توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱HONEY BEE MATING OPTIMIZATION SCHEME FOR LOAD FREQUENCY CONTROL
نویسنده(ها): ، ، ،
اطلاعات انتشار: ششمین کنفرانس بین‌المللی مسائل فنی و فیزیکی در مهندسی قدرت، سال
تعداد صفحات: ۶
This paper presents an adaptive Honey Bee Mating Optimization (HBMO) algorithm to tune optimal gains of a Proportional Integral Derivative (PID) controller for Load Frequency Control (LFC) design in an interconnected power system. The problem of robustly tuning of the PID based LFC design is formulated as an optimization problem according to the time domain–based objective function which is solved by the HBMO technique that has a strong ability to find the most optimistic results. To demonstrate the effectiveness of the proposed method a two–area interconnected power system is considered as the test system under different operating conditions. The simulation results are shown to maintain the robust performance in comparison with the particle swarm optimization based tuned PID controller and also classical controllers.<\div>

۲Multi–Stage Fuzzy Controller for UPFC Using PSO to Damp Power System Oscillations
نویسنده(ها): ، ، ،
اطلاعات انتشار: سومین کنفرانس تخصصی حفاظت و کنترل سیستم های قدرت، سال
تعداد صفحات: ۷
The Unified Power Flow Controller (UPFC) is the most versatile device in the FACTS devices which has emerged to enhance power system stability spectrum and dynamic performance. In this paper, a new Multi–Stage Fuzzy (MSF) DC–voltage regulator is proposed for the UPFC to damp power system low frequency oscillations. For a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membershipfunctions. For this reason, in the proposed MSF type PID controller the membership functions are tuned automatically using Particle Swarm Optimization (PSO) method. The aim is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantages of PSO and fuzzy system control techniques and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the new proposed control strategy is evaluated under different operating conditions in comparison with the classical controllers to demonstrate its robust performance through time simulation studies and some performance indices.<\div>
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