STUDY OF VOLTAGE STRESSES INSIDE REGENERATIVE DRIVE

This paper investigates the effect of voltage stress on the voltage insulation components inside a regenerative active-front-end adjustable-speed drive (ASD). It shows that a high potential voltage insulation issue may exist on various components inside the ASD and cause earlier failures. A simplified system model to describe this phenomenon is described, and the voltage stresses of different components inside the ASD under different grounded conditions and filter capacitor are analyzed. It is concluded that among different grounding systems, a high-resistance grounded system gives minimum stress. Appropriately, designing the insulation components is critical to protect the drive

Daily Peak Load Forecast Using Artificial Neural Network

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer

Artificial Neural Network Model for Hourly Peak Load Forecast

Artificial Neural Network model for short-term demand forecast of hourly peak load is proposed in this paper. For learning of the ANN model Levenberg-Marquardt algorithm is adopted because of its ability to handle the large number of non-linear load data. The training of network is done by using hourly peak load data of preceding five years from the period of forecast and the temperature data. The validation of the developed ANN model is tested with historical load data of BESCOM (Bangalore Electricity Supply Company Limited) power system.  The comparison of conventional methods and ANN model with respect to percentage error is evaluated, from the results it has been found that the proposed ANN model with optimal number of hidden layer neurons gives accurate predictions. Keywords: Artificial Neural Network, Normalization, Forecasting JEL Classifications: C8, Q47

Prediction of pollution flashover voltages of ceramic string insulators under uniform and non-uniform pollution conditions

Present paper emphasizes on the development of a new mathematical model to estimate the contamination flashover voltages of ceramic string insulators under varying uniform and non-uniform pollution conditions subjected to AC voltages. The proposed model is developed based on dimensional analysis of the factors which commonly influence the process of contamination flashover of insulators. The new model for string insulators has been validated using previous authors both experimental and analytical results for total of fifteen string insulators including porcelain, glass string insulators and one porcelain log rod insulator. The validation of the new model indicated that results of proposed model for string insulators are in good agreement with published experimental and model results

EFFECT OF CONDUCTION BAND ON THE PERFORMANCE OF POLLUTED CERAMIC INSULATOR SPECIMENS

Pollution-induced flashover of outdoor ceramic insulators is one of the critical factors which governs power system reliability. Deposition of the air-borne particulates and condensation of vapors on the surface forms layers of pollution which cause degradation in their performance. To mitigate this problem, several techniques such as silicon grease coating, RTV coating, increase in creepage distance, and regular washing of the insulators have been tried with varied success. All these methods are quite expensive and add to the overall cost of power delivery. It is observed that due to the deposition of contaminants on the surface of the ceramic insulators, the field around the insulator becomes highly nonuniform which may lead to flashover. In the present work, a simple and inexpensive method that can increase the pollution withstand capability of the polluted ceramic insulators is proposed. As a part of the proposed method, ceramic specimens were prepared, conduction bands were placed and flashover experiments were conducted with and without conduction bands under clean and polluted conditions. From the test results, it can be concluded that by optimally placing the conduction band on the surface of the ceramic insulators, their pollution withstand capability can be significantly enhanced