Forecasting dissolved gas content in power transformers plays a significant role in detecting incipient faults and maintaining the safety of the power system. Though various forecasting models have been developed, there is still room to further improve prediction performance. In this paper, a new forecasting model is proposed by combining mixed kernel function-based support vector regression (MKF-SVR) and genetic algorithm (GA). First, forecasting performance of SVR models constructed with a single kernel are compared, and then Gaussian kernel and polynomial kernel are retained due to better learning and prediction ability. Next, a mixed kernel, which integrates a Gaussian kernel with a polynomial kernel, is used to establish a SVR-based forecasting model. Genetic algorithm (GA) and leave-one-out cross validation are employed to determine the free parameters of MKF-SVR, while mean absolute percentage error (MAPE) and squared correlation coefficient (r²) are applied to assess the quality of the parameters. The proposed model is implemented on a practical dissolved gas dataset and promising results are obtained. Finally, the forecasting performance of the proposed model is compared with three other approaches, including RBFNN, GRNN and GM. The experimental and comparison results demonstrate that the proposed model outperforms other popular models in terms of forecasting accuracy and fitting capability.

Gas insulated switchgear equipment (GIS) is widely used in power system, and more attention has been paid to discharge defects than mechanical defects. However, since mechanical defects are a major cause of the failure in GIS, it is of great significance to carry out relevant research on mechanical defects. Detection and diagnosis methods of mechanical defects based on vibration signal are studied in this paper. Firstly, vibration mechanisms of GIS are analyzed. Due to structural differences between single phase insulated type GIS and three phase insulated type GIS, there are big differences in vibration mechanisms between the two types of GISs. Secondly, experimental research on mechanical defects is carried out based on a 110 kV GIS equipment and a self-developed vibration detection system; results show that mechanical defects can be diagnosed by analyzing signal amplitude, frequency spectrum and waveform distortion rate, and a large current is more beneficial for diagnosing mechanical defects. Lastly, field application has been carried out on 220 kV GIS equipment, and a poor contact defect is found, demonstrating that abnormal diagnosis can be realized by method proposed in this paper. Experimental research and field application demonstrate the feasibility and effectiveness of detection and diagnosis method for mechanical defects based on vibration signal and provide experience for subsequent engineering application.

Frequency response analysis (FRA) demonstrates significant advantages in the diagnosis of transformer winding faults. The instrument market desires intelligent diagnostic functions to ensure that the FRA technique is more practically useful. In this paper, a hierarchical dimension reduction (HDR) classifier is proposed to identify types of typical incipient winding faults. The classifier procedure is hierarchical. First, measured frequency response (FR) curves are preprocessed using binarization and binary erosion to normalize FR data. Second, the pre-processed data are divided into groups according to the definition of dynamic frequency sub-bands. Then, hybrid algorithms comprised of two conventional and two novel quantitative indices are used to reduce the dimension of the FR data and extract the features for identifying typical types of transformer winding faults. The classifier provides an integration of a priori expertise and quantitative analysis in the furtherance of the automatic identification of FR data. Twenty-six sets of FR data from different types of power transformers with multiple types of winding faults were collected from an experimental simulation, literature, and real tests performed by a grid company. Finally, real case studies were conducted to verify the performance of the HDR classifier in the automatic identification of transformer winding faults.

In 3D full-Maxwell simulations of very fast transients (VFTs), both accuracy and simulation efficiency pose challenges. The existing method cannot set simulation conditions appropriately, resulting in subpar simulation accuracy for the VFT process and unnecessary time consumption. Moreover, for the very fast transient overvoltage (VFTO) waveforms estimated by simulations, there is still a lack of specific analysis on waveform characteristics and appropriate verification methods. In this paper, a novel method that significantly improves both the accuracy and the time efficiency of the 3D full-Maxwell simulation of the VFT process is proposed. The wave process generated by breakdown is also analyzed to obtain the VFTO waveform characteristics for subsequent verification. Simulation cases, based on both a simplified GIS model and an actual GIS model, are demonstrated and discussed. Furthermore, a specially designed field test on the typical 1100 kV GIS, taking into account the residual voltage, is conducted for the simulation verification for the first time. The simulation and field test results align well in both the time domain and the time-frequency domain. Additionally, the novelty of this proposed method is demonstrated by comparing it with other existing method in case studies. The accuracy of the method has been shown to improve significantly, and the simulation time consumption can be reduced with the same computational machine and simulation configuration.

Transient voltage generated in ultra-high voltage (UHV) transmission system has a severe impact on the insulation state of gas-insulated transmission lines (GIL). In order to monitor the transient voltage process occurring in UHV GIL during operation, this paper constructs a transient voltage ultra-wideband (UWB) online monitoring system based on capacitive voltage division. This system has been applied in an 1100 kilovolt (kV) GIL utility tunnel project. It can be used to analyze the characteristics of the recorded transient voltage waveforms for distinguishing different types of insulation failure. In this paper, through the case studies in time domain and time–frequency domain, the case of SF₆ gap breakdown and that of post insulator flashover have differentiated characteristics in instantaneous frequency. Additionally, a case of secondary discharge is successfully estimated through the time–frequency distribution of the transient voltage. In order to find the malfunctioning chamber of GIL rapidly, a two-terminal TW-based fault location method based on this monitoring system is developed in this paper. The case study validates the locating accuracy which directly support for shortening the maintenance time of GIL.