Abstract
With the rapid development of power grids, the voltage level and capacity of high-voltage shunt reactors (HVSRs) are increasing year by year, and HVSR faults are increasing, especially HVSRs core winding faults. The HVRSs core winding faults seriously threaten the safe and stable operation of power grids. To solve this problem, we propose a feature extraction method for HVSR core winding faults identification to improve the accuracy of faults identification. This method relies on the combined application of qualitative analysis of phase space reconstruction (PSR) and quantitative calculation of an improved K-means clustering method. First, we employ PSR to qualitatively analyze the HVSR vibration signal and extract the phase trajectory feature quantity of the vibration signal. Then, this paper uses an improved K-means clustering optimized by an improved grasshopper optimization algorithm (GOA) to cluster the phase trajectory features, so as to obtain the cluster center coordinates. Furthermore, we employ the distance from the cluster center displacement vector to the origin and the angle change to perform quantitative calculations to realize fault identification. Finally, the fault simulation experimental data sets of 10kV and 20kV HVSRs verify the effectiveness of the proposed method. The experiment results show that the proposed method has better accuracy and can truly reflect the state characteristics of the HVSR core winding. The proposed high-accuracy method helps to improve the efficiency of on-site HVSRs condition assessment and maintenance.
Highlights
A high-voltage shunt reactor (HVSR) is a core component of power systems
HVSR chaotic analysis phase trajectory feature extraction and IGOA-K-means clustering identification, we summarize the detailed process as follows: Step 1) The collected raw vibration signal is preprocessed for noise reduction and detrending
COMPARISON OF CLUSTERING METHODS Here, to verify the superiority of the IGOA-K-means clustering proposed in this paper, we employ the phase trajectory data extracted in Case I with rich vibration mode information to compare the performance of the traditional K-means, grasshopper optimization algorithm (GOA)-K-means and IGOA-K-means clustering methods
Summary
A high-voltage shunt reactor (HVSR) is a core component of power systems. An HVSR has variable functions, such as compensating reactive power, reducing line power loss, suppressing the increase in power frequency voltage, and preventing resonant voltage [1], [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Baoping Cai. electrical failures that cause abnormal HVSR vibrations, such as winding looseness, iron core looseness, internal components falling off, and interturn short circuits, may lead to a reduction in HVSR short-circuit resistance, local power failures or even large-scale power outages [4]. A 220 kV overhead line trip caused by internal winding faults in an HVSR resulted in serious power failure in a large area and led to enormous economic losses [5]. In view of this threat, it is vital to devise an effective core winding failure feature extraction approach for core winding failure of HVSRs with abnormal vibrations.
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