Нейро-сетевое моделирование в задачах прогнозирования режимов работы электрических сетей

Abstract

В статье сформирована нейро-фаззи сеть с учетом температурного мониторинга воздушной линии. Отличительной особенностью, предложенной сети, являются возможность обработки информации, заданной в разных шкалах измерения, и высокое быстродействие для прогнозирования режимов работы электрической сети.

У статті сформована нейро-фаззі мережа з урахуванням температурного моніторингу повітряної лінії. Відмінною особливістю, запропонованої мережі, є можливість обробки інформації, яку задано в різних шкалах вимірювання, і висока швидкодія для прогнозування режимів роботи електричної мережі

Purpose. Form a neuro-fuzzy network based on temperature monitoring of overhead transmission line for the prediction modes of the electrical network. Methodology. To predict the load capacity of the overhead line architecture provides the use of neuro-fuzzy network based on temperature monitoring of overhead line. The proposed neuro-fuzzy network has a fourlayer architecture with direct transmission of information. To create a full mesh network architecture based on hybrid neural elements with power estimation accuracy of the following two stages of the procedure: - in the first stage a core network (without power estimation accuracy) is generated; - in the second stage architecture and network parameters are fixed obtained during the first stage, and it is added to the block estimation accuracy, the input signals which are all input, internal and output signals of the core network, as well as additional input signals. Results. Formed neuro-fuzzy network based on temperature monitoring of overhead line. Originality. A distinctive feature of the proposed network is the ability to process information specified in the different scales of measurement, and high performance for prediction modes mains. Practical value. The monitoring system will become a tool parameter is measuring the temperature of the wire, which will, based on a retrospective analysis of the accumulated information on the parameters to predict the thermal resistance of the HV line and as a result carry out the calculation of load capacity in real time.