Application of data mining techniques to identify structural congestion problems under uncertainty

Sánchez‐Úbeda, Eugenio F.; Peco, J.; Raymont, P.; Gómez, Tomás; Bañales, Santiago; Hernandez, A.

doi:10.1109/ptc.2001.964622

Cited by 4 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Y={y 1 ,y 2 ,…,y n }. Y {-1,1} (18) The boundary function comprises hyper plane g(x)=w,x + b (19) Where, w and b shall be derived in such a way that unseen data can be classified correctly. This is achieved by maximizing the margin of separation between the two classes.…”

Section: B Support Vector Machine For Classificationmentioning

confidence: 99%

“…One of them is used to identify congestion problem in transmission planning. The methodology allows to identify not only the transmission paths and corridors which have congestion problems, but also the scenarios producing these critical situations [18]. Other methodologies used to identify fault on power system are wavelet analysis [6,7,8,14,15,16], artificial neural networks [7], [14], and Support Vector Machine (SVM) [6,7,12,17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Mining Based Receiver Operating Characteristic (ROC) for Fault Detection and Diagnosis in Radial Distribution System

Sawitri¹,

Muntasa²,

Purnama³

et al. 2009

JTS

View full text Add to dashboard Cite

This paper discusses data mining applications, especially the use of support vector machines to identify a fault in the 13 nodes radial distribution system of IEEE standard. The identification process was carried out with support vector machine (SVM). Prior to the identification process by SVM, feature extraction was carried out using wavelet transformation for signal decomposition and signal size reduction. To determine the performance of the identification, ROC (Receiver Operating Characteristic) analysis was used. Based on the curve formed by the ROC a fault in the radial distribution can be identified by SVM with "good" performance This is indicated by the value of the best cutoff point is 0.8 and area under the curve is 0.85854.

show abstract

Section: B Support Vector Machine For Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Mining Based Receiver Operating Characteristic (ROC) for Fault Detection and Diagnosis in Radial Distribution System

Sawitri¹,

Muntasa²,

Purnama³

et al. 2009

JTS

View full text Add to dashboard Cite

show abstract

“…Thus, a decision tree can provide rules such as “if gas price is lower than 4.00 and the demand is lower than 300000, then the company's profit is low”, which shows the power of decision trees in terms of interpretability. Note that this approach has been applied successfully in many problems (Wehenkel, 1997; Sánchez‐Úbeda et al , 2001).…”

Section: Applying Decision Treesmentioning

confidence: 99%

Medium‐term risk analysis in electricity markets: a decision‐tree approach

Mosquera

Reneses

Sánchez‐Úbeda

2008

International Journal of Energy Sector Management

View full text Add to dashboard Cite

The purpose of this paper is to analyze medium-term risks faced by electrical generation companies in competitive environments. Market risks faced by generation companies are caused by several variables subject to uncertainty. Hydro conditions, fuel (coal and natural gas) prices, system demand, and CO2 emission price are the risk factors considered in the paper. Taking into account these risk factors, generation companies have to take decisions that would affect their economic results and their risk exposure. Design/methodology/approach – This paper proposes a methodology to support the risk-analysis decision-making process. Firstly, different scenarios of risk factors are generated. Then, a market equilibrium model is used in order to assess the impact of the different sources of uncertainty. Finally, decision trees are used in order to analyze the variables subject to interest, such as electricity prices or companies’ profits. Research limitations/implications – The proposed methodology can be enhanced to take into account scenarios of more risk factors, such as equipment failure or agents’ behavior. Another future enhancement could be a detailed study of correlation between different risk factors. Findings – A realistic case study is presented, showing the advantages of these techniques for medium-term risk-analysis and decision-making processes. Several decision trees have been generated to assess the impact of the different risk factors in electricity prices and companies’ profits. These decision trees provide valuable information for companies when facing their risk-management process. Originality/value – The approach presented here constitutes a valuable support to gain useful information for wise decision making and to hedge against risk.

show abstract