Informatics Publishing Limited

Vivek Mani Tripathi ¹, D. D. Tewari ¹, H. D. Tiwari ², Sheela Tiwari ³, M. P. Uppadhya ³

Affiliations
1 Ecology and Environ. Biology Lab, Department of Botany, M.L.K. P.G. College, Balrampur, U.P., IN
2 Department of Chemistry, M.L.K. P.G. College, Balrampur, U.P., IN
3 Department of Economics, B.N.K.B. P.G. College, Akbarpur, U.P., IN

Abstract

Present paper deals with the physico-chemical characteristic of River Rapti in District Balrampur. Most physical and chemical parameters like colour, odour, temperature, pH, EC, TDS, DO, BOD, COD, sodium, potassium, magnesium and nitrate are reported beyond the tolerance limits which indicates the degradation of water quality. The degradation of water quality is reported maximum at Nahar Balaganj and Bhagwatiganj spots.

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Sheela Tiwari ¹, Ram Naresh ², Rameshwar Jha ³

Affiliations
1 Department of Instrumentation and Control Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, Punjab, IN
2 Department of Electrical Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, IN
3 IET Bhaddal Technical Campus, Ropar, Punjab, IN

Abstract

This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.

Keywords

System Identification, Recurrent Neural Networks, Static Backpropagation (BP).

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