21-03-2014, 11:57 AM
Novel Techniques for Real Time Computing Critical Clearing Time SIME-B and CCS-B
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Abstract
Real time transient stability assessment mainly depends on real-time prediction.
Unfortunately, conventional techniques based on offline analysis are too slow and unreliable in
complex power systems. Hence, fast and reliable stability prediction methods and simple stability
criterions must be developed for real time purposes. In this paper, two new methods for real time
determining critical clearing time based on clustering identification are proposed. This article is
covering three main sections: (i) clustering generators and recognizing critical group; (ii) replacing the
multi-machine system by a two-machine dynamic equivalent and eventually, to a one-machine-
infinite-bus system; (iii) presenting a new method to predict post-fault trajectory and two simple
algorithms for calculating critical clearing time, respectively established upon two different transient
stability criterions. The performance is expected to figure out critical clearing time within 100ms-
150ms and with an acceptable accuracy.
Introduction
Power system stability has been recognized as an
important problem for secure system operation since
the1920s [1, 2]. Many major blackouts caused by power
system instability have illustrated the importance of this
phenomenon [3]. Transient instability has been the
dominant stability problem on most systems, and has been
the focus of much of the industry’s attention concerning
system stability.
Large-disturbance rotor angle stability or transient
stability is concerned with the ability of the power system
to maintain synchronism when subjected to a severe
disturbance, such as a short circuit on a transmission line.
Transient stability depends on both the initial operating
state of the system and the severity of the disturbance.
Instability is usually in the form of aperiodic angular
separation due to insufficient synchronizing torque,
manifesting as first swing instability. Instability that may
result occurs in the form of increasing angular swings of
some generators leading to their loss of synchronism with
other generators. The basic factor is how the power outputs
of synchronous machines vary as their rotor angles change.
SIME revisited
Literature [9] investigated on SIME. SIME is a transient
stability method based on a generalized OMIB. The
fundamental difference between the original version of
Extended Equal-Area Criterion (EEAC) and SIME is that
EEAC relies on a time-invariant OMIB that constructs by
assuming the classical simplified machine and network
modeling and by “freezing” once and for all the machine
rotor angles at t 0 , the initial time of the disturbance
inception.
Conclusion
This paper proposed two new methods for transient
stability assessment: SIME-B and CCS-B with introduction
of PMUs. CTT is determined in extremely short time,
obviously, is eligible for real time analysis. The new model
applied for predicting post-fault trajectory based on angular
velocity prediction successfully overcomes problem of
accelerating power estimation, which is unsmooth. SIME-
B method can be executed even faster sincethe time
angular velocity reaches the maximum, t B , is fixed.
However, in some cases, a large deviation of Pa will lead
to inaccurate results. On the other hand, CCS-B seems to
be a promising method because of its precision and rapid
convergence.