Journal ArticleDOI
Artificial neural-net based dynamic security assessment for electric power systems
Dejan J. Sobajic,Yoh-Han Pao +1 more
TLDR
This work focuses on examination of that complex mapping and investigation of the influence of the various parameters on CCT, and on synthesizing such complex and transparent mappings.Abstract:
In the post-fault dynamic analysis of interconnected power systems, the critical fault clearing time (CCT) is one of the parameters of paramount importance. Critical clearing time is a complex function of pre-fault system conditions (operating point, topology, system parameters), fault structure (type and location) and post-fault conditions that are in part dependent on the protective relaying policy. To define analytically such a relationship would be highly desirable but diversity of variable involved makes this task extremely complicated. Our efforts focus on examination of that complex mapping and investigation of the influence of the various parameters on CCT. The evaluation of CCT involves elaborate computations that often include time-consuming solutions of nonlinear on-fault system equations. Existing conventional pattern recognition techniques are incapable of synthesizing such complex and transparent mappings. Thus, when a human operator tells the machine learning unit (that is the pattern recognizer) that system state belongs to a certain class, say "emergency", the pattern recognizer merely records that classification mindlessly and is not able to look at the pattern with insight and discover what underlies the "emergency" nature of pattern. It is, therefore, highly desirable to have a mechanism which when presented with a sequence of class labeled patterns not only learns an internal structure which allows it to generalize and to classify other and to classify other patterns correctly, but also is able to shed some light on what combination of features give rise to the particular class membership.read more
Citations
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Journal ArticleDOI
Electric load forecasting using an artificial neural network
TL;DR: In this article, an artificial neural network (ANN) approach is presented for electric load forecasting, which is used to learn the relationship among past, current and future temperatures and loads.
Journal ArticleDOI
Short-term load forecasting using an artificial neural network
TL;DR: In this paper, an artificial neural network (ANN) method is applied to forecast the short-term load for a large power system, where the load has two distinct patterns: weekday and weekend-day patterns.
Journal ArticleDOI
Economic load dispatch for piecewise quadratic cost function using Hopfield neural network
TL;DR: In this article, the authors presented a new method to solve the problem of economic power dispatch with piecewise quadratic cost function using the Hopfield neural network, which is much simpler and the results are very close to those of the numerical method.
Journal ArticleDOI
Advancement in the application of neural networks for short-term load forecasting
TL;DR: An improved neural network approach to produce short-term electric load forecasts is proposed, which includes a combination of linear and nonlinear terms which map past load and temperature inputs to the load forecast output.
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Short-term Load Forecasting Using Artificial Neural Network
TL;DR: A new neural network training algorithm is presented which reduces the required training time considerably and overcomes many of the shortcomings presented by the conventional back-propagation algorithm.
References
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Neural networks and physical systems with emergent collective computational abilities
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Journal ArticleDOI
Neurons with graded response have collective computational properties like those of two-state neurons.
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Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.