Total Factor Productivity Growth and Output Growth in Indian Electronics Industry in the Liberalization Era: An Empirical Examination

This paper analyses the efficiency and productivity growth of Electronics industry, which is considered one of the vibrant and rapidly growing manufacturing industry sub-sectors of India in the liberalization era since 1991. The main objective of the paper is to examine the extent and growth of Total Factor Productivity (TFP) and its components namely, Technical Efficiency Change (TEC) and Technological Progress (TP) and its contribution to total output growth. In this study, the electronics industry is broadly classified into communication equipments, computer hardware, consumer electronics and other electronics, with the purpose of performing a comparative analysis of productivity growth for each of these sub-sectors for the time period 1993-2004. The paper found that the sub-sectors have improved in terms of economies of scale and contribution of capital.The change in technical efficiency and technological progress moved in reverse directions. Three of the four industry witnessed growth in the output primarily due to TFPG and the contribution of input growth to output growth had been negative/negligible, except for Computer hardware where contribution from both input growth and TFPG to output growth were prominent. The paper explored the possible reasons that addressed the issue of low technical efficiency and technological progress in the industry.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices