NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS

• Datum: 2016
• Hauptverfasser: Moroz, A. N., Cheremisin, N. M., Cherkashina, V. V., Kholod, A. V.
• Format: Artikel
• Sprache: English; Russian
• Veröffentlicht: National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2016
• Schlagworte: electric grid; neural grid; neuro-fuzzy grid; temperature monitoring of air electric line; prediction modes of electric grid; 621.315
• Online Zugang: http://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12
• Назва журналу: Electrical Engineering & Electromechanics

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 four-layer 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.