ABSTRACT:
Dynamic Voltage Restorer (DVR) is a
custom power device used as an effective solution in protecting sensitive loads
from voltage disturbances in power distribution systems. The efficiency of the
control technique, that conducts the switching of the inverters, determines the
DVR efficiency.Proportional-Integral-Derivative (PID) control is the general technique
to do that. The power quality restoration capabilities of this controller are
limited, and it produces significant amount of harmonics – all of which stems
from this linear technique’s application for controlling non-linear DVR. As a solution,
this paper proposes an Artificial Neural Network (ANN) based controller for
enhancing restoration and harmonics suppression capabilities of DVR. A detailed
comparison of Neural Network controller with PID driven controller and Fuzzy logic driven controller
is also illustrated, where the proposed controller demonstrated superior performance
with a mere 13.5% Total Harmonic Distortion.
KEYWORDS:
1. Power
quality
2. Dynamic
Voltage Restorer (DVR)
3. PID
4. Fuzzy logic
5. Artificial
Neural Network (ANN)
SOFTWARE: MATLAB/SIMULINK
Fig. 1. Block diagram of the proposed DVR system to mitigate
voltage
instabilities.
EXPECTED SIMULATION RESULTS:
Fig. 2. Three phase sag mitigation based on ANN controlled DVR.
(a) Instantaneous voltage at stable condition; (b) Instantantaneous voltage
when sag occurs; (c) Voltage required to mitigate voltage sag; (d) Output
voltage of the inverter circuit; (e) Generated PWM for inverter; (f)
Instantaneous voltage after voltage restoration.
Fig. 3. Restored Voltage Using (a) PID controller; (b) Fuzzy
controller; (c) ANN controller; (d)THD comparison: the least THD can be seen at
ANN based DVR, the range of the harmonics is also truncated by a huge amount by
this method.
CONCLUSION:
DVRs are a popular choice for enhancing power quality in power
systems, with an array of control system on offer to drive these devices. In
this paper, application of ANN to operate DVR for providing better performance
than existing systems to mitigate voltage sag, swell, and harmonics has been
demonstrated. Problem statement and theoretical background, structure of the
proposed method, training procedure of the ANN used have been described in
detail. Simulation results showing the DVR performance during voltage sag have
been presented. Comparison of the proposed method with the popular PID
controller, and nonlinear Fuzzy controller has been carried out, where the proposed
ANN controller appeared as the best option to restore system voltage while
mitigating THD to the greatest extent.
REFERENCES:
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