Abstract
The article studies and develops the methods for assessing the degree of participation of power plants in the general primary frequency control in a unified energy system (UES) of Russia based on time series analysis of frequency and power. To identify the processes under study, methods of associative search are proposed. The methods are based on process knowledgebase development, data mining, associative research, and inductive learning. Real-time identification models generated using these algorithms can be used in automatic control and decision support systems. Evaluation of the behavior of individual UES members enables timely prevention of abnormal and emergency situations. Methods for predictive diagnostics of generating equipment in terms of their readiness to participate in the primary frequency control are also proposed. In view of the non-stationarity of the load in electrical networks, the algorithms have been developed using wavelet analysis. Case studies are given showing the operating of the proposed methods.
Highlights
Mathematics 2021, 9, 2875. https://The modern system for frequency and active power mode control in Russia should, to a great extent, agree with the international requirements [1]
We have proposed a methodology for assessing the participation of the
In this paper,generating we have proposed a methodology for approach assessingto the participation of the power system’s equipment in the general primary frequency control (GPFC)
Summary
The modern system for frequency and active power mode control in Russia should, to a great extent, agree with the international requirements [1]. According to [5], the identification of the power plant or unit participation in the general primary frequency control (GPFC) is carried out when frequency deviations exceed. For assessing the participation degree of various power plants in the GPFC, the System Operator (SO, the Central Dispatch Office of the Russian Unified Energy System) has an archive of frequency and power realizations over a time span around the significant frequency jump This scanty information far from all pieces of equipment can be obtained in the automatic mode. The advantage of this technique, based on the use of a knowledge base and real-time identification models, is noted in comparison with the frequently conducted expensive control tests. An identification algorithm with continuous self-tuning in real-time based on virtual models is, used [13]
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