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International Journal for Innovative Research in Science & Technology

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Paper Title:- Development of ANN and AFIS Models for Age Predictionof in-Service Transformer Oil Samples

Paper Title:- Development of ANN and AFIS Models for Age Predictionof in-Service Transformer Oil Samples

Author Name:- Mohammad Aslam Ansari 

Department of Electrical Engineering 

Abstract:- Power transformer is one of the most important and expensive equipment in electrical network. The transformer oil is a very important component of power transformers. It has twin functions of cooling as well as insulation. The oil properties like viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant suffer a change with respect to time. Hence it is necessary that the oil condition be monitored regularly to predict, if possible, the remaining lifetime of the transformer oil, from time to time. Six properties such as moisture content, resistivity, tan delta, interfacial tension and flash point have been considered. The data for the six properties with respect to age, in days, has been taken from literature, whereby samples of ten working power transformers of 16 to 20 MVA installed at different substations in Punjab, India have been considered. This paper aims at developing ANN and ANFIS models for predicting the age of in-service transformer oil samples. Both the the models use the six properties as inputs and age as target. ANN (Artificial Neural Network) model uses a multi-layer feedforward network employing back propagation algorithm, and ANFIS (Adaptive Neuro Fuzzy Inference System) model is based on Sugeno model. The two models have been simulated for estimating the age of unknown transformer oil samples taken from generator transformers of Anpara Thermal Power Project in state of U.P. India. A comparative analysis of the two models has been made whereby ANFIS model has been found to yield better results than ANN model.     

Keywords: ANN, ANFIS, Power Transformer, Regression, Performance, Backpropagation Algorithm   

I.         Introduction

Power transformer is one of the most important constituent of electrical power system. The transformer oil, a very important ingredient of power transformers, acts as a heat transfer fluid and also serves the purpose of electrical insulation. Its insulating property is subjected to the degradation because of the ageing, high temperature, electrical stress and other chemical reactions. Hence it is necessary that the oil condition be monitored regularly. This will help to predict, if possible, the in-service period or remaining lifetime of the transformer oil, from time to time.

       There are several characteristics which can be measured to assess the present condition of the oil. The main oil characteristics are broadly classified as physical, chemical and electrical characteristics; some of these are viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant. There exists a co-relation among some of the oil properties and suffer a change in their values with respect to time [2]. This variation of oil properties with respect to time has been utilised to develop the two models as said earlier

      The training data for the proposed work have been obtained from literature, whereby ten working transforms of 16 to 20 MVA, 66/11 KV installed at different substations in the state of Punjab, India have been considered. The six properties of transformer oil such as breakdown voltage (BDV), moisture, resistivity, tan delta, interfacial tension and flash point have been considered as inputs and age as target. Test data have been taken from generator transformers of 250 MVA, 15.75kV/400kV from Anpara Thermal Power Project in state of U. P., India.

II.     “Ann” and “Anfis” methods

It is known that classical models need linear data for their processing, therefore models like ANN and ANFIS that are based on soft computing techniques, play an important role for solving these kinds of non-linear problems.

        Neural networks exhibit characteristics such as mapping capabilities or pattern association, generalization, robustness, fault tolerance, parallel and high speed processing. Neural networks can be trained with known examples of a problem to acquire knowledge about it. Once trained successfully, the network can be put to effective use in solving unknown or untrained instances of the problem. ANN model which uses multilayer feed forward network is based on back propagation (BP) learning algorithm of neural network. Backpropagation gives very good answers when presented with inputs never seen before. This property of generalization makes it possible to train a network on giving set of input-target pairs and get good output.

         ANFIS stands for Adaptive Neural Fuzzy Inference System. Using a given input/output data set, the toolbox function ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either a backpropagation algorithm alone, or in combination with a least squares type of method. This allows the fuzzy systems to learn from the data they are modelling. These techniques provide a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data. This learning method works similarly to that of neural networks.

III.       Development of ann model

The proposed ANN model uses “Levenburg-Marquardt (trainlm) algorithm which is independent of learning rate, hence by simply changing the number of neurons in hidden layer, training and testing error could be reduced. A total of 700 data sets obtained from literature [2] were arranged in tabular form and used for training the neural network. The model uses a simple two layer network, one hidden layer and one output layer. Input layer comprises of six neurons, one for the each input, while the output layer has a single neuron for a single output, the age of oil sample.

         It has been found that network architecture that uses 20 neurons in hidden layer gave the best performance with a regression of 0.999 and mean square error (MSE) of 83.0 ( data is non –normalized, so error looks large ) . The training continued for 184 iterations with training functions logsig in hidden layer and purelin in output layer respectively.

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Validation of Failure of Beater Shaft of Double Roller Ginning Machine using Mathematical Failure Analysis Approach

Abstract:- A cotton gin is a machine that quickly and easily separates cotton fibers from their seeds, allowing for much greater productivity than manual cotton separation. The fibers are processed into clothing or other cotton goods, and any undamaged seeds may be used to grow more cotton or to produce cottonseed oil and meal. The cotton gin is a machine used to separate cotton fibers from the seed. The double roller ginning machine consists of various parts such as beater shaft, leather roller, moving knife, fixed knife, feeder, etc.         During the ginning operation the shaft fails at certain location. The actual failure position of shaft shown in fig no A was studied and the failure analysis using theories of failure was used to identify and validate the place or point of failure.

Keywords: Ginning machine, beater shaft, theories of failure, SFD, BMD, failure analysis

I.       Introduction

Ginning, in its strictest sense, refers to the process of separating cotton fibers from the seeds. The cotton gin has as its principal function the conversion of a field crop into a salable commodity. Thus, it is the bridge between cotton production and cotton manufacturing.  Ginning is the first and most important mechanical process by which seed cotton is separated into lint (fiber) and seed and machine used for this separation is called as gin. It consists of two spirally grooved leather roller pressed against a fixed knife, are made to rotate at about 90-120 rpm. Two moving blades combined with seed grids constitutes a central assembly known as beater which oscillates by means of a crank or eccentric shaft, close to the fixed knife. When the seed cotton is fed to the machine in action, fibers adhere to the rough surface of the roller are carried in between the fixed knife and roller in such a way that the fibers are partially gripped between them. The oscillating knife beats the seed and separates the fibers. This process is repeated for number of times and due to push-pull-hit action the fibers are separated from the seed, carried forward on the roller and dropped out of machine. The ginned seeds drop down through the grid which is oscillating along with beater.

Fig. 1: Double Roller Gin machine

The beater assembly is the innermost and major part of the double roller gin and is sandwiched symmetrically between the stationary knives as shown in fig.  It is composite unit consisting of moving knives and seed grids situated on the either side of the beater shaft. It has anchor shaped cross section and its axle is situated 100cm above ground and 15cm from each roller. Beater trough with perforations having concave edge with radius of 565cm and angle of 144 degree between two arms is provided to stop unginned cotton to fall down into seed chute.

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