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This paper presents a novel switch current stress reduction technique for a single switch boost-flyback high step up dc–dc converter. Coupled inductor-based converters do not have a sufficiently higher inductance value of their windings. This creates current surge during transient and steady state, which in turn increases current stress at main switch. To compensate this difficulty in coupled inductor-based boost flyback converter, an inductor–diode parallel combination has been connected in series with the switch. This technique increases the effective inductance of the coupled inductor primary winding during switch turn-on operation and helps to reduce current stress. Steady-state operating principle with the proposed technique is analyzed in continuous conduction mode. The current reduction technique is discussed with a proper mathematical derivation. To verify theoretical findings, simulation study is carried out in MATLAB/Simulink R2014b. Finally, a laboratory-based 40-W prototype converter is designed to show the effectiveness of the proposed switch current reduction technique. [ABSTRACT FROM AUTHOR]

In this paper, component values of analog active filters are selected based on the manufacturer's values of E series. The selection is based on optimization algorithms and here one is nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), and another one is the physics-based method called Sine Cosine Algorithm (SCA), are used for active filter design. The capability of optimization of the above algorithms is evaluated by considering the two active filters of a 4th order Butterworth and State variable filter. The performances of each algorithm are analyzed by applying to above two different filter structures, where the component values are determined by making compatible with different E series manufacturer. [ABSTRACT FROM AUTHOR]

The component value selection for active analogue filter design is an important issue to improve the performances and to make compatible with existing parameters value. The Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are efficient intelligent evolutionary algorithms for solving optimization problems. Both techniques are used to minimize the total design error of a 4th order Butterworth low pass active filter. This would be realized with component values which are compatible with E12 series. In addition, stability of filter is guaranteed by minimization of gain sensitivity product. By considering the minimization of Gain Sensitivity Product (GSP), the sixteen variables of objective function are reduced to eight variables which speed up the iteration process. The simulation results prove the efficiency of algorithms for the design of analogue active filter by optimizing the component values based on E12 compatible with minimization of GSP by minimising the design error. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of leakage current and its dependency on common mode voltage in transformer less single-phase grid connected photovoltaic (PV) system. Further a new carrier-based PWM method is derived for H bridge single-phase grid-tied PV inverter to minimize leakage current. The proposed modulation strategy is compared with conventional techniques through simulation in MATLAB R2018a. The superiority of the proposed technique is validated in laboratory prototype of a 500 W single-phase grid-tied PV system. The hardware results validate the theoretical findings in appropriate. [ABSTRACT FROM AUTHOR]

In conventional design method, the parameters of an active filter cannot be selected based on manufactured parameters such as E series. However, this can be feasibly obtained using optimization technique through an exhaustive search on all possible combination of preferred values of E series by providing the coding system. The exhaustive search for low pass second order filter is carried out through Meta-heuristic optimization Algorithms. The cost function for error minimization including compatible to E series and gain adaptive are used here as objective function for finding out minima through well known algorithms. The inclusion of gain constrained concept eliminates the extra op-amp cascading to state variable filter circuit which reduces the noise and dimension of cost function; in addition, improves the stability of the system. The performances of Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) and Sine-Cosine Algorithm (SCA), nature and physics-inspired artificial intelligence techniques are evaluated for state variable filter design considering gain constrained. Each of above algorithms is applied to filter structure and performances of them are also evaluated when filter design is realized with components selected from different manufactured E series. [ABSTRACT FROM AUTHOR]