Pattern analysis and classification for security evaluation in power networks

Kalyani, Sheetal

doi:10.1016/j.ijepes.2012.07.065

Cited by 33 publications

(14 citation statements)

References 38 publications

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“…Static security problems can be assessed by adopting decision tree algorithms; however, it is possible to over-fit or trap decision tree algorithms into local minimum points [27]. In addition, the support vector machine (SVM) algorithm is another machine learning method for static security assessment [28,29]. In Kalyani et al [28] and Kalyani et al [29], considering line overload and voltage deviation indices in the power system, SVM algorithms are employed for evaluating static security.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, the support vector machine (SVM) algorithm is another machine learning method for static security assessment [28,29]. In Kalyani et al [28] and Kalyani et al [29], considering line overload and voltage deviation indices in the power system, SVM algorithms are employed for evaluating static security. Nevertheless, the SVM algorithm is difficult to implement if the scale of the training sets is too large.…”

Section: Literature Reviewmentioning

confidence: 99%

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Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Sun

2018

Applied Sciences

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As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Sun

2018

Applied Sciences

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“…The selection of variables of previously published works mainly applies the filter method that has to go through many stages (Dong et al, 2012;Kalyani & Swarup, 2013;Swarup, 2008). With this method, the first is based on the statistical criteria to filter out the good variables first, then ask the identifier to evaluate the accuracy to find the appropriate set of variables.…”

Section: Introductionmentioning

confidence: 99%

BPSO&1-NN algorithm-based variable selection for power system stability identification

Au¹,

Cuong²,

Giang³

et al. 2020

IJAEMS

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Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.

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“…Along with the continuous urbanization in China, the scale of regional power systems keeps on increasing, which brings a great challenge to guarantee the secure and reliable operation of regional power systems [1,2]. To meet this challenge, it is extremely important to study power system security under both normal and faulty conditions so as to allow system operators to take proper control measures when system security levels become low.…”

Section: Introductionmentioning

confidence: 99%

A Security Level Classification Method for Power Systems under N-1 Contingency

Zhang³

et al. 2017

Energies

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Security assessment is crucial for the reliable and secure operation of power systems. This paper proposes a security level classification (SLC) method to analyze the security level of power systems both qualitatively and quantitatively. In this SLC method, security levels are graded according to a comprehensive safety index (CSI), which is defined by integrating the system margin index (SMI) and load entropy. The SMI depends on the operating load and the total supply capacity (TSC) under N-1 contingency, and the load entropy reflects the heterogeneity of load distribution calculated from entropy theory. In order to calculate the TSC under N-1 contingency considering both of the computational accuracy and speed, the TSC is converted into an extended conic quadratic programming (ECQP) model. In addition, the load boundary vector (LBV) model is established to obtain the capacity limit of each load bus, and thus detect potential risks of power systems. Finally, two modified practical power systems and the IEEE 118-bus test system are studied to validate the feasibility of the proposed SLC method.

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Pattern analysis and classification for security evaluation in power networks

Cited by 33 publications

References 38 publications

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

BPSO&1-NN algorithm-based variable selection for power system stability identification

A Security Level Classification Method for Power Systems under N-1 Contingency

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