Optimal Performance of DFIG Integrated with Different Power System Areas Using Multi-Objective Genetic Algorithm

Elkasem, Ahmed. H. A.; Kamel, Salah; Rashad, Ahmed

doi:10.1109/mepcon.2018.8635243

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…This AI methodology allows you to analyze and prevent any type of failure that you want to monitor. These methods must be adequately validated before their costly implementation in the real system (Elkasem et al 2018). In this sense, the use of prototypes or test benches is convenient for the validation of fault diagnosis techniques.…”

Section: Introductionmentioning

confidence: 99%

Vibration analysis for fault detection in wind turbines using machine learning techniques

Vives

2022

Adv. in Comp. Int.

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The implementation of machine learning techniques allows to prevent in advance the degeneration of any component present in a wind turbine, as well as the detection and diagnosis of sudden failures. This methodology allows automatic and autonomous learning to predict, detect and diagnose electrical and mechanical failures in wind turbines. Four different failure states have been simulated due to bearing vibrations in wind turbines, comparing traditional techniques, such as frequency analysis, as well as the implementation of AI, using the KNN and SVM methodology. This contribution evaluates different methodologies for monitoring, supervision and fault diagnosis based on the implementation of machine learning algorithms adapted to the different components and faults of the wind turbine. Implementing these techniques, allows to anticipate a breakdown, reduce downtime and costs, especially if they are offshore.

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Section: Introductionmentioning

confidence: 99%

Vibration analysis for fault detection in wind turbines using machine learning techniques

Vives

2022

Adv. in Comp. Int.

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“…These several optimization techniques are applied for finding the optimal controller parameters to overcome the LFC problem and achieve more system security. The utilized techniques by researchers in the LFC issue such as; grasshopper optimization algorithm [25], ant colony optimization technique [26], Jaya algorithm [27], particle swarm optimizer [28], firefly algorithm [29], hybrid pattern search shuffled-frog leaping algorithm [30], multi-objective genetic algorithm [31], grey wolf optimizer [32], sine cosine algorithm [33], harris hawks optimizer and salp swarm algorithm [34], lightning-attachment procedure optimization (LAPO) [35] and improved LAPO [36]. However, these techniques achieve exceptional performance by ensuring effectual LFC design.…”

Section: Introductionmentioning

confidence: 99%

Frequency Stability of AC/DC Interconnected Power Systems with Wind Energy Using Arithmetic Optimization Algorithm-Based Fuzzy-PID Controller

et al. 2021

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This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA’s proficiency has been verified over several other powerful optimization techniques; differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies; different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques.

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Application of Genetic Algorithm-Based Controllers in Wind Energy Systems for Smart Energy Management

Boopathi

Jagatheesan

Samanta

et al. 2023

Springer Tracts in Nature-Inspired Computing

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Optimal Performance of DFIG Integrated with Different Power System Areas Using Multi-Objective Genetic Algorithm

Cited by 6 publications

References 14 publications

Vibration analysis for fault detection in wind turbines using machine learning techniques

Vibration analysis for fault detection in wind turbines using machine learning techniques

Frequency Stability of AC/DC Interconnected Power Systems with Wind Energy Using Arithmetic Optimization Algorithm-Based Fuzzy-PID Controller

Application of Genetic Algorithm-Based Controllers in Wind Energy Systems for Smart Energy Management

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