The purpose of the paper is to determine the basic electrical characteristics and to develop a calculation method and algorithm for optimizing the design parameters of autotransformers intended for use in a melting ice scheme with a non-inductive circuit on 6-10 kV overhead power lines. Methodology. The development of the technical and economic model and the method for calculation of the design parameters of the autotransformer for melting ice is performed on the basis of a systematic approach. Optimization of structural characteristics of autotransformers is carried out using a combined algorithm based on the spatial grid method, adapted to the case of a mixed space of discrete and continuous independent variables, and the specifics of the technical and economic model of the autotransformer. The proposed combined optimization algorithm is implemented in the Delphi environment. Results. Based on the required specific melting power, the main electrical characteristics of autotransformers intended for use in the meltingicr scheme with a non-inductive circuit on 6-10 kV overhead lines, which were the basis for optimizing their design parameters, have been calculated. The technical and economical model of autotransformer for melting ice, which is defined by nine independent variables and describes its cost and technical parameters, is developed. On the basis of the obtained electrical characteristics, optimization of the design parameters of a series of autotransformers is carried out, which includes three standard sizes, differing in maximum length of the transmission line. Originality. A method of calculation of structural parameters of autotransformers for ice melting is proposed, the peculiarity of which is the use of the criterion of the minimum of the cost of the active part and taking into account the conditioned by the circuit of connection of the autotransformers the technical restrictions of errors on the value and angle of secondary current, which are important from the point of view of ensuring the permissible deviation of the specific power of melting ice. Practical value. Optimal correlations of geometrical sizes and electromagnetic loads of autotransformers for ice melting, their cost indicators, as well as the main design characteristics of the magnetic circuit and windings are established. Results of design calculation of autotransformers are sufficient for introduction of their serial production in industrial conditions. References 11, tables 2, figures 4.
Purpose. Forming 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. References 10, figures 1. Key words: electric grid, neural grid, neuro-fuzzy grid, temperature monitoring of overhead transmission line, electric grid modes prediction.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. Библ. 10, рис. 1. Ключевые слова: электрическая сеть, нейросеть, нейро-фаззи сеть, температурный мониторинг воздушной линии, прогнозирование режимов работы электрической сети.Introduction. An important factor in the operation of electrical networks (EN), is an increase in load and the aging power grid equipment, which is typical for most industrialized countries. The capacity of the EN is reduced over time due to branching and complexity of network configuration. This fact increases the burden of overhead transmission lines (OL) as the backbone and distribution ones. The lack of information about the real parameters of the OL forces to use close to reality calculations of allowable power network modes. In most cases, they do not correspond to the actual operating conditions of networks, which leads to a significant reduction in transit overflows of power and overload of EN elements.To eliminate unacceptable overload of EN elements measures of emergency control are provided. Devices of OL automatic overload limiting are designed for emergency...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.