Implementation of transient stability assessment using artificial neural networks

Eltigani, Dalia M.; Ramadan, Kamal; Zakaria, Elfadil

doi:10.1109/icceee.2013.6634018

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…It can be concluded that different emergency control measures have various effects on the enhancement of the voltage stability margin, and prediction results provide the basis and guidance for the formulation of optimal ODOP-based emergency control strategy. In addition, considering other parameters in the MILP optimisation problem (21)(22)(23)(24)(25), the unit power adjustment coefficient of test system can be obtained through the simulation, and it is estimated as 9572 MW/Hz. According to the Guidelines for Calculation and Analysis of Power Grid Security and Stability, the power grid frequency fluctuation should not exceed ± 0.1 Hz.…”

Section: Voltage Stability Emergency Control Coordination Strategymentioning

confidence: 99%

“…However, the calculation accuracy of the above-mentioned methods is marginally insufficient, and the corresponding control measures are relatively conservative, especially in the application to large-scale power grids [22,23]. Computational intelligence has been gradually applied to the power system emergency control [24][25][26][27]. A fuzzy-logic based load shedding approach has been proposed in ref.…”

Section: Introductionmentioning

confidence: 99%

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CNN‐LSTM based power grid voltage stability emergency control coordination strategy

Zhang

Qin

Gao

et al. 2023

IET Generation Trans & Dist

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The occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method.

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Section: Voltage Stability Emergency Control Coordination Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CNN‐LSTM based power grid voltage stability emergency control coordination strategy

Zhang

Qin

Gao

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Also, along with the new wave of deep learning, some Neural Network based methods are proposed. With powerful modeling ability of neural networks, Eltigani [16] realize assessing the transient stability. Zhou [17] present a method for long-term voltage stability monitoring based on Artificial Neural Network which requires training before online deployment.…”

Section: B Related Workmentioning

confidence: 99%

A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location

Ling

Qiu

et al. 2021

IEEE Trans. Big Data

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Synchronized measurements of a large power grid enable an unprecedented opportunity to study the spatialtemporal correlations. Statistical analytics for those massive datasets start with high-dimensional data matrices. Uncertainty is ubiquitous in a future's power grid. These data matrices are recognized as random matrices. This new point of view is fundamental in our theoretical analysis since true covariance matrices cannot be estimated accurately in a high-dimensional regime. As an alternative, we consider large-dimensional sample covariance matrices in the asymptotic regime to replace the true covariance matrices. The self-adjoint polynomials of largedimensional random matrices are studied as statistics for big data analytics. The calculation of the asymptotic spectrum distribution (ASD) for such a matrix polynomial is understandably challenging. This task is made possible by a recent breakthrough in free probability, an active research branch in random matrix theory. This is the very reason why the work of this paper is inspired initially. The new approach is interesting in many aspects. The mathematical reason may be most critical. The realworld problems can be solved using this approach, however.

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“…Also, some data-driven methods for correlation analysis are proposed recently, such as the principal components analysis, the artificial neural networks, the support vector machine [13]. Eltigani utilizes artificial neural networks (ANNs) in assessing the transient stability [14]. In his approach, the power system is described by an equivalent single machine infinite bus system, which cannot reflect accurately the actual state of the system.…”

Section: B Related Workmentioning

confidence: 99%

A Correlation Analysis Method for Power Systems Based on Random Matrix Theory

et al. 2017

IEEE Trans. Smart Grid

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The operating status of power systems is influenced by growing varieties of factors, resulting from the developing sizes and complexity of power systems; in this situation, the modelbased methods need be revisited. A data-driven method, as the novel alternative, on the other hand, is proposed in this paper: it reveals the correlations between the factors and the system status through statistical properties of data. An augmented matrix, as the data source, is the key trick for this method; it is formulated by two parts: 1) status data as the basic part, and 2) factor data as the augmented part. The random matrix theory (RMT) is applied as the mathematical framework. The linear eigenvalue statistics (LESs), such as the mean spectral radius (MSR), are defined to study data correlations through large random matrices. Compared with model-based methods, the proposed method is inspired by a pure statistical approach, without a prior knowledge of operation and interaction mechanism models for power systems and factors. In general, this method is direct in analysis, robust against bad data, universal to various factors, and applicable for real-time analysis. A case study, based on the standard IEEE 118-bus system, validates the proposed method.

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Implementation of transient stability assessment using artificial neural networks

Cited by 7 publications

References 3 publications

CNN‐LSTM based power grid voltage stability emergency control coordination strategy

CNN‐LSTM based power grid voltage stability emergency control coordination strategy

A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location

A Correlation Analysis Method for Power Systems Based on Random Matrix Theory

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