1. Introduction
SF6 high-voltage circuit breakers (HVCBs) constitute essential electrical switching devices within power systems, concurrently fulfilling the critical functions of circuit interruption, closure, and provision of control and protective measures [
1,
2,
3]. Comprehensively assessing the health status of SF6 HVCBs is a prerequisite for determining their safety control levels and implementing equipment maintenance strategies.
Currently, numerous researchers have proposed an abundance of methods for evaluating the operating state of HVCBs, yielding substantial achievements. These methods can be primarily categorized into the following two types.
The first classification pertains to conventional techniques of measurement and surveillance, which leverage sensors and instrumentation for the real-time monitoring of circuit breaker parameters. Faults within HVCBs are identified through the analysis of parameters such as electrical or vibration signals. For example, in reference [
4], both mechanical and electrical signals are gathered and subjected to analysis, culminating in the proposal of a fault diagnosis methodology for HVCBs predicated on the fusion of multisensory information. Ref. [
5] introduces an enhanced rule for the combination of multi-sensor evidence aimed at optimizing the data acquired from diverse sensors. Qiuyu Yang et al. present a method for assessing the state of spring-operated HVCB dampers based on vibration time frequency imagery [
6]. Ref. [
7] employs the characteristics of control coil currents as modeling data and advances an online amalgamated HVCB fault diagnosis approach, thereby augmenting diagnostic precision and the capacity for learning.
The second category of methods involves the integration of intelligent algorithms with data-driven approaches to evaluate the health status of circuit breakers [
8]. Data-driven analysis, based on the degree of correlation between data, integrates technologies such as data mining, extensive storage, fuzzy mathematics, expert systems, and machine learning to establish empirical models. These models combine a wealth of data with the actual operating conditions of the equipment to model and predict the operating state of circuit breakers. In Ref. [
9], to extract and simulate the correlation between circuit breaker characteristic indicators and operating conditions, an adaptive error back propagation neural network (BPNN) was constructed with parameter improvements. Yao et al. employed a combination of fractal technology and probabilistic neural network for a circuit breaker fault diagnosis method capable of classifying and identifying faults [
10]. Advanced artificial intelligence methods, including fuzzy logic, k-means clustering, cluster trees, and artificial neural networks, were introduced in [
11] to improve the health assessment of HVCBs. Ref. [
12] proposes a comprehensive diagnosis of mechanical faults in HVCBs using a multi-channel integrated convolutional neural network based on multi-data fusion. Diahovchenko et al. developed a fuzzy logic-based assessment method for SF6 HVCBs, determining the equipment most in need of maintenance and assisting in prioritizing maintenance plans [
13].
While the scholarly endeavors mentioned above have achieved significant advancements, certain areas persist that necessitate refinement. Firstly, some methodologies exhibit a lack of comprehensive consideration for evaluation factors, often resulting in the exclusion of environmental data from the assessment model due to challenges in quantifying the influence of environmental indicators on circuit breaker operational status. Secondly, the unequal impact of varying factors on evaluation outcomes remains inadequately addressed. Lastly, when dealing with input indicators featuring both discrete and continuous data, prevailing analytical approaches frequently rely on a uniform data-driven model, potentially overlooking the intrinsic disparities between continuous and discrete data types, thereby impacting the precision of the evaluation model.
In response to these challenges, this study proposes an operational status assessment methodology for SF6 HVCBs predicated on the integrated data-driven analysis (IDDA) model. Initially, a comprehensive database is established, comprising fault precursor indicators of diverse data types that encapsulate the mechanical, electrical, and insulation performance of the circuit breaker, alongside environmental attributes. Subsequently, to mitigate the imbalances induced by variations in distinct factors affecting system stability, a data-driven model grounded in critical factor stability (CFS) is deployed to objectively assess the relative significance of environmental factors. Lastly, an optimized fuzzy inference (OFI) system, integrated with probabilistic fuzzy and hierarchical fuzzy techniques, is employed to process continuous numerical indicators, thus streamlining the complexity of the model. The results of the case analysis indicate that the proposed SF6 HVCB operational status assessment method can enhance the accuracy of the evaluation.
2. Data Preprocessing
2.1. Data Collection
The operational stability of SF6 HVCBs relies heavily on the mechanical, electrical, and insulation performances. A comprehensive assessment of SF6 HVCBs’ operational condition necessitates the consideration of these three key performances. Currently, the primary focus of circuit breaker monitoring includes dynamic contact travel, coil current, vibration signals, stored energy motor current, and various other parameters [
14]. Given the multitude of signals available for safety assessment, selected signals should exhibit the following characteristics:
They should accurately represent variations in different components or operating states of the circuit breaker.
They should promptly detect changes in the equipment’s status.
They should furnish precise and dependable monitoring data.
They should ease measurement and analysis.
We amalgamated the aforementioned characteristics and extensively consulted experts and on-site operators to gather the following signal data.
Mechanical performance primarily pertains to the efficacy of mechanical structures such as the opening and closing mechanism, contact system, and mechanical connectors, which reflect operational processes and mechanical status. An SF6 HVCB characterized by abbreviated opening and closing durations and rapid operational velocity facilitates expeditious disconnection and reconnection of electrical circuits. Measurement of opening and closing time and speed enables the assessment of spring and operating mechanism sensitivity and reliability. Contact travel directly impacts the circuit breaker’s contact state and arc formation. Excessive contact travel may augment wear and energy loss. Hence, measuring contact travel and ensuring it falls within an appropriate range are imperative to mitigate issues such as contact wear and poor contact.
Electrical performance encompasses the breaking capacity of SF6 HVCBs, stability of the control system, and reliability of electrical connections. Breaking current value is gauged through a current transformer to characterize circuit breaker breaking performance, reflecting remaining life and relative contact wear. Operating current and voltage values of opening and closing coils ascertain the condition of coil magnet operation and control circuit functionality, providing insights into coil status and locking device operation.
SF6 gas serves as the arc extinguishing medium for circuit breakers, with its insulation performance directly impacting safety and stability. To evaluate the insulation capability and arc extinguishing performance of SF6 HVCBs, temperature, pressure, density, and leakage of SF6 gas must be measured. Insufficient gas pressure or density decrease may result in local discharge of the arc extinguishing medium.
Environmental factors indirectly affect insulation, electrical, and mechanical performance within SF6 HVCBs, thereby influencing operational status. Therefore, recording surrounding environmental conditions is essential. Additionally, according to the equipment ledger and maintenance records, collect records of operational conditions.
2.2. Data Categorization
The data collected has been categorized into discrete and continuous indicators. Discrete indicators predominantly pertain to environmental variables such as weather, pollutants, mechanical vibration, external physical damage, and electromagnetic disturbances. Each indicator encompasses distinct factors, as delineated in
Table 1.
Continuous indicators encompass mechanical, electrical, and insulation performance metrics. These metrics, along with their associated safety thresholds [
11], are detailed in
Table 2. Should a specific indicator surpass its safety threshold, it is deemed a rare factor, potentially resulting in irregular equipment conditions.
Notably, SF6 HVCBs of different rated voltage levels have varying opening and closing coil voltages. signifies the rated voltage of SF6 HVCBs, denotes the voltage of the opening coil, represents the voltage of the closing coil, and denotes the resistance value of the measured coil.
The operational status of SF6 HVCBs based on events is categorized into four levels: Excellent, Fine, Abnormal, and Severe. Different levels correspond to different treatment methods. The corresponding set of comments is presented in
Table 3.
2.3. Establishment of the Data Matrix
In order to facilitate subsequent data-driven analysis, all input data undergo preprocessing to form a data processing space. Considering that the collected data of continuous indicators such as opening and closing time, speed, and total contact travel involve multiple scales and dimensions, it is necessary to first normalize the value of indicators. The formula is as follows:
Here, represents any one of the indicator values in indicator , where and , respectively, denote the maximum and minimum values of the indicator, and is the normalized indicator value.
Then, let
be a set containing the identifiers of each status record, and let
be a group containing 17 indicators, where each indicator
is composed of a set of indicator factors,
. Let
be a group containing four operational status ratings. To handle discrete and continuous indicators separately, the set containing indicators is divided into two parts and represented in matrix form, where discrete indicator factors are denoted as
, and continuous indicator factors are denoted as
:
Based on the above assumptions, an integrated database space matrix, denoted as M, can be constructed:
Here, represents one factor of indicator recorded in status record , corresponding to any factor in the respective column; is the multi-factor-determined operational status rating of the SF6 HVCB, where .