Interactive Trial and Error Learning Method for Distributed Channel Bonding: Model, Prototype Implementation, and Evaluation

Khan, Zaheer; Lehtomäki, Janne

doi:10.1109/tccn.2019.2897695

Cited by 9 publications

(9 citation statements)

References 22 publications

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“…In terms of performance, the choice of the gains for these regulators plays an important role. It is commonly referred to use the "Ziederger method" to choose these gains [35]. In this case, the WECS could undergo a large overshoot followed by a long settling period.…”

Section: Resultsmentioning

confidence: 99%

Internal Model Control (IMC)-Based Active and Reactive Power Control of Brushless Double-Fed Induction Generator with Notch Filter

Memon

Mustafa

Laghari

et al. 2022

International Transactions on Electrical Energy Systems

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The increase in demand for electricity and, in particular, green energy has put renewable energy systems at the focal point of energy policy worldwide. The higher reliability of brushless doubly fed induction generators (BDFIGs) makes them suitable for offshore and remote wind energy generation (WEG) applications. Besides, controlling the active and reactive powers in an electrical power system is critical for optimal voltage regulation, reduced power losses, and enhanced utilization of installed equipment. However, the existing literature on BDFIG’s active and reactive power control highlights the poor dynamic response and high transients with harmonic generation during inductive load insertion. It is because the Ziegler technique was employed to select PI gains, and the instantaneous reactive power theory was used to mitigate harmonics. Considering that, this paper proposes a vector control (VC) method for BDFIGs in wind turbines, in which the proportional-integral (PI) gains for internal model control (IMC) are optimized to improve the dynamic response of the active and reactive power during inductive load insertion. The proposed method reduces the complexity, time consumption, and uncertainty in making the optimal choice. In addition, to reduce a double fundamental frequency component to the point-of-common-coupling (PCC) voltage, the excellent characteristics of the notch filter are utilized in the grid-side converter (GSC)-based vector control scheme. The simulation results in MATLAB/Simulink show that the proposed IMC-based vector control scheme with a notch filter provides satisfactory results with a minimum peak value compared to existing techniques.

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Section: Resultsmentioning

confidence: 99%

Internal Model Control (IMC)-Based Active and Reactive Power Control of Brushless Double-Fed Induction Generator with Notch Filter

Memon

Mustafa

Laghari

et al. 2022

International Transactions on Electrical Energy Systems

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“…Out-of-the-box MABs mainly decide which are the best channel widths to be used when no further information, neither from the network nor from user requirements, is considered. The goal is to maximize WLAN performance [156], [157], [162]. When traffic loads and other performance metrics are considered, such as delay and throughput, DRL techniques are successfully applied [158], [159].…”

Section: Channel Bondingmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

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“…Generally, the "trial and error" method is utilized to select these gains. 31 The mentioned method of selecting the PI gains is a manual tuning process and depends on the experience and expertise of the control designer. Besides, it is time-consuming and does not yield optimal outcomes generally.…”

Section: Mathematical Modeling Of Bdfigmentioning

confidence: 99%

Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

Memon

Mustafa

Jumani

et al. 2021

Int Trans Electr Energ Syst

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Summary Considering the superior performance and comparatively lower maintenance cost of the brushless double‐fed induction generator (BDFIG) than the conventional double‐fed induction generator (DFIG), this article explores its application in wind energy conversion systems (WECS) with a novel control strategy. The proposed control strategy utilizes the intelligence of the Salp swarm algorithm (SSA)–based vector control scheme to optimally regulate the speed, torque, current, active, and reactive power of the proposed WECS during the sharp changes in the wind speed and load. To overcome the demerits associated with the conventional “trial and error” method of PI regulator tuning, the SSA is utilized to optimally select their proportional and integral gains automatically. SSA accomplishes the mentioned task by iteratively minimizing the considered error integrating objective function through an offline optimization method; thus, it provides the least error and consequently optimal dynamic response of the proposed BDFIG‐based WECS at the end of the optimization process. The effectiveness and performance of the proposed control strategy are validated in terms of optimal speed, torque, and active and reactive power regulation and is compared with the internal model control and particle swarm optimization algorithm–based vector control schemes under identical operating conditions and system configurations. The proposed control scheme for the considered BDFIG‐based WECS obtains the best optimal dynamic response among the considered control schemes; thus, it proves its efficacy and essence.

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Interactive Trial and Error Learning Method for Distributed Channel Bonding: Model, Prototype Implementation, and Evaluation

Cited by 9 publications

References 22 publications

Internal Model Control (IMC)-Based Active and Reactive Power Control of Brushless Double-Fed Induction Generator with Notch Filter

Internal Model Control (IMC)-Based Active and Reactive Power Control of Brushless Double-Fed Induction Generator with Notch Filter

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

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