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The paper investigates the dynamic operation of novel control scheme for both Static Synchronous Compensator (STATCOM) and Static Synchronous Series Compensator (SSSC) based on a new full model comprising a 48-pulse Gate Turn-Off thyristor voltage source converter for combined reactive power compensation and voltage stabilization of the electric grid network. The complete digital simulation of the STATCOM and SSSC within the power system is performed in the MATLAB/Simulink environment using the Power System Blockset (PSB). The STATCOM scheme and the electric grid network are modeled by specific electric blocks from the power system blockset, while the control system is modeled using Simulink. Two novel controllers for the STATCOM and SSSC are presented in this paper based on a decoupled current control strategy. The performance of both STATCOM and SSSC schemes connected to the 230-kV grid are evaluated. The proposed novel control schemes for the STATCOM and SSSC are fully validated by digital simulation.

Abstruct-The paper presents a rule-based fuzzy logic controller to control the output power of a pulse width modulated (PWM) inverter used in a stand alone wind energy conversion scheme (SAWECS). The self-excited induction generator used in SAWECS has the inherent problem of fluctuations in the magnitude and frequency of its terminal voltage with changes in wind velocity and load. To overcome this drawback the variable magnitude, variable frequency voltage at the generator terminals is rectified and the dc power is transferred to the load through a PWM inverter. The objective is to track and extract maximum power from the wind energy system (WES) and transfer this power to the local isolated load. This is achieved by using the fuzzy logic controller which regulates the modulation index of the PWM inverter based on the input signals: the power error e = (Pref -Po) and its rate of change e. These input signals are fuzzified, that is defined by a set of linguistic labels characterized by their membership functions predefined for each class. Using a set of 49 rules which relate the fuzzified input signals (e, 6) to the fuzzy controller output U , fuzzy set theory and associated fuzzy logic operations, the fuzzy controller's output is obtained. The fuzzy set describing the controller's output (in terms of linguistic labels) is defuzzified to obtain the actual analog (numerical) output signal which is then used to control the PWM inverter and ensure complete utilization of the available wind energy. The proposed rule-based fuzzy logic controller is simulated and the results are experimentally verified on a scaled down laboratory prototype of the SAWECS. NOMENCLATUREVR VI ZDC dc link current. Po Rectifier output power. P, , , TG Efficiency of induction generator. rlR Efficiency of rectifier. W m ws i d s a l p v d s Minv vw Wind velocity. dc voltage at rectifier output. dc voltage at inverter input. Maximum output power of wind energy system. Rotor speed of induction generator. Synchronous speed of induction generator. d-axis component of stator current. q-axis component of stator current. d-axis component of stator voltage. Modulation index of PWM inverter.Radius and swept area of wind turbine. Tip speed ratio (A = R w T / V~) .Rotor speed of wind turbine. Average torque conversion coefficient. Average torque at wind turbine shaft. Average torque at induction generator shaft. Gear box ratio.

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