فهرست مطالب ali karami-mollaee

Integrating large-scale wind farms with the main grid poses several challenges to operating the power utilities. One of the most important is to extract the maximum possible power in different operating conditions from the installed capacity. One of the common solutions in wind power plants equipped with doubly-feed induction generators is to control the output power of the rotor by using a proportional-integral controller.In this paper, a modified chaos-based imperialist competition algorithm is used to manage the control coefficients of twenty wind turbines (2 MW) located in a microgrid. The results show that the use of chaos theory improves the quality of convergence tracking of maximum power points during rapid changes in wind speed. In the classical proportional-integral controller, when the wind speed changes rapidly, the generated torque contains a large ripple and this controller is unable to overcome the nonlinear and ripple torque properties. This weakness increases the stress on the system and can damage the structural equipment of the generator. At the same time, the results obtained using the chaotic-based imperialist competition algorithm show that not only the ripple content is significantly reduced, but also more than 40% of its subsurface value is reduced.

Voice coil motors (VCM) are used in very small equipment such as mobile phone cameras and the use of a robust control feature as sliding mode control (SMC) is inevitable in them. The most important property of SMC is its invariant against matched disturbances and uncertainties which is due to the using of Sign function in input control signal. The invariant property is stronger than robustness. But, this method is not invariant with respect to the mismatched uncertainties i.e. is variant and sensitive. This is the challenge of SMC in VCM because of existents mismatched uncertainty in their models. To solve this problem in this paper, input control signal is calculated at first via SMC and then, coefficients of the sliding surface are determined in such a way that the effect of mismatched uncertainties or disturbances is removed in closed loop system and invariant property is retained. The proposed approach is simple in concept and realization. Moreover, due to the inaccessible system states, a new nonlinear observer is proposed for system model identification. In simulation, the electro-mechanical model of motor has been used which has both matched and mismatched uncertainties. Simulation results show the effectiveness of this approach.

Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such a system, we must to know and to identify the new states or the plant model should be completely known. To solve this problem, we suggest two online neural networks to identify and to obtain a model for the unknown nonlinear system. In the first approach, the neural network training law is based on the available system states and the bound of observer error is not proved to converge to zero. The advantageous of the second training law is only using of the system output and the observer error converges to zero based on the Lyapunov stability theorem. To verify these approaches Duffing-Holmes chaotic systems (DHC) is useD

This paper describes load torque estimation (LTE) issue in induction motors (IM) with uncertainty and disturbance, using dynamic sliding mode control (DSMC). In DSMC the chattering is removed due to the integrator (or low pass filter) which is placed before the input control of the plant. In the other word, the Sign function appears in derivative of input control signal and will not appear in the input control signal of the plant. However, in DSMC the augmented system (the system plus the integrator) is one dimension bigger than the actual system and then, the plant model should be completely known. To solve this problem, a new adaptive fuzzy observer (AFO) has been proposed. The advantage of the proposed approach is to have the system controlled as well as its main task i.e. LTE, which is important in practical implementation. Simulation results are presented to demonstrate the preference of the approach.

The most important property of sliding mode control (SMC) is invariant against matched uncertainties، which is due to the using of Sign function and this Sign function produces chattering. Moreover، SMC is not invariant with respect to the mismatched uncertainties، which is its other problem. In this paper to solve these two problems، using of multiple surface dynamic sliding mode control (DSMC) is proposed. In DSMC the chattering is removed due to the integrator where is placed before the input control signal of the plant. However، in DSMC the augmented system (the system plus the integrator) is one dimension bigger than the actual system and then، the plant model should be completely known. To solve this problem، an observer is proposed called integral-chain observer or ICO. To counteract with mismatched uncertainty، any system dynamics are considered as a distinct nonlinear system and multiple sliding surfaces is defined. One of the advantages of the proposed approach is the upper bound of the uncertainty not used in DSMC and ICO، which is important in practical implementation. Then، a design procedure is described and simulation result is presented to demonstrate the approach.