A Novel On-Line Substation Instrument Transformer Health Monitoring System Using Synchrophasor Data

Zhang, Lin; Chen, Heng; Wang, Qiushi; Nayak, Neeraj; Gong, Yanfeng; Bose, Anjan

doi:10.1109/tpwrd.2019.2905426

Cited by 32 publications

(10 citation statements)

References 21 publications

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“…Event-based analysis of micro-PMU measurements has received increasing attention in recent years. The majority of the work in this area has focused on monitoring the utility equipment [5], load modeling [6], cybersecurity [7], state estimation [8], and stability analysis [9]. A few studies have also focused on analyzing events in solar generation units and distributed energy resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

Khaledian

Mohsenian‐Rad

2022

IEEE Trans. Smart Grid

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Section: Literature Reviewmentioning

confidence: 99%

Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

Khaledian

Mohsenian‐Rad

2022

IEEE Trans. Smart Grid

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“…The widespread use of digital systems for collecting and transmitting data in the power industry has made it possible to implement on-line diagnostic systems for highvoltage equipment, including metering transformers. Thus, the authors of [29] propose the system for metering transformers' technical state through on-line monitoring based on the substation model and vector measurements, in which anomalies become a signal for the system to take measures with respect to the state of the individual elements of the power equipment under consideration.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

et al. 2022

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This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F1-score, was estimated to be 83%.

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“…Synchrophasor technology uses monitoring devices, called phasor measurement units, which take high-speed measurements of phase angles, voltage and frequency that are time stamped with high-precision clocks. The measurements, typically taken 30 times a second, can quickly track system changes undetectable through traditional monitoring systems used in the industry [1]. This makes new energy management applications possible, including model validation [2], dynamic state estimation [3], oscillation monitoring, islanding detection and wide area monitoring and control [4].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

Dobson

Martin

et al. 2021

IEEE Trans. Smart Grid

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This paper develops a practical framework of Area Angle Monitoring (AAM) to monitor in real time the stress of bulk power transfer across an area of a power transmission system. Area angle is calculated from synchrophasor measurements in real time to provide alert to system operators if the area angle exceeds pre-defined thresholds. This paper proposes a general method to identify the warning threshold of area angle and a simplified method to quickly update area angle thresholds under significant topology change. A mitigation strategy to relieve the area stress is also proposed. In order to handle the limited coverage of synchrophasor measurements, this paper proposes a method to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application scenario of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment for real-time application of AAM with livestream and recorded synchrophasor data is described.

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A Novel On-Line Substation Instrument Transformer Health Monitoring System Using Synchrophasor Data

Cited by 32 publications

References 21 publications

Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

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