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A Mixed-Strategy based Gravitational Search Algorithm (MS-GSA) is proposed in this paper, in which three improvement strategies are mixed and integrated in the standard GSA to enhance the optimization ability. The first improvement strategy is introducing elite agent's guidance into movement function to accelerate convergence speed. The second one is designing an adaptive gravitational constant function to keep a balance between the exploration and exploitation in the searching process. And the third improvement strategy is the mutation strategy based on the Cauchy and Gaussian mutations for overcoming the shortages of premature. The MS-GSA has been verified by comparing with 7 popular meta-heuristics algorithms on 23 typical basic benchmark functions and 7 CEC2005 composite benchmark functions. The results on these benchmark functions show that the MS-GSA has achieved significantly better performance than other algorithms. The effectiveness and significance of the results have been verified by Wilcoxon's test. Finally, the MS-GSA is employed to solve the parameter identification problem of Hydraulic turbine governing system (HTGS). It is shown that the MS-GSA is able to identify the parameters of HTGS effectively with higher accuracy compared with existing methods. [ABSTRACT FROM AUTHOR]

With the total installed capacity of small hydro generator cluster (SHGC) continues to grow rapidly, the effect of SHGC will have affected to the security and stability of the main network in China, especially to power oscillations of passageway of ultra‐high‐voltage AC and DC. Thus, a precise equivalent model of SHGC is proposed in this study. In the proposed model, the fifth‐order model of generator is adopted as the equivalent generator model, the excitation system is simplified into proportional feedback control model and exponential load model is used as equivalent load model. In addition, Grey Wolf optimiser with chaotic local search is designed to identify the parameters of the equivalent model. Finally, the proposed equivalent technique is applied in one simulation experiment and one practical experiment. The experimental results demonstrate that the validity and accuracy of the proposed equivalent model and identification method in solving dynamic equivalent of SHGC in engineering practice. [ABSTRACT FROM AUTHOR]

The hydraulic turbine governing system (HTGS) is a crucial control system of hydroelectric generating units (HGUs). Parameter identification of HTGS is an important issue for the modeling and control of HGUs. The parameter identification problem of HTGS is more difficult if the elastic water hammer model is considered in the system, and existing algorithms are not effective to solve it. To solve this new problem, a modified gravitational search algorithm (MGSA) has been proposed in which modifications have been made to improve the performance of the GSA from two aspects. First, the constant attenuation factor is replaced by a hyperbolic function to generate a better gravitational constant to balance the global exploration and local exploitation during different searching stages. Second, agent mutation is introduced to increase the diversity of agents and to strengthen the ability to jump out of the local minima of the GSA. The performance of the MGSA has been verified by 13 typical benchmark problems, and the experimental results and statistical analysis demonstrate that the proposed MGSA significantly outperforms the standard GSA and some other popular optimization algorithms. The MGSA is then employed in the parameter identification of a nonlinear model of HTGS with an elastic water hammer, and the experimental results indicate that MGSA locates more precise parameter values than the compared methods. [ABSTRACT FROM AUTHOR]

Abstract: Heuristic optimization has shown its superiority in handling identification problem of complicated system, for methods based on heuristic optimization do not have special requirements on model structures of the target system. In this paper, a piecewise function based gravitational search algorithm (PFGSA) is proposed and applied in parameter identification of automatic voltage regulator (AVR) system. In the proposed algorithm, a piecewise function is designed as the gravitational constant function to replace the traditional exponential equation. The piecewise function provides more rational gravitational constant to control the convergence of algorithm, and thus excellent searching ability is likely to be achieved. Moreover a new weighted objective function is proposed in the identification frame. Comparative experimental studies are conducted to test the searching ability of PFGSA and to verify the performance of proposed identification strategy, while genetic algorithm, particle swarm optimization and GSA are employed for comparison. The experimental results show that PFGSA performs the best on term of accuracy and stability in the parameter identification of AVR system, and the proposed identification strategy is effective. [Copyright &y& Elsevier]

Abstract: Parameter identification of hydraulic turbine governing system (HTGS) is crucial in precise modeling of hydropower plant and provides support for the analysis of stability of power system. In this paper, a newly developed optimization algorithm, called gravitational search algorithm (GSA), is introduced and applied in parameter identification of HTGS, and the GSA is improved by combination of the search strategy of particle swarm optimization. Furthermore, a new weighted objective function is proposed in the identification frame. The improved gravitational search algorithm (IGSA), together with genetic algorithm, particle swarm optimization and GSA, is employed in parameter identification experiments and the procedure is validated by comparing experimental and simulated results. Consequently, IGSA is shown to locate more precise parameter values than the compared methods with higher efficiency. [ABSTRACT FROM AUTHOR]

Inter type-2 fuzzy model has been confirmed to be more effective in Takagi–Sugeno (T–S) fuzzy model identification compared to type-1 fuzzy model. It is indisputable that some algorithms based on inter type-2 fuzzy model have already been developed and shown remarkable modeling performance. To further improve the modeling accuracy, the optimization methods and the neural network are taken into consideration. In this paper, an evolving modified inter type-2 fuzzy c-regression model (MIT2-FCRM) algorithm based on gravitational search algorithm (GSA) and a consequent parameter identification method based on extreme learning machine algorithm with forgetting factor for processing online sequences (namely WOS-ELM) were proposed. Then a novel approach for T–S fuzzy modeling was presented, in which, the coefficients of the upper and lower hyperplanes were obtained by evolving MIT2-FCRM algorithm based on GSA, a hyper-plane-shaped membership function (MF) was utilized to identify the antecedent parameters of the T–S fuzzy model, and WOS-ELM was employed to identify the consequent parameters. The modeling results of six examples indicate that the proposed approach is superior to other studies in terms of identification accuracy, compact fuzzy rules and noise resistance ability. [ABSTRACT FROM AUTHOR]

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite's memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method. [ABSTRACT FROM AUTHOR]