Modeling of AC-DC power system feeding a controlled buck-boost converter

Chanpittayagit, Rangsan; Areerak, Kongpol

doi:10.1109/ecticon.2014.6839720

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…As a result, the equivalent circuit of the considered power system can be represented in the DQ frame as depicted in Fig. 2 by fixing the rotating frame on the phase of the switching function (φ 1 =φ) [21]. In Fig.…”

Section: State-space Averaging Modelmentioning

confidence: 99%

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The Optimal Controller Design of Buck-Boost Converter by using Adaptive Tabu Search Algorithm Based on State-Space Averaging Model

Pakdeeto¹,

Chanpittayagit²,

Areerak³

2017

Journal of Electrical Engineering and Technology

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-Normally, the artificial intelligence algorithms are widely applied to the optimal controller design. Then, it is expected that the best output performance is achieved. Unfortunately, when resulting controller parameters are implemented by using the practical devices, the output performance cannot be the best as expected. Therefore, the paper presents the optimal controller design using the combination between the state-space averaging model and the adaptive Tabu search algorithm with the new criteria as two penalty conditions to handle the mentioned problem. The buck-boost converter regulated by the cascade PI controllers is used as the example power system. The results show that the output performance is better than those from the conventional design method for both input and load variations. Moreover, it is confirmed that the reported controllers can be implemented using the realistic devices without the limitation and the stable operation is also guaranteed. The results are also validated by the simulation using the topology model of MATLAB and also experimentally verified by the testing rig.

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Section: State-space Averaging Modelmentioning

confidence: 99%

“…The more details how to derive the considered power system using the DQ and GSSA methods can be found in [21].…”

Section: State-space Averaging Modelmentioning

confidence: 99%