CONTROL OF THE CAPACITY OF THE POWER GRID IN THE TASKS OF FORECASTING THE ELECTRICAL LOAD

Abstract

The paper considers the software-modeling complex of the power grid management system and its elements. The relevance of the work is due to the requirements of the current legislation for forecasting electricity consumption to solve the problem of maintaining a balance of capacity between the generating side and electricity consumption. The developed algorithms and control methods are used as part of a software-modeling complex for managing the power grid and power equipment, the most relevant is the use of autonomous consumers and micro-grids in local power systems. For the effective conduct of experimental research, an experimental methodology was developed, including the stage of development of the experimental plan-program; the choice of means of conducting the experiment; conducting the experiment; processing and analysis of experimental data. It is shown that it is possible to use the technical and information basis of a hierarchical automated information measuring system for monitoring and accounting of electricity to build a technological management system of a regional grid company. It is shown that the smart meters of the intelligent electricity metering system (ISU) are in continuous communication with the producer and consumer of energy, that is, monitoring takes place in real time. The developed neural network model (NS) model reduces the task of short-term forecasting of power consumption to the search for a matrix of free coefficients by training on available statistical data (active and reactive power, ambient temperature, date and index of the day, predictive estimates of power consumption of the forecasting model, some connections, the power system of the magnitude of the consumed active and reactive power has an acceptable level of prediction error. A neural network has been developed to estimate the capacity, calculate and predict the temperature of the cores of a power cable line in real time based on data from the temperature monitoring system, and taking into account changes in the current load of the line. The analysis of the obtained characteristics showed that the maximum deviation of the data received from the neural network from the data of the training sample was less than 3%, which is quite an acceptable result. The comparison of the forecast values with the actual ones allows us to speak about the adequacy of the chosen network model and its applicability in practice for the reliable operation of the cable system of power supply to consumers. The analysis of the results showed that the more the insulation material of the power cable line is aged, the greater the temperature difference between the original and the aged sample.