Showing papers in "Electric Power Systems Research in 1995"

TL;DR: In this article, an adaptive ARMA (autoregressive moving-average) model is developed for short-term load forecasting of a power system, and the results of 24-hours and one-week-ahead forecasts show that the adaptive algorithm is more accurate than the conventional Box-Jenkins approach, especially for the 24-hour case.

TL;DR: In this paper, an artificial neural network (ANN) model for short-term load forecasting (STLF) is presented, which is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual ‘next one hour' ANN models.

TL;DR: A comprehensive survey of the conceptual aspects as well as recent algorithmic developments for distribution system restoration can be found in this article, where several fundamentally different approaches are discussed together with the factors affecting the assumptions of the underlying concepts and the various criteria used in the different approaches.

TL;DR: In this article, a new method of fault section estimation in power systems using genetic algorithms (GAs) is presented, and the simulation results show that the GA based method can find multiple optimal solutions directly and efficiently in a single run, which is very suitable for complex fault diagnosis problems.

TL;DR: Novel artificial neural network based weather-load and weather-energy models have been developed to forecast electric load and energy for 24 months ahead and provide better accuracy than previously developed models.

TL;DR: A new approach using fuzzy dynamic programming to decide the optimal location and size of compensation shunt capacitors for distribution systems with harmonic distortion and it is found that the system loss and harmonic distortion can be reduced very effectively by this proposed algorithm.

TL;DR: In this paper, a new approach based on genetic algorithms to solve the thermal unit commitment problem of electric power systems is presented, which is very suitable for solving combinatorial optimization problems.

TL;DR: In this article, the authors proposed a fast method for finding the maximum load, especially the reactive power demand, at a particular load bus before reaching the voltage stability limit, which was tested on the IEEE 14-, 30-, and 118-bus systems and the results obtained were compared with those found by other methods.

TL;DR: In this paper, a procedure for optimal allocation of sectionalizing switches in radial distribution systems is proposed, aimed at minimizing unsupplied energy caused by network failures, where Opportunities for alternative source supply made possible by network reconfiguration are considered.

TL;DR: In this paper, a new 0-1 integer programming method of feeder reconfiguration for loss minimization in distribution systems is proposed, where the composition of loads for various feeders is different and their loading patterns vary with time.

TL;DR: The results indicate that the speed and selectivity of the NN approach are quite adequate for a number of different transmission and distribution monitoring, control, and protection applications.

TL;DR: In this article, the authors describe the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for distribution feeders, where the principal functions of this diagnostic system are: (i) detection of fault occurrence, (ii) identification of faulted sections, and (iii) classification of faults into types, e.g. HIF (high impedance faults) or LIFs (low impedance faults).

TL;DR: In this paper, a highly adaptable and robust short-term load forecasting algorithm developed using hybrid modeling techniques is proposed to account for the changing base load component, including an adaptive autoregressive modeling technique enhanced with partial autocorrelation analysis to model the random component of the load.

TL;DR: A neural network preestimation filter to identify most forms of gross errors, including conforming bad data, in raw measurements before state estimation rather than afterwards is presented.

TL;DR: In this article, a biologically inspired genetic algorithm (GA) is used to design a distribution system with minimal feeder length, energy losses and load imbalance between transformers, subject to voltage drop and capacity constraints.

TL;DR: In this paper, the application of a fuzzy optimization technique to optimal power flow calculations has been presented, and the developed method has been tested on a large-scale power system and the results show that this method is promising for handling uncertain constraints in practical power systems.

TL;DR: In this paper, a reliable online method to accomplish short-term load forecasting is presented that automatically adapts to changing conditions and requires very little human intervention.

TL;DR: The fuzzy neural network proposed generates the fuzzy rules from historical data while learning and is capable of approximating any continuous load profile on a compact set to good accuracy.

TL;DR: An accurate method for calculating the resistance of a substation grounding grid buried in two-layer earth is proposed, based on the image theory and the synthetic-asymptote approach.

TL;DR: This paper reviews some of the important works reported in the literature on the application of fuzzy logic and various issues related to the applicationof fuzzy logic are discussed.

TL;DR: In this article, a fuzzy logic controller is designed to implement variable series capacitor compensation in the transmission network of interconnected power systems, which is one facet of flexible AC transmission systems employed to enhance the stability of the system.

TL;DR: In this article, a Monte Carlo simulation is used to obtain a more detailed characterization of the probability distribution of production costs beyond the expected value, and a model involving the stochastic processes underlying the generator outages is presented.

TL;DR: This paper presents the new generation of artificial neural netoworks (ANNS) for solving the task of power system operation planning based on the theory of radial basis functions for approximations, and shows that this new algorithm is superior to back-propagation ANNs and optimal configured back- propagation ANNs achieved with genetic algorithms.

TL;DR: In this paper, the skin effect and saturation effect on the rotor resistance, magnetizing and leakage reactances were investigated and handy expressions that can predict the machine parameters as functions of the slip were derived.

TL;DR: In this paper, an alternative formulation of the AC-DC load flow problem is presented, which shows that neglecting the converter transformer reactance results not only in reduced accuracy but also in less confidence in the validity of the solution.

TL;DR: In this article, the authors investigated the application of artificial intelligence techniques and eventually checked their positive contribution in the field of short-term load forecasting, focusing on the construction problems of an integrated tool specifically designed to meet the needs of utility forecasters and power system operators.

TL;DR: The identification method may not be complete, but it successfully discriminates arcing faults from other disturbances, providing security enhancement for distribution protection.

TL;DR: In this paper, the authors present the basic concepts of power system reliability assessment and review the relability indices and methodologies from a planning viewpoint and also from an economic cost-benefit perspective.

TL;DR: In this article, the feasibility of installing a 2.5 MW wind farm within an isolated power system in Western Australia is examined and the performance of these alternators under disturbance conditions is assessed and the test results are used to derive improved computer representations for the generators.

TL;DR: In this paper, a minimal cut set based approach for the reliability evaluation of an optimally planned transmission system (Part I) and obtains a configuration of transmission lines to achieve a desired failure rate set by the planner (Part II) is presented.