Abstract
This paper analyzes the electrical parameters of areas that contain distributed generation facilities. It demonstrates the necessity of stricter requirements to the performance of energy system automated systems, especially in island operation. It is noted that implementing a special procedure for truncating the standard sequential analysis (Wald analysis) for automated decision-making is a time-consuming process. By ensuring the constancy of first- and second-kind errors at each step of the Wald analysis, one can generate adaptable settings. The paper uses evidence from an automated underfrequency load shedding unit to show that a modified sequential analysis algorithm performs twice as fast. The authors present guidelines on how to adopt this modified algorithm in existing or newly developed energy system automated systems.
Similar content being viewed by others
REFERENCES
Wald, A., Sequential Analysis, New York: Wiley, 1947.
Shiryaev, A.N., Statisticheskii posledovatel’nyi analiz. Optimal’nye pravila ostanovki (Statistical Sequential Analysis. Optimal Terminal Rules), Moscow: Nauka, 1976.
Davarifar, M., Rabhi, A., Hajjaji, A., and Daneshifar, Z., Real-time diagnosis of PV system by using the sequential probability ratio test (SPRT), Proc. 16th Int. Power Electronics and Motion Control Conf. and Exposition, Antalya, Turkey, September 21–24, 2014, Piscataway, NJ: Inst. Electr. Electron. Eng., 2014.
Basharinov, A.E. and Fleishman, B.S., Metody statisticheskogo posledovatel’nogo analiza i ikh radiotekhnicheskie prilozheniya (Statistical Sequential Analysis and Its Radio Engineering Application), Moscow: Sovetskoe Radio, 1962.
Anderson, T.W., A modification of the sequential probability ratio tests to reduce the sample size, Ann. Math. Stat., 1960, vol. 31.
Bussgang, J.J. and Marcus, M.B., Truncated Sequential Hypothesis Tests: Memorandum RM-4268-APRA, Santa Monica, CA: Rand Corp., 1964.
Sochman, J. and Matas, J., Waldboost-learning for time constrained sequential detection, Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Piscataway, NJ: Inst. Electr. Electron. Eng., 2005, vol. 2.
Lorden, G., Structure of sequential tests minimizing an expected sample size, Z. Wahrscheinlichkeitstheor. Verw. Geb., 1980, vol. 51, no. 3.
Aivazyan, S.A., Distinguishing of close hypotheses about the fensity of the distribution in the scheme of the generalized sequential criterion, Teor. Veroyatn. Ee Primen., 1965, vol. 10, no. 4.
Kulikov, A.L. and Ilyushin, P.V., Application of the Wald sequential procedure for automatic control of the modes of power districts with distributed generation facilities, Energetik, 2019, no. 6.
Fu, K.-S., Sequential Methods in Pattern Recognition and Machine Learning, Amsterdam: Elsevier, 1968.
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated by F. Baron
About this article
Cite this article
Kulikov, A.L., Ilyushin, P.V. & Loskutov, A.A. High-Performance Sequential Analysis in Grid Automated Systems of Distributed-Generation Areas. Russ. Electr. Engin. 92, 90–96 (2021). https://doi.org/10.3103/S1068371221020073
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S1068371221020073