Hasmat Malik

EMD and ANN based intelligent fault diagnosis model for transmission line

In the presented work, an intelligent model for fault classification of a transmission line is proposed. Ten different types of faults (LAG, LBG, LCG, LABG, LBCG, LCAG, LAB, LBC, LCA and LABC) have been considered along with one healthy condition on a simulated transmission line system. Post fault current signatures have been used for feature extraction for further study. Empirical Mode Decomposition (EMD) method is used to decompose post fault current signals into Intrinsic Mode Functions (IMFs). These IMFs are used as input variables to an artificial neural network (ANN) based intelligent fault classification model. Relief Attribute Evaluator with Ranker search method is used to select the most relevant input variables for fault classification of a three-phase transmission line. Proposed approach is able to select most relevant input variables and gives better result than other combinations. Ours is a first attempt at using EMD for feature selection in fault classification of transmission lines.

Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis

Abstract The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H2, C2H4/C2H6, and C2H2/CH4 as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods, i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research.

Optimal Design of Power Transformer Using Genetic Algorithm

The paper presents power transformer design, using genetic algorithm (GA) and simulated annealing (SA) by minimizing total active cost, keeping in view the constraints imposed by international standards and power transformer specifications. The design results using conventional method (CM) have been compared with those obtained by applying GA and SA techniques and it is quite evident that the dimensions as well as active cost have been reduced in comparison to CM using same set of constraints. The results of GA and SA have been obtained using optimization tool box MATLAB Release 9.1 which have not been applied for power transformer design so far. Present paper firstly provides efficient and reliable solution for the design optimization problem with several variables and secondly, it guarantee that the obtained solution is a global optimum. Hence, this paper demonstrates a better and efficient solution for power transformer design using the said optimization techniques.

Feature selection using RapidMiner and classification through probabilistic neural network for fault diagnostics of power transformer

The diagnosis of incipient fault is important for power transformer condition monitoring. The incipient faults are monitored by conventional and artificial intelligence based models. The key gases, percentage value of gases and ratio of Doernenburg, Roger, IEC methods are input variables to artificial intelligence (AI) models which affects the accuracy of incipient fault diagnosis so selection of most influencing relevant input variable is an important research area. With this main objective, RapidMiner software is applied to IEC TC 10 and related datasets having different operating life to find most influencing input variables for incipient fault diagnosis in AI models. The RapidMiner identifies %CH₄, %C₂H₂, %H₂, %C₂H₆, C₂H₄/C₂H₆, C₂H₂/CH₄, C₂H₂/H₂ and CH₄/H₂ as the most relevant input variables in incipient fault diagnosis and it is used for fault diagnosis using different artificial intelligence (AI) approach i.e. fuzzy-logic (FL) and . The compared results shows that AI models give better results at proposed input variables used as an input vector. PNN gives highest accuracy of 98.28, proving proposed input variables can be used in transformer fault diagnosis research.

Application Research Based on Artificial Neural Network (ANN) to Predict No-Load Loss for Transformer's Design

Transformer is one of the vital components in electrical network which play important role in the power system. The continuous performance of transformers is necessary for retaining the network reliability, forecasting its costs for manufacturer and industrial companies. The major amounts of transformer costs are related to its no-load loss, so the cost estimation processes of transformers are based on reduction of no-load loss. This paper presents a new method for classification of transformer no-load losses. It is shown that ANNs are very suitable for this application since they present classification success rates between 78% and 96% for all the situations examined. The method is based on Multilayer Perceptron Neural Network (MPNN) with sigmoid transfer function. The Levenberg-Marquard (LM) algorithm is used to adjust the parameters of MPNN. The required training data are obtained from transformer company.

Application of Modern Technology for Fault Diagnosis in Power Transformers Energy Management

A transformer is the most important equipment for power supply to consumers. For uninterrupted power supply to consumers proper maintenance particularly preventive maintenance is very much necessary. The failure in magnetic, electric and dielectric circuits as well as structural failure may cause extensive damage to the equipment and surroundings. Proper operation and maintenance procedure may help to prevent failure and extend life of operation of the transformer. Continuous monitoring to health of the transformer and analyzing the history of past failures may indicate the type of maintenance required & facilitate identifying incipient faults. An overview of recently developed mythologies has been covered in the present paper along with planned strategy for curbing catastrophic failures creeping In service-transformers. The methods suggested in the paper are helpful in reducing failure and extending life of both the power transformers & distribution transformers.

Selection of most relevant input parameters using WEKA for artificial neural network based concrete compressive strength prediction model

In this paper, a novel approach to predict concrete compressive strength (CCS) at high strength level using artificial neural network (ANN) is proposed. The proposed approach is implemented to train, test and validate using available real 1030 datasets of USI machine learning repository. Data sets utilized to predict CCS included with eight input variables (i.e., blast furnaces slag, cement, fly ash, superplasticizer, water, coarse aggregate, fine aggregate and age) to ANN model which affects the accuracy of CCS prediction. Therefore, the selection of the most relevant input variables to the ANN model is necessary. With this objective, InfoGain Attribute Evaluator with Ranker Search Method using WEKA (a data mining implementation) is applied to find the most relevant input variables. Identified 7 most relevant input variables are used as input to ANN model to predict the CCS. The results obtained validates that the combination of input variables selected through WEKA gives higher prediction accuracy than any other combination of input variables. This method is used to predict the CCS in high strength level.

Application research based on modern-technology for transformer Health Index estimation

Diagnosis of power transformers becomes important due to the age of the transformer in service. Diagnosis methods can be separated in integral or differential methods. This paper describes a realistic Health Index formulation method for power transformers using readily available data. The method considers practical limitations on obtaining data, and the possible constraints on the parameters. This Health Index estimation considers not only typical test results such as dissolved gas analysis (DGA), oil quality, furan, and power factor, but also other parameters such as frequency response analysis (FRA), turn ratio, tap changer and bushing condition, physical observations, load history, maintenance work orders, and In-Service age of transformer. The calculation includes condition ratings, weighting factors, and assigned scores for specific condition parameters. By using a multi-criteria analysis approach, the method combines the various factors into a condition-based Health Index.

Artificial neural network based intelligent model for wind power assessment in India

Wind resource assessment is essential to evaluate the future wind power generation from a wind farm. As wind power generation depends directly on wind speed, therefore accurate wind speed prediction facilitates wind power generation. In this paper generalized regression neural network is employed for accurate wind speed prediction. The performance of proposed approach is evaluated using publically available dataset of different cities in India. Air temperature, earth temperature, relative humidity, daily solar radiation, elevation, latitude, heating degree days, cooling degree days, longitude and atmospheric pressure are used as input variables. Correlation coefficient of 0.99909 is obtained during training and 0.95143 during testing of GRNN model. The proposed GRNN model is then utilized to find wind speed and power potential of major wind power generating sites of Andhra Pradesh, India. A comparison between the measured and forecasted wind speed and power values validate that generalized regression neural network is an appropriate technique for long term wind speed and power prediction.

Cost Analysis of Transformer's Main Material Weight with Artificial Neural Network (ANN)

Transformer is one of the vital components in electrical network which play important role in the power system. The continuous performance of transformers is necessary for retaining the network reliability, forecasting its costs for manufacturer and industrial companies. The major amount of transformer costs are related to its raw materials, so the cost estimation process of transformers are based on amount of used raw material. This paper presents a new method to estimate the weight of main materials for transformers. The method is based on Multilayer Perceptron Neural Network (MPNN) with sigmoid transfer function. The Levenberg-Marquard (LM) algorithm is used to adjust the parameters of MPNN. The required training data are obtained from transformer company.