A backend framework for the efficient management of power system measurements

McCamish, Benjamin; Meier, Rich; Landford, Jordan; Bass, R.; Chiu, David; Cotilla‐Sanchez, Eduardo

doi:10.1016/j.epsr.2016.05.003

Cited by 8 publications

(5 citation statements)

References 19 publications

(21 reference statements)

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“…The synchrophasor data collected and coalesced by phasor data concentrators pose two problems: inconsistent data and management of massive volume of data. McCamish et al presented a PMU data management and archival system that provides efficient data retrieval, and features an event detection algorithm based on a correlation matrix [95].…”

Section: ) Statistics Based Methodsmentioning

confidence: 99%

“…Similar to signal processing techniques, many of the statistical analysis based methods also rely on data from multiple buses in the network [95], [96], [98], [102], [110], [121] entailing communication cost. The performance of many of these techniques is also dependent on user-defined threshold [105], [106], [109], [112], [116], [121].…”

Section: ) Statistics Based Methodsmentioning

confidence: 99%

“…Event Detection Category Applied Technique [70], [80], [83], [85], [86], [136] Signal Processing Wavelet Transform [70], [72], [75], [79], [90]- [92], [138], [139] Filtering [81], [89], [140], [141] Fourier Transform [94], [100], [104]- [107], [116], [120], [142] Statistical Analysis PCA [95], [99], [103], [110], [113], [115], [121] Clustering and Correlation [123], [127], [131]- [133] Machine Learning/Deep Learning CNN [125], [128], [134] LSTM Among these requirements, frequency response and active power control are critically important. Figure 6 shows the deterioration of the U.S Eastern Interconnection (EI) frequency response to a 4.5 GW generator loss under different levels of PV penetration [148]*.…”

Section: Referencementioning

confidence: 99%

See 2 more Smart Citations

Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

Farooq

Bass

2022

IEEE Access

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Modern power systems characterized by complex topologies require accurate situational awareness to maintain an adequate level of reliability. Since they are large and spread over wide geographical areas, it is inevitable that failures will occur. Various generation and transmission disturbances, such as generator and transmission line tripping and load disconnection, give rise to a mismatch between generation and demand, which manifest as frequency events. These events can take the form of negligible frequency deviations or more severe emergencies that can precipitate cascading outages, depending on the severity of the disturbance and efficacy of remedial action schema. The impacts of such events have become more critical recently due to increased levels of renewable penetration and distributed energy resources, which have caused a decline in system synchronous inertia. Due to the repercussions, it is indispensable to arrest such disturbances on time by activating primary frequency control measures. In this paper, a comprehensive systematic literature review is presented on the techniques used for event detection in power systems and the methods of primary frequency response in modern power systems. The paper also highlights the impacts of severe frequency events within power systems.

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Section: ) Statistics Based Methodsmentioning

confidence: 99%

Section: ) Statistics Based Methodsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

Farooq

Bass

2022

IEEE Access

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“…The measuring equipment from abnormal operating conditions may lead to errors in measurement (Yan et al, 2015;Chen et al, 2010). In particular, the magnetic bias phenomenon in potential transformers (PT) and current transformers (CT) equipment would cause measurement errors (Mccamish et al, 2016). Also, the non-synchronous problem on data collection could cause errors since the sampling time of some devices is asynchronous .…”

Section: The Source Of Abnormal Data In Distribution Networkmentioning

confidence: 99%

An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

Kuang

Qin²,

et al. 2021

Front. Energy Res.

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For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.

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“…transformers cause measurement errors in current and voltage phasors [5]. Under these conditions, the state estimations based on the weighted least squares method suffer from masking and smearing effects, thus yielding inaccurate results.…”

Section: Introductionmentioning

confidence: 99%

A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

Algikar¹,

Xu²,

Yarahmadi³

et al. 2023

Preprint

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In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.

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A backend framework for the efficient management of power system measurements

Cited by 8 publications

References 19 publications

Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow

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