Transient stability assessment of power systems described with detailed models using neural networks

Karami, Ali; Esmaili, Soodabeh

doi:10.1016/j.ijepes.2012.08.071

Cited by 65 publications

(27 citation statements)

References 27 publications

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“…Configuration and perceptron neural network parameters included 3 layers: input layer, hidden layer and output layer. Weighting update algorithm and Levenberg-Marquardt bias was recommended by fast calculation [5,6,12]. There are 152 sets of information in the neural network database, 70% of this information is used for training, 15% for testing, and 15% for cross-validation.…”

Section: Variables and Initial Samplesmentioning

confidence: 99%

Load Shedding Control Strategy Based on Transient Instability Evaluation of Power System Using Artificial Neural Network and Analytic Hierarchy Process Algorithm

Trong¹,

Ngoc²,

Huy³

et al. 2017

Preprint

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Emergency control load-shedding is a key solution to prevent blackouts in the power system. This paper proposed a new model of emergency controls load shedding based on the fast identification of the unstable state of the power system. K-means clustering algorithm divided the instability mode into the clusters. The results of analysis of this cluster were used as the basis for classification control. Building load shedding strategies is consisted of the pre-designed rules based on AHP algorithm. When the recognition of the power system "instability" is detected, the signal of load shedding control is triggered immediately, therefore the decision time is greatly shortened comparing to the traditional methods. The effectiveness of the proposed method was tested on the IEEE 39-bus to overcome the limitations of the last traditional methods.

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Section: Variables and Initial Samplesmentioning

confidence: 99%

Load Shedding Control Strategy Based on Transient Instability Evaluation of Power System Using Artificial Neural Network and Analytic Hierarchy Process Algorithm

Trong¹,

Ngoc²,

Huy³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The nonzero coefficients returned by the constrained QP optimization define the support vector set. In general, for mass nonlinear and inseparable data, α i and offset b of the Lagrange multipliers are calculated by solving quadratic Equation (10) with constraints (11):…”

Section: Svm Classifiermentioning

confidence: 99%

“…Meanwhile, these factors highlight the importance of power system stability analysis and control. The transient stability analysis methods can generally be divided into time-domain simulation methods, straightforward methods based on the transient energy function [1], extended equal area methods [2] and data-mining methods [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. However, the traditional methods, like the time-domain simulation and transient energy function methods, have some disadvantages, such as requiring accurate parameters and information about the network configuration during the fault and being time consuming.…”

Section: Introductionmentioning

confidence: 99%

Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method

Zhou

et al. 2016

Energies

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To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM) models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter-wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS), which are validated by receiver operating characteristic (ROC) analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologies, and the transient stability predicting capability of the SVM model based on the GTCFS is extensively tested. The experimental results show that the selected GTCFS features improve the prediction accuracy with high computational efficiency. The proposed method has distinct advantages for transient stability prediction when faced with incomplete Wide Area Measurement System (WAMS) information, unknown operating conditions and unknown topologies and significantly improves the robustness of the transient stability prediction system.

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“…Proposed an optimization based technique for control coordination of POD and FACTs controllers for optimal oscillations damping in multi-machine power system. Damping of power system oscillations between inter connected areas is very important for the system secure operation [5] [9] [10].…”

Section: Facts Controller Designmentioning

confidence: 99%

“…Loss of synchronism can occur between one machine and the rest of the system, or between groups of machines, with synchronism maintained within each group after separating from each other. The change in electromagnetic torque of a synchronous machine following a perturbation can be resolved into two components [2] [4] [5]:  Synchronizing torque component, in phase with rotor angle deviation.  Damping torque component, in phase with the speed deviation.…”

Section: Introductionmentioning

confidence: 99%

Comparison and Performance Analysis of FACTs Controller in System Stability

Ramasamy¹

2016

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Transient stability assessment of power systems described with detailed models using neural networks

Cited by 65 publications

References 27 publications

Load Shedding Control Strategy Based on Transient Instability Evaluation of Power System Using Artificial Neural Network and Analytic Hierarchy Process Algorithm

Load Shedding Control Strategy Based on Transient Instability Evaluation of Power System Using Artificial Neural Network and Analytic Hierarchy Process Algorithm

Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method

Comparison and Performance Analysis of FACTs Controller in System Stability

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