Voltage phase angle variation in relation to wind power

Allen, A.; Santoso, Surya; Grady, W.M.

doi:10.1109/pes.2010.5590001

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The calculation doesn't break down the whole hour of information without a moment's delay, yet rather investigates a window of PMU information, appointed to the variable yWindow. The subroutine examination on the window of information yWindow is applied for each moving window of information until the finish of the whole hour of the signal y [12].…”

Section: B Screening Algorithm Developmentmentioning

confidence: 99%

Placement of PMU Optimally and Detection of Events in Power System

R*¹,

Lakshmikantha²,

Sundar³

2019

IJITEE

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Smart grid is an advanced electrical supply system in which data is conveyed bi directionally utilizing computerized correspondence innovation. Observing, investigating, controlling and correspondence happens inside the inventory network accordingly improving proficiency, lessening vitality utilization and so on. A Phasor Measurement Unit (PMU) measures electrical signals in system. PMUs are utilized in the transmission side of the system. These are introduced at different places on the network and are time synchronized utilizing Global Positioning System (GPS). Thus real time synchronized continuous estimations are obtained from different estimation points on the network. PMU helps in automating the system. A system of PMUs called Wide Area Measurement System (WAMS) can be utilized for huge scale monitoring of the network. Power outages and load shedding in the system can avoided.PMUs can synchronously gauge voltage and current, phase angle, power, energy import & export, frequency, frequency deviations, etc. These parameters can be utilized to tell about the state of the system. The optimal placement of PMUs & Event detection techniques from the synchronized PMU data are determined using MATLAB software. Programming PMUs to detect the events in the system & placing it optimally can help diminish the huge expense and equipment intricacy.

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Section: B Screening Algorithm Developmentmentioning

confidence: 99%

Placement of PMU Optimally and Detection of Events in Power System

R*¹,

Lakshmikantha²,

Sundar³

2019

IJITEE

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“…Thus any change in the power injection at the bus will cause the state vector to change. In [18], the effect of wind generation on the phase angle is established using field data from an experimental work.…”

Section: Variable Energy Resourcesmentioning

confidence: 99%

Event-Triggered State Estimation for Variable Energy Resources Management

Francy¹,

Farid²,

Adegbege³

2012

9th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2012)

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In recent years, the power generation portfolio has expanded to include variable energy resources (VERs) such as wind and solar generation to meet increasing energy demand and environmental objectives. These sources inject uncertain amounts of power at time scales faster and generally dissimilar to that previously found in typical load profiles. As a result, in order to keep the balance of load and generation, the state of all power system buses becomes highly variable. Conventional measurement and state estimation (SE) techniques under these conditions may introduce errors which may impede the acquisition of an accurate picture of the system state. This paper considers an Event Triggered State Estimation (ETSE) for power networks incorporating VER. The proposed framework represents an improvement over Classical State Estimation (CSE) as it captures the variability associated with the VER while maintaining a high fidelity of the power network states. The effectiveness of the ETSE is demonstrated as compared to existing CSE on a standard IEEE 14 bus system.

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“…Operating the power grid at increased wind penetration levels leads to many concerns, including low inertial and voltage issues [2] and large phase angle differences [3]. Due to vulnerability playing an increasing role in how electricity is generated and consumed, this has resulted in power system stability becoming a significant factor for utilities to manage and innovate [3] effectively.…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Phase Angle Difference Analysis Using PMU Data on grids with Varying Wind Penetration

Duggan¹,

Brogan²,

Liu³

et al. 2021

2021 56th International Universities Power Engineering Conference (UPEC)

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Phase angle differences on a power system are strongly correlated to active power transfer and system topology. The transition from centralized thermal generation to distributed generation is driving phase angle variation in unprecedented ways. In this study, PMU data from three sites across Ireland are studied along with other power system metrics, including system demand and wind generation. Linear regression is used to analyse phase angle variation from power system metrics; the results are used to identify system operating parameters and predict future operating conditions as Ireland's wind resource expands. It is demonstrated that anticipated wind expansion may cause phase angle variation across the network that exceeds operational thresholds. The methods of wrangling and joining different power system data sources and the sequential forward selection regression function can be applied to the vast amounts of time series data generated from power system operation.

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Voltage phase angle variation in relation to wind power

Cited by 7 publications

References 8 publications

Placement of PMU Optimally and Detection of Events in Power System

Placement of PMU Optimally and Detection of Events in Power System

Event-Triggered State Estimation for Variable Energy Resources Management

Phase Angle Difference Analysis Using PMU Data on grids with Varying Wind Penetration

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