Adaptive ANN based Single PI Controller for Nine-Level PUC Inverter

Babaie, Mohammad; Sharifzadeh, Mohammad; Al‐Haddad, Kamal

doi:10.1109/epec47565.2019.9074823

Cited by 22 publications

(5 citation statements)

References 36 publications

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“…A feed-forward network architecture was chosen while constructing the neural network (Babaie et al, 2019), (Shehzad et al, 2021). In a feed-forward neural network, the node connections don't form a cycle.…”

Section: Mathematical Modelingmentioning

confidence: 99%

AI based controller optimization for VSC-MTDC grids

et al. 2022

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Electric power industry is continually adopting new techniques to improve the reliability and efficiency of the energy system and to cope with the increasing energy demand and the associated technical challenges. In recent years, the maturation of Artificial Intelligence (AI) led researchers to solve various problems in the power system by using AI techniques. Voltage Source Converter is the result of advancements in the field of power electronics and semiconductors technology, which holds a promising future for the realization of smart grid, renewable energy integration, and HVDC transmission system. Usually hit and trial method or the design engineer’s experience is used for the manual tuning of the PI controllers, which cannot yield superior performance. The process becomes more complicated when multiple grids are involved, such as in VSC-based MTDC grids. This research article use a deep learning optimization technique for the tuning of the VSC controllers, which resulted in quick settling time, better slew rate, less undershoot and low overshoot. The deep learning neural network is trained through the Particle Swarm Optimization (PSO) algorithm to produce the best possible tuned or optimally tuned parameters for the controllers. The optimal tuning of the controller will result in an overall better performance of the converter and the grid. A four-layered deep learning neural network and a three-terminal MTDC grid were designed and simulated in MATLAB/SIMULINK environment.

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Section: Mathematical Modelingmentioning

confidence: 99%

AI based controller optimization for VSC-MTDC grids

et al. 2022

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“…Citing the classical ones, such as Neutral Point Clamped (NPC) [11,12], Flying Capacitor (FC) [13,14] and Cascaded H-bridge (CHB) [15], these inverters use a large number of components and even more than one DC source. For those reasons, researchers have developed new topologies of multilevel inverters such as Packed U-Cells (PUC) [16][17][18], which needs a low number of passive and active switches and provides a high number of levels without using filters or sensors, and Split Packed U-Cells (SPUC) [19], which is able to combine capacitors together, and also uses a small number of components with an uncomplicated PWM control technique. The SPUC inverter is very competitive in PV systems in standalone mode [20,21], as well as in grid-connected mode as published in [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

Ouardi

Gadari

Mokhlis

et al. 2023

Eng

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A new Maximum Power Point Tracking (MPPT) method, consisting in combining the Incremental Conductance (INC) algorithm with the Hysteresis control, was developed and applied to a standalone photovoltaic (PV) system to generate the maximum power of the PV array. The INC allows one to search for the Maximum Power Point (MPP). The hysteresis improves the accuracy of tracking the MPP very fast even after severe changes in weather conditions and has no oscillations around the MPP. The five-level S-Packed U Cells (SPUC5) inverter is used to transform the produced DC voltage to AC voltage; it generates five-level output voltage with a small number of switches and only DC source voltage. The capacitors of the SPUC5 are controlled by the Pulse Width Modulation (PWM) in order to balance their voltages. The proposed PV system was established and trained in the MATLAB/Simulink environment under various irradiation conditions. A comparison between different MPPT methods, INC-PWM and INC-PI, was investigated in order to examine the effectiveness of the developed MPPT technique in particular, and of all the PV system components. The results of the simulation validate the effectiveness of the suggested MPPT algorithm as well as the used SPUC5 inverter.

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“…On the other hand, a seven-level voltage can be produced when the dc capacitor voltage is balanced to the one-third of dc input voltage. Owing to the reduced number of switching devices and dc capacitor requirement, the performance of PUC inverters is investigated in various applications such as active filter [14,15], dynamic voltage restorer [16], rectifier [17,18], stand-alone inverter [18][19][20][21], and grid-connected inverter [20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…The main reason of this comes from the fact that the design of PUC7 topology was based on employing six switching devices and one dc capacitor whose voltage should be adjusted to one-third of the input dc voltage. Extension to nine-level increases the number of switching devices from six to eight and dc capacitors from one to two which increases the controller complexity considerably [28,29].…”

Section: Introductionmentioning

confidence: 99%

Proportional‐resonant based control strategy for grid‐connected packed‐E cell inverters with Lyapunov filter‐based PLL

Biricik

Kömürcügil²,

Ahmed

et al. 2022

IET Generation Trans & Dist

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Adaptive ANN based Single PI Controller for Nine-Level PUC Inverter

Cited by 22 publications

References 36 publications

AI based controller optimization for VSC-MTDC grids

AI based controller optimization for VSC-MTDC grids

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

Proportional‐resonant based control strategy for grid‐connected packed‐E cell inverters with Lyapunov filter‐based PLL

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