# Electrical Event Detection and Monitoring Data Storage from Wide Area Measurement System

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## Abstract

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## 1. Introduction

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- Ref. [6] performs an analysis on the dimensionality of the PMU data for both normal and abnormal conditions, using an algorithm based on the changes detected within the subspace created by the dimensionality reduction.
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- Ref. [7] describes a method based on Principal Component Analysis (PCA) capable to locates electrical power system faults exposed to different types of disturbances by combining the input data of phasor synchronous meters.
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- Ref. [8] presents methods to detect events and storage reduction data using Principal Component Analysis (PCA) method with a second-order differential method. The proposed method for data reduction is based on an event-driven and self-adjusting sliding window.
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- Ref. [9] uses Random Matrix Theory (RMT) as data processing tools to estimate the state of large power systems. The developed algorithm performs a high-dimensional analysis and compares it with the RMT predictions for anomaly detections in the electrical system.
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- Ref. [10] proposes a dedicated method based on rules for events detection, such as monitoring normal operating limits.
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- Ref. [11] obtains data from PMUs in a reduced form using the local outlier factor algorithm to detect and locate events.
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- Ref. [12] detects and locates single-phase-to-ground faults by correlating the values of electrical quantities and the status of the power system.
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- Ref. [13] presents a PMU anomaly detection that classifies events, outliers, and the lack of measurements. This system is based on stacking machine learning techniques to obtain a higher level of accuracy and increased performance with high-dimensional data. After capturing data from PMUs, the isolation forest technique is applied, which provides scores that classify the data as normal or anomalous (which are the events). These scores feed two other K-Means and LoOP techniques, whose results are multiplied vectorially, and which result in probabilities that are applied to Pearson’s correlation with other PMUs to verify whether an event is occurring.
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- Ref. [14] presents a convolutional neural network (CNN)-based model to detect frequency disturbance events.
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- Ref. [15] exploits the statistic properties of the PMU dataset and generate a hypothesis testing framework to detect power system events using sample covariance of the PMU data collected during the system operations.
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- Ref. [16] uses neural network-based event detection and classification algorithms that requires thousands of confirmed events as training labels.
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- Ref. [17] develops a bidirectional anomaly generative adversarial network (GAN) algorithm to detect power system events with the introduction of conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique.

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- the techniques usually used for event detection are: principal component analysis, state extraction method, non-nested generalized examples; random matrix, isolation forest, K-means, LoOP, among others
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- most works consider application of a centralized approach to control
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- few works consider real-time application aspects of real systems
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- most of the data analytics techniques are being used in the analysis of electricity distribution problems.

- (i)
- status detection: PMUs have an algorithm that generates information about the device’s status, which may indicate data error, PMU error, modified data, loss of satellite communication, among others
- (ii)
- violations of operational limits
- (iii)
- finally, the application of PCA technique and Pearson correlation to monitor measurement values and detect violations of operational thresholds

## 2. Description of PMU System of State of Paraná (Brazil)

## 3. Data Anomaly Detection Using Principal Component Analysis

- Timestamps: date (day, month, year) and time (hour, minute, second and thousandth of a second).
- Meter status: data error, GPS signal, ordering, triggers, configuration change, modified data, timing quality, synchronism.
- Electrical system data: frequency, rate of change of frequency, voltage phasors (magnitude and angle), current phasors (magnitude and angle), analogue and digital channels.

## 4. Results

#### 4.1. Study of Event 1

#### 4.2. Study of Event 2

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- in the substations of Copel GeT: Apucarana (in the north of Parana State), Cascavel (in the west of Parana State), Bateias (in the east of Parana State) and Maringa (in the north of Parana State are substations of Copel GeT [1]
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- in the Power Station of Copel GeT: José Richa, Ney Braga and Salto Santiago (Paraná) [1]
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- in the substations of São Paulo: Araraquara (in the north of São Paulo State), and Itatiba (in the east of São Paulo) [1]

## 5. Conclusions

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- status detection made possible by algorithm embedded at PMUs that generates information about the device’s status, which may indicate data error, PMU error, modified data, loss of satellite communication, identify numerical problems such as absence of values (Not a Number—NaN), null values among others. The purpose of this functionality is to monitor the status of the devices, account for reported situations and generate a report indicating potential PMUs for maintenance
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- detection of numerical non-conformities as values outside the range of operation
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- systemic event detection: a set of techniques to monitor measurement values and detect violations of operational thresholds.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Overholt, P.; Ortiz, D.; Silverstein, A. Synchrophasor Technology and the DOE: Exciting Opportunities Lie Ahead in Development and Deployment. IEEE Power Energy Mag.
**2015**, 13, 14–17. [Google Scholar] [CrossRef] - Lu, C.; Shi, B.; Wu, X.; Sun, H. Advancing China?s Smart Grid: Phasor Measurement Units in a Wide-Area Management System. IEEE Power Energy Mag.
**2015**, 13, 60–71. [Google Scholar] [CrossRef] - Sattinger, W.; Giannuzzi, G. Monitoring Continental Europe: An Overview of WAM Systems Used in Italy and Switzerland. IEEE Power Energy Mag.
**2015**, 13, 41–48. [Google Scholar] [CrossRef] - Jones, K.D.; Cano, E.B.; Chen, H.; Robinson, F.; Thomas, K.; Gardner, R.M. Strategies for success with synchrophasors. IEEE Power Energy Mag.
**2015**, 13, 29–35. [Google Scholar] - Madani, V.; Giri, J.; Kosterev, D.; Novosel, D.; Brancaccio, D. Challenging Changing Landscapes: Implementing Synchrophasor Technology in Grid Operations in the WECC Region. IEEE Power Energy Mag.
**2015**, 13, 18–28. [Google Scholar] [CrossRef] - Xie, L.; Chen, Y.; Kumar, P.R. Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis. IEEE Trans. Power Syst.
**2014**, 29, 2784–2794. [Google Scholar] [CrossRef] - Gonzalez, C.; Vazquez, E.; Sellschopp, F. Fault location diagnosis based on synchronized phasor measurements. IEEE Lat. Am. Trans.
**2015**, 13, 645–650. [Google Scholar] [CrossRef] - Ge, Y.; Flueck, A.J.; Kim, D.-K.; Ahn, J.-B.; Lee, J.-D.; Kwon, D.-Y. Power System Real-Time Event Detection and Associated Data Archival Reduction Based on Synchrophasors. IEEE Trans. Smart Grid
**2015**, 6, 2088–2097. [Google Scholar] [CrossRef] - He, X.; Ai, Q.; Qiu, R.C.; Huang, W.; Piao, L.; Liu, H. A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory. IEEE Trans. Smart Grid
**2015**, 8, 674–686. [Google Scholar] [CrossRef] - Liang, X.; Wallace, S.A.; Nguyen, D. Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data. IEEE Trans. Ind. Appl.
**2017**, 53, 1789–1798. [Google Scholar] [CrossRef] - Liu, G.; Chen, H.; Sun, X.; Quan, N.; Wan, L.; Chen, R. Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detec-tion and Localization. IEEE Access
**2017**, 6, 4982–4993. [Google Scholar] - Wang, Y.; Chen, Q.; Hong, T.; Kang, C. Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Trans. Smart Grid
**2018**, 10, 3125–3148. [Google Scholar] [CrossRef] [Green Version] - Khaledian, E.K.; Pandey, S.; Kundu, P.; Srivastava, A.K. Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP. IEEE Trans. Smart Grid
**2021**, 12, 3. [Google Scholar] [CrossRef] - Wang, W.; Yin, H.; Chen, C.; Till, A.; Yao, W.; Deng, X.; Liu, Y. Frequency Disturbance Event Detection Based on Synchrophasors and Deep Learning. IEEE Trans. Smart Grid
**2020**, 11, 3593–3605. [Google Scholar] [CrossRef] - Ling, Z.; Qiu, R.C.; He, X.; Chu, L. A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location. IEEE Trans. Big Data
**2019**, 7, 548–558. [Google Scholar] [CrossRef] - Shi, J.; Foggo, B.; Yu, N. Power System Event Identification Based on Deep Neural Network with Information Loading. IEEE Trans. Power Syst.
**2021**, 36, 5622–5632. [Google Scholar] [CrossRef] - Cheng, Y.; Yu, N.; Foggo, B.; Yamashita, K. Online Power System Event Detection via Bidirectional Generative Adversarial Networks. IEEE Trans. Power Syst.
**2022**, 37, 4807–4818. [Google Scholar] [CrossRef] - Krefta, G.F.; Pimentel, C.E.F. Inclusão das Usinas UHE GPS, UHE GNB, UHE GBM e UHE GJR no Sistema de Medição Sincrofasorial da Copel GeT. In Proceedings of the XXV SNPTEE Seminário Nacional de Produção e Transmissão de Energia Elétrica, GPC/043, Belo Horizonte, Brazil, 10–13 November 2019; pp. 1–8. [Google Scholar]
- IEEE Std C37.118-2005 (Revision of IEEE Std 1344-1995); IEEE Standard for Synchrophasors for Power Systems. IEEE Standards Association: Piscataway, NJ, USA, 2006; pp. 1–57.
- IEEE Std C37.118.2-2011 (Revision of IEEE Std C37.118-2005); IEEE Standard for Synchrophasor Data Transfer for Power Systems. IEEE Standards Association: Piscataway, NJ, USA, 2011; pp. 1–53.
- Monfreda, M. A Powerful Interpretative Tool at the Service of Analytical Methodology. In Principal Component Analysis; Sanquansat, P., Ed.; InTech: London, UK, 2012; p. 49. [Google Scholar]
- Rumsey, D. How to Interpret a Correlation Coefficient R, 2nd ed.; The Ohio State University: Columbus, OH, USA, 2019. [Google Scholar]

**Figure 1.**Localization of PMUs (green diamonds)—Copel GeT (adapted from [18]).

**Figure 2.**Data of frequency, rate of change of frequency (df/dt), magnitude and angle of voltage and current of phase A).

**Figure 3.**Data of frequency, rate of change of frequency (df/dt), magnitude and angle of voltage and current of phase A, normalized.

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## Share and Cite

**MDPI and ACS Style**

Parede, V.T.; Aoki, A.R.; Teixeira, M.D.; Fernandes, T.S.P.; Barreto, N.E.M.; Grando, F.L.; da Silva, V.A.; Guerra, F.A.; Ramos, M.P.; da Costa, C.H.;
et al. Electrical Event Detection and Monitoring Data Storage from Wide Area Measurement System. *Energies* **2023**, *16*, 1713.
https://doi.org/10.3390/en16041713

**AMA Style**

Parede VT, Aoki AR, Teixeira MD, Fernandes TSP, Barreto NEM, Grando FL, da Silva VA, Guerra FA, Ramos MP, da Costa CH,
et al. Electrical Event Detection and Monitoring Data Storage from Wide Area Measurement System. *Energies*. 2023; 16(4):1713.
https://doi.org/10.3390/en16041713

**Chicago/Turabian Style**

Parede, Vinicius Tertulino, Alexandre Rasi Aoki, Mateus Duarte Teixeira, Thelma S. Piazza Fernandes, Nathan Elias Maruch Barreto, Flavio Lori Grando, Vanderlei Aparecido da Silva, Fabio Alessandro Guerra, Milton Pires Ramos, Clayton Hilgemberg da Costa,
and et al. 2023. "Electrical Event Detection and Monitoring Data Storage from Wide Area Measurement System" *Energies* 16, no. 4: 1713.
https://doi.org/10.3390/en16041713