Search

The application of evolutionary computation methods in search and optimization has been growing over the past few decades. As a promising approach in metaheuristic optimization algorithms, differential evolution (DE) has been attracting increasing attention for wide engineering applications including power engineering. Different from conventional evolutionary algorithms using predefined probability distribution function for mutation process, differential evolution exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective functions topographical information toward the optimization process, and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. In this paper, detailed numerical studies are carried out to propose the characterization of the performance of several DE mutation methods with and without fitness sharing scheme. All the approaches using the proposed modified DE are presented on an example in power system planning. [ABSTRACT FROM AUTHOR]

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis. [ABSTRACT FROM AUTHOR]

Power flow feasibility boundaries are constraints limiting the operating transmission capability and the available stability (security) margin of the system. The convexity properties of this boundary are important to the analytical description of its shape (including the needs of approximation) and for the calculation of security margins. These analyses are significantly simplified if the boundary is convex. The conjecture that the boundary is convex in the space of system parameters has been used over a long time. However, some alarming experimental counterexamples already exist which show that this boundary is not always convex. In this letter, an analytical approach is presented to analyze the convexity properties of the power flow feasibility boundary. In particular, the approach helps to identify nonconvex parts of the boundary. Numerical case studies illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Load model is one of the most important elements in power system simulation and control. Based on more than 20 years of practice using load characteristic recorders in the measurement-based load modeling, this paper proposes the Expectation Composite Load Model to predict unseen data. The methods for load bus classification and parameter identification are also provided. The generalization capability of the proposed load modeling is validated by the measured load dynamics and system dynamics during two three-phase short circuit tests of the NE power grid in China. In order to evaluate the measurement-based load model, the Probabilistic Collocation Method (PCM) is applied to quantitatively analyze uncertainties of the simulation responses raised by the parameters. [ABSTRACT FROM AUTHOR]

A load model is one of the most important elements in power system simulation and control. Recently, the constant impedance, constant current, and constant power load in combination with the induction motor load have been widely used as the composite load model, whose parameters are all identified from the field measurements in measurement-based load modeling practices so far. However, there is virtually no research conducted on whether all these parameters could really be identified. This paper investigates the possibility on reducing the number of composite load model parameters to be identified from field measurements. This paper first shows that direct application of the IEEE load motor parameters in the composite load model may be inadequate on describing the load dynamics over different operating status. Then the perturbation method is used to derive the trajectory sensitivities of the equivalent motor parameters, based on which the reduction on the identified parameters of the composite load model is presented. Two cases of measurement-based load modeling in North China and Northeast China are studied to illustrate the validity of the reduction method. It is shown that the reduction does not lose the model's capability on describing the load dynamics. The reduction on the number of identified parameters not only provides a possible way to solve the multi-valued load model problem based on the current practices on measurement-based load modeling, but it also facilitates building of the load model with more components included in it. Meanwhile, reducing the identified parameters reduces the identification time; thus, the proposed strategy significantly enhances the efficiency of the load modeling work. [ABSTRACT FROM AUTHOR]