Gradient Descent Learning for Utility Current Compensation using Active Regenerative PWM Filter
Problem statement: Harmonic analysis is a primary matter of power quality assessment. Its main intention is to check the utility whether it is delivering the loads without any deviations in voltages and currents. The problem is due to proliferation of Electronic converters and power electronics which gave birth to numerous new applications, offering unmatched comfort to the customers. Approach: Harmonics should be maintained within the limits said in standards like IEEE 519 and others such as IEEE 1159 for safeguarding the utility. This was provided by many mitigation technologies like passive, shunt and series filtering, active conditioners, but they were lack of some demerits like huge cost, many controllers and circuit components. So for controlling the harmonic loads the converter with four quadrant characteristics was implemented and this converter act as shunt active filter as well as rectifier simultaneously without any additional circuitry. For having better harmonic reduction in addition, many controllers like p-q Theorem based controller, Fuzzy and gradient descent based neural network is also used. Results: The simulation results gives the compared source current wave forms for various controllers with individual harmonic mitigations. Conclusion: The pure utility current is obtained by using this intelligent neural filter without any additional components and without any extra controllers than the conventional methods.
Copyright: © 2011 R. Balamurugan and G. Gurusamy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- nonlinear loads
- solid-state electronics
- Total Harmonic Distortion (THD)
- Active Regenerative Filters (ARF)
- p-q theorem based controller
- Fuzzy Logic Controller (FLC)
- gradient descent back propagated neural network