Synchro-Measurement Application Development Framework: An IEEE Standard C37.118.2-2011 Supported MATLAB Library

Naglič, Matija; Popov, Marjan; Meijden, Mart A. M. M. van der; Terzija, Vladimir

doi:10.1109/tim.2018.2807000

Cited by 25 publications

(27 citation statements)

References 19 publications

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“…Furthermore, it gives insights into the operation of classical relay protection systems in the control room [19][20][21][22]. This also opens up possibilities to design other functions, such as a control function for corona losses which can be beneficial for the process of planning and the procurement of losses.…”

Section: Advanced Protection Functions Of the Wampac Systemmentioning

confidence: 99%

Model for 400 kV Transmission Line Power Loss Assessment Using the PMU Measurements

Pavičić¹,

Holjevac

Ivanković³

et al. 2021

Energies

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This paper presents an advanced model for monitoring losses on a 400 kV over-head transmission line (OHL) that can be used for measured data verification and loss assessment. Technical losses are unavoidable physical effects of energy transmission and can be reduced to acceptable levels, with a major share of technical losses on transmission lines being Joule losses. However, at 400 kV voltage levels, the influence of the electrical corona discharge effect and current leakage can have significant impact on power loss. This is especially visible in poor weather conditions, such as the appearance of fog, rain and snow. Therefore, loss monitoring is incorporated into exiting business process to provide transmission system operators (TSO) with the measure of losses and the accurate characterization of measured data. This paper presents an advanced model for loss characterization and assessment that uses phasor measurement unit (PMU) measurements and combines them with end-customer measurements. PMU measurements from the algorithm of differential protection are used to detect differential currents and angles, and this paper proposes further usage of these data for determining the corona losses. The collected data are further processed and used to calculate the amount of corona losses and provide accurate loss assessment and estimation. In each step of the model, cross verification of the measured and calculated data is performed in order to finally provide more accurate loss assessment which is incorporated into the current data acquisition and monitoring systems.

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Section: Advanced Protection Functions Of the Wampac Systemmentioning

confidence: 99%

Model for 400 kV Transmission Line Power Loss Assessment Using the PMU Measurements

Pavičić¹,

Holjevac

Ivanković³

et al. 2021

Energies

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“…Any higher frequency components present in the PMU measurements, as a consequence of excited local plant oscillations, may result in erroneous generator slow coherency identification [28]. Moreover, actual PMU measurements are affected by transients, and system imposed high-frequency components such as measurement noise [29]. Therefore, for generator slow coherency identification, it is prudent to preprocess the measurements in order to retain only the inter-area oscillation frequencies of interest.…”

Section: B Data Preprocessingmentioning

confidence: 99%

“…The PMU measurements are sent to the SEL-5073 PDC for aggregation and time-alignment. The aggregated PMU measurements are further sent to a PC running the Synchro-measurement Application Development Framework (SADF) [29], where the proposed coherency identification algorithm and the benchmark method are implemented and executed as an online MATLAB program in real-time. To provide time synchronization between all components, a GE RT430 grandmaster GPS clock is used for IRIG-B and IEEE 1588 Precise Time Protocol based time dissemination.…”

Section: Simulation Platformmentioning

confidence: 99%

Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Naglič

Popov

Meijden

et al. 2020

IEEE Trans. Smart Grid

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In an electric power system, slow coherency can be applied to identify groups of the generating units, the rotors of which are swinging together against each other at approximately the same oscillatory frequencies of inter-area modes. This serves as a prerequisite-step of several emergency control schemes to identify power system control areas and improve transient stability. In this paper, slow coherent generators are grouped based on the direction and the strength of electromechanical coupling between different generators. The proposed algorithm performs low-pass filtering of generator frequency measurements. It adaptively determines the minimal number of the measurements to be processed in an observation window, and performs data selectivity to prevent mixing of interfering coherency indices. Finally, it adaptively tracks grouping changes of slow coherent generators and determines a finite number of groups for an improved affinity propagation clustering. The proposed algorithm is implemented as an online MATLAB program and verified in real-time using RTDS power system simulator with the integration of actual synchronized measurement technology components as hardware-in-the-loop. The obtained results demonstrate the effectiveness of the proposed algorithm for robust and near real-time identification of grouping changes of slow coherent generators during the quasi-steady-state and electromechanical transient period following a disturbance. where he was involved in modeling SF6 circuit breakers. His current research interests include future power systems, large-scale power system transients, intelligent protection for future power systems, and wide-area monitoring and protection. Prof. Popov is a member of CIGRE. He has actively participated in WG C4.502 and WG A2/C4.39. He was a recipient of the IEEE PES Prize Paper Award and the IEEE Switchgear Committee Award in 2011. He is an Associate Editor of the International Journal of Electric Power and Energy Systems. Mart A. M. M. van der Meijden (M'10) received the M.Sc. degree (cum laude) in electrical engineering from the Eindhoven University of Technology, Eindhoven, The Netherlands, in 1981.He is leading research programs on intelligent electrical power grids, reliable, and large-scale integration of renewable (wind and solar) energy sources in the European electrical power systems and advanced grid concepts. He has more than 30 years of working experience in the field of process automation and the transmission and the distribution of gas, district heating, and electricity. Since 2003, he has been with TenneT TSO, Arnhem, The Netherlands, Europe's first cross-border grid operator for electricity, where he is the Manager of Research and Development/Innovation and was responsible for the development of the TenneT long-term vision on the electrical transmission system. He has been a Full Professor (part-time) with the

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“…Typically, the WAMPAC system utilizes the advanced Synchronized Measurement Technology (SMT) [5][6][7] as a key building block to deliver time synchronized measurements (synchro-measurements) of electrical quantities from system-wide dispersed locations. Supported by precise time synchronization, the SMT comprises of intelligent electronic devices (IED) or Phasor Measurement Units (PMU), and Phasor Data Concentrators (PDC), connected over ICT infrastructure into a hierarchically organized network [8], as illustrated in Fig. 3.1.…”

Section: Wide Area Monitoring Protection and Controlmentioning

confidence: 99%

Grid Awareness Under Normal Conditions and Cyber-Threats

Naglič

Joseph

Pan

et al. 2018

Intelligent Integrated Energy Systems

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The situational grid awareness is becoming increasingly important for power system operations due to smaller operational margins, wide range of uncertainties entailed by renewables and highly critical infrastructure failures due to potential cyber-attacks. In this chapter, we look at some of the state-of-the-art technologies to monitor events in a power system under normal operating condition, followed by detection algorithms for regular business risk events, such as faults and equipment failures, and finally, we look into methods for quantifying vulnerability of under the rare and men-orchestrated cyber-attacks. First, we outline an architecture of a central piece of today's grid awareness system, Wide Area Monitoring, Protection and Control technology. Next, we review an event detection method used to identify and record faults and failures in the grid. Finally, we present a method for vulnerability assessment of grids under cyber-attacks.

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Synchro-Measurement Application Development Framework: An IEEE Standard C37.118.2-2011 Supported MATLAB Library

Cited by 25 publications

References 19 publications

Model for 400 kV Transmission Line Power Loss Assessment Using the PMU Measurements

Model for 400 kV Transmission Line Power Loss Assessment Using the PMU Measurements

Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Grid Awareness Under Normal Conditions and Cyber-Threats

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