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. Index Terms-ART neural networks, controlled system islanding, load shedding, remedial action scheme RAS, wide area protection.

h i g h l i g h t sResults of an EES system demonstration project carried out in the UK. Approaches to the design of trials for EES and observation on their application. A formalised methodology for analysis of smart grids trials. Validated models of energy storage. Capability of EES to connect larger quantities of heat pumps and PV is evaluated. a b s t r a c tThe UK government's CO 2 emissions targets will require electrification of much of the country's infrastructure with low carbon technologies such as photovoltaic panels, electric vehicles and heat pumps. The large scale proliferation of these technologies will necessitate major changes to the planning and operation of distribution networks. Distribution network operators are trialling electrical energy storage (EES) across their networks to increase their understanding of the contribution that it can make to enable the expected paradigm shift in generation and consumption of electricity.In order to evaluate a range of applications for EES, including voltage control and power flow management, installations have taken place at various distribution network locations and voltage levels. This article reports on trial design approaches and their application to a UK trial of an EES system to ensure broad applicability of the results. Results from these trials of an EES system, low carbon technologies and trial distribution networks are used to develop validated power system models. These models are used to evaluate, using a formalised methodology, the impact that EES could have on the design and operation of future distribution networks.

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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.

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.