Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

Chetouani, Elmostafa; Errami, Youssef; Obbadi, Abdellatif; Sahnoun, Smail

doi:10.11591/eei.v10i5.2843

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…)𝑄 𝑠_𝑟𝑒𝑓 (23) The rotor current 𝑖 𝑟𝑑 and 𝑖 𝑟𝑞 are controlled by LADRC controllers as shown in Figure 5. These expressions can be rewriten using the canonical form given by ( 24) and (25). ; 𝑢 𝑞 = 𝑣 𝑟𝑞 (29)…”

Section: Linear Active Disturbance Rejection Control For Rotor Side C...mentioning

confidence: 99%

“…The control block of the GSC is shown in Figure 6, which includes a direct current (DC) bus voltage control loop to keep it constant and a current control loop in the filter (𝑅 𝑓 , 𝐿 𝑓 ) which is used to generate reference 𝑉 𝑓𝑑_𝑟𝑒𝑓 and 𝑉 𝑟𝑞_𝑟𝑒𝑓 voltages which will be applied to the input of the PWM command of the converter [25], [26]. The filtre currents expression can be writen using the canonical form given by ( 30) and (31).…”

Section: Linear Active Disturbance Rejection Control For Grid Side Co...mentioning

confidence: 99%

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Proposed robust ADRC control of a DFIG used in wind power production

Laafou

Madi²,

Moumani³

et al. 2022

Bulletin EEI

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This work presents the control of the double fed induction generator (DFIG) used in the wind conversion system (WCS). The studied model is based on the control of the two converters grid side converter (GSC) and rotor side converter (RSC), in order to extract the maximum of power and also to keep the direct current (DC) bus voltage constant. The control strategy used here is based on linear active disturbance rejection control (LADRC) controllers. These are known, in the field of control systems, by their performance and robustness and allow a very good rejection of disturbances which can affect the wind system. In addition, this kind of controllers could help to meet the requirements of robustness against the variations of the parameters of the system and the uncertainties of its mathematical model. To validate the effectiveness of the proposed control, the system was modeled and tested by numerical simulation using MATLAB/Simulink software. The results carried out are discussed and analyzed in terms of references tracking, robustness and disturbance rejection. The results obtained have shown shown that the proposed control is promising and useful.

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Section: Linear Active Disturbance Rejection Control For Rotor Side C...mentioning

confidence: 99%

Section: Linear Active Disturbance Rejection Control For Grid Side Co...mentioning

confidence: 99%

Proposed robust ADRC control of a DFIG used in wind power production

Laafou

Madi²,

Moumani³

et al. 2022

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Global best values can be calculated from individual best amounts. The improved controller settings were found in the last iteration's global best values [31], [32].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…where Vij(d) is the particle ith speed in a j dimension at iteration d, xij(d) is the particle ith location in a j dimension at iteration d, Pbest is the perfect previous location of ith particle, Gbest is the perfect particle among all the population, W is the inertia weight factor, (C1 and C2) are the acceleration constants, (r1 and r2) are the random integers between [0-1], and n is the swarm size [32], [33].…”

Section: Particle Swarm Optimization Mathematical Modelmentioning

confidence: 99%

Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system

2022

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This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.

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“…This laid the groundwork for the development of contemporary optimisation techniques for best PI controller parameter adjustment [25]. Researchers have suggested a variety of sophisticated controllers to address the shortcomings of conventional PI controller, particle swarm optimization (PSO) is most widely utilised artificial intelligence techniques [26]. PSO algorithm's drawbacks include a poor rate of convergence during repetitive processes and an ease with which it easily enters local optimums in highdimensional space.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

2023

IJATEE

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Power flow optimization is a critical aspect of power and control system design, aiming to minimize running costs while ensuring control variable tolerances and maximizing power delivery. The utilization of flexible alternating current transmission system (FACTS) particularly unified power flow controllers (UPFCs), enhances a system's power transfer capacity. UPFCs offer versatile adjustments for active and reactive power lines and voltage levels simultaneously, contributing to improved system performance and stability. A novel approach for enhancing flexibility and control in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) was proposed. The technique involved the integration of a fuzzy-based control strategy for UPFC and the utilization of a cascaded adaptive neuro fuzzy inference system (ANFIS) to manage UPFC's series and shunt converters. Additionally, an improved butterfly optimization algorithm (IBOA) was introduced to ensure stability for DFIG-WECS. The methods were implemented and validated through simulations using MATLAB software. The effectiveness of the proposed approach was demonstrated through experimental results. The integration of cascaded ANFIS-based UPFC control and IBOA with WECS resulted in increased system power quality and stability. The total harmonic distortion (THD) of the system is reduced by 1.8%, indicating an enhancement in power quality. The application of UPFC and innovative control methodologies results in optimal power flow (OPF), maintaining stringent control variable tolerances in power systems. This research presents a systematic approach to enhancing flexibility and control in a DFIG-based WECS through the innovative use of UPFC and advanced control techniques. The proposed fuzzy-based UPFC control, in conjunction IBOA, significantly improves power quality and stability, thereby enhancing the overall efficiency of the system.

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Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

Cited by 9 publications

References 26 publications

Proposed robust ADRC control of a DFIG used in wind power production

Proposed robust ADRC control of a DFIG used in wind power production

Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

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