Synchrophasor Missing Data Recovery via Data-Driven Filtering

Konstantinopoulos, Stavros; Mijolla, Genevieve M. De; Chow, Joe H.; Lev-Ari, H.; Wang, Meng

doi:10.1109/tsg.2020.2986439

Cited by 21 publications

(8 citation statements)

References 20 publications

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“…The predictions will be utilized to fill in potential missing data in the PMU data. For a detailed discussion on the filter formulation, stability and performance on various event types, the reader is referred to [26]. Fig.…”

Section: Adaptive Filtering Methodsmentioning

confidence: 99%

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Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

Wang

Chow

Osipov

et al. 2021

IEEE Open J. Power Energy

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There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.

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Section: Adaptive Filtering Methodsmentioning

confidence: 99%

“…Fig. 8 shows the performance of OLAP-t compared to three other methods for recovering missing data in an oscillation event [26]. The error metric utilized is the average absolute error of the estimated measurements, defined as…”

Section: Adaptive Filtering Methodsmentioning

confidence: 99%

Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

Wang

Chow

Osipov

et al. 2021

IEEE Open J. Power Energy

Self Cite

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“…Reference [25] uses low-rank tensor factorization and subspace selection (known as OnLine Algorithm for PMU data processing, or OLAP) to replace missing values. Reference [26] proposes an OLAP specializing in the temporal aspect of the PMU data that primarily uses past values to fill in missing data, and reference [27] advocates that this version is the most advanced algorithm in the field of PMU missing value replacement. However, these matrix completion-based methods are not able to handle extreme cases where all or a majority of PMUs are out of service for a period of time, such as when GPS signals are out-of-service or malfunctioning.…”

Section: Introductionmentioning

confidence: 99%

Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling

et al. 2023

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This paper develops a forecasting-based missing value replacement model for Phasor Measurement Unit (PMU) data during power system events. The proposed forecasting model leverages a sequence-to-sequence (Seq2Seq) long short-term memory (LSTM) network with an attention mechanism, which is trained with both pre-event and post-event data. The trained forecasting model is utilized to accurately estimate and recover missing measurements in PMU data. To improve the accuracy of the proposed model, we introduced two novel techniques: (1) integrating a prior knowledge matrix into the attention mechanism that effectively preserves correlations within PMU data, and (2) decoupling the magnitude and direction components of the residual forecast so that such forecasting models are separately trained, which boosts the resistance to the noisy signal. Numerical studies on real-world PMU data collected from the North American electric power grid demonstrate that our proposed model outperforms baseline models in terms of accuracy for all key power grid measurements. Furthermore, our model exhibits robust missing data recovery performance even when nearly all of the grid event data is missing.

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“…On the other hand, data loss is a common issue in wide-area measurement systems, which may stem from the exceptions or errors in network regarded as the first choice for removing outliers of PMU data [11]. Adaptive [12] and collaborative [13] filter with low-rank matrix algorithm are proposed for processing multi-channel PMU measurements with abnormal observations. However, existing filtering-based approaches typically have a low recovery accuracy when facing data with low signal-to-noise ratios (SNRs).…”

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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Synchrophasor Missing Data Recovery via Data-Driven Filtering

Cited by 21 publications

References 20 publications

Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling

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

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