Digital Identification Algorithms for Primary Frequency Control in Unified Power System

• Published in: Mathematics (Basel) Vol. 9; no. 22; p. 2875
• Main Authors: Bakhtadze, Natalia, Maximov, Evgeny, Maximova, Natalia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Algorithms; associative search; Automatic control; Control systems; Data mining; Decision support systems; Electrical networks; Electricity; Emergency equipment; Energy; Frequency analysis; Frequency control; general primary frequency control; inductive learning; intelligent identification; Knowledge bases (artificial intelligence); Machine learning; Participation; Power plants; Power supply; predictive diagnostics of generating equipment; process knowledgebase; Regions; Supply & demand; Systems analysis; Time series; Turbines; Wavelet analysis; Europe; Russia
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9222875

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.