With increasing harmonic pollution in the power system, real-time monitoring and analysis of harmonic variations have become important. Because of limitations associated with conventional algorithms, particularly under supply-frequency drift and transient situations, a new approach based on non-linear leastsquares parameter estimation has been proposed as an alternative solution for high-accuracy evaluation. However, the computational demand of the algorithm is very high and it is more appropriate to use Hopfield type feedback neural networks for real-time harmonic evaluation. The proposed neural network implementation determines simultaneously the supply-frequency variation, the fundamental-amplitude/phase variation as well as the harmonics-amplitude/phase variation. The distinctive feature is that the supply-frequency variation is handled separately from the amplitude/phase variations, thus ensuring high computational speed and high convergence rate. Examples by computer simulation are used to demonstrate the effectiveness of the implementation. A set of data taken on site was used as a real application of the system.

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A power system stabilizer (PSS) design method, which aims at enhancing the damping of multiple electromechanical modes in a multi-machine system over a large and pre-specified set of operating conditions, is introduced in this paper. With the assumption of normal distribution, the statistical nature of the eigenvalues corresponding to different operating conditions is described by their expectations and variances. A probabilistic eigenvalue-based optimization problem used for determining PSS parameters is then formulated. Differential evolution (DE) is applied for solving this highly nonlinear optimization problem. Different strategies for control parameter settings of DE have been studied to verify the robustness of DE in PSS optimization problems. The performance of the proposed PSS, with a conventional lead/lag structure, has been demonstrated based on two test systems by probabilistic eigenvalue analysis and nonlinear simulation.

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