الفهرس Only 14 pages are availabe for public view
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Abstract
State estimation is a fundamental tool for control and monitoring of electrical power systems. The state estimation process is based on a statistical criteria that estimates the true value of the state variables of the system to minimize or maximize the selected criteria. The most common and familiar method used with state estimation is the weighted least squares method. The power systems state estimation probbm using least squares method is considered as a global optimization problem which solved by an iterative procedure, the Newton method, where the objective function is the sum of the squares of the differences between the measured and true values being measured (expressed as a function of the unknown state variables- voltage magnitudes and phase, angles) recognizing that there are errors in the measured quantities and that there may be redundant measurements, with each squared difference divided or ”weighted” by the variance of the meter error.
In this thesis, a new approach in optimizing the state estimation problem using genetic algorithm is introduced. The algorithm is applied to a case study. The algorithm have been tested for different approaches of genetic operators and shows how the different parameters of the genetic algorithm was tuned to fit the problem. Sensitivity of the proposed algorithm, sensitivity to genetic algorithm parameters is discussed. A comparison with the classical least squares method shows better measurements residual.
Another application of genetic algorithm to power system state estimation is presented, that is bad data identification. The identification of multiple bad data, especially when mutually interacting, may be difficult to handle with the known procedures based on the normalized or weighted residuals. That is, this identification procedure often can’t pinpoint a single bad measurement but instead identifies a group of measurements one of which is bad, in addition the successive elimination of the measurement with the largest normalized residual in some cases mav result in the suppression of correct measurements instead of the bad data.
The identification problem is formulated here as that of picking bad data from a set of suspect in easurements i n o rder t o fulfill t he r equirements o f m aintaining o bservability and eliminating the minimum number of measurements. Genetic algorithm is used as stochastic search to find the optimal solutionFinally suggestions are given for improving the presented algorithms and improving the performance of the genetic algorithms parameters in order to get better results.