Neural networks and fuzzy logic in power electronics

Lin, Bor‐Ren; Hoft, R.G.

doi:10.1016/0967-0661(94)90579-7

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…Supervised machine learning techniques is very popular in the application of power electronic converters as it reduces the complexity and process the results in a very short time [2]- [4]. ANN is aided in real time modeling apprach for power electronic converters to model thermal stress, and switching loss, and reported a unique performance in computation burden and resource utilisation of FPGA [5].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning controller in matrix converter based on model predictive control algorithm

Gounder,

Subramanian

2023

IJPEDS

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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.

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Section: Introductionmentioning

confidence: 99%