Q-Learning based Maximum Power Extraction for Wind Energy Conversion System With Variable Wind Speed

Kushwaha, Ashish; Gopal, M.; Singh, Bhim

doi:10.1109/tec.2020.2990937

Cited by 60 publications

(19 citation statements)

References 14 publications

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“…Video image processing technology is more and more widely used in athletes' sports training and sports competitions [5]. It has become a research hotspot, in foreign countries, such as the United States; the "Eagle Eye" test system has been applied to tennis competitions; it is mainly used to monitor the game; when the player disagrees with the landing position of the tennis ball, the three-dimensional image of the landing position of the ball can be retrieved [6].…”

Section: Introductionmentioning

confidence: 99%

Tennis Technology Recognition and Training Attitude Analysis Based on Artificial Intelligence Sensor

2022

Journal of Sensors

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In order to solve the problem of traditional tennis serving technique teaching, subjective, and experience-based teaching methods, the author proposes a method to extract tennis training movements based on artificial intelligence sensor video analysis. This method requires the use of ordinary cameras and computer technology; video analysis technology is used to guide tennis teaching and training. The result obtained is as follows: in the experimental group and the control group, at the end of the teaching stage, the movement characteristics of each link of the body are quite different, which can reach 15%-20%; when the experimental group and the control group did the “scratching back” movement during the teaching period, at the end of teaching, the students in the experimental group were close to high-level tennis players in their movement skills, and the movement gap was only about 5%. It is proved that the method proposed by the author provides an objective and scientific basis for measuring the referee’s penalty level. In the grass-roots tennis teaching and training, it has important value and significance and has a great promotion prospect.

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Section: Introductionmentioning

confidence: 99%

Tennis Technology Recognition and Training Attitude Analysis Based on Artificial Intelligence Sensor

2022

Journal of Sensors

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“…In addition to the three critical applications above, other applications of RL in power systems include electricity market [140], [141], network reconfiguration [142], service restoration [143], [144], emergency control [145], maximum power point tracking [146], [147], cyber security [148], [149], maintenance scheduling [150], protective relay control [151], electric vehicle charging navigation [152], demand response customer selection [153], power flexibility aggregation [154], etc.…”

Section: Other Applicationsmentioning

confidence: 99%

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Chen

Tang

et al. 2022

IEEE Trans. Smart Grid

184

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With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well.

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“…Kushwaha et al [21] proposed an intelligent sensor for variable wind speed using a maximum-power-pointtracking based Q-learning algorithm. Reddy et al [22][23][24][25] applied an efficient evolutionary algorithm, efficient multi-objective optimization, glowworm swarm optimization and a harmony search algorithm to obtain an optimal power flow solution.…”

Section: India Ranks Fourth On the Renewable Energymentioning

confidence: 99%

Ant lion algorithm for optimized controller gains for power quality enrichment of off-grid wind power harnessing units

Kodakkal¹,

Rajagopal

Raju

et al. 2020

Chin. J. Electr. Eng.

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The proposed system uses an algorithm that works on the admittance of the system, for estimating the reference values of generated currents for an off-grid wind power harnessing unit (WPHU). The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds. The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU. The wind power harnessing unit comprising of a squirrel cage induction generator, a star-delta transformer, a battery storage system and the control unit are modeled using Matlab/Simulink R2019. An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line. The response of the system during the dynamic loading depends on the best possible compensator proportional-integral (PI) gains. The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence, high efficiency and fast calculating speed. It is therefore used to extract the optimal values of frequency and voltage PI gains. The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up. This algorithm is proven to have a quick response, maintain the required frequency, suppress the current harmonics, regulate voltage, help in balancing the load and compensating for the neutral current.

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Q-Learning based Maximum Power Extraction for Wind Energy Conversion System With Variable Wind Speed

Cited by 60 publications

References 14 publications

Tennis Technology Recognition and Training Attitude Analysis Based on Artificial Intelligence Sensor

Tennis Technology Recognition and Training Attitude Analysis Based on Artificial Intelligence Sensor

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Ant lion algorithm for optimized controller gains for power quality enrichment of off-grid wind power harnessing units

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