RBF Neural Network approach for security assessment and enhancement

Srilatha, N.; Yesuratnam, G.; Deepthi, M

doi:10.1109/tdc.2014.6863489

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Generation Shift Sensitivity Factor (GSSF) method [6] is used for rescheduling of generators. This method also requires calculation of the contribution of each generation to all the congested lines; an efficient method has been reported in [7].…”

Section: Illustration Of Sse Using Generation Reschedulingmentioning

confidence: 99%

Security assessment and enhancement using RBFNN with feature selection

Srilatha

Yesuratnam

2014

2014 North American Power Symposium (NAPS)

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Secure operation of the power system in real time requires assessment of rapidly changing system conditions. Traditional security evaluation method involves running full load flow for each contingency, making it infeasible for real time application. This paper presents Radial Basis Function Neural Network (RBFNN) approach with feature selection for static security assessment and enhancement. The security of the system is assessed based on the intensity of contingencies. The necessary corrective control action to be taken in the event of insecure state is also proposed and the effect of this action has also been observed in order to enhance the security. RBFNN improves the response time compared to other neural networks. Feature selection of the input patterns is done to reduce the dimensionality to a large extent, maintaining the classification accuracy. This method is illustrated using New England 39 bus system.

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Section: Illustration Of Sse Using Generation Reschedulingmentioning

confidence: 99%

Security assessment and enhancement using RBFNN with feature selection

Srilatha

Yesuratnam

2014

2014 North American Power Symposium (NAPS)

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“…Probabilistic methods that determine the probability distributions for the stability of the system are suitable for system planning due computational requirements [5]. On the other hand, pattern recognition methods such as artificial neural networks (ANNs) assess the security of an operating point (OP) as they develop a mapping between the features representing the states of the system and its security through the use of a knowledge base (KB) generated off-line [6][7][8][9][10][11][12][13][14]. As a specific implementation of ANNs, multi-layer perceptrons (MLPs) can be used for the prediction of security indices during the DSA of a large power system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons

Beyranvand

Kucuktezcan

Çataltepe

et al. 2018

ijisae

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In this study, the effect of feature selection methods on the performance of multi-layer perceptrons used for the dynamic security assessment of electric power systems is investigated. The existence of many measurable parameters (features) characterizing the power system security status complicates the use of multi-layer perceptron both in terms of prediction accuracy and training time. In this paper, the dynamic security of a power system subject to a number of critical contingencies is assessed as the critical clearing time of any credible fault is predicted by a multi-layer perceptron. In addition to the study of two different feature selection methods, which are Minimum Redundancy Maximum Relevance (mRMR), and Regressional ReliefF (RReliefF), a novel multi-layer perceptron based feature selection method is proposed to be applied in the prediction of security indices. The performance of the feature selection methods on the dynamic security assessment is investigated on a 16-generator, 68-bus test system.

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“…Whereas in Swarup and Corthis (2006), a SOM is used for the static security assessment of a power system. Another implementation of RBF ANNs is presented in Srilatha et al (2014).…”

Section: Artificial Neural Network In Electrical Applicationsmentioning

confidence: 99%

Artificial recurrent neural networks for the distributed control of electrical grids with photovoltaic electricity

Avila¹

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Realizando el acto de defensa y lectura de la Tesis en la E.T.S.I.T., Madrid, el día de de 2016.Acuerdan otorgar la calificación de:Y, para que conste, se extiende firmada por los componentes del tribunal, la presente diligencia ResumenEl sistema eléctrico actual no ha evolucionado desde sus orígenes. Esto ha hecho que emerjan diferentes problemas los cuales son necesarios afrontar para incrementar el rendimiento de las redes eléctricas. Uno de estos problemas es el crecimiento de la demanda, mientras que otros, como las Tecnologías de la Información y las Comunicaciones (TIC) o la Generación Distribuida (GD), se han desarrollado dentro de la red eléctrica recientemente sin ser propiamente integradas dentro de ella. Esta Tesis afronta los problemas derivados del manejo y la operación de las redes eléctricas existentes y su evolución hacia lo que se considera la red eléctrica del futuro o Smart Grid (SG).El SG nace de la convergencia de cinco aspectos: i) la red eléctrica, ii) TICs, iii) energías renovables, iv) almacenamiento eléctrico y v) Gestión de la Demanda Eléctrica (GDE). Esta Tesis consiste en un primer paso hacia el SG uniendo e integrando los cinco aspectos claves para su desarrollo y despliegue en el futuro cercano. Para ello, la mejora del estado de la red eléctrica se consigue a través del suavizado del consumo agregado. Para lograr este objetivo, se propone el uso de un algoritmo que procese la información proveniente de las TICs para que todas las partes de la red eléctrica se puedan beneficiar. Algunos de estos beneficios son: mejor uso de las infraestructuras, reducción de su tamaño, mayor eficiencia, reducción de costes e integración de GD, entre otros. El algoritmo propuesto está basado en una aproximación distribuida en la que los usuarios son hechos partícipes de sus decisiones, siendo capaces de manejar sus flujos de potencia con este objetivo. El algoritmo se ha implementado siguiendo una estrategia basada en la GDE combinada con el control automático de la demanda que ayude a integrar los Recursos Energéticos Distribuidos (RED) (energías renovables y sistemas de almacenamiento eléctrico), que lo conducen hacia un concepto innovador denominado Gestión de la Activa de la Demanda Eléctrica (GADE).En esta Tesis, una aproximación basada en la Inteligencia Artificial (IA) ha sido utilizada para implementar el algoritmo propuesto. Este algoritmo ha sido construido utilizando Redes Neuronales Artificiales (RNAs), más concretamente Redes Neuronales Recurrentes (RNRs). El uso de RNAs ha sido motivado por las ventajas de trabajar con sistemas distribuidos, adaptativos y no lineales. Y la elección de las RNRs se ha basado en sus propiedades dinámicas, las cuales encajan perfectamente con el comportamiento dinámico no lineal de la red eléctrica. Además, un controlador neural es utilizado para manejar cada elemento de la red eléctrica, incrementado la eficiencia global a través del suavizado del consumo agregado y maximizando el autoconsumo de los RED disponibles. Sin embargo, no existe ningún tipo de...

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RBF Neural Network approach for security assessment and enhancement

Cited by 5 publications

References 9 publications

Security assessment and enhancement using RBFNN with feature selection

Security assessment and enhancement using RBFNN with feature selection

A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons

Artificial recurrent neural networks for the distributed control of electrical grids with photovoltaic electricity

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