Expert system application for the loading capability assessment oftransmission lines

This paper describes the application of an expert system for the evaluation of the short-term thermal rating and temperature rise of overhead conductors. The expert system has been developed using a database and Leonardo expert system shell which is gaining popularity among commercial tools for developing expert system applications. The expert system has been found to compare well when evaluated against site tests. A practical application is given to demonstrate the usefulness of the expert system developed     

Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

This paper presents a new strategy for wind speed forecasting based on a hybrid machine learning algorithm, composed of a data filtering technique based on wavelet transform (WT) and a soft computing model based on the fuzzy ARTMAP (FA) network. The prediction capability of the proposed hybrid WT+FA model is demonstrated by an extensive comparison with some other existing wind speed forecasting methods. The results show a significant improvement in forecasting error through the application of a proposed hybrid WT+FA model. The proposed wind speed forecasting strategy is applied to real data acquired from the North Cape wind farm located in PEI, Canada.     

A new recursive neural network algorithm to forecast electricity price for PJM day‐ahead market

This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day‐ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi‐step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short‐term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72 h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short‐term price forecasting. Copyright © 2009 John Wiley & Sons, Ltd.     

A knowledge based tutoring system for teaching fault analysis

A knowledge based tutoring system is used to support the education of power engineering students. The aim of this project is to make teaching and learning more productive and efficient by employing modern technologies. It seeks to find new methods to teach large numbers of students with no increase in staff. The tutoring system is based on an expert system shell. It provides a functionally interacting set of theory and problems, and supports student progress through monitoring and assessment. This paper describes the development of the tutoring system for teaching electrical engineering subjects, and in particular, fault analysis in power systems. The expert system based software has been successfully used by power engineering students. They found this software easy to use and understand, and it has become an extra teaching tool     

Sensitivity analysis of neural network parameters to improve the performance of electricity price forecasting

This paper presents a sensitivity analysis of neural network (NN) parameters to improve the performance of electricity price forecasting. The presented work is an extended version of previous works done by authors to integrate NN and similar days (SD) method for predicting electricity prices. Focus here is on sensitivity analysis of NN parameters while keeping the parameters same for SD to forecast day‐ahead electricity prices in the PJM market. Sensitivity analysis of NN parameters include back‐propagation learning set (BP‐set), learning rate (η), momentum (α) and NN learning days (d_NN). The SD parameters, i.e. time framework of SD (d=45 days) and number of selected similar price days ( N =5) are kept constant for all the simulated cases. Forecasting performance is carried out by choosing two different days from each season of the year 2006 and for which, the NN parameters for the base case are considered as BP‐set=500, η=0.8, α=0.1 and d_NN=45 days. Sensitivity analysis has been carried out by changing the value of BP‐set (500, 1000, 1500); η (0.6, 0.8, 1.0, 1.2), α (0.1, 0.2, 0.3) and d_NN (15, 30, 45 and 60 days). The most favorable value of BP‐set is first found out from the sensitivity analysis followed by that of η and α, and based on which the best value of d_NN is determined. Sensitivity analysis results demonstrate that the best value of mean absolute percentage error (MAPE) is obtained when BP‐set=500, η=0.8, α=0.1 and d_NN=60 days for winter season. For spring, summer and autumn, these values are 500, 0.6, 0.1 and 45 days, respectively. MAPE, forecast mean square error and mean absolute error of reasonably small value are obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by developed NN model based on the most favorable case are accurate and efficient. Copyright © 2008 John Wiley & Sons, Ltd.     

Network equivalents and expert system application for voltage andVAR control in large-scale power systems

This paper presents an application of the steady-state network equivalents and an expert system for voltage and reactive power (VAr) control in large-scale power systems. A steady-state network equivalencing technique is used to construct the “three-tier” subsystem which is adequate to solve voltage violation problems. An expert system utilises the sensitivity tree method to select the optimal set of control actions to alleviate the voltage problem. The expert system was developed using the VP-EXPERT system shell. VP-EXPERT interacts with the power system analysis software providing analysis of the network sensitivity matrix and data for the knowledge base. Practical application of the developed expert system is demonstrated on the example of the Hydro-Electric Commission (HEC) power system of Tasmania, however the proposed approach is not limited by the system size