Transformer Internal and Inrush Current Fault Detection Using Machine Learning

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external faults.However,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush currents.CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays.Moreover,iden-tification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed.For the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal fault.The transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush currents.During inrush currents,CT temperature is increased.Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates maloperation.Hybrid wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identification of the false tripping of differential protection system of transformer.

SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

Haemoglobin Measurement from Eye Anterior Ciliary Arteries through Borescope Camera

Nowadays,smartphones are used as self-health monitoring devices for humans.Self-health monitoring devices help clinicians with big data for accurate diagnosis and guidance for treatment through repetitive measurement.Repetitive measurement of haemoglobin requires for pregnant women,pediatric,pulmonary hypertension and obstetric patients.Noninvasive haemoglobin measurement through conjunctiva leads to inaccurate measurement.The inaccuracy is due to a decrease in the density of goblet cells and acinar units in Meibomian glands in the human eye as age increases.Furthermore,conjunctivitis is a disease in the eye due to inflammation or infection at the conjunctiva.Conjunctivitis is in the form of lines in the eyelid and covers the white part of the eyeball.Moreover,small blood vessels in eye regions of conjunctiva inflammations are not visible to the human eye or standard camera.This paper proposes smartphone-based hae-moglobin(SBH)measurement through a borescope camera from anterior ciliary arteries of the eye for the above problem.The proposed SBH method acquires images from the anterior ciliary arteries region of the eye through a smartphone attached with a high megapixel borescope camera.The anterior ciliary arteries are projected through transverse dyadic wavelet transform(TDyWT)and applied with delta segmentation to obtain blood cells from the ciliary arteries of the eye.Furthermore,the Gaussian regression algorithm measures haemoglobin(Hb)with more accuracy based on the person,eye arteries,red pixel statistical parameters obtained from the left and right eye,age,and weight.Furthermore,the experimen-tal result of the proposed SBH method has an accuracy of 96%in haemoglobin measurement.

Modified Mackenzie Equation and CVOA Algorithm Reduces Delay in UASN

In Underwater Acoustic Sensor Network(UASN),routing and propagation delay is affected in each node by various water column environmental factors such as temperature,salinity,depth,gases,divergent and rotational wind.High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity.High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes.In this paper,the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing.Golden Ratio Optimization Method(GROM)and Gaussian Process Regression(GPR)predicts propagation delay of each node in UASN using temperature,salinity,depth,dissolved gases dataset.Dissolved gases,rotational and divergent winds,and stress plays a major problem in UASN,which increases propagation delay and energy consumption.Predicted values from GPR and GROM leads to node selection and Corona Virus Optimization Algorithm(CVOA)routing is performed on the selected nodes.The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes,based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds.From simulation results,CVOA Algorithm performs better than traditional DF and LION algorithms.