMU data. However, since bad data and synchronization errors may be introduced by the data loss [11,12], gross error [13], manipulation attacks [14,15], and time stamp shift [16,17], the results with field data may contain errors [18].…”

Section: Introductionmentioning

confidence: 99%

A new four‐step method to identify the parameters of transmission line based on SCADA data

Kong

Lü

Que

et al. 2022

IET Generation Trans & Dist

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Accurate parameters of transmission line (ParTL) are the basis of power system analysis. For high-voltage transmission line (TL), remote terminal units (RTUs) are widely installed to provide supervisory control and data acquisition (SCADA) measurements. Based on multiple snapshots of SCADA data at both ends of the TL, a four-step method to identify ParTL is proposed in this paper. In detail, based on the π-model of TL, the relationship between voltage phase angle difference (VPADs) and parameter identification is presented. And then, the general estimation formulation for VPADs is established. Furthermore, four steps to simplify and solve the formulation are detailed, that is, firstly after the approximated relationship between VPADs and reactance are derived, the estimation of VPADs at multiple snapshots is converted into the optimal problem with reactance of TL, just one variable, and the VPADs are estimated roughly; secondly based on the median estimation, the ParTL are identified roughly; thirdly, combined with Taylor series expansions and the alternating current power flow model, the VPADs are estimated accurately; fourthly, the ParTL are identified accurately. Finally, the results with simulated and field data verify the effectiveness and practicality of the proposed method.

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“…Unfortunately, its performance will deteriorate when data losses occur. Clustering analysis such as subspace clustering [21] and the density-based spatial clustering of applications with noise (DBSCAN) algorithm [22,23] can also be used to detect outliers and restore them to their actual values efficiently. However, as most existing methods, clustering analysis is not good at recovering PMU measurements with low SNRs, either.…”

Section: Introductionmentioning

confidence: 99%

Robust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition

Pei

Wang

et al. 2022

IET Generation Trans & Dist

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The development of cyber-physical power systems raises concerns about the data quality issue of phasor measurement units (PMUs). Low signal-to-noise ratios (SNRs) and data losses caused by malicious electromagnetic interference, false data injections, and equipment malfunctioning may jeopardize the data integrity and availability necessary for power system monitoring, protection, and control. To ensure grid resiliency, this paper proposes a robust fast PMU measurement recovery (RFMR) algorithm based on improved singular spectrum analysis (SSA) of Hankel structures. It utilizes single or multiple channels of PMU time-series to restore the problematic phasor measurements with low-SNR noises and data losses. Additionally, the traditional singular value decomposition (SVD) and Tucker decomposition (TD) in RFMR are replaced by randomized SVD (RSVD) and sequential TD (STD) to reduce the computational complexity in single-channel and multi-channel RFMR, respectively. Numerical case studies demonstrate that the proposed algorithm can recover the noise-contaminated measurements with higher accuracy than existing methods, such as matrix/tensor decomposition approaches and robust principal component analysis (RPCA), and effectively complement the missing data with the observed measurements corrupted by low SNRs. Moreover, the latency margins of various power system synchrophasor application scenarios can be satisfied with the reduced computational complexity.

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Method of amplitude data recovery in PMU measurements that considers synchronisation errors

Cited by 5 publications

References 31 publications

Generative Adversarial Network-based Data Recovery Method for Power Systems

Generative Adversarial Network-based Data Recovery Method for Power Systems

A new four‐step method to identify the parameters of transmission line based on SCADA data

Robust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition

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