Generator‐based threshold for transient stability assessment

Isbeih, Younes J.; Moursi, Mohamed Shawky El; Xiao, Weidong; El-Saadany, Ehab F.

doi:10.1049/iet-stg.2018.0292

Cited by 7 publications

(5 citation statements)

References 48 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of these action set is detailed in the following sub-sections. Stepwise approach (Action Set) Generator Rejection Generator Tripping followed by Load curtailment [AS (1) → AS (4) ] AS (1) → AS (4) → AS (5) → AS (7) AS (1) → AS (4) → AS (5) → AS (8) AS (1) → AS (4) → AS (6) Load Shedding Load Shedding [AS (1) → AS (2) ] AS (1) → AS (2) → AS (3) AS (1) : Identification of action type; AS (2) : Decision of load shedding; AS (3) : Calculation of magnitude for load shedding; AS (4) : Detection of location for generator rejection; AS (5) : Post generator rejection, detection of location of load shedding ; AS (6) : Calculation of magnitude for load shedding ; AS (7) : calculation of magnitude for load shedding; AS (8) : Post generator rejection, calculation of magnitude for load shedding.…”

Section: B Decision Rule Based Inferencementioning

confidence: 99%

“…Similarly, the reactive component LQk to be shed is calculated in proportion to ratio of pre-disturbance reactive and real power component, and LPk. Overall, the set-of-actions for the situation ( 𝑗𝑗 ∉ 𝑐𝑐 ) is labelled as AS (1) → AS (4) → AS (6) , while the action strategy for the situation ( 𝑃𝑃 𝑗𝑗0 < 𝑑𝑑𝑒𝑒𝑐𝑐𝑃𝑃 ) is labelled as AS (1) → AS (4) → AS (5) → AS (8) . In both instances, the magnitude of load to be shed from all the load in the system is calculated as,…”

Section: Action Set As(1) → As(4) → As(5) → As(8) and Action Set As(1...mentioning

confidence: 99%

“…To approximate the transient stability boundary, literature also investigates critical clearing time (CCT) estimates, which signify the maximum allowable time to clear a fault. The mahalanobis-kernel regression [7] and generator-based threshold computation [8] are a few state-of-the-art methods to compute CCT for the generators. Cui et al [9] present a combinational transfer learning strategy to improve TS prediction using convolutio nal neural networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Unified Scheme to Enhance the Stability of Solar Energy Integrated Power System in Real-Time

Shrivastava,

Siddiqui,

Verma

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Solar energy penetration in power grids helps to maintain power balance between generation and demand, thus enhances power grid performance. However, these integrations reduce grids' time margin to respond against sudden frequency changes and re-establishing generation-demand equivalency. Thereby, it poses challenges to the performance and stability of the power system. It therefore becomes increasingly important to comprehend the real-time system data, identify and initiate necessary remedies to sustain the healthy system's operation. This article presents a data-driven unified methodology for enhancing grid stability in solar energy-penetrated power network. The proposed method is a two-stage unified framework that incorporates Prompt Instability Evaluation (PIE) to evaluate impending system transient instability in first stage. In the second stage for a system unstable operation a Decision Assisted Adaptive Control (DAAC) is developed and implemented for corrective emergency control. A novel PIE is presented to perform a postdisturbance transient stability assessment using short-synchronized moving data. The PIE assesses the upcoming transient instability within the first few cycles following fault inception. Next, a novel DAAC is proposed to design an emergency remedial scheme for identifying location (where), magnitude, and type in real-time for unstable operations. The DAAC utilizes a novel Decision Rule Based Inference (DRBI) to evaluate suitable action sets that may be deployed by the DAAC to sustain system stability. The simulation results demonstrate the suitability of the proposed study on the system's performance in the absence/presence of solar energy with topological variations.

show abstract

Section: B Decision Rule Based Inferencementioning

confidence: 99%

Section: Action Set As(1) → As(4) → As(5) → As(8) and Action Set As(1...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Unified Scheme to Enhance the Stability of Solar Energy Integrated Power System in Real-Time

Shrivastava,

Siddiqui,

Verma

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Jorge performed an assessment on the frequency and transient stability of the RESs penetrated grid, in which the author focused on the transient instability in the power system by considering the grid parameters [9]. A study [10], proposed a systematic approach to analyzing the transient stability of the 39-bus New England power system, where the authors considered the generator-based threshold as a limit function. Similarly, Perilla et al investigated the degree of the transient stability enhancement in the wind turbine integrated power system via parametric sensitivity and sophisticated approaches [11].…”

Section: Introductionmentioning

confidence: 99%

Parametric Sensitivity Analysis of Rotor Angle Stability Indicators

Shrestha

González-Longatt

2021

Energies

View full text Add to dashboard Cite

With the increasing penetration rate of Power Electronic Converter (PEC) based technologies, the electrical power systems are facing the problem of transient stability since the PEC based technologies do not contribute to the system inertia, and the proportion of synchronous generators (i.e., the source of inertia) is in decreasing rate. In addition, PEC based technologies’ components have poor inherent damping. It is very important to analyze the system characteristics of a power system to minimize the potential instabilities during the contingencies. This paper presents the parametric sensitivity analysis of the rotor angle stability indicators for the 39-bus New England power system. The indicators of rotor angle stability analysis such as critical fault clearing time (CCT), Eigenvalue points, damping ratio, frequency deviation, voltage deviation, and generator’s speed deviation are identified and analyzed for three case scenarios; each scenario has six sub-cases with different inertia constants. The results show that the CCTs for each component will be reduced if the inertia reduces at any section of a multi-machine power system. Although the applied three scenarios with six sub-cases are identified to be stable in this analysis, the decreasing inertia constant has significant impact on the power system dynamics.

show abstract

“…The dynamic response of a power system is governed by a set of highly nonlinear differential and algebraic equations (DAE) which describe the behavior of the synchronous generators and its associated control systems, loads, renewable power generation, flexible AC transmission devices (FACTs) in addition to the transmission network [2], [3]. When a power system is subjected to small changes, the DAE model can be linearized about the equilibrium point.…”

mentioning

confidence: 99%

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

Azman

Isbeih

Moursi

et al. 2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.

show abstract

Generator‐based threshold for transient stability assessment

Cited by 7 publications

References 48 publications

Data-Driven Unified Scheme to Enhance the Stability of Solar Energy Integrated Power System in Real-Time

Data-Driven Unified Scheme to Enhance the Stability of Solar Energy Integrated Power System in Real-Time

Parametric Sensitivity Analysis of Rotor Angle Stability Indicators

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

Contact Info

Product

Resources

About