Circuit Breaker Condition Assessment Research Articles

Comprehensive condition assessment of circuit breakers in a power network for maintenance scheduling

AbstractEnhancing preventive maintenance (PM) by using condition monitoring of circuit breaker (CB) can reduce maintenance costs, if enough information about the condition of this expensive equipment are provided. Here, using recorded coil current data of CBs in a power system network and on the basis of their calculated failure probability function, an algorithm is developed for comprehensive CB condition assessment. For this purpose, first, CBs are prioritized according to their cumulative failure probability functions. Second, by introducing healthy/reference CBs and comparing them with the CB with the highest priority, the condition of the latter is assessed that whether it needs maintenance or not. At last, by creating two sets of reference CBs with healthy/reference features and/or mechanisms and comparing them with the features and/or mechanisms of the CB with the highest priority, the failure part of the considered CB that needs to be repaired will be identified. The proposed methodology is straightforward, completely rule‐based and relies just on recorded data and the results have enough clarity and reliability.

A practical knowledge-based ranking approach to identify critical circuit breakers in large power systems

Condition assessment of circuit breakers (CBs) based on the installation of online monitoring systems (OLMs) is increasingly demanded by utilities. However, equipping all CBs with OLMs is neither technically nor economically feasible in a large power system. Therefore, the prioritization of the CBs is very important to identify critical components for equipping with such expensive tools (OLMs). The limited information along with the large size of power systems result in the inapplicability of the conventional ranking methods, in practice. To cope with this problem, this paper proposes a simple yet effective method based on the analytic hierarchy process (AHP) for organizing the experts’ knowledge. The prioritization of the CBs has been solved as a multi-criteria decision-making problem (MCDM). The proper criteria have been defined to incorporate the practical concerns and reliability points regarding the limited data in a large scale system. In addition, in order to avoid the inconsistency of the decision matrix, the problem has been divided into three-level, i.e. ranking of substations, CBs in each substation, and CBs in the network. The applicability of the approach has been verified through successful implementation in a large-scale power transmission network to determine the critical substations and CBs for equipping with OLMs.

Research on data-driven method for circuit breaker condition assessment based on back propagation neural network

As the maintenance requirements are changed with the health status of equipment, in order to develop an optimal maintenance strategy, a data-driven nonlinear method is proposed to online assess the operating condition of circuit breakers. From the historical data resources with different timeliness, feature indicators are extracted based on the confidence improved by Bayesian probability. Then, an adaptive error back propagation (BP) neural network is improved to model the nonlinear correlation between the feature indicators and the operating conditions of the circuit breaker, by additional momentum factor, self-adaptive learning rate and improved momentum. Finally, combined with the inspection test and online monitoring data, the panoramic operating condition pf the equipment is objectively graded by the output model. Taking 500kV SF6 high-voltage circuit breaker as an example, combined with the data provided by China Yunnan Power Grid, the effectiveness of the proposed method is proved by sample tests and method comparison.

Fault Identification for Circuit Breakers Based on Vibration Measurements

Mechanical condition assessment of circuit breakers (CBs) in the power system, if correctly applied, can make an optimal decision on whether, where, and how to perform maintenance. This article presents a new approach to the detection and identification of CB's mechanical states, using its vibration data during switching operations. The morphological correlation coefficient (MCC), which can detect whether or not a fault is present in a CB, is introduced first. Then, a quantitative methodology to specify the specific fault type of the CB, i.e., the ensemble empirical mode decomposition (EEMD)-Hilbert marginal spectrum energy entropy (HMSEE), combined with a classifier [e.g., support vector machine (SVM)], is proposed. By using this approach, the mechanical condition of CBs can be reliably estimated. Some typical mechanical faults of a 12-kV vacuum CB were simulated in the test laboratory to evaluate the effectiveness of the proposed approach. The application in two field cases demonstrates the applicability of the proposed approach.

Circuit breaker condition assessment through a fuzzy‐probabilistic analysis of actuating coil's current

Circuit breaker (CB) is a component providing significant electrical protection for power transmission and distribution systems. Hence, an effective condition monitoring system for the CBs is of imminent importance. This study presents a fuzzy-probabilistic-based condition assessment algorithm to assess the CB operation performance by monitoring its trip/close coil current (C.C.), and suggests a simple yet effective condition assessment tool. The C.C. patterns are acquired by measurements carried out on 60 CBs (72.5 kV, SF6) under faulty and normal conditions. To intelligently assess the CB's operating state (i.e. normal, alarm and emergency), in terms of its reliability, the optimum C.C. features with minimum interdependence were obtained. Furthermore, a fuzzy-probabilistic method is proposed based on the results of the statistical analyses of the features. The feasibility and applicability of the proposed method in CB condition assessment is verified against the experimental measured data.

Applicability of auxiliary contacts in circuit breaker online condition assessment

Circuit breaker (CB) due to its important roles in protection of power system has been recognized as a critical component. In order to assess the condition of CBs, a prevalent and conventional diagnosis method is time measurement of the mechanical operations (called timing test) through the travel curve (T.C.). However, it is an inappropriate technique for the online assessment. The major objective of this paper is to reveal the correlation between the T.C. and auxiliary contacts (A.Cs) of CB. On the basis the results, timing of A.Cs is proposed as easy-to-access, easy-to-measure, and non-invasive diagnosis parameters in online condition assessment of mechanism of CBs. The investigations are established upon measurements carried out on sixty CBs (72.5kV, SF6) under healthy and faulty conditions. The new approach is conducted through an adaptive neuro-fuzzy inference system (ANFIS). The feasibility and applicability of the proposed method in CB condition assessment is verified against the experimental measured data.

Intelligent maintenance model for condition assessment of circuit breakers using fuzzy set theory and evidential reasoning

This paper proposes a circuit breaker (CB) condition assessment model which combines the fuzzy set theory, the analytical hierarchy process (AHP) and evidential reasoning (ER). Four assessment factors, that is, (i) electrical characteristics, (ii) mechanical characteristics, (iii) insulation characteristics and (iv) miscellaneous factors, are selected as the condition parameters. A hierarchical assessment index system is constructed based on preventive tests data, intelligent electronic device monitoring data, maintenance data and operating environment. The inputted condition parameters indices are processed by the membership functions of a fuzzy distribution to obtain relative impairment grades which represent the relative grade of the CB condition towards the fault. The weights of the CB condition factors and indices are generated by the AHP based on experts’ interviews and feedbacks, and the final assessment is determined by ER according to the decision factors combined. An intelligent maintenance model is built with field examples to demonstrate the effectiveness of the proposed model in assessing the CB operation conditions.

Circuit-Breaker Automated Failure Tracking Based on Coil Current Signature

The online condition assessment of circuit breakers (CBs) has been increasingly requested by power utilities in recent years. Trip and close coil current (CC) signature is an effective and noninvasive parameter in CB online condition monitoring. This paper gives an insight into the impacts of the various failures on the coil current waveform, as well as on the CB operation time. The failures and their causes are categorized based on the outcome of these investigations. Finally, a new algorithm using trip/close CC is proposed to detect the mode and cause of CB incipient failures. In this study, the CC patterns are acquired by measurements carried out on (healthy and faulty) CBs of 72.5-kV and 24-kV rating voltages, having SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> insulation medium, and are equipped with a spring-drive mechanism.

Design of trip current monitoring system for circuit breaker condition assessment

A distributed system, which supports circuit breaker maintenance and asset management, is described. It uses a client/server architecture for propagating expert knowledge from switchgear maintenance experts directly to maintenance operatives for on-site circuit breaker condition assessment and diagnosis. Prior research in the field of distribution level circuit breaker condition monitoring has shown the trip coil of a circuit breaker yields a current profile that, when tripped, can subsequently be interpreted as an indicator of plant health. Exploiting existing circuit breaker test equipment, a centralised archive of asset condition is built from routine tests permitting experts to examine trends in the data and pass their definition of the operating conditions to personnel in the field. This provides diagnostic support to engineers in the field. The system is currently in use as the subject of a pilot study conducted by SP PowerSystems intended to improve its ongoing maintenance and asset management activities.