Automated power management strategy for wind power generation system using pitch angle controller

Sitharthan, R.; Sundarabalan, C.K.; Devabalaji, K.R.; Yuvaraj, T.; Imran, Arif

doi:10.1177/0020294019827330

Cited by 141 publications

(12 citation statements)

References 28 publications

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“…In the parameters set, the iteration count, limits, population, and the objective function are fed. The ANFIS completed its training when it reached its maximum iteration or when it reaches the objective function [ 35 – 37 ]. The training dataset has been used for performance evaluation with the test dataset.…”

Section: Methodsmentioning

confidence: 99%

ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Kumar¹,

Al‐Turjman²,

Srinivas

et al. 2021

Neural Comput & Applic

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Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10 –3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

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Section: Methodsmentioning

confidence: 99%

ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Kumar¹,

Al‐Turjman²,

Srinivas

et al. 2021

Neural Comput & Applic

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“…The developed PEHS model consists of a wind catcher made up of PVC material. The windcatcher is designed in such a way that it is possible to capture wind from all directions [20][21][22]. The wind capture is mounted over a cantilever.…”

Section: Modelling and Function Of The Proposed Piezoelectric Energy Harvesting System For Wind-induced Vibrationmentioning

confidence: 99%

Piezoelectric energy harvester converting wind aerodynamic energy into electrical energy for microelectronic application

Sitharthan

Yuvaraj

Padmanabhan

et al. 2021

IET Renewable Power Gen

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Piezoelectric Energy Harvesting Systems play a vital role in energizing microelectronic devices with the low-frequency operation. Here, a novel piezoelectric energy harvesting device has been developed for low power electronic devices. The developed Piezoelectric Energy Harvesting Systems consists of a cantilever with poles projecting outwards and the cantilevers one end is connected to the wind-catcher, and another end is connected to the torsional spring. The developed Piezoelectric Energy Harvesting Systems signifies its application in energizing microelectronic devices. The cantilever is placed inwards to the piezoelectric crystal stack. When the wind strikes, a vortex is created in the windcatcher, which oscillates the cantilever and generates stress in the piezoelectric crystal stack to develop electric energy. The output voltage obtained from the Piezoelectric Energy Harvesting Systems does not affect any input frequency of the piezoelectric crystal. The result obtained shows that the developed Piezoelectric Energy Harvesting Systems generates 120-200 eV with 2.9 × 10 16 -4.84 × 10 16 Hz frequency considering an elementary charge unit as 40 for a variable wind flow of 4-9 m/s. This research aims to develop an efficient wind-based Piezoelectric Energy Harvesting Systems for low powered microelectronic devices.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The developed ENN further consists of 10 neurons in the hidden layer and corresponding 10 neurons in context layer and one neuron in the output layer, respectively. The step by step weight updating of layers in developed ENN is as follows, 29‐32 …”

Section: Torque Ripple Minimizations Of Pmsm Using Fl‐dtcmentioning

confidence: 99%