1. Introduction
The high-voltage electrical system of electric locomotive, consisting of pantographs, insulators, potential transformers, arresters, circuit breakers, and traction transformers, transfers electrical energy to the traction drive system to propel the train [
1]. The arrester is positioned on locomotive roof to mitigate the increase in voltage of the safeguarded equipment [
2], preventing them from the harm caused by external and internal overvoltages [
3,
4]. The zinc oxide (ZnO) valve plates are commonly used in locomotive arresters due to their superior pressure-sensitive features [
5]. In recent years, as reported in some literature electric locomotives have experienced arrester burnout or explosion accidents during operation, especially when harmonic resonance of the traction supply network happens [
6,
7]. Such events have resulted in severe consequences, including train stoppages and power outages in the traction supply network, which have negatively impacted the safe and stable operation of the railway system. Therefore, it is crucial to carry out relevant studies for revealing the fault mechanism of locomotive arresters, and implementing specific health management measures, which should be beneficial for achieving the maintenance mode transformation from traditional planned maintenance to state maintenance and preventive maintenance.
Existing studies mainly investigate the fault characteristics and mechanisms of arresters in the power grid when exposed to fundamental power frequency (50 Hz considered in this work) overvoltage or thunder impulse [
8,
9,
10,
11]. However, this is distinct from the situations of the locomotive arrester faults. References [
12] and [
13] indicate that electric locomotive arresters are at certain risk of burnout or explosion in the presence of high frequency harmonic resonance in the traction supply network. Moreover, studies based on some tests point out that the primary cause of locomotive arrester faults is that the arrester is exposed to high-frequency and high-amplitude resonance overvoltage from the traction supply network for a relatively long duration [
14]. This leads to an enormous increase in arrester leakage current so as to exacerbate heating and break the thermal equilibrium, resulting in heat accumulation. As a result, the thermal capacity limit of the arrester is surpassed. It should be noted that the frequency band of the resonant overvoltage in the traction supply network, typically ranging from several hundreds to several thousands hertz [
15,
16,
17], is much higher than the counterpart of general overvoltages in the grid. Although this explanation of the locomotive arrester faults is reasonable, there is a lack of detailed tests about the electrical characteristics of the ZnO arrester operating under the high-frequency harmonic over-voltage, which will further reveal the arrester burnout and explosion mechanism.
On the other hand, a combination of planned maintenance and post repair is currently adopted for locomotive arresters [
18]. In practical engineering, there exists some inadequate or excessive maintenance. With the continuous advancements in sensor, microprocessor, and other technologies relevant to online monitoring, the prognostics and health management (PHM) is with increasing application prospect for realizing intelligent operation and maintenance of locomotive arresters [
19,
20].
Currently available techniques for evaluating the electrical equipment health status, which is the key information of the PHM, are primarily model-based and data-driven methodologies [
21]. The model-based prediction methods rely on physical or mathematical model of the target, i.e., electrical equipment. This model is usually developed by analytical solutions which is based on a comprehensive understanding of the operation and fault mechanisms of the equipment. Under the prerequisite of reasonable and comprehensive modelling, this kind of methods exhibit robust specificity and high reliability. Nevertheless, the modelling is generally not a trivial task, and the model validation poses challenges. Furthermore, the model must be adjusted to follow the changes on the target. For example, the model of an arrester cannot be directly used for an insulator, even if they have certain similarities. Besides, the fact is that for different types of arresters the model still requires modification. This means limited adaptability and inadequate generalization capability.
Alternatively, based on the acquisition of monitoring signals associated with electrical equipment deterioration, the data-driven methods can evaluate the equipment operation state, warn its faults, and even forecast its remaining life based on some modern intelligent algorithms. Typically, there are mainly two approaches to implement a data-driven PHM system. First, the equipment health state is predicted by statistical method based on the fault probability distribution derived from a substantial quantity of operation and maintenance data [
22]. However, all the data must conform to strict requirements of both sample size and temporal dimension. Besides, since it is with few links to the equipment operation principle, this approach generally fails to elucidate the events associated with the equipment faults. The second is state parameter monitoring based approach [
23]. Operation state as well as residual life of the equipment can be evaluated through comparing the monitoring data with the failure thresholds which are typically come out of the insight to the equipment physical properties usually acquired from sufficient tests. This means that test data covering the entire equipment life cycle is required for understanding the ageing pattern or performance decline of the equipment. However, this is not always satisfied. On the whole, for the time being, research on PHM of electric locomotive high-voltage equipment is just in its infancy. Specifically, less attention has been paid on locomotive arrester PHM.
As mentioned above, regarding the accidents of locomotive arrester burnout and explosion, this study simulates the high-frequency overvoltage of the traction supply network, which allows to investigate the wide frequency band electrical characteristics of the arrester, explaining that it is prone to fault during resonance. Subsequently, a PHM for locomotive arrester is proposed based on online monitoring design as well as fault warning and health evaluation algorithms, which is beneficial to the operation and maintenance of arresters in the future.
The rest of this article is organized as follows.
Section 2 presents a concise analysis of the static volt-ampere characteristics and equivalent circuit models of the arrester. Then high-frequency tests of a typical model of locomotive arrester as well as its ZnO valve plates are performed. The tested results under fundamental and harmonic voltages as well as their combinations explain how the operational properties of arrester are affected by variations in voltage amplitude and frequency. A PHM method for locomotive arrester is proposed in
Section 3 including an online monitoring scheme for acquiring and preprocessing relevant data, and the fault warning and health assessment algorithms.
Section 4 draws the conclusion.
Author Contributions
Conceptualization, X.P., H.Z., K.S. and G.X.; methodology, X.P. H.Z. and K.S.; software, K.S. and G.X.; validation, P.W., K.S. and G.X.; formal analysis, X.P., H.Z., P.W. and K.S.; investigation, P.W. and G.X.; data curation, P.W. and G.X.; writing—original draft preparation, P.W.; writing— review and editing, X.P., H.Z. and K.S.; supervision, X.P., H.Z. and K.S.; project administration, H.Z.; funding acquisition, X.P.and H.Z.; All authors have read and agreed to the published version of the manuscript.