An efficient control strategy for two connected microgrids (MGs) is proposed to ensure stable and economic operation. One of the most important means of improving energy efficiency is to achieve the best response for sudden and stochastic disturbances to which the MGs are subjected. Traditionally, MGs are controlled using a linear controller, such as conventional proportional-integral (PI) controller. Fuzzy PI (FPI) controller-based model reference adaptive control that can adapt to a wide range of operating conditions for regulating the voltage is investigated and its performance is compared with the conventional linear PI controller that is not able to mitigate these disturbances efficiently. Parameters of the proposed controller are optimised using an advanced optimisation technique called global porcellio scaber algorithm (GPSA). Performance of the controllers is demonstrated on two connected microgrids for a number of scenarios such as load variations, weather fluctuations and faults. Simulation results verify that the proposed control strategy is effective and feasible under various operating conditions for this system. The results also show that the dynamic performance of the system with the model reference adaptive fuzzy PI (MRAFPI) controller is better than that with the most common controller used for this application, the conventional PI controller, for different operating conditions. wileyonlinelibrary.com/iet-gtd IET Gener. Transm. Distrib.
Integrating large-scale wind turbine generators (WTGs) may have significant impacts on power system operation such as system frequency, voltage profile, stability and reliability. This paper studies the stability and performance of the wind energy conversion system (WECS) based on Static Var Compensator (SVC). Without reactive power compensation, the integration of wind farm based on induction generators (IGs) in a network may lead to the voltage collapse in the system and hence it becomes unstable. The paper also shows that a dynamic reactive power compensation using Static Var Compensator (SVC) at the point of common coupling (PCC) is successful in maintaining the system voltage at acceptable level and hence increases stability of the system. Moreover, this paper presents, using advanced optimization techniques based on artificial intelligence (AI) such Harmony Search Algorithm (HS), Self-Adaptive Global Harmony Search Algorithm (SGHS), Firefly Algorithm (FA) and Improved Firefly Algorithm (IFA)as to tune the parameters of PI controllers for SVC and pitch angle.
Large-scale integration of wind turbine generators (WTGs) may have significant impacts on power system operation with respect to system frequency and bus voltages. This paper studies the effect of Static Var Compensator (SVC) connected to wind energy conversion system (WECS) on voltage profile and the power generated from the induction generator (IG) in wind farm. Also paper presents, a dynamic reactive power compensation using Static Var Compensator (SVC) at the a point of interconnection of wind farm while static compensation (Fixed Capacitor Bank) is unable to prevent voltage collapse. Moreover, this paper shows that using advanced optimization techniques based on artificial intelligence (AI) such as Harmony Search Algorithm (HS) and Self-Adaptive Global Harmony Search Algorithm (SGHS) instead of a Conventional Control Method to tune the parameters of PI controller for SVC and pitch angle. Also paper illustrates that the performance of the system with controllers based on AI is improved under different operating conditions. MATLAB/Simulink based simulation is utilized to demonstrate the application of SVC in wind farm integration. It is also carried out to investigate the enhancement in performance of the WECS achieved with a PI Controller tuned by Harmony Search Algorithm as compared to a Conventional Control Method.
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