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Accurate localization of partial discharge in GIS equipment remains a key focus of daily maintenance for substations, which can be achieved through advanced detection and location techniques, as well as regular maintenance and testing of the equipment. However, there is currently an issue with low accuracy in the localization algorithm. Aiming at the problems of low precision and local optimization of the swarm intelligence algorithm in partial discharge localization system of GIS equipment, this paper proposes a 3D localization algorithm based on a time difference of arrival (TDOA) model of the improved artificial fish swarm algorithm (IAFSA). By introducing the investigation behaviour of the artificial bee colony(ABC) algorithm into the artificial fish swarms algorithm (AFSA), this algorithm is more efficient to jump out of the local extremum, enhance the optimization performance, improve the global search ability and overcome the premature convergence. Furthermore, more precise positioning can be achieved with dynamic parameters. The results of the testing function show that IAFSA is significantly superior to AFSA and particle swarm optimization (PSO) in terms of positioning accuracy and stability. When applied to partial discharge localization experiments, the maximum relative positioning error is less than 2.5%. This validates that the proposed method in this paper can achieve high-precision partial discharge localization, has good engineering application value, and provides strong support for the safe operation of GIS equipment. [ABSTRACT FROM AUTHOR]

Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%. [ABSTRACT FROM AUTHOR]

Foam is a kind of ideal fluid for profile control in petroleum engineering, which has attracted intense interests of scholars globally in recent years. In this study, a foam system stabilized with anionic surfactants and clay particles was proposed for profile control in reservoirs, and the formulation was optimized experimentally. Moreover, flooding experiments in visible porous media models and in sandpacks were conducted to test the plugging effect of the foam system on reservoirs, and the effects of different factors such as gas–liquid ratio, temperature and permeability on profile control were also evaluated. According to the experimental results, the clay-HY-2 system was elected for its satisfactory foamability, stability, and salinity resistance, and the optimum concentrations of HY-2 and clay particle are 0.6 wt% and 5.0 wt%, respectively. Compared with traditional foam fluids, the clay-HY-2 system can form denser and smaller bubbles in high- and middle-permeable layers, enhancing the plugging effect there, while there are less bubbles in low-permeable layers, i.e., the restriction on the flow in narrow structures is slight. The clay-HY-2 foam can perform the efficient and uniform profile control effect on sandpacks when the foam quality is around 50%. The resistance factor of the foam decrease gradually with the increasing temperature, however, the resistance factor remains higher than 350.0 when the temperature reaches 80.0 °C. When the permeability exceeds 1502.0 mD, the clay-HY-2 foam can perform deep profile control in reservoirs, and the resistance factor are not sensitive to the change of permeability when it exceeds 3038.0 mD. Besides, the site application case shows that the clay-HY-2 foam do have good profile control effect on reservoirs, i.e., improving oil production and declining water cut. [ABSTRACT FROM AUTHOR]