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Comparative analysis of Solar Generation System with 21- CHB-MLI integrated SAPF based ANN and AGPSO tuned PI controller to enhance power quality

Agrawal Seema, Kumar Mahendra, Palwalia D. K.

Journal of Power Technologies

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2022

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Vol. 102, nr 4

121--131

EN

This paper represents comparative analysis of artificial neural network (ANN) and AGPSO tuned PI controller based power quality improvement solar generation system. Now a day's Power quality is a major problem due to non-liner load based on power electronics. SAPF is solution to overcome such power quality issues in dynamic manner. With the use of both soft computing controllers based Shunt active power filter, it is tried to reduce harmonics (distortions), compensate reactive power, enhance power quality and power factor correction of supply voltage. System comprises 21-Level cascaded H-bridge inverter supplied from photovoltaic panel, series coupling inductor and self supported DC (capacitor) bus. Voltage harmonics of supplied voltage from PV is reduced by 21-level cascades H-bridge inverter in which switching signal is generated by carrier based in phase level shifted pulse width modulation technique. Incremental conductance (IC) MPPT technique is incorporated to maximize PV panel output. Phase locked loop based unit template generation and Levenberg Marquardt algorithm trained ANN and AGPSO tuned PI controller based DC bus voltage regulation is utilized for current quality improvement in SAPF. Comparative results show the effectiveness of ANN controller than A GPSO tuned PI controller. Suggested model is simulated in Matlab/Simulink 2016(b) for effectiveness.

2

Performance analysis of SRF PLL type-3 with dynamic feed forward frequency estimator for abnormal grid conditions

Agrawal Seema, Kumar Mahendra, Palwalia D. K.

Journal of Power Technologies

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2021

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Vol. 101, nr 2

87--95

EN

A phase-locked loop (PLL) is a closed-loop feedback control device that synchronizes its output signal with an input signal in both frequency and time. This paper presents a robust method for tracking against adverse conditions of the fundamental sequence component of utility voltage. The proposed phase-locked loop (PLL) is a hybrid configuration of the phase- locked loop type-3 (SRF type-3) synchronous reference frame coupled with a feed-forward frequency estimator using a selective harmonic filtering technique. A SRF type -2 based PLL has a secondary closed path. Under the ramp frequency transition, it has a steady-state step and a frequency error. In the proposed PLL, the constant gain loop filter is used to eliminate the phase and frequency error. Feed-forward operation with selective harmonic pre-filtering enables fast-tracking with a low tracking error of the reference signal. From the results it is evident that the proposed PLL achieves a high bandwidth and quick dynamic response without endangering the stability and filtering capability. The proposed system has been tested through MATLAB Simulink platform under abnormal conditions.

3

Performance Measure of Shunt Active Power Filter Applied with Intelligent Control Technique

Agrawal Seema, Chourasiya Seemant, Palwalia Dheeraj Kumar

Journal of Power Technologies

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2020

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Vol. 100, nr 3

272--278

EN

This paper describes a shunt active power filter for compensating harmonic currents and reactive power. The shunt active power filter (SAPF) was implemented with a phase pulse modulated current control voltage source inverter. The proposed system is based on PLL synchronization with reduced fuzzy logic controller (RD-FLC). These control techniques for SAPF prove that source current is sinusoidal even when load is nonlinear. The CC-VSI inverter switching is done according to gating pulse obtained from the hysteresis controller using RD-FLC controller. The proposed system is investigated using simulation results. The effectiveness of the controller is observedin the form of total harmonic distortion, reactive power compensation and settling time of dc link voltage under non linearload condition. This technology reduced the rules of fuzzy logic controller from 49 to 37.