Study of Phasor Measurement Unit and Its Applications

Yadav, K. L.; Priyadarshi, Alok; Rathore, Vishal

doi:10.1007/978-981-15-7994-3_22

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…In distribution networks with larger dimensions, optimization must be done in such a way that economic costs are minimized and the observability index is maximized. When the actual large-scale distribution system is considered, there is no need to install μ-PMUs in all buses, and the number of units is determined based on a trade-off between economic benefits and 4,6,7,9,11,12,14,15,17,18,20,21,23,24,26,27,28,29,31,32,34,35,36,37,39,40,42,43,45,46,47,49 observability maximization. Therefore, in this type of distribution network, there is no need for full observability, and μ-PMUs are installed only at high-sensitivity buses that are determined by optimization algorithm to achieve economic goals, simultaneously.…”

Section: Discussionmentioning

confidence: 99%

“…Traditionally, the distribution network was monitored by supervisory control and data acquisition (SCADA) system, which their measurements were not accurate and coordinated, their sampling rate was longer, and they were not able to provide direct phase angle data. So, μ-PMUs introduces as the most effective and prominent measuring devices in monitoring, controlling, and protecting smart distribution systems [6].…”

Section: Concepts and Motivationsmentioning

confidence: 99%

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A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Abdolahi

Taghizadegan

Banaei

et al. 2021

IET Science Measure & Tech

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A reliability-based optimal μ-PMU (micro-phasor measurement unit) placement scheme is suggested for efficient observability enhancement of smart distribution grids at steady-state and contingencies conditions. This article introduces a unique method for the μ-PMU allocation in reconfigurable smart distribution grids in which communication system requirements and zero injection nodes (ZINs) are considered. The original objective function and limitations are proposed aiming at minimizing the capital cost, including communication links and installation costs of μ-PMU, optical power ground wire cost, power losses cost, and reliability cost as well as obtaining the maximum number of measurement redundancy constrained to full system observability in the presence of ZINs and tie switches. The suggested method is formulated as a mixed-integer linear programming problem applied to find optimal μ-PMU locations considering the cost of communication infrastructure and co-optimize the system switching plan simultaneously. In this regard, CPLEX-a high-performance mathematical solver-is used to solve the proposed mixed-integer linear optimization problem to reach the global optimality. The simulations are performed on 33, 69, and 85-bus radial distribution networks, and comprehensive simulation studies show the effectiveness of the suggested method.

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Section: Discussionmentioning

confidence: 99%

Section: Concepts and Motivationsmentioning

confidence: 99%

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Abdolahi

Taghizadegan

Banaei

et al. 2021

IET Science Measure & Tech

View full text Add to dashboard Cite

show abstract

“…Due to their improved accuracy and high sampling rate for measuring phase angles, PMUs have been widely accepted in electric power systems for wide-area control, protection, and monitoring [ 3 , 4 ]. The advanced applications of PMUs in power systems can improve their reliability and security [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Alghassab,

Hatata,

Sokrana

et al. 2024

Heliyon

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“…A phasor measuring unit (PMU) is a tool utilized within a power grid that determines both the magnitude and phase angle of such an electrical phasor element (like voltage or current) utilizing a shared time source for timing. PMUs may also supply real-time phasor time data for important power system applications including corrective action schemes, oscillation detection, and condition estimates [2][3][4][5][6].…”

mentioning

confidence: 99%

Power Data Quality Improvement Through PMU Bad Data Detection Based on Deep Complex Network

Kabra

Rani²

2022

International Journal of Electrical Engineering and Computer Science

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Phasor Measurement Units (PMUs) enable the switching of devices in various power signal modes. A jitter or glitch in a signal cause bad data and also the PMU data will spike due to a disturbance or a transmitting data mistake. As a result of these difficulties, PMU data suffer from different degrees of data quality problems. To detect the bad data, several approaches have been already utilized however it provides some disadvantages such as complexity due to the utilization of dual identical systems separately for analyzing both real and imaginary values of PMU. Likewise, the bad data due to the topology variations have not been optimally identified. To overcome these issues a Robust Bad Data Detection Technique has been proposed in which a Deep complex neural network (DCNN) is incorporated to process the complex number having both voltage magnitude and phase angle. Deep complex Networks are also proposed with the conjunction of topology processor and AC state estimator (SE). Moreover, instead of Batch normalization weight normalization is altered due to the fusion of recurrent timestamps for measuring voltage magnitude and phase angle. The comparative analysis is done in terms of accuracy , Bad data detection capability , bad data detection range and running time with existing techniques The proposed technique provides accuracy of about 99.5% which is higher than the existing techniques.

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Study of Phasor Measurement Unit and Its Applications

Cited by 7 publications

References 38 publications

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Power Data Quality Improvement Through PMU Bad Data Detection Based on Deep Complex Network

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