Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids

Seyedi, Younes; Karimi, Houshang; Grijalva, Santiago

doi:10.1109/tii.2018.2865765

Cited by 24 publications

(9 citation statements)

References 30 publications

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“…Literature [20] discusses the integration of threedimensional information technology and distribution network management from the process construction of infor-mation system to solve the problems in distribution network management. In literature [21], by using 3D scene simulation technology and object-oriented design method, a method combining 2D GIS with scene simulation technology is proposed. The literature [22] examines the application mode of a 3D GIS system in power grid design, operation, and maintenance from multiple perspectives of overhead transmission lines and substation design and construction.…”

Section: Related Workmentioning

confidence: 99%

Research on Simulation of Distribution Network Engineering Scene Based on 3D GIS Technology

Wang

2022

Wireless Communications and Mobile Computing

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With the rapid development of the power system and the increasing complexity of the power grid structure, users’ demand for information-based management of the power distribution network is growing, and the need to improve various power production service standards is becoming increasingly urgent. Geographic information systems (GIS) are a natural combination of geographic and data attributes that are particularly well suited to the management of urban pipeline systems such as distribution and telecommunication networks. This paper discusses the current application characteristics and challenges of 3D GIS in the power system. A 3D graphic analysis model of the power grid and its realization method are proposed using 2D GIS and scene simulation technology combined with object-oriented design method. The problems that exist in current urban distribution facility management are solved in this paper through the effective integration of information construction and distribution facility management, and the goal of refined management is achieved on this basis. More and more complex 3D spatial data can be processed more realistically and intuitively with 3D GIS.

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Section: Related Workmentioning

confidence: 99%

Research on Simulation of Distribution Network Engineering Scene Based on 3D GIS Technology

Wang

2022

Wireless Communications and Mobile Computing

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“…In work by Yigit, Gungor, and Baktir [2], linear state estimation has been performed to improve parallel computation for big data screening, gathering, and processing, with the aim to provide intelligent grid monitoring via abnormal event detection. Several studies have been performed investigating big μPMU data to identify anomalous events in the power distribution network [5][6][7][8][9][10]. These methods focus on the data-driven approach employing machine learning algorithms to improve solar farm behavioural awareness [5].…”

Section: Background Of Power Grid Systemmentioning

confidence: 99%

“…These methods focus on the data-driven approach employing machine learning algorithms to improve solar farm behavioural awareness [5]. Some of the methods perform statistical approaches, where the absolute deviation around median combined with dynamic window sizes have been used for event detection [6]. Other methods used supervised or semisupervised machine learning for identifying different event, where the knowledge of the event already provided as a priori [7][8][9].…”

Section: Background Of Power Grid Systemmentioning

confidence: 99%

High-resolution data-driven anomalous event detection from solar farm data using clustering large applications based upon randomized search

Maitreyee¹,

Dey²,

Rana³

et al. 2021

Preprint

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Solar energy is a significant and fast-growing source of low-carbon electricity. The usual means of utilityscale solar farm condition monitoring is limited by poor measurement accuracy and low-resolution data collection rates. A micro-synchrophasor measurement unit (µPMU) has been adapted and integrated with a power quality monitor (PQM). This apparatus provides the high-resolution, high-precision, time-stamped data needed by analysts to make solar farms more costeffective and to better understand decentralize grid behaviour. The resulting big data necessitates applying machine learning (ML) for automatic event forecasting, fault detection, and site maintenance. The limited availability of data knowledge, data volume, and performance issues drives the exploration of data-driven based unsupervised ML methods on this occasion. Clustering Large Applications based upon RANdomized Search (CLARANS) algorithm has been employed owing to its suitability to categorise events from the big data. CLARANS has been performed to recognize inefficient voltage phase unbalance. The voltage and current waveforms and related issues such as, voltage dip or voltage sag and phase imbalance events have been considered among multiple data streams and various power distribution issues for this investigation. Ten consecutive days of empirical data have enabled this research. Altogether, 250.92 million power quality data points have been tested and validated.

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“…One of the emerging applications of micro-PMUs is to study "events" in power distribution systems. Event-based studies of micro-PMU measurements have a wide range of use cases, such as in situational awareness [2], equipment health diagnostics, such as for inverters [3], capacitor banks [4], transformers [5], distribution-level oscillation detection and analysis [6], fault analysis and fault location [7].…”

Section: A Background and Motivationmentioning

confidence: 99%

“…The event detection component in this paper can be broadly compared with the other data-driven studies such as in [2], [3], [8]- [13]. Some methods are based on principles in statistics.…”

Section: Literature Reviewmentioning

confidence: 99%

Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements

Aligholian,

Shahsavari,

Stewart

et al. 2020

Preprint

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Distribution-level phasor measurement units, a.k.a, micro-PMUs, report a large volume of high resolution phasor measurements which constitute a variety of event signatures of different phenomena that occur all across power distribution feeders. In order to implement an event-based analysis that has useful applications for the utility operator, one needs to extract these events from a large volume of micro-PMU data. However, due to the infrequent, unscheduled, and unknown nature of the events, it is often a challenge to even figure out what kind of events are out there to capture and scrutinize. In this paper, we seek to address this open problem by developing an unsupervised approach, which requires minimal prior human knowledge. First, we develop an unsupervised event detection method based on the concept of Generative Adversarial Networks (GAN). It works by training deep neural networks that learn the characteristics of the normal trends in micro-PMU measurements; and accordingly detect an event when there is any abnormality. We also propose a two-step unsupervised clustering method, based on a novel linear mixed integer programming formulation. It helps us categorize events based on their origin in the first step and their similarity in the second step. The active nature of the proposed clustering method makes it capable of identifying new clusters of events on an ongoing basis. The proposed unsupervised event detection and clustering methods are applied to real-world micro-PMU data. Results show that they can outperform the prevalent methods in the literature. These methods also facilitate our further analysis to identify important clusters of events that lead to unmasking several use cases that could be of value to the utility operator.

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Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids

Cited by 24 publications

References 30 publications

Research on Simulation of Distribution Network Engineering Scene Based on 3D GIS Technology

Research on Simulation of Distribution Network Engineering Scene Based on 3D GIS Technology

High-resolution data-driven anomalous event detection from solar farm data using clustering large applications based upon randomized search

Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements

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