A data-driven analysis of capacitor bank operation at a distribution feeder using micro-PMU data

Shahsavari, Alireza; Farajollahi, Mohammad; Stewart, Emma; Meier, Alexandra von; Alvarez, Lilliana; Cortez, Ed; Mohsenian‐Rad, Hamed

doi:10.1109/isgt.2017.8085984

Cited by 40 publications

(27 citation statements)

References 7 publications

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“…Considering the related literature on micro-PMUs, so far, most studies have focused on detecting the presence of and/or scrutinizing the characteristics of certain events, whose source locations are assumed to be known. The events that have been previously studied include capacitor bank switching [4], transformer tap changing [10], inverter misoperation [11], and load switching [6]. Importantly, the above studies are complementary to what we do in this paper, because once the source of an event is located by using the proposed method in this paper, one can use the techniques in [4], [6], [10], [11] to further the event and its characteristics.…”

Section: B Related Workmentioning

confidence: 74%

“…Answering the above question is the key to achieving situational awareness in power distribution systems, so as to keep track of how various grid equipment, assets, DERs, and loads operate or misoperate. The applications are diverse, ranging from identifying incipient failures [1], [4] or cyber-attacks [5], to recruiting demand side resources to construct a self-organizing power distribution system [6]- [8]. Here, an event is defined rather broadly to include any major change in a component across the distribution feeder.…”

Section: A Motivationmentioning

confidence: 99%

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Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data

Farajollahi

Shahsavari

Stewart

et al. 2018

IEEE Trans. Power Syst.

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131

119

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A novel method is proposed to locate the source of events in power distribution systems by using distribution-level phasor measurement units, a.k.a., micro-PMUs. An event in this paper is defined rather broadly to include any major change in any component across the distribution feeder. The goal is to enhance situational awareness in distribution grid by keeping track of the operation (or misoperation) of various grid equipment, assets, distribution energy resources, loads, etc. The proposed method is built upon the compensation theorem in circuit theory to generate an equivalent circuit to represent the event by using voltage and current synchrophasors that are captured by micro-PMUs. Importantly, this method makes critical use of not only magnitude but also synchronized phase angle measurements, thus, it justifies the need to use micro-PMUs, as opposed to ordinary RMS-based voltage and current sensors. The proposed method can work with data from as a few as only two micro-PMUs. The effectiveness of the developed method is demonstrated through computer simulations on the IEEE 123-bus test system, and also on micro-PMUs measurements from a real-life 12.47 kV test feeder in Riverside, CA. The results verify that the proposed method is accurate and robust in locating the source of different types of events on power distribution systems.

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Section: B Related Workmentioning

confidence: 74%

Section: A Motivationmentioning

confidence: 99%

Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data

Farajollahi

Shahsavari

Stewart

et al. 2018

IEEE Trans. Power Syst.

Self Cite

131

119

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“…The data streams that are generated by micro-PMUs introduce prominent Big-Data challenges to the power distribution industry. The key to address these challenges is to turn the micro-PMU data-streams into a set of events that are worth studying [4]- [6]. This of course requires developing new tools and techniques that can detect, identify, and analyze the type of events that occur on distribution networks and recorded by often only a few available micro-PMUs.…”

Section: A the Challenges And The Related Literaturementioning

confidence: 99%

“…Micro-PMUs provide precise time-stamped GPS-synchronized reading of voltage and current phasors; on all three phases and once every 8.333 milliseconds [1]. There is a growing interest among electric utilities to deploy micro-PMUs in their distribution networks for different applications, c.f., [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Event Analysis in Distribution Feeders Using Distribution Synchrophasors

Shahsavari

Farajollahi

Stewart

et al. 2019

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

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This paper proposes a machine learning (ML) approach to detect, identify, and analyze the events that occur on distribution networks using data streams from real-world distribution-level phasor measurement units (PMUs). First, we develop two statistical event detection methods. One is based on testing absolute values around median and the other one is based on testing residuals on a non-linear estimation. Both methods use moving windows as well as dynamic window size. This allows us to detect events of different types and durations. Next, we use field expert knowledge to assign labels to the detected events and subsequently develop a multi-class support vector machine classifier to classify power quality events. Finally, we apply the above developed techniques to detect, identify, and analyze the events in a micro-PMU data stream from a real-world test site in Riverside, CA. We particularly study the oscillation events that occur somewhere across the distribution feeder itself, where their impacts are observed remotely by the available micro-PMUs.

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“…A previous LBNL study [15] presented an algorithm for detecting events in voltage-magnitude timeseries data by identifying edges -significant changes in voltage magnitude measurements -and an approach for clustering sets of events to distinguish them from one another (e.g., distinguishing capacitor bank switching from transformer tap changes). In another study [61], the team conducted a data-driven experimental analysis of capacitor bank switching operation events on a distribution feeder using an µPMU data set.…”

Section: Synchrophasor Data Analytics On the Distribution Gridmentioning

confidence: 99%

Big-Data Analytics for Electric Grid and Demand-Side Management

Yin

Ghatikar

Piette

2019

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A data-driven analysis of capacitor bank operation at a distribution feeder using micro-PMU data

Cited by 40 publications

References 7 publications

Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data

Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data

A Machine Learning Approach to Event Analysis in Distribution Feeders Using Distribution Synchrophasors

Big-Data Analytics for Electric Grid and Demand-Side Management

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