Decision Trees-Aided Self-Organized Maps for Corrective Dynamic Security

Voumvoulakis, E.M.; Hatziargyriou, Nikos D.

doi:10.1109/tpwrs.2008.920194

Cited by 34 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For each pattern, a security class label is assigned according to the possibility of different instances occurred in the pattern. For pattern , its class label can be determined by the following rule: (16) where and , respectively, denote the number of "secure" and "insecure" instances in , and is the threshold to measure the occurrence possibility of a class in a pattern (it is set as 50% in this paper).…”

Section: Pattern-based Dynamic Secure/insecure Regionsmentioning

confidence: 99%

“…In the literature, most of the reported methods consider only one single contingency [16]- [18]. But in practice, depending on the DSA results, it is usually needed to include more than one contingency in the preventive control.…”

Section: E Multi-contingency Controlmentioning

confidence: 99%

“…In [15], DT is utilized for predicting one-shot stabilizing controls. In [16], load shedding strategies are inferred from inverse reading of the DT. In [17], a hybrid method using NN and DT is designed for preventive generation rescheduling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

Zhang

Guan

et al. 2012

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

This paper presents a statistical learning-based method for preventive dynamic security control of power systems. Critical operating variables regarding system dynamic security are first selected via a distance-based feature estimation process. An unsupervised learning procedure called pattern discovery (PD) is then performed in the space of the critical variables to extract the subtle structure knowledge called patterns. The patterns are geometrically non-overlapped hyper-rectangles, representing the system dynamic secure/insecure regions and can be explicitly presented to provide decision support for real-time security monitoring and situational awareness. By formulating the secure patterns into a standard optimal power flow (OPF) model, the preventive control against dynamic insecurities can be efficiently and transparently attained. The proposed method is validated on the New England 39-bus system considering both single-and multi-contingency conditions.

show abstract

Section: Pattern-based Dynamic Secure/insecure Regionsmentioning

confidence: 99%

Section: E Multi-contingency Controlmentioning

confidence: 99%

See 1 more Smart Citation

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

Zhang

Guan

et al. 2012

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…Several applications involving decision trees have been addressed in real-time transient stability prediction and assessment, voltage security monitoring and estimation, loss of synchronism detection and timing of controlled separation in power systems [11]- [19]. A recent approach has combined DT with another data mining tool for prediction performance improvement in the field of dynamic security controls [20].…”

Section: A Pmu and Decision Tree Principlesmentioning

confidence: 99%

“…6(a). The idea of using corrective DTs is similar to the work presented in [20], where additional classification techniques are applied to the nodes with poor performance.…”

Section: B Corrective Dtsmentioning

confidence: 99%

Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements

Diao

Sun

Vittal

et al. 2009

IEEE Trans. Power Syst.

226

View full text Add to dashboard Cite

Voltage collapse is a critical problem that impacts power system operational security. Timely and accurate assessment of voltage security is necessary to detect post-contingency voltage problems in order to prevent a large scale blackout. This paper presents an online voltage security assessment scheme using synchronized phasor measurements and periodically updated decision trees (DTs). The DTs are first trained offline using detailed voltage security analysis conducted using the past representative and forecasted 24-h ahead operating conditions. The DTs are also updated every hour by including newly predicted system conditions for robustness improvement. The associated synchronized critical attributes are obtained in real time from phasor measurement units (PMUs) and compared with the offline thresholds determined by the DTs to assess security. This approach is tested on the American Electric Power (AEP) system and properly trained DTs perform well in assessing voltage security. Several new ideas to improve DT performance are also introduced.Index Terms-Decision trees, online security assessment, phasor measurement units, voltage collapse.

show abstract

Unsupervised Learning and Hybrid Methods

Hatziargyriou¹,

Voumvoulakis²

2016

Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence

View full text Add to dashboard Cite

Decision Trees-Aided Self-Organized Maps for Corrective Dynamic Security

Cited by 34 publications

References 15 publications

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements

Unsupervised Learning and Hybrid Methods

Contact Info

Product

Resources

About