Fault Diagnosis of Wind Turbine Pitch System Based on Multiblock KPCA Algorithm

Abstract

When the wind turbine pitch system is in operation, due to the strong coupling of the internal structure of the system, it is difficult to accurately locate the fault only relying on prior knowledge. And when using the data-driven contribution graph method for fault location, due to the influence of the fault variable, the contribution value of the non-fault variable will become larger, which will cause a tailing effect and misdiagnosis. In this paper, a multiblock kernel principal component algorithm (MBKPCA) is proposed. In this algorithm, the variables of the pitch system operation process are divided into several variable blocks based on the historical fault data and perform faults based on the kernel principal component algorithm for each variable block diagnosis. Taking historical data of an area in North China as an actual calculation example, the proposed block kernel principal component analysis algorithm is compared with the traditional kernel principal component analysis algorithm. The results show that the MBKPCA can effectively reduce the "tailing effect" of the fault variable on the non-fault variable when the contribution graph method is used to identify the fault source, and the method can accurately locate the fault source and achieves higher detection accuracy. INDEX TERMS fault diagnosis， kernel principal component algorithm，wind turbine pitch system， characteristic variables

Fault diagnosis model of main equipment in substation based on multi-mode time-frequency combination

“…The high-voltage side threephase of the main substation equipment is connected to the high-voltage side three-phase marked on the instrument panel one by one, and the low-voltage side of the main substation equipment is connected to the low-voltage side of the instrument to ensure good contact [8]. The switch waveform measured in this test project can be used to determine whether the switch is correct, and whether there are any faults such as mechanism jamming, insulation fault, contact burning, and transmission shaft fracture, providing strong data support for partial discharge detection of substation main equipment [9]. The detection of UHF signals generated by small defects in the main equipment of a substation can be used to determine partial discharges through signal distribution characteristics within the cycle, providing a reference for subsequent fault diagnosis.…”