Design and Implementation of Artificial Neural Networks to Predict Wind Directions on Controlling Yaw of Wind Turbine Prototype

Dzulfikri, Zaky; Nuryanti, Nuryanti; Erdani, Yuliadi

doi:10.18196/jrc.1105

Cited by 12 publications

(8 citation statements)

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“…The study's findings show that an effective ANN control method for wind energy conversion systems with PMSM has been developed. Wind turbine system performance may be improved using a model developed by researchers using Markov and ANN [106,117]. With these strategies, it has been demonstrated that generator speed fluctuations are reduced and increasing the safety of the WT system and the ability to generate more electricity from varying wind speeds.…”

Section: Neural Network Controllersmentioning

confidence: 99%

Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions

et al. 2023

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The use of renewable energy techniques is becoming increasingly popular because of rising demand and the threat of negative carbon footprints. Wind power offers a great deal of untapped potential as an alternative source of energy. The rising demand for wind energy typically results in the generation of high-quality output electricity through grid integration. More sophisticated contemporary generators, power converters, energy management, and controllers have been recently developed to integrate wind turbines into the electricity system. However, a comprehensive review of the role of converters in the wind system’s power conversion, control, and application toward sustainable development is not thoroughly investigated. Thus, this paper proposes a comprehensive review of the impact of converters on wind energy conversion with its operation, control, and recent challenges. The converters’ impact on the integration and control of wind turbines was highlighted. Moreover, the conversion and implementation of the control of the wind energy power system have been analyzed in detail. Also, the recently advanced converters applications for wind energy conversion were presented. Finally, recommendations for future converters use in wind energy conversions were highlighted for efficient, stable, and sustainable wind power. This rigorous study will lead academic researchers and industry partners toward the development of optimal wind power technologies with improved efficiency, operation, and costs.

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Section: Neural Network Controllersmentioning

confidence: 99%

Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions

et al. 2023

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“…A computational intelligence called ANN (Artificial Neural Network) imitates the organic neural network behavior of living things. Numerous issues, including control [41], detection [42], forecasting [43], [44], and more, have been resolved using ANN approaches. The technique operates by analyzing and computing previously approved data training [45].…”

Section: E Levenberg Marquardt Artificial Neural Network (Lm-ann) Alg...mentioning

confidence: 99%

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

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To facilitate wireless sensor networks deployment in dense jungle environments, the challenges of unreliable wireless communication links used for routing data between nodes and the gateway, and the limited battery energy available from the nodes must be overcome. In this paper, we introduce the Self-Organized Wireless Sensor Network (SOWSN) to overcome these challenges. To develop the traits needed for such SOWSN nodes, three types of computational intelligence mechanisms have been featured in the design. The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. The second feature is the introduction of the fuzzy logic ANFIS-optimized Near Ground Propagation Model to predict the wireless transmission link quality and power transfer between transmitters. The third feature is the introduction of the (Levenberg Marquardt artificial neural network (LM-ANN) for Adaptive Dynamic Power Control to further optimize the transmitter power levels, radio modulation, Spreading Factor configurations, and settings of the employed SOWSN LoRaWAN nodes based on predicted wireless transmission link quality parameters. The introduced features were extensively evaluated and analyzed using simulation and empirical measurements. Using clustering, near-ground propagation, and adaptive transmission power control features, a robust wireless data transmission system was built while simultaneously providing power conservation in SOWSN operation. The payload loss can be improved using SAW clustering from 1275 bytes to 5100 bytes. The result of power conservation can be seen from the reduction of transmission power in SOWSN nodes with the increase of transmission time (TOA) as its side effect. With the original power transmission at 20 dBm, original TOA time at 96.832 milliseconds for all nodes, and SNR 3 as input, transmission power was reduced to 12.76 dBm and the TOA increased to 346.78 milliseconds for all nodes.

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“…NN (Neural Network) is a computational intelligence inspired by the brain biological neural networks that mimic brain behavior from living things. NN methods have been used to solve many problems, from motor control [106], human detection [107], forecasting [108], [109], and many more. The system will do its job by considering previously accepted examples (called data training) [110].…”

Section: B Nn (Neural Network)mentioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

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In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

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Design and Implementation of Artificial Neural Networks to Predict Wind Directions on Controlling Yaw of Wind Turbine Prototype

Cited by 12 publications

References 0 publications

Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions

Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

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