Abstract: We present a theory for optimal filtering of infinite sets of random signals. There are several new distinctive features of the proposed approach. First, we provide a single optimal filter for processing any signal from a given infinite signal set. Second, the filter is presented in the special form of a sum with p terms where each term is represented as a combination of three operations. Each operation is a special stage of the filtering aimed at facilitating the associated numerical work. Third, an iterative scheme is implemented into the filter structure to provide an improvement in the filter performance at each step of the scheme. The final step of the concerns signal compression and decompression. This step is based on the solution of a new rank-constrained matrix approximation problem. The solution to the matrix problem is described in this paper. A rigorous error analysis is given for the new filter.
Abstract: Estimation of voltage stability based on optimal
filtering method is presented. PV curve is used as a tool for voltage stability analysis. Dynamic voltage stability estimation is done by
using particle filter method. Optimum value (nose point) of PV curve can be estimated by estimating parameter of PV curve equation
optimal value represents critical voltage and
condition at specified point of measurement. Voltage stability is then estimated by analyzing loading margin condition c stimating equation. This
maximum loading
ecified dynamically.
Abstract: A theory for optimal filtering of infinite sets of random
signals is presented. There are several new distinctive features of the
proposed approach. First, a single optimal filter for processing any
signal from a given infinite signal set is provided. Second, the filter is
presented in the special form of a sum with p terms where each term
is represented as a combination of three operations. Each operation
is a special stage of the filtering aimed at facilitating the associated
numerical work. Third, an iterative scheme is implemented into the
filter structure to provide an improvement in the filter performance at
each step of the scheme. The final step of the scheme concerns signal
compression and decompression. This step is based on the solution of
a new rank-constrained matrix approximation problem. The solution
to the matrix problem is described in this paper. A rigorous error
analysis is given for the new filter.