A novel zero voltage transition boost converter and artificial neural network‐based estimation of converter efficiency

Abstract

In this paper, a novel zero voltage transition (ZVT) boost converter is proposed, and the overall efficiency of the converter is predicted with an artificial neural network (ANN) model. In the proposed converter, the main switch is turned on by ZVT and turned off by zero voltage switching (ZVS). Also, the other semiconductor elements operate by soft switching (SS). Besides, the proposed snubber cell has the bidirectional direct power transfer feature. The theoretical analyzes of the converter are verified by an prototype having 50 V DC input voltage, 100 V DC output voltage, 250 W output power, and 100 kHz switching frequency. The overall efficiency of the converter in hard switching (HS) condition is increased from 87.2% to 95.4% thanks to proposed snubber cell. Moreover, the efficiency of converter at HS operation is estimated with ANN. For this estimation, 110 efficiency values are obtained based on the different switching frequency and the output power values. When the actual efficiency measurements and the estimation results obtained with the ANN model are compared, it is seen that the results overlap and is obtained very close result to the truth by ANN. Thus, owing to the ANN model, the semiconductor power elements will not need to be operated at high frequencies and overheating, and the damaging to the elements will be prevented. Finally, the efficiency curve measurement of the converter takes long time in the experimental study when it takes highly short time as a few minutes in the estimation with ANN.

Intelligent wide‐area damping controller for static var compensator considering communication delays

“…Since dynamic inputs and outputs can be easily mapped using the neural network, which has led to the development of various neural network-based adaptive control schemes. [31][32][33][34] These controllers have also been applied with the SVC for oscillation damping application and have been found to overperform the conventional controllers. [20][21][22][23][24] In this study, a linear-neuro-adaptive controller (LNAC) is presented to provide enhanced oscillation damping under varied operating scenarios.…”

“…The accurate identification of the plant is achieved by the convergence of (34), which corresponds to eðkÞ ! 0.…”

“…The learning algorithm's stability and the speed of convergence described by (34) rely upon the appropriate learning rate. Small value of the learning rate guarantee convergence, but the learning becomes sluggish, and its large value may produce oscillations and result in divergence.…”

A novel capacitor‐voltage reduced bidirectional PWM DC‐DC buck‐boost converter for renewable energy battery charge system

“…These converters are half‐bridge or full‐bridge, and their gain depends on the transformer's turn ratio 13–18 . Transformer design becomes very difficult due to the leakage inductance losses at high voltages 19 . Generally, dual‐active bridge converters are used in bidirectional applications.…”

“…[13][14][15][16][17][18] Transformer design becomes very difficult due to the leakage inductance losses at high voltages. 19 Generally, dual-active bridge converters are used in bidirectional applications. However, these converters include many elements, and this necessity causes high costs and high transmission losses.…”