کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6854376 1437428 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm
ترجمه فارسی عنوان
شناسایی پارامترهای یک مدل غیر خطی سیستم حاکم بر توربین هیدرولیک با یک چکش آب الاستیک بر اساس یک الگوریتم جستجوی گرانشی اصلاح شده
کلمات کلیدی
شناسایی پارامتر، سیستم کنترل توربین هیدرولیک، چکش آب انعطاف پذیر، الگوریتم جستجوی گرانشی، الگوریتم جستجو گرانشی اصلاح شده،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 50, April 2016, Pages 177-191
نویسندگان
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