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القائمة البريدية
التصويت: مؤتمر التخطيط الافتراضي والمدن الاليكترونية
للكاتب .
A. E. Mansour
Electrical Engineering Department, Faculty Of Engineering, Al-Azhar University
ABSTRACT
This paper presents, for large-scale power systems, a distributed bad-data pre-cleaning (BDPC)technique with subsequent hierarchical recursive static state estimation (RSSE) method when themeter placement used is optimal. The optimality here means that the meter configuration does
satisfy the necessary and sufficient conditions for the detection and identification of bad-data with
minimum meter number and minimum cost of the elements contained. The overall power system is
considered as a group of subsystems connected with tie-lines or transformers (ties). For eachsubsystem and its ties the local BDPC is achieved at once before applying the RSSE in each ofthem. The BDPC technique uses a new group of bad-data indicators (RV', RPI, RPFD, RQI,
RQFD) for the detection, identification, and correction processes in all available types of
measurements. The fast RSSE is based on the direct use of the measurements redundancy
together with the network flow equations to achieve a recursive estimate that verifies the network
conditions at stationary operation. A reasonable data exchange between the interconnected
subsystems is exploited to handle bad-data in ties measurements at the subsystem level. A
comparison with a well known algorithm [21,22] is given as well. The proposed algorithm has
been applied on the AEP 30-bus test system. The results gained on this network demonstrate that
the presented technique will be a powerful tool for on-line monitoring and control in the largescale
power systems.
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