Characterizing and quantifying noise in PMU data

Brown, Michael S.; Biswal, Milan; Brahma, Sukumar; Ranade, Satish J.; Cao, Huiping

doi:10.1109/pesgm.2016.7741972

Cited by 193 publications

(80 citation statements)

References 8 publications

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“…This assumption is quite realistic as the signal to noise ratio in the µPMU measurements is in the range of 40 to 50 dB [18], [19]. The errors in the instrumentation channel, e.g., CTs, PT, and control cables, are larger but stable which allows for filtering these errors utilizing consecutive measurements [20].…”

Section: Distribution System State Estimation Problemmentioning

confidence: 99%

Physics-Aware Neural Networks for Distribution System State Estimation

Zamzam

Sidiropoulos

2020

IEEE Trans. Power Syst.

123

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The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation. Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage. Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model. In this paper, we propose a novel learning model that utilizes the structure of the power grid. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. This prevents overfitting and reduces the complexity of the training stage. We also propose a greedy algorithm for phasor measuring units placement that aims at minimizing the complexity of the neural network required for realizing the state estimation mapping. Simulation results show superior performance of the proposed method over the Gauss-Newton approach.

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Section: Distribution System State Estimation Problemmentioning

confidence: 99%

Physics-Aware Neural Networks for Distribution System State Estimation

Zamzam

Sidiropoulos

2020

IEEE Trans. Power Syst.

123

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“…In this context, the presented routine can provide an accurate and frequently updated noise characterization and thus avoid discrepancy between model inputs and real measurements [15], [16]. To the best of the Authors' knowledge, some analysis have been carried out on the noise affecting PMU estimates [17], [18], but a thorough and rigorous modeling routine for the actual measurement noise on the acquired waveforms (and thus applicable to other smart meters) is still missing.…”

Section: Introductionmentioning

confidence: 99%

Statistical Model of Measurement Noise in Real-World PMU-based Acquisitions

Frigo¹,

Derviškadić²,

Bach

et al. 2019

2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

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In this paper, we present a processing technique to determine the statistical distribution of additive measurement noise in real-world acquisitions, with specific reference to Phasor Measurement Unit (PMU) applications in Active Distribution Networks (ADNs). The proposed approach identifies the power signal fundamental component, as well as harmonic and interharmonic interferences, and models the measurement noise as a Gaussian random variable. First, we describe the algorithm main stages and the criteria for the most suitable parameter setting. Then, we carry out a numerical validation inspired by IEEE Std. C37.118.1 test conditions. Finally, we validate the proposed approach on real-world measurements acquired by a PMU in the distribution network of EPFL campus.

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“…In order to justify the effectiveness of our proposed approach in real-world conditions including noise, measurement errors and communication errors, we add noise and errors to the measurements and compare the performance of different models. More specifically, three types of modifications of measurements are added: 1) Gaussian noise: We add Gaussian noises to the data so that the signal to noise rate (SNR) is 45 dB, as introduced in [35]. The noise has zero mean and the standard deviation, σ noise , is calculated as σ noise = 10 − SNR 20 .…”

Section: ) Fully-connected Neural Network (Fcnn): a Three-layermentioning

confidence: 99%

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

Chen

Zhang

et al. 2020

IEEE J. Select. Areas Commun.

208

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This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCNs superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses. research on metal oxide varistors and high voltage polymeric metal oxide surge arresters. From 2014 to 2015, he was a Visiting Professor with the

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Characterizing and quantifying noise in PMU data

Cited by 193 publications

References 8 publications

Physics-Aware Neural Networks for Distribution System State Estimation

Physics-Aware Neural Networks for Distribution System State Estimation

Statistical Model of Measurement Noise in Real-World PMU-based Acquisitions

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

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