Parameter optimization of PI controller by PSO for optimal controlling of a Buck converter's output

Muhurcu, Gulcin; Köse, Ercan; Mühürcü, Aydın; Kuyumcu, Ali

doi:10.1109/idap.2017.8090234

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Nowadays, optimisation schemes have attracted attention based on their good performance. The PSO algorithm is a well‐known optimisation technique, which can be considered as an evolutionary computation technique [36]. This algorithm is reached by research on swarm; indeed, this method, to manipulate algorithms, utilises evolutionary operators.…”

Section: Voltage Control Strategymentioning

confidence: 99%

Adaptive neuro‐fuzzy inference systems (ANFIS) controller design on single‐phase full‐bridge inverter with a cascade fractional‐order PID voltage controller

Saadat¹,

Ghamari²,

Mollaee³

et al. 2021

IET Power Electronics

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Adaptive neuro-fuzzy inference system (ANFIS) technique is a significant alternative of research which is structured with a combination of two soft-computing strategies of fuzzy logic and artificial neural network. The design of ANFIS controller for a single-phase fullbridge inverter with pulse width modulation is demonstrated here in the presence of different disturbances. Moreover, an LC filter is designed to decrease the disturbing harmonics which the stability of the filter can be noted as an important issue. Based on the fuzzy C-mean clustering method used for decreasing fuzzy rules, the computational burden has been improved resulting in faster dynamic performance. This method considers the system as a black-box structure which omits the need for an exact model of system and can be an appropriate technique for ill-defined systems. Additionally, to deal with the variations of supply DC voltage, a fractional-order proportional-integral-derivative controller is designed which is tuned by particle swarm optimiser algorithm and can generate a sinusoidal reference for the system input. This double-loop control technique is known as cascade control strategy. It can be seen that ANFIS scheme provides appropriate results with less computational burden and simple structure with optimised responses in challenging conditions. The capability of the proposed method is validated for different operating conditions through simulation and experimental results.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Voltage Control Strategymentioning

confidence: 99%

Adaptive neuro‐fuzzy inference systems (ANFIS) controller design on single‐phase full‐bridge inverter with a cascade fractional‐order PID voltage controller

Saadat¹,

Ghamari²,

Mollaee³

et al. 2021

IET Power Electronics

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“…For proportional-integral (PI) and PID controllers, a hybrid approach based on fuzzy logic is described. Mühürcü et al [6] proposed a buck converter concept that enhanced a discrete time PI controller. By enhancing the PSO algorithm's integral gain (Ki) and proportional gain (Kp), the control procedure efficiency is maintained at an acceptable level.…”

Section: Introductionmentioning

confidence: 99%

Tuning proportional integral controller to enhance the photovoltaic system performance

Nadhim,

Ibrahim

2023

Bulletin EEI

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This paper presented a closed loop buck-boost converter controlled by using proportional and integral controller (PIC) for photovoltaic (PV) applications. The mathematical equations of design the solar panel type KC200GT and buck–boost converter is illustrated. The electrical behaviors of solar panel are examined at 1,000 light radiation and room temperature (25 °C) situation. A PIC tuning is difficult with conventional methods such as graphs and mathematical analysis, so for that reason two approaches are used for tuning and optimization of PIC, particle swarm optimization (PSO), and Zeigler–Nichols (ZN). The suggested closed loop application through the converter preserves the constant voltage in the output spite of alterations to the input voltage, decreases settling time, overshoot and ripple voltage to minimum value that enhancement the efficiency of the converter. The results show that the PSO approach is improved than the ZN approach. The proposed model is constructed by using SimPower system tool box in MATLAB package.

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“…A. Bakar et al [7] presented the design of PI controller for a boost converter using Real Time Interface (RTI) to achieve faster transient response which can be observed by using dSPACE RTI (an open source repository software package). G. Mühürcü et al [8] presented the design of Buck Converter which was added to discrete time PI controller. The efficiency of control process is maintained at high level by optimizing the proportional gain ‫ܭ(‬ ) and integral gain ‫ܭ(‬ ) in PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Closed Loop Proportional Integral (PI) Controlled Boost Converter and 3-phase Inverter for Photovoltaic (PV) Applications

Kadhim¹

2019

alkej

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This research deals with the design and simulation of a solar power system consisting of a KC200GT solar panel, a closed loop boost converter and a three phase inverter by using Matlab / Simulink. The mathematical equations of the solar panel design are presented. The electrical characteristics of the panel are tested at the values of 1000 for light radiation and 25 °C for temperature environment. The Proportional Integral (PI) controller is connected as feedback with the Boost converter to obtain a stable output voltage by reducing the oscillations in the voltage to charge a battery connected to the output of the converter. Two methods (Particle Swarm Optimization (PSO) and Zeigler- Nichols) are used for tuning PI controller to enhance the overall performance of the system. The Sine Pulse Width Modulation (SPWM) technique was used to obtain a pure 3-phase in low distortion. The voltage and current output values of ± 14V and ± 2.8A are obtained, and the Total Harmonic Distortion (THD) value is 4.20%.

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Parameter optimization of PI controller by PSO for optimal controlling of a Buck converter's output

Cited by 11 publications

References 15 publications

Adaptive neuro‐fuzzy inference systems (ANFIS) controller design on single‐phase full‐bridge inverter with a cascade fractional‐order PID voltage controller

Adaptive neuro‐fuzzy inference systems (ANFIS) controller design on single‐phase full‐bridge inverter with a cascade fractional‐order PID voltage controller

Tuning proportional integral controller to enhance the photovoltaic system performance

Design and Simulation of Closed Loop Proportional Integral (PI) Controlled Boost Converter and 3-phase Inverter for Photovoltaic (PV) Applications

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