Experimental Technology and Management

[Objective] As an important protection and control device in the power system, the switchgear inevitably experiences different types of partial discharges because of its harsh working environment. Thus, the accurate identification of fault types is crucial to ensure the safe and stable operation of the power system and prevent equipment damage. However, at the present stage, the characterization of fault information during partial discharge fault identification in the switchgear cabinet is difficult, and the accuracy of partial discharge fault identification is low. In this study, we propose the automatic optimization of successive variational mode decomposition(SVMD) and support vector machine(SVM) parameters based on the IIVY algorithm to realize efficient identification of different partial discharge types. [Methods] First, we develop three multistrategy fusion methods using spatial pyramid matching chaotic mapping for initialization parameters, adaptive t-distribution for decision updates, and dynamic adaptive power selection for mode updates. On this basis, we propose the IIVY algorithm. Second, we develop a partial discharge feature extraction strategy based on IIVY-SVMD-MPE. This uses the IIVY algorithm to adaptively select the SVMD penalty factor α, combine it with correlation coefficients to filter the three largest IMF components, extract the multiscale permutation entropy(MPE), and construct the multidimensional fusion feature dataset. Third, we establish a switchgear localized discharge fault identification model based on IIVY-SVM using the IIVY algorithm to select the three largest IMF components for the MPE and construct a multidimensional fusion feature dataset. Finally, we establish a fault identification model based on IIVY-SVM for efficient identification of partial discharge types. The IIVY algorithm adaptively optimizes the penalty parameter C and kernel parameter σ in SVM, resulting in a fault identification model with the optimal parameter combination. [Results] This study combines the experimental data, compares the 10 fault identification models, and draws the following conclusions:(1) The IIVY algorithm proposed in this study is more advantageous than the three original optimization algorithms in the hyperparameter adaptive optimization under the same conditions, which proves the high efficiency of the proposed improvement strategy.(2) The pattern recognition model SVM is more suitable for partial discharge fault identification than BP and ELM.(3) MPE can be used to extract the fault features carried by the signal more comprehensively.(4) The adoption of a single signal processing or feature extraction method has a large impact on the accuracy of fault recognition, and the model proposed in this study can efficiently process the original signal and extract fault features.(5) Overall, the comprehensive recognition accuracy of the fault recognition model proposed in this study reaches 98.8%, in which the recognition accuracies of the pin–plate discharges, discharges along the surface, suspended discharges, and air gap discharges are 100%, 100%, 100%, 95% and 100%, 95% and 100%. [Conclusions] By establishing a multi-strategy fusion method based on spatial pyramid matching chaotic mapping, adaptive t-distribution, and dynamic adaptive weighting based on the IIVY algorithm, we propose and establish a partial discharge feature extraction method based on IIVY-SVMD-MPE and a partial discharge fault identification model based on IIVY-SVM, utilize the IIVY algorithm adaptive optimization of the SVMD penalty factor α with the penalty parameter C and kernel parameter σ in SVM, and realize the fault recognition model. The test results showed that the fault identification model established in this study has an identification accuracy of 98.8%, which effectively improves the fault identification accuracy and stability and provides a reference for partial discharge fault identification in the switchgear.