Ibrahim Helal

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.

Wide area transient stability prediction using on-line Artificial Neural Networks

This paper proposes a real-time wide area protection system which 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. Rate of change of bus voltages and angles for six cycles after fault tripping and/or clearing is used to train a two layers ANN. 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.

Fuzzy linear regression for measurement of harmonic components in a power system

The paper presents a new approach for the measurement of power system harmonic components and is based on fuzzy linear regression. In this approach, the non-sinusoidal voltage or current waveform is written as a linear function. The coefficients of this function are assumed to be fuzzy numbers having a certain middle and spread value. The problem is formulated as a linear optimization problem, where the objective is to minimize the spread of voltage or current samples. Effects of critical parameters on the proposed algorithm are studied in the text. The proposed approach is tested using several numerical tests, which form the basis to our conclusions at the end of the paper.

A wide area synchrophasor based ANN transient stability predictor for the Egyptian Power System

This paper proposes an Artificial Neural Networks (ANN) based technique for transient stability prediction. The ANN makes use of the advent of Phasor Measurements Units (PMU) for real-time prediction. Rate of change of bus voltages and angles is used to train a two layers ANN. Potential of the proposed approach is tested using the Egyptian Power System (EPS) as a study system.

A Heuristic-Based Approach for Enumerating The Minimal Path Sets in a Distribution Network

Determination of minimal path sets and minimal cut sets in a distribution network is essential not only to evaluate the load point reliability indices but also to investigate the different scenarios for power restoration in case of system major cuts. Moreover the order of the minimal cut sets and the number of the minimal path sets gives indication of where the redundant components are needed to improve the load point reliability indices. Most of existing methodologies to determine the minimal path sets and minimal cut sets use complex programming techniques especially when bidirectional flow links are encountered. Developing a reliability graph for each load point adds to the complexity of these methodologies. This paper introduces a simple heuristic-based approach to determine minimal path sets from graph model which, in turn, can be used to enumerate the minimal cut sets. Beside representing the bi-directional flow with one link, this approach uses a generic reliability graph model for multiple source, multiple load points network.