Feature selection for intelligent stability assessment of power systems

Zhang, Rui; Xu, Yan; Hill, David J.

doi:10.1109/pesgm.2012.6344780

Cited by 20 publications

(22 citation statements)

References 21 publications

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“…In Table 2, the recognition accuracy of ANN2 and ANN1 were 95.4% and 96.8% respectively. This is an acceptable result with the previous study, and accuracy from 94% -97% [5,8].…”

Section: Calculate the Important Factor Of The Load Based On Ahp Algosupporting

confidence: 86%

“…The data is normalized before training. This process is included the steps: identifying variable sets and initial data, selecting the variable sets, evaluating the variable set [5][6]. In this paper, we introduce two standards to select variables: Fisher standard and Scatter matrices standard.…”

Section: Variables and Samplesmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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“…In Table 2, the recognition accuracy of ANN2 and ANN1 were 95.4% and 96.8% respectively. This is an acceptable result with the previous study, and accuracy from 94% -97% [5,8].…”

Section: Calculate the Important Factor Of The Load Based On Ahp Algosupporting

confidence: 86%

Section: Variables and Samplesmentioning

confidence: 99%

Section: Variables and Initial Samplesmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The SU reduces the data redundancy in a dataset in order to improve the accuracy and reduce the computational effort of the DT classifier for DSA significantly. The idea presented in this paper is instigated by the utilization of SU for DT classifier-based applications in power systems as reported in [21,22].…”

Section: The State Of the Artmentioning

confidence: 99%

Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

Al-Gubri¹,

Ariff²

2018

Turk J Elec Eng & Comp Sci

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This paper proposes a novel algorithm based on an advanced feature selection technique for the decision tree (DT) classifier to assess the dynamic security in a power system. The proposed methodology utilizes symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier-based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of the DT classifier. The effectiveness of the proposed technique is demonstrated on the modified IEEE 30-bus test system model. The results show that the DT classifier with SU outperforms the DT classifier without SU. The performance of the proposed algorithm indicates that the DT classifier with SU is able to assess the dynamic security of the system in near real time. Therefore, it is able to provide vital information for protection and control applications in power system operation.

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“…In this paper, a feature selection technique called RELIEFF [22] is employed to select the most relevant features for PD. Other feature selection methods can be referred in [23].…”

Section: B Feature Selectionmentioning

confidence: 99%

Advanced Pattern Discovery-based Fuzzy Classification Method for Power System Dynamic Security Assessment

Luo

Dong

Chen

et al. 2015

IEEE Trans. Ind. Inf.

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1 Abstract-Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD) based fuzzy classification scheme for the DSA. Firstly, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when apply it to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the dynamic security assessment of a power system.

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Feature selection for intelligent stability assessment of power systems

Cited by 20 publications

References 21 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

Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

Advanced Pattern Discovery-based Fuzzy Classification Method for Power System Dynamic Security Assessment

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