Power Analysis Using Various Types of Wind Turbines

Ganthia, Bibhu Prasad; Mohanty, Monalisa; Maherchandani, Jai Kumar

doi:10.4018/978-1-7998-7447-8.ch010

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…This process is key to achieving the expected results in our models. The dataset that was used for this project was taken from a scada system of windmills that are installed in Turkey and are generating energy [ 30 , 31 ].…”

Section: Machine-learning Models For Wind-power-generation Predictionmentioning

confidence: 99%

“…For the study of this dataset, more columns have been added [ 30 , 31 ]: Month A month column was added based on the date column. Mean Wind Speed In this new column, the wind speed is rewritten in 0.5 intervals—for example: if the wind speed is between 3.25 and 3.75, it becomes 3.5; and if the wind speed is between 3.75 and 4.25, it becomes 4.…”

Section: Machine-learning Models For Wind-power-generation Predictionmentioning

confidence: 99%

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COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

Martín¹,

Flórez

González‐Briones

et al. 2023

Sensors

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The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today’s devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.

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Section: Machine-learning Models For Wind-power-generation Predictionmentioning

confidence: 99%

Section: Machine-learning Models For Wind-power-generation Predictionmentioning

confidence: 99%

COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

Martín¹,

Flórez

González‐Briones

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…After that, in most cases, it applies appropriate rules before asserting new knowledge into the knowledge base. An inference engine can be used to provide the ability for an explanation, allowing it to describe the chain of reasoning to a user that led towards a certain conclusion by tracing back the rules that were fired as a result of the assertion [23]- [25]. Because the system has no memory constraints, it can be utilized to store as much data as needed and remember it during its applications [26]- [28].…”

Section: Expert Systemmentioning

confidence: 99%

Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB

Ganthia,

Mohanty,

Mohapatra

et al. 2023

Control Syst. Optim. Lett.

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The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners in making important decisions on unit commitments, energy trading, system security reliability, and optimal reserve capacity. The objective is to produce a desktop version of a personal computer-based complete expert system that can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes a study of load forecasting in smart grids, a detailed study of the architecture and configuration of Artificial Neural Network (ANN), Mathematical modeling and implementation of ANN using MATLAB, and a detailed study of load forecasting using the backpropagation algorithm.

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“…This proposed model is active and faster responsive for both steady saturated mode and transient mode. [13][19] [28] The transient due to sudden speed change can be effectively noted from this model. In further section the use of various modes of techniques for the adaptive controller described and discussed.…”

Section: Wind Energy Conversion Systemsmentioning

confidence: 99%

“…In further section the use of various modes of techniques for the adaptive controller described and discussed. [14][20-21][27] [22][23][24][25][26][27][28][29]…”

Section: Wind Energy Conversion Systemsmentioning

confidence: 99%

Radial Basis Function Artificial Neural Network Optimized Stability Analysis in Modified Mathematical Modeled Type-III Wind Turbine System Using Bode Plot and Nyquist Plot

Ganthia¹,

Barik²,

Nayak³

2022

ECS Trans.

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Stability is the major factor that should be maintained in every power system. To predict and to optimize the nonlinear parameters this research provides a control system transient analysis using Bode plot and Nyquist plot to regulate the stability in wind power generation. The rotor speed should be balanced with respect to the generation of generator power for viable wind power generation. This study introduces a sliding mode controller for controlling wind speed and preserving system stability, and it may be improved using an Artificial Neural Network based Radial Basis Function Neural Network to eliminate nonlinearities induced by changing wind speed. The tip speed ratio approach is utilized in this study to harvest the most power from wind energy. To optimize this TSR method, a PI-RBFN tuned sliding mode controller was utilized to get maximum power while minimizing active power losses. This proposed approach may be used to address nonlinearities in the pitch angle caused by changing wind speed. As a result, the resilience of the redesigned Type-III wind turbine system is investigated using MATLAB simulink in this study. The simulation results are compared to the current DFIG-based modified Type-III wind turbine method.

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Power Analysis Using Various Types of Wind Turbines

Cited by 8 publications

References 15 publications

COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB

Radial Basis Function Artificial Neural Network Optimized Stability Analysis in Modified Mathematical Modeled Type-III Wind Turbine System Using Bode Plot and Nyquist Plot

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