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Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Self-Constructing Fuzzy Neural Fractional-Order Sliding Mode Control of Active Power Filter.

Juntao Fei, Zhe Wang, Qi Pan

IEEE Transactions on Neural Networks and Learning Systems

|May 4, 2022

Summary

This study introduces a novel fractional-order sliding mode control (FOSMC) scheme enhanced by a self-constructing recurrent fuzzy neural network (SCRFNN) to effectively mitigate power system harmonic distortions and improve robustness against uncertainties.

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Area of Science:

Electrical Engineering
Control Systems
Artificial Intelligence

Background:

Power systems face harmonic distortions due to nonlinearities and uncertainties.
Traditional control methods struggle with complex dynamics and parameter variations.
Effective harmonic mitigation is crucial for power quality and system stability.

Purpose of the Study:

To propose a fractional-order sliding mode control (FOSMC) scheme for harmonic distortion mitigation.
To integrate a self-constructing recurrent fuzzy neural network (SCRFNN) for handling uncertainties.
To enhance power system robustness and control performance.

Main Methods:

Development of a fractional-order sliding mode controller (FOSMC) for stability and chatter reduction.
Utilization of SCRFNN to approximate unknown system dynamics and adapt to environmental fluctuations.
Incorporation of feedback connections in SCRFNN for improved temporal problem handling.

Main Results:

The combined FOSMC-SCRFNN scheme ensures tracking error and its derivative converge to zero.
Experimental validation confirms the effectiveness of the proposed control strategy.
The scheme demonstrates superior performance in harmonic suppression and robustness compared to existing methods.

Conclusions:

The proposed FOSMC with SCRFNN is a highly effective method for mitigating harmonic distortions in power systems.
The integration of fractional-order calculus and adaptive neural networks significantly enhances control system performance and robustness.
This approach offers a promising solution for improving power quality and system stability under uncertain operating conditions.