Fast real power contingency ranking using a counterpropagation network

Lo, K.L.; Peng, L.J.; MacQueen, John; Ekwue, A.O.; Cheng, Danling

doi:10.1109/59.736256

Cited by 53 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [43] and [44] neural networks are applied to the contingency selection problem. A more recent contingency-ranking algorithm based on neural networks is presented in [45]. An interesting method of contingency selection based on substation topology is proposed in [46].…”

Section: Resultsmentioning

confidence: 99%

Multiple Element Contingency Screening

Davis

Overbye

2011

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

The main focus of this work is to efficiently determine the double-outage contingencies that threaten the operation of the power system. This work is necessary because enormous numbers of double-outage contingencies exist for even relatively small systems, and new standards require system operators to begin considering more than single-outage contingencies. Without an efficient method for predicting the severe contingencies, the entire set of double-outage contingencies has to be solved, and this means solving many millions of contingencies. Several algorithms are presented to detect double-outage contingencies that result in violations. The algorithms use varying amounts of information. However, at most they use linear sensitivities, line limit information, and line flow information. The output of the screening algorithms is compared to the full set of results for the double-outage contingency analysis, solved using the dc power flow. The results show that, even using a very limited amount of information about the system, it is possible to predict a very high percentage of the double-outage contingencies that result in violations.ii

show abstract

Section: Resultsmentioning

confidence: 99%

Multiple Element Contingency Screening

Davis

Overbye

2011

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…The assessment addresses whether, after a disturbance, the system reaches a steady state operating point without violating system operating constraints called security constraints. These constraints ensure the power in the network is properly balanced as given by (11), bus voltage magnitudes and thermal limit of transmission lines are within the acceptable limits given by (12). Violations, if any, are identified as an insecure state that could result in a black-out.…”

Section: A Static Security Assessment (Ssa)mentioning

confidence: 99%

“…For releasing of the problem in [17], [18], [19] redundant attributes data is segregated from relevant and important ones which require a high accuracy, otherwise, is encountered with mistake responses.…”

Section: B Feature Selectionmentioning

confidence: 99%

“…in recent years, there is growing trend in applying these methods for the operation and control of power system. Pattern Recognition (PR) techniques have shown great important as a means of estimating the severity of contingencies [11]. Artificial neural network systems obtained popularity in respect of the conventional methods as they are efficient in discovering similarities among large bodies of data and synthesize complex mappings accurately and rapidly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Static security assessment using radial basis function neural networks based on growing and pruning method

Javan

Mashhadi

Rouhani

2010

2010 IEEE Electrical Power &Amp; Energy Conference

View full text Add to dashboard Cite

Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. This paper presents a novel method based on growing and pruning training algorithm using radial basis function neural network (GPRBFNN) and winner-take-all neural network (WTA) to examine whether the power system is secure under steady-state operating conditions. Hidden layer neurons have been selected with the proposed algorithm which has the advantage of being able to automatically choose optimal centers and distances. A feature selection technique-based class separability index and correlation coefficient has been employed to identify the inputs for the GPRBF network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus and IEEE 30-bus systems.Index Terms -Static security assessment, Radial basis function neural network, Class separability index, Correlation coefficient, Winner-take-all neural network.

show abstract

“…Peng et al proposed efficient contingency ranking of transmission lines using real power performance index. Artificial neural network (ANN) is used to do the contingency ranking for the practical power system [6].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Intelligent Monitoring of Critical Lines in the Restructured Power Market

Rajasekaran¹,

Sathiyamoorthy²

2016

View full text Add to dashboard Cite

Restructured electric market environment allows the power wheeling transactions between the power producers and customers to meet the growing load demand. This will lead to the possible of congestion in the transmission lines. The possible contingencies of power components further worsen the scenario. This paper describes the methodology for the identification of critical transmission line by computing the real power and reactive power performance indices. It also demonstrates the importance of fuzzy logic technique used to rank the transmission lines according to the severity and demonstrated on IEEE-30 bus system.

show abstract

Fast real power contingency ranking using a counterpropagation network

Cited by 53 publications

References 12 publications

Multiple Element Contingency Screening

Multiple Element Contingency Screening

Static security assessment using radial basis function neural networks based on growing and pruning method

Fuzzy Based Intelligent Monitoring of Critical Lines in the Restructured Power Market

Contact Info

Product

Resources

About