Application of artificial neural networks for online \nvoltage stability monitoring and enhancement of an \nelectric power system
Notice bibliographique
Résumé
Due to economic reasons arising out of deregulation and open market of electricity, \nmodem day power systems are being operated closer to their stability limits. When a fault \noccurs, there is a great possibility of occurrence of cascading outages, as observed in the \nAugust 2003 Blackout in the North-East USA and Canada. Power system voltage \nstability is one of the challenging problems faced by the utilities. Innovative methods and \nsolutions are required to evaluate the voltage stability of a power system and implement \nsuitable strategies to enhance the robustness of the power system against voltage stability \nproblems. This is the motivation behind the research carried out as a part of the PhD \nprogram and presented in this dissertation. \nArtificial neural networks (ANNs) have gained widespread attention from \nresearchers in recent years as a tool for online voltage stability assessment. Two major \nareas requiring investigation are identified after doing a thorough survey of the existing \nliterature on online voltage stability monitoring using ANN. The first one is the effective \nmethod of selecting important features among numerous possible measurable parameters \nas potential inputs to the ANN. The second one is the feasibility of using a single ANN \nfor monitoring voltage stability for multiple contingencies. In the first phase of the \nresearch, a regression-based method of computing sensitivities of the voltage stability \nmargin with respect to different parameters is proposed. Using the sensitivity \ninformation, important features are chosen selectively to train separate Multilayer \nPerceptron Networks (MLP) to monitor voltage stability for different contingencies. \nIn the second phase of the research, an enhanced Radial Basis Function Network \n(RBFN) is proposed for online voltage stability monitoring. Important features of the \nproposed RBFN are: (1) the same network is trained for multiple contingencies, thus \neliminating the need for training different ANNs for different contingencies, (2) the \nnumber of neurons in the hidden layers is decided automatically using a sequential \nlearning strategy, (3) the RBFN can be adapted online, with changing operating scenario, \n(4) a network pruning strategy is used to limit the growth of the network size as a result \nof the adaptation process. \nIn the next phase of the research, a sensitivity-based voltage stability \nenhancement method is proposed, considering multiple contingencies. Considering the \nlimitations of the existing analytical methods, the sensitivities of the voltage stability \nmargin with respect to parameters are found by using the RBFN proposed in the second \nphase of the research. Using the sensitivity information, correct amounts of generation \nrescheduling are found by using linear optimization. Case studies are presented \nthroughout different sections of the thesis to illustrate the application of the proposed \nmethods.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».