A.C. Liew

An adaptive linear combiner for on-line tracking of power system harmonics

The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components of power system signals mixed with noise and decaying DC components.

Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting

This paper describes the implementation and forecasting results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric load forecasting. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, as well as, on weekends, public holidays, and days before and after public holidays. Furthermore, use of fuzzy logic effectively handles the load variations due to special events. The fuzzy-neural network (FNN) has been extensively tested on actual data obtained from a power system for 24-hour ahead prediction based on forecast weather information. Very impressive results, with an average error of 0.62% on weekdays, 0.83% on Saturdays and 1.17% on Sundays and public holidays have been obtained. This approach avoids complex mathematical calculations and training on many years of data, and is simple to implement on a personal computer.

Classification of power system disturbances using a fuzzy expert system and a Fourier linear combiner

This paper presents a hybrid scheme using a Fourier linear combiner and a fuzzy expert system for the classification of transient disturbance waveforms in a power system. The captured voltage or current waveforms are passed through a Fourier linear combiner block to provide normalized peak amplitude and phase at every sampling instant. The normalized peak amplitude and computed slope of the waveforms are then passed on to a diagnostic module that computes the truth value of the signal combination and determines the class to which the waveform belongs. Several numerical tests have been conducted using EMTP programs to validate the disturbance waveform classification with the help of the new hybrid approach which is much simpler than the recently postulated ANN or wavelet based approaches.

An adaptive neural network approach for the estimation of power system frequency

Abstract A new approach to the estimation of power system frequency using an adaptive neural network is presented in this paper. This approach uses a linear adaptive neuron or an adaptive linear combiner called “Adaline” to identify the parameters of a discrete signal model of the power system voltage. Here, the learning parameters are adjusted to force the error between the actual and the computed signal samples to satisfy a stable difference error equation, rather than to minimize an error function. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. The technique is shown to be capable of tracking power system conditions and is immune to the effects of harmonics and random noise.

Extension of dynamic model of impulse behavior of concentrated grounds at high currents

A dynamic model which describes the impulse behavior of concentrated grounds at high currents is described in this paper. This model is an extension of previous models in that it can successfully account for the surge behavior of concentrated grounds over a much wider range of current densities. It is able to describe the well known effect of ionization of soil as well as the observed effect of discrete breakdowns and filamentary arc paths at much higher currents. Results of verification against experimental results are also presented.

A neural network short-term load forecaster

Abstract This paper presents a neural network based approach to short-term load forecasting, which plays an important role in the day to day operation and scheduling of power systems. A four-layer feedforward neural network, trained by a back-propagation learning algorithm, has been applied for forecasting the hourly load of a power system. In this paper, the performance of the network is compared with some carefully chosen experimental methods. This new approach promises to provide results unobtainable with more traditional time series methods. It is shown that, with careful network design, the back-propagation learning procedure is an effective way of training neural networks for electrical load prediction. The choice of transfer function is an important design issue in achieving fast convergence and good generalization performance. The network is trained on real data from a power system and evaluated for short-term forecasting with hourly feedback. The network learns the training set nearly perfectly and shows accurate prediction with 1.07% error on weekdays and 1.80% error on weekends.

A real-time short-term load forecasting system using functional link network

This paper presents a new functional-link network based short-term electric load forecasting system for real-time implementation. The load and weather parameters are modelled as a nonlinear ARMA process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced functional link net. The adaptive mechanism with a nonlinear learning rule is used to train the link network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast.

Fault impedance estimation algorithm for digital distance relaying

Fault impedance is one of the major parameters that must be estimated accurately in digital distance relaying application. In this paper, a new algorithm is proposed based on symmetrical components theory. The proposed algorithm has computational advantage over previously suggested symmetrical components based algorithms. A procedure for applying shunt fault conditions to the sequence equations to estimate fault impedance of the protected transmission line is discussed. The Alternative Transient Program (ATP) that is available on personal computers was used in evaluating the proposed algorithm. ATP models a power system and simulates many fault conditions on a selected transmission line. Fault data obtained were used in calculating fault impedance using the proposed algorithm. Fault impedance estimates were inserted in relay characteristics to determine suitability of the proposed algorithm for first zone distance protection. Sample results of these studies which show stable fault distance estimates are presented and discussed in the paper. >

Multi-objective generation dispatch using bi-criterion global optimisation

This paper proposes a bi-criterion global optimisation approach to determine the most appropriate generation dispatch solution taking into account the fuel cost, the environmental cost and the requirement of the operation security of power networks. The approach is based on the simulated annealing technique and the formation of a bi-criterion objective function. Its effectiveness is demonstrated through an example, in which the sample test system is a 3-area interconnected and longitudinal system.

A new approach to monitoring electric power quality

The paper presents an adaptive neural network approach for the estimation of harmonic distortions and power quality in power networks. The neural estimator is based on the use of linear adaptive neural elements called adalines The learning parameter of the proposed algorithm is suitably adjusted to provide fast convergence and noise rejection for tracking distorted signals in the power networks. Several numerical tests have been conducted for the adaptive estimation of harmonic components, total harmonic distortions, power quality of simulated waveforms in power networks supplying converter loads and switched capacitors. Laboratory test results are also presented in support of the performance of the new algorithm.