The Dynamic Sliding Mode Controller with Observer of Coincident Perturbations and States for Buck Converter of Fuel Cell Source

Buck converter has been widely used in the DC renewable energy system application. The Fuel Cell (FC) based DC renewable energy is offered as a high-performance and low-emission power supply, which replaces conventional DC sources. Its relevant control system has regulated the output voltage under input voltage and load resistance variations to track the desired reference signal. To control the current sensorless-based buck converter with matched and mismatched uncertainties, the sys-tem must be modeled in such a way that by measuring the output voltage, both the inductor current and system perturbations can be estimated. The purpose of the work is suggestion of a novel dynamic sliding mode controller (DSMC) based on observer of coincident perturbations and states (CSPO) to enhance its controllability and tracking performance. The significance of the work lies in low cost and reduced losses due to the inductor current measurement. Lacking an exact value for inductor current, it is not possible to estimate and compensate the perturbations caused by parametric uncertainties in the buck converter. These objectives were achieved by modeling in the canonical form. The canonical model somehow converts both the matched and mismatched perturbations into the matched perturbation, in which the system states and perturbations can be merely estimated using only an output volt-age value and a CSPO. The most important results are the fastness and robustness of the DSMC to control the buck converter and compensate the effect of mismatched uncertainties and nonlinear dis-turbances and chattering phenomenon

Novel chattering free binomial hyperbolic sliding mode controller for asymmetric cascaded E-type bonded T-type multilevel inverter-based dynamic voltage restorer to meliorate FRT capability of DFIG-based wind turbine

The endurance capability of Doubly Fed Induction Generator (DFIG)-based wind turbine to network against different disturbances is an important criterion to meet the recent grid codes requirements. This generator has to provide the required energy under the perturbation conditions which is principally specified by Fault Ride-Through (FRT) capability. It refers to safe and continuous operation of DFIG without interruption which has been performed by Dynamic Voltage Restorer (DVR) using fast and accurate compensation of the voltage fluctuations. This paper aims to introduce an innovative quasi-sinusoidal voltage synthesizer for DVR to enhance the power quality and power delivery. Hence, a new Asymmetric Cascaded E-type Bonded T-type Multilevel Inverter (ACEBTMI) with binary geometric progression form of DC sources is proposed to provide high-step staircase sinusoidal voltage containing low semiconductor switches. Then, accurate triggering signals for the suggested ACEBTMI corresponding to the voltage fluctuations have been provided by new Chattering Free Binomial Hyperbolic Sliding Mode Controller (CFBHSMC). To validate compensation capability of the proposed DVR, ACEBTMI part has been compared with some traditional and familiar multilevel inverters, and also CFBHSMC part has been compared with SMC, FLC and PID controllers. In precise, the simulation results have definitely confirmed the accurate tracking and robust control performance of CFBHSMC to support ACEBTMI-based DVR which enables DFIG to ride-through different voltage perturbations

Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm

Abstract Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios