M.M. Mansour

A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays

The coordination of directional overcurrent relays (DOCR) is treated in this paper using particle swarm optimization (PSO), a recently proposed optimizer that utilizes the swarm behavior in searching for an optimum. PSO gained a lot of interest for its simplicity, robustness, and easy implementation. The problem of setting DOCR is a highly constrained optimization problem that has been stated and solved as a linear programming (LP) problem. To deal with such constraints a modification to the standard PSO algorithm is introduced. Three case studies are presented, and the results are compared to those of LP technique to demonstrate the effectiveness of the proposed methodology.

New heuristic strategies for reactive power compensation of radial distribution feeders

We introduce two new heuristic techniques for reactive power compensation in radial distribution feeders. The methods can be considered as generalization ideas that emerged from recent heuristic search strategies and lead to better results. The methods formulae are derived, and the techniques are applied to three feeders. Results of the proposed approaches are compared with previous methods to show the superiority of the proposed methods. To show the closeness or remoteness from the optimal solution after implementing these methods, a new algorithm using the variational technique is presented to obtain the optimal capacitor allocation according to available standard sizes of capacitors.

An Intelligent Wide Area Synchrophasor Based System for Predicting and Mitigating Transient Instabilities

Increasing expansion of power systems and grids are accompanied nowadays by innovation in smart grid solutions to maintain systems stability. This paper proposes an intelligent wide area synchrophasor based system (IWAS) for predicting and mitigating transient instabilities. The IWAS incorporates artificial neural networks (ANN) for transient stability prediction. The ANN makes use of the advent of phasor measurements units (PMU) for real-time prediction. Coherent groups of generators-which swing together-is identified through an algorithm based on PMU measurements. A remedial action scheme (RAS) is applied to counteract the system instability by splitting the system into islands and initiate under frequency load shedding actions. The potential of the proposed approach is tested using New England 39 bus system.

A computational comparison of steady state load shedding approaches in electric power systems

This paper presents a formulation of the optimal steady state load shedding problem that uses the sum of the squares of the difference between the connected active and the reactive load and the supplied active and reactive power. The latter are treated as dependent variables modelled as functions of bus voltages only. An investigation of the performance of the proposed algorithm over a range of generation deficits as well as overload conditions is presented. Testing is done using IEEE 14, 30, 57, and 118 bus power systems, representing small and medium power systems. The optimal results are compared with results obtained using two earlier approaches. The results obtained using the proposed approach appear to give a better optimal state of the power system.

Application of a new frequency estimation technique to power systems

A new technique for estimating the frequency contents in a signal is applied to power systems. The technique uses two sets of measured samples: samples from the signal itself and samples from a filtered signal obtained by passing the original signal through a continuous-time filter. The estimates of the frequencies contained in the signal are obtained from the eigenvalues of the generalized eigenproblem of two matrices formed from the two sets of samples. The technique assumes estimating multiple sinusoids which makes it suitable in cases with large harmonic distortion. Results show that by the proper choice of the sampling time, the eigenproblem condition is improved such that accurate estimates become attainable in case of noise. Further improvement in the estimate accuracy is achievable by utilizing correlated samples. The technique is also applied to estimating varying frequency.

Out-of-step prediction based on artificial neural networks

The application of artificial intelligence to power systems has resulted in an overall improvement of solutions in many implementations. This paper presents a new approach to the prediction (detection) of out-of-step synchronous generators based on artificial neural networks (ANNs). The paper describes the ANN architecture adopted as well as the selection of input features for training the ANN. A feedforward model of the neural network based on the stochastic back-propagation training algorithm has been used. The capabilities of the developed algorithm for the prediction of the out-of-step condition have been tested through computer simulation for a typical case study. The results of using the proposed algorithm reveal a high classification performance.

Out-of-step detection based on pattern recognition

Abstract This paper presents a new approach for out-of-step detection of synchronous generators with the ability to initiate early tripping for non-recoverable swings while avoiding tripping on recoverable swings. The approach is based on the K -means clustering pattern recognition technique. The capabilities of the developed algorithm are tested through computer simulation for a typical case study. While being feasible for on-line implementation, the introduced algorithm shows high classification performance.

An Artificial Neural Network Based Protection Approach Using Total Least Square Estimation of Signal Parameters via the Rotational Invariance Technique for Flexible AC Transmission System Compensated Transmission Lines

Abstract This article proposes an approach for the protection of transmission lines with flexible AC transmission systems based on artificial neural networks using the total least square estimation of signal parameters via rotational invariance technique. The required features for the proposed algorithm are extracted from the measured transient currents and voltages waveforms using the total least square estimation of signal parameters via rotational invariance technique. Since these transient waveforms are considered as a summation of damped sinusoids, the total least square estimation of signal parameters via rotational invariance technique is used to estimate different signal parameters, mainly damping factors, frequencies, and amplitudes of different modes contained in the signal. Those features are employed for fault detection and faulted phase selection using artificial neural networks. Two types of flexible AC transmission system compensated transmission lines, namely the thyristor-controlled series capacitor and static synchronous compensator, are considered. System simulation and test results indicate the feasibility of using neural networks with the total least square estimation of signal parameters via rotational invariance technique in the fault detection, classification, and faulted phase selection of flexible AC transmission system compensated transmission lines.

A modified particle swarm optimizer applied to the solution of the economic dispatch problem

In this paper, a modified approach, based on the particle swarm optimizer (PSO) is presented and explained in detail. As a case study, this PSO technique is applied to the solution of the economic dispatch (ED) problem of thermal generating units. A piecewise quadratic function is used to represent the fuel cost of each generating unit. The B-coefficient method is used to model the transmission losses. A modification to the standard PSO algorithm is proposed to allow dealing with the power balance equality constraint. Unlike the traditional methods, this modification does not depend on penalizing the infeasible solutions using a pre-defined penalty function. The application of the proposed method to several case studies shows that it is applicable to large networks with multigenerating units including transmission losses with very promising results.

Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface

Abstract This paper presents the hourly load forecasting results of the Egyptian unified grid (EUG). The technique is based on a generalized model combining the features of ANN and an expert system. The above methodology makes the technique robust, updatable and provides for operator intervention when necessary. This property makes it especially suitable for the EUG where the load patterns are influenced mostly because of social activities, and weather contributes very little to load forecast. For example, many social occasions depend on religious preferences which cannot be decided well in advance. This technique has been tested with one year data of EUG during 1993. The results clearly demonstrate the advantage of the above methodology over statistical based techniques. The average absolute forecast errors for the proposed methodology is 2.63% with a standard deviation of 2.62% whereas, the conventional multiple regression method scores an average absolute error of 4.69% with a standard deviation of 4.03%.