Review on Optimal Wind Farm Control Techniques and Prospects of Artificial Intelligence

Isa, Abubakar; Chhor, Johnny; Krajiski, Philip; Kipke, Vile

doi:10.1109/iecon.2019.8926889

Cited by 5 publications

(2 citation statements)

References 60 publications

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“…Nowadays, the gearless PMSG based WECS is the most rapidly expanding 19 because of the self-excitation, requires less maintenance, has lower losses and cost, and has high efficiency. 9 The success of turbine control in wind energy applications is a very important issue to ensure efficient performance with low operating and maintenance costs, 20,21 for that reason, many literature studies appear to be a priority, 22 such as research on MPPT control, 4 pitch angle control, 23 integration to network and frequency regulation, 24 fault-tolerant cooperative control. 25 Among these research areas, this article mainly focuses on the MPPT technique which is applied by controlling the electrical power converter.…”

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confidence: 99%

Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS

Gouabi

Hazzab

Habbab

et al. 2021

Adaptive Control & Signal

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Summary This article proposes an adaptive and modified Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm, using fuzzy logic step size controller, for a wind energy conversion system (WECS) based on wound rotor synchronous generator. The modified P&O controller uses additional current information of the dc‐link, to decrease power oscillation around MPP, and choose the correct direction of the perturbation, while wind speed increase condition, depending on the sign of dc‐link current change. So, in this contribution, a modified drift‐free P&O algorithm was chosen because it gives better results, but the duty cycle step size in the proposed MPPT controller has been varied adaptively, using a fuzzy logic inference system based on the variation of the rectifier output power and the previous change of the duty cycle to get fast convergence until the MPP is reached in increasing and decreasing wind speed conditions. Performances and robustness of the proposed speed sensorless MPPT controller are evaluated in an experiment implementation, using a WECS emulator and dSpace DS1104 controller board, where experimental results show that the proposed method guarantees fast convergence with better energy quality and high robustness against speed or load change conditions.

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confidence: 99%

Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS

Gouabi

Hazzab

Habbab

et al. 2021

Adaptive Control & Signal

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“…The existing solutions for reducing wake effects in WF system can be categorized into two types, such as optimal placement of WTGs in WF [8], [9], and coordinated operation of WTGs to reduce aerodynamic losses [10], [11]. The first solution is realized by optimal design of WF system, while the second solution is determined by the WF operators during the operation of WF system.…”

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confidence: 99%

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Bui

Nguyen

2020

IEEE Access

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In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.

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Three-Dimensional Dynamic Wake Model for Real-Time Wind Farm Simulation

Kipke,

Sourkounis

2024

2024 32nd Mediterranean Conference on Control and Automation (MED)

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Review on Optimal Wind Farm Control Techniques and Prospects of Artificial Intelligence

Cited by 5 publications

References 60 publications

Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS

Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Three-Dimensional Dynamic Wake Model for Real-Time Wind Farm Simulation

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