This paper focuses on advanced analysis techniques and design considerations of DC interference generated by HVDC electrodes during normal bipolar and temporary monopolar operations on neighboring metallic utilities, with a special emphasis on buried gas and oil pipelines. This study examines the level of pipeline corrosion, the safety status in the vicinity of exposed appurtenances and the impact of DC interference on the integrity of insulating flanges and impressed current cathodic protection (ICCP) systems. Computation results obtained for different soil models show that different soils can lead to completely different DC interference effects. The results and conclusions presented here can be used as a reference to analyze the severity of DC interference on pipelines due to proximate HVDC electrodes.

For reasons of low accuracy of artificial survey leakage, a gas pipeline leakage diagnosis method based on BP neural networks and D-S theory is presented by introducing WSN and information fusion theory. Two sub-neural networks are established at normal node to simplify network structure. The leakage characteristic parameters of negative pressure wave and acoustic emission signals are used as input eigenvector respectively for primary diagnosis. Through making preliminary fusion result s as the basic probability assignment of evidence, the impersonal valuations are realized. Finally, all evidences are aggregated at normal and sink node respectively by using the improved combination rules. The method makes full use of redundant and complementary leakage information. Numerical example shows that the proposed improves the leakage diagnosis accuracy and decreases the recognition uncertainty.

The image definition identification method based on the composite model of wavelet transform and neural networks is stronger in image edge character extraction, nonlinear process, self-adapted study and pattern recognition. The paper puts forward an evaluation method of image definition based on the focusing mechanism of simulating persons eyes by neural networks and on the composite model of wavelet transformation and neural networks. The wavelet component statistics obtained by the wavelet transform are taken as the inputs of the 5 layer RBF neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step size to adjust the network weights. The compound model is first trained by 75 images from the training set, and then is tested by 102 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.