One of the key tasks of the overhead line power equipment inspection based on aerial images acquired by unmanned aerial vehicles is to determine whether the insulators are faulty. However, the fault area on the insulator string occupies a relatively small portion of the entire image, which will make detection difficult. This article presents an intelligent fault detection method for overhead line insulators based on aerial images and improved you only look once (YOLOv3) deep learning technology. In our model, a densely connected feature pyramid network (FPN) is proposed. First, this network can improve the utilization rate of the strong semantic information of deep features and the localization information of shallow features, thereby improving the small insulator fault (missing-cap) detection performance of the YOLOv3 model. Second, this network reduces the number of parameters of the YOLOv3 model, resulting in a low risk of network over-fitting for small datasets. The experimental results on the CPLID dataset show that our model has higher detection accuracy in localization of overhead line insulators and detection of insulator missing-cap faults compared with the existing works.

In this paper, a set of dissolved gas analysis (DGA) new feature combinations is selected as input from the mixed DGA feature quantity, and an improved krill herd (IKH) algorithm optimized support vector machine (SVM) transformer fault diagnosis model is established to solve the problem that the single characteristic gas or characteristic gas ratio which are utilized as the DGA feature quantity cannot fully reflect the transformer fault classification. The following work has been done in this paper: (1) IEC TC 10 fault data and other 117 sets of fault data in China are preprocessed in order to reduce the influence on diagnosis results causing by the edge data in the fuzzy area. (2) The SVM parameters and 11 features are encoded by a binary code technique. (3) A preferred DGA feature set for fault diagnosis of power transformers is selected by genetic algorithm (GA) and SVM. (4) GA is utilized to optimize the parameters of SVM. Combining with cross-validation principle, a transformer fault diagnosis model based on IKH algorithm to optimize support vector machine is established. The fault diagnosis results based on the new fault sample show that the proposed DGA feature set increase the accuracy by 26.78% and 10.83% over the DGA full data and IEC ratios. Moreover, the accuracy of IKHSVM is better than the GASVM, Back-Propagation neural network (BPNN) and particle swarm optimization optimized support vector machine (PSOSVM), the accuracy rate are 85.71%, 75%, 64.29% and 71.43% respectively which proves the validity of the proposed fault diagnosis model.

Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This paper proposes and develops a Genetic Algorithm Support Vector Machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency domain spectroscopy. Therefore, a novel model for predicting frequency domain spectroscopy curves is firstly reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this work is in an exploration of the reported model, as an intelligent based moisture diagnosis tool for power transformers.

The operating temperature and the ampacity are important parameters to reflect the operating state of cross-linked polyethylene (XLPE) submarine high voltage (HV) cables, and it is of great significance to study the electrothermal coupling law of submarine cable under the seawater flow field. In this study, according to the actual laying conditions of the submarine cable, a multi-physical coupling model of submarine cable is established based on the electromagnetic field, heat transfer field, and fluid field by using the COMSOL finite element simulation software. This model can help to analyze how the temperature and ampacity of the submarine cable are affected by different laying methods, seawater velocity, seawater temperature, laying depth, and soil thermal conductivity. The experimental results show that the pipe laying method can lead to the highest cable conductor temperature, even exceeding the maximum heat-resistant operating temperature of the insulation, and the corresponding ampacity is minimum, so heat dissipation is required. Besides, the conductor temperature and the submarine cable ampacity have a linear relationship with the seawater temperature, and small seawater velocity can significantly improve the submarine cable ampacity. Temperature correction coefficients and ampacity correction coefficients for steady-state seawater are proposed. Furthermore, the laying depth and soil thermal conductivity have great impact on the temperature field and the ampacity of submarine cable, so measures (e.g., artificial backfilling) in areas with low thermal conductivity are needed to improve the submarine cable ampacity.

Conventional dielectric response measurement techniques, for instance, recovery voltage measurement (RVM), frequency domain spectroscopy (FDS) and polarization–depolarization current (PDC) are effective nondestructive insulation monitoring techniques for oil-impregnated power transformers. Previous studies have focused mainly on some single type of dielectric measurement method. However, the condition of oil paper insulation in transformer is affected by many factors, so it is difficult to predict the insulation status by means of a single method. In this paper, the insulation condition assessment is performed by grey relational analysis (GRA) technique after carefully investigating different dielectric response measurement data. The insulation condition sensitive parameters of samples with unknown insulation status are extracted from different dielectric response measurement data and then these are used to contrast with the standard insulation state vector models established in controlled laboratory conditions by using GRA technique for predicting insulation condition. The performance of the proposed approach is tested using both the laboratory samples and a power transformer to demonstrate that it can provide reliable and effective insulation diagnosis.

Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures.