The method of Support Vector Machine (SVM) based on Dissolved Gas Analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings, such as the fuzzy boundaries of DGA data, and SVM parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and a hybrid improved Seagull Optimization Algorithm to optimize the SVM (TISOA-SVM). Firstly, KPCA is used to extract features from DGA feature quantities. In addition, TISOA is further proposed to optimize the SVM parameters to build the optimal diagnosis model based on SVM. For the SOA, three improvement methods are proposed. An improved tent map is used to replace the original population initialization to improve population diversity. In addition, the nonlinear inertia weight and random double helix formula are proposed to improve the optimization accuracy and efficiency of the SOA. Then, benchmarking functions are used to test the optimization performance of TISOA and six algorithms, and the results show that TISOA has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on KPCA and TISOA-SVM is obtained, and it is noteworthy that three examples are tested to verify the diagnostic performance of the proposed method. These results show that the proposed method has higher diagnostic accuracy, shorter diagnosis time, stronger significance and validity than other methods. Therefore, a research idea is provided for solving practical engineering problems in the field of fault diagnosis.INDEX TERMS Power transformer, fault diagnosis, kernel principal component analysis, support vector machine, hybrid improved seagull optimization algorithm.

Power transformer fault diagnosis exerts a vital part in the safe operation of power system. The PSO-SVM based on transformer fault diagnosis still has some shortcomings, such as slow convergence speed and easy to fall into local optimization. This dissertation proposes a transformer diagnosis method based on Improve Particle Swarm Optimization to support Vector Machine (MPSO-SVM). Adding disturbance to Particle swarm optimization (PSO) to disturb the position of such precocious particles, so as to get rid of local optimum. The case analysis represents that the diagnostic accuracy of MPSO-SVM is higher than that of PSO-SVM and Generalized Regression Neural Network (GRNN), and MPSO-SVM can effectively promote the fault diagnosis performance of transformer.

Aimed at that ubiquitous three-phase unbalance problem in low-voltage distribution networks, the spotted hyena optimizer (SHO) algorithm is used to optimize the commutation strategy of the three-phase load unbalance. A multitarget swapping mathematical model was designed, and the objective was quickly resolved by relying on the excellent commutation strategy of the SHO. Finally, a case analysis was carried out on the data of a station area in Enshi, Hubei Province, and the results verify this swapping strategy can effectively reduce the unbalance rate.

In the practical application of large-scale photovoltaic module monitoring, adopting wireless sensor network (WSN) technology is a method worth researching. With increasing nodes in the wireless sensor network, widely existing clock skew, increased geometrically, is bringing about greater energy consumption. Due to the random distribution of nodes, in order to improve the transmission efficiency and reduce the computational load of the coordinator, the node processor needs to the use edge computing for preliminary analysis. This paper puts forward an improved energy-efficient reference broadcast synchronization algorithm (ERBS). This algorithm firstly calculates the average phase offset of nonadjacent nodes in the network after receiving a message. It then uses the least square method to solve the clock skew to achieve high-precision synchronization of the whole network. Simulation results show that compared with RBS, the time synchronization precision of ERBS is greatly improved and synchronization times are greatly reduced, decreasing energy consumption significantly.

In order to avoid clock skew in WSN (Wireless Sensor Networks) for large-scale photovoltaic modules monitoring, an improved time synchronization algorithm, TSP-GDM (Time Synchronization Protocol with Gaussian Delay Model) is proposed in this paper. Interdependence of local time stamps is established between network nodes according to a linear clock model. Local exchange and share of local time stamps in nodes are achieved by means of wireless transmission. An estimation method with the Gaussian Delay Model is designed to deal with the estimation problems of the node clock offset. The synchronization accuracy of the proposed method is verified with the MATLAB simulation. It is found that TSP-GDM can be applied to synchronous topology of large-scale photovoltaic modules monitoring with a higher synchronization accuracy. Compared to RBS (Reference Broadcast Synchronization), TPSN (Timing-synchronization Protocol for Sensor Networks) and RTSP (Recursive Time-Sync Protocol for WSN), its synchronization accuracy in an inner layer has been increased by 22.57 µs, 15.7 µs and 4.26 µs respectively.INDEX TERMS Gaussian delay model, photovoltaic modules monitoring, time synchronization, WSN.