In this paper, a new control approach using an optimal linear control with prescribed degree of stability for modular multi-level converters (MMC) is presented and analyzed. The proposed controller relies on a linear quadratic regulator with integral action which brings the ability of state variable reference tracking for modular multi-level converters. Since MMC is a complex system with several state variables, a unified control system design for this system is vital. The proposed controller of this study is designed to obtain wider stability margin thanks to the implementation of prescribed degree of stability concept to minimize the quadratic performance index of the control structure. By means of this method, the poles of the closed-loop system will be shifted to the desired places in the left half side of the S-plane. The main advantages of this control strategy compared to previous methods are that it will be possible to control the state of energy for each phase separately, while there will be superior tolerance to nonlinearities and the enhanced stability margin with less sensitivity to plant-parameter variations. The performance of the designed controller is verified through MATLAB^TM simulations (The MathWorks, Natick, MA, USA) with the nonlinear model of MMC.

This paper presents a type of solar thermal power plant and also studies the efficiency of the common type of this distributed generation resource. In order to improve the efficiency of the power station, several techniques are studied such as the combined cycle plant, the thermoelectric generator and the intelligent control application. The thermodynamic analysis of the power plant and the performance of the equipment with an intelligent control system are validated by the developed experimental prototype.

This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.