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This study examines the price estimation capability of MAIS (Multi-Agent Intelligent Simulator) when two types of agents with different learning capabilities coexist in a power trading market. This study identifies that the proposed MAIS, considering the coexistence of different types of agents, can improve its estimation accuracy of wholesale electricity price. This study also reexamines the estimation...
The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the...
A novel approach for fast detection of power islands in a distribution network using the transient signals generated during the islanding event is investigated. Performance of several pattern recognition techniques in classifying the transient generating events as islanding or non-islanding was examined. Discrete wavelet transform of the transient current signals are utilized to extract feature vectors...
This paper discusses a feature extraction technique with genetic programming (GP) and bootstrap to improve interpretation accuracy of dissolved gas analysis (DGA) fault classification in power transformers, dealing with highly versatile or noise corrupted data. Initial DGA data are preprocessed with bootstrap to equalize the sample numbers for different fault classes, thus improving subsequent extraction...
The pattern recognition approach for security analysis (SA) of power systems has been presented as a promising tool for on-line applications. This paper applies a learning-based nonlinear classifier, which is a support vector machine (SVM) for SA. Three single SVM are trained to classify the state of the system: secure, alert and emergency. The final classification is obtained combining the output...
This paper describes a short time electrical energy demand forecast system using two different techniques of artificial intelligence: recurrent artificial neural networks and support vector regression. A brief analysis of the demand over the electrical energy network connection points is also done.
In this paper some patterns based on discrete wavelet transform are studied for detection and identification of both, low frequency disturbances, like flicker and harmonics, and high frequency disturbances, like transient and sags. The wavelet function Daubichies is used as base function in detection and identification because of its frequency response and information time localization properties...
With the development of power markets, electricity price especially the market clearing price (MCP) forecasting is becoming more and more important in such new competitive markets since the MCP forecasting is the basis of decision making for participants in electricity market. In this paper the problem of modeling market clearing price forecasting in deregulated markets is studied. And electricity...
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