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This paper presents a new technique for the detection of islanding conditions in electrical power systems. This problem is especially prevalent in systems with significant penetrations of distributed renewable generation. The proposed technique is based on the application of principal component analysis (PCA) to data sets of wide-area frequency measurements, recorded by phasor measurement units. The PCA approach was able to detect islanding accurately and quickly when compared with conventional RoCoF techniques, as well as with the frequency difference and change of angle difference methods recently proposed in the literature. The reliability and accuracy of the proposed PCA approach is demonstrated using a number of test cases, which consider both islanding and non-islanding events. The test cases are based on real data, recorded from several phasor measurement units located in the UK power system.

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This paper investigates neural network based estimation of NO x emissions in a thermal power plant, fed with both oil and methane fuels. Two types of neural network namely a novel 'eng-genes' architecture and a Multilayer Perceptron (MLP) have been developed, both being optimised using genetic algorithms. Due to the local nature of the NO x generation process, operational information on the burner cells of the combustion chamber has been considered. Neural networks, with different numbers of hidden nodes have been tested on a set of three-dimensional data of the simulated combustion chamber. It is shown that, the proposed 'eng-genes' neural network can produce accurate estimations with better generalisation performance than MLP.