In planning electric power systems, it is always necessary to assess whether small-disturbance (SD) instability phenomena occur at prefixed system operating conditions. This analysis can become very difficult when the problem data are uncertain. In such cases the use of deterministic approaches is inadequate and the application of probabilistic analysis techniques is the most feasible alternative. This paper presents a new and practical probabilistic approach for the assessment of SD stability in multimachine power systems taking into account the uncertainties associated with bus load forecasting and treating loads as random uncorrelated variables with normal distributions. This approach proves suitable for determining the risk of SD instability for each expected system operating condition and for systematically individualizing all factors that can affect the probability of SD instability in large power systems. A numerical example illustrates the capability of the proposed technique.
Probabilistic assessment of small disturbance stability in multimachine power systems / Brucoli, Michele; La Scala, Massimo; Torelli, Francesco. - In: INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE. - ISSN 0020-7721. - STAMPA. - 18:6(1987), pp. 1091-1102. [10.1080/00207728708964034]
Probabilistic assessment of small disturbance stability in multimachine power systems
Michele Brucoli;Massimo La Scala;Francesco Torelli
1987-01-01
Abstract
In planning electric power systems, it is always necessary to assess whether small-disturbance (SD) instability phenomena occur at prefixed system operating conditions. This analysis can become very difficult when the problem data are uncertain. In such cases the use of deterministic approaches is inadequate and the application of probabilistic analysis techniques is the most feasible alternative. This paper presents a new and practical probabilistic approach for the assessment of SD stability in multimachine power systems taking into account the uncertainties associated with bus load forecasting and treating loads as random uncorrelated variables with normal distributions. This approach proves suitable for determining the risk of SD instability for each expected system operating condition and for systematically individualizing all factors that can affect the probability of SD instability in large power systems. A numerical example illustrates the capability of the proposed technique.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.