K.A. Clements

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The use of large digital computers in control centers has made it possible to track the changing conditions in the power system with a mathematical model in the computer. This real-time model can be used to assess the security of the present system as well as to check out possible control strategies. In this paper the various steps in constructing the model(More)
This paper develops a Bayesian-based hypothesis testing procedure to be applied in conjunction with topology error processing via normalized Lagrange multipliers. As an advantage over previous methods, the proposed approach eliminates the need of repeated state estimator runs for alternative hypothesis evaluation. The identification process assumes that the(More)
This paper introduces backtracking and trust region methods into power system state estimation. The traditional Newton (Gauss-Newton) method is not always reliable particularly in the presence of bad data, topological or parameter errors. The motivation was to enhance convergence properties of the state estimator under those conditions, and together with QR(More)
A method for topology error identification based on collinearity tests involving Lagrange multipliers and the columns of the corresponding covariance matrix is presented. It relies on a geometric interpretation of the multipliers associated with the equality constraints that model circuit breaker status in generalized state estimation. The method is(More)
The analog measurements used for state estimation generally include systematic errors introduced by the deviations in gain, zero offset, and nonlinearity of the instruments, random errors caused by the degree of precision of various instruments in the measurement streams, and installation errors due to the use of erroneous instrument ratios, transducer(More)
This paper presents the sequential-quadratic programming technique combined with the method of importance sampling in order to solve the stochastic optimal power flow (OPF). It is widely recognized that it is impossible to model all possible contingencies. Instead, we employ Monte Carlo importance sampling techniques to obtain an estimate of the expected(More)
Estimating the state of a large electric power system is an ill-conditioned computational task simply because of its size. This paper describes a technique which reduces the large state estimation problem to a set of smaller least-squares problems plus the solution of a square system. An observability algorithm developed earlier is used to partition the(More)
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