Photovoltaic Integrated Shunt Active Power Filter with Simpler ADALINE Algorithm for Current Harmonic Extraction

Abstract

This manuscript presents a significant work in improving the current harmonics extraction algorithm and indirectly improving the injection current produced by a single-phase Photovoltaic Shunt Active Power Filter (PV SAPF). Improvement to the existing adaptive linear neuron (ADALINE) technique has been carried out, leading to the formation of a simpler ADALINE; it is expected to perform as fast as the current harmonics extraction algorithm. Further analysis on the DC link capacitor control algorithm, called "self-charging with step size error cancellation", was also done to inspect the performance of the algorithm in a single-phase photovoltaic shunt active power filter system. Both algorithms, configured in single-phase PV SAPF, were simulated in MATLAB/Simulink (R2012b). A laboratory prototype was developed, and the algorithms were computed on a TMS320F28335 Digital Signal Processing (DSP) board for hardware implementation purposes. From the acquired results, the simpler ADALINE algorithm has effectively performed with lower total harmonic Distortion (THD) and outstanding compensation. The established algorithm of self-charging with step size error cancellation works well with single-phase PV SAPF and has shown less overshoot, a fast response time, and minimal energy losses.

Integration of CFA 2 FB control scheme with modified shunt active line conditioner ( MSALC ) connected distribution system for mitigation of harmonics

“…Further, some of the merits are 25 : reduction of harmonics and three-phase neutral return current, improvement of power factor, impact minimization upon the distribution system, voltage regulation and eliminate risk of resonance. [30][31][32][33][34]…”

Machine Learning-Based Adaline Neural PQ Strategy for a Photovoltaic Integrated Shunt Active Power Filter

“…In the frequency-based techniques, the computational burden caused by the needed time in collecting adequate samples gives poor dynamic performances in comparison with the time domain strategies [37]. These latter offer better performance in terms of response time, but lack accuracy particularly under varying loads [38].…”

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

“…To improve the algorithmic quality, studies have been focused on reducing time delays of filters [1][2][3], reducing the computation times of the control algorithms [1], [4][5][6][7], and reducing the time spent on other algorithms [8][9][10][11]. The time delay due to the algorithms is called response time delay [12][13][14][15][16][17], whereas time delay caused by the hardware limitations is called reaction time delay [17][18][19][20][21]. For hardware improvement, researchers have tried to adapt faster data acquisition units [22][23][24], more accurate and faster measurement sensors [25][26][27], to increase the switching frequency of power electronics switches [28][29][30] and to optimize LC filter parameters [31][32][33][34][35].…”