SynchroWaveform Measurement Units and Applications

Bastos, Alvaro Furlani; Santoso, Surya; Freitas, Walmir; Xu, Wilsun

doi:10.1109/pesgm40551.2019.8973736

Cited by 20 publications

(13 citation statements)

References 20 publications

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“…during recent years with different applications in power systems. The evolution has started with simple devices such as chart recorders and analog meters to more sophisticated devices such as phasor measurement units (PMU) [1]. These devices are capable of sending data up to one frame every 1 to 4 seconds (SCADA) or 30 to 120 frames-per-second (PMUs and µ-PMUs).…”

Section: Introduction Sensors and Measurement Devices Have Been Evolvingmentioning

confidence: 99%

“…While PMUs are very capable in steady-state and nominal frequency conditions, their measurements may have errors under transient conditions and non-nominal frequencies. However, in recent years, there has been more interest in using the next generation of high fidelity measurement devices, known as synchro waveform measurement units (SWMU) [1]- [2] or continuous point-on-wave (CPOW) units. These devices have the capability of measuring the time-synchronized voltage and current waveforms in high sampling rates and capture the transient behavior of power networks in a wide-area manner.…”

Section: Introduction Sensors and Measurement Devices Have Been Evolvingmentioning

confidence: 99%

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Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

Niazazari

Livani

Ghasemkhani

et al. 2021

2020 52nd North American Power Symposium (NAPS)

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This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-ofthe-art classifiers.

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Section: Introduction Sensors and Measurement Devices Have Been Evolvingmentioning

confidence: 99%

Section: Introduction Sensors and Measurement Devices Have Been Evolvingmentioning

confidence: 99%

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

Niazazari

Livani

Ghasemkhani

et al. 2021

2020 52nd North American Power Symposium (NAPS)

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show abstract

“…A common source of field data measurements is power quality monitors (PQM), which record instantaneous voltage and current waveforms with a high time resolution (hundreds of samples per cycle). The latest version of these devices allows the addition of precise and synchronized time stamps to the measured data, expanding the suitability of the recorded data to more advanced applications [7]. Traditionally, PQMs employ a limited set of triggering features to detect disturbances within the dataset and, once they have been detected, store a few waveform cycles as individual events [8].…”

Section: Introductionmentioning

confidence: 99%

Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

Bastos

Santoso

2021

Energies

Self Cite

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In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.

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“…Such measurements are provided by a new generation of sensors, called waveform measurement units (WMUs). WMUs provide GPS-synchronized measurements in time-domain [11]. They capture the voltage and current waveforms during various events, both faults and PQ events.…”

Section: Introductionmentioning

confidence: 99%

“…The reporting rate of a typical WMU is 256 samples per cycle [12], which is much higher than that of a D-PMU which at most reports two samples per cycle [5]. Data from WMUs have been used recently to investigate harmonic addition or cancellation, to monitor power lines, or to detect subsynchronous resonance [11], [13].…”

Section: Introductionmentioning

confidence: 99%

Event Location Identification in Distribution Networks Using Waveform Measurement Units

Izadi

Mohsenian‐Rad

2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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A new method is proposed to identify the location of events in distribution networks using data from waveform measurement units (WMUs). When an event occurs, WMUs provide GPS-synchronized measurements of voltage and current waveforms in time-domain that are captured during the event. Given such data, the proposed method identifies the bus number where the event occurred. Here, an event is defined rather broadly as any major change in the voltage or current waveforms. An event may have various causes, such as capacitor bank switching, load switching, a minor fault, etc. The first step in the proposed method is to characterize the oscillatory modes of the captured waveform; namely their frequency, damping rate, magnitude, and angle. The next step is to model the underlying circuit at the dominant mode of the event. The final step is to locate the cause of the event based on certain forward and backward calculations on the obtained circuit model. As few as only two WMUs, one at the beginning of the feeder and one at the end of the feeder, are sufficient to identify the location of the event. The performance of the developed method is verified on the IEEE 33-bus test system. The results verify the accuracy and robustness of the proposed method in identifying the location of events in distribution networks.

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SynchroWaveform Measurement Units and Applications

Cited by 20 publications

References 20 publications

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

Event Location Identification in Distribution Networks Using Waveform Measurement Units

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