scholarly journals A Unified Power Quality Conditioner for Power Quality Issue Mitigationer Using Neuro Fuzzy Controller

Author(s):  
R. MANIVASAGAM

Abstract This manuscript grants a control system for Power Quality (PQ) issues of Unified Power Quality Conditioner (UPQC) based hysteresis controller and augmentation the PQ with the help of numerous optimization methods. In numerous attractive features of UPQC, like voltage sags, voltage swells negative series current and harmonics. Foremost aim of PQ issues is the recompense of the D-FACT devices that origins an undesired effect on bearing. Optimal values for converter of UPQC its presentation can be meaningfully enhanced PQ. This paper discoursed about the UPQC physical process asserts the required meliorate the PQ at DC link integrated. The adaptive control technique for hysteresis developed by the modifying PQ with the use of the UPQC method is also briefly excused. The measurement of voltage sag and swell will be improved and sinusoidal after inoculating current and voltage to the UPQC source current and source voltage. The evaluation results inevitably show the effectiveness of the voltage sag and the voltage swell in the UPQC system based on the Neuro Fuzzy Control (NFC) controller is improved.

2014 ◽  
Vol 61 (11) ◽  
pp. 5851-5860 ◽  
Author(s):  
Raphael J. Millnitz dos Santos ◽  
Jean Carlo da Cunha ◽  
Marcello Mezaroba

Author(s):  
Budi Srinivasarao ◽  
G. Sreenivasan ◽  
Swathi Sharma

Since last decade, due to advancement in technology and increasing in the electrical loads and also due to complexity of the devices the quality of power distribution is decreases. A Power quality issue is nothing but distortions in current, voltage and frequency that affect the end user equipment or disoperation; these are main problems of power quality so compensation for these problems by DPFC is presented in this paper. The control circuits for DPFC are designed by using line currents, series reference voltages and these are controlled by conventional Neuro-Fuzzy controllers. The results are observed by MATLAB/SIMULINK model.


The Unified Power Quality Conditioner (UPQC) is the most flexible solution for all power quality problems. The UPQC is one of the APF family members where shunt and series APF functionalities are integrated together to achieve superior control over several power quality problems simultaneously. Reference signal extraction is the most important part of any control. Feedback control requires the minimum information about the process also the corrective action is taken when the control variable is changed but it has some drawbacks that it does not provide any predictive action for any known disturbance. The proposed control technique uses feed forward plus feedback control with synchronously rotating reference frame based reference signal extraction that not only measures the disturbances (voltage sag here) but also take corrective action to compensate for the same before they actually disturb the system. The applications of feed forward control along with the feedback control are able to compensate for the measured disturbance with the desired speed of response that enables faster restoration of voltage at load end. The suggested method is implemented by simulation in MATLAB/SIMULINK to demonstrate the improvement in response time.


2020 ◽  
Author(s):  
iftikhar manzoor

UPQC Based power quality conditioner covers all power quality problems. Problems covers by UPQC are Voltage Sag, Voltage Swell, voltage imbalance, Power factor correction, harmonics mitigation.


Author(s):  
Chirag Patel ◽  
Ranjit Mahanty

The paper presents a three-phase unified power quality conditioner using a fuzzy controller. The fuzzy controller replaces the conventional PI controller in this work. The results obtained through the fuzzy controller are found superior to those obtained through conventional PI controller in terms of dynamic response. This is because of the fact that the fuzzy controller is based on a linguistic variable set theory and does not require a mathematical model of the system. Moreover, the tedious method of tuning the PI controller is not required in case of fuzzy controller. Simulations are carried out using MATLAB/Simulink to validate the theoretical findings.


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