The Impact of PMU Data Precision and Accuracy on Event Classification in Distribution Systems

Grando, Flavio Lori; Lazzaretti, André E.; Moreto, Miguel

doi:10.1109/tsg.2021.3126268

Cited by 8 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where O i denotes different subgraphs that can be divided into q subgraphs; and vol(O i ) is the sum of the weights for each subgraph. We introduce indicator vector h j given by: C′ (3,4) C′ (2,3) C′ (1,2) C′ (2,4) C′ (1,5) C′ (6,7) C′ (7,8) C′ (8,9) C′ (6,8) C′ (8,10) C′ (1,4) Fig. 2.…”

Section: Correlation Clustering Of Pqmdsmentioning

confidence: 99%

“…Most data loss events in existing studies have involved at most three continuous datapoints [2]. Additionally, most studies have not focused on less probable events such as large amounts of continuous or non-continuous data loss [10].…”

Section: A Missing Data Models For Pqmdsmentioning

confidence: 99%

“…S TATE perception is essential for the correct interconnection of all the components in a power system to guarantee safe and stable operation [1]. Power-quality monitoring and wide-area measurement systems can provide representative data for state perception owing to their real-time operation, synchronization, and accuracy [2]. In those systems, power-quality monitoring devices (PQMDs) and phasor measurement units are critical components [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Harmonic Data Recovery Method Based on Multivariate Norm Matrix

Wang,

Xu,

Song

et al. 2023

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.

show abstract