This paper focuses on the use of extremely large data sets in power system operation, control, and protection, which are difficult to process with traditional database tools and often termed big data. We will discuss three aspects of using such data sets: feature extraction, systematic integration for power system applications, and examples of typical applications in the utility industry. The following analytics tasks based on big data methodology are elaborated upon: corrective, predictive, distributed and adaptive. The paper also outlines several research topics related to asset management, operation planning, realtime monitoring and fault detection/protection that present new opportunities but require further investigation.

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The role of big data in improving power system operation and protection

High-voltage circuit breakers (HVCBs) play an important role in power systems, which can control and ensure the power grids are working properly. Real-time fault diagnosis of HVCBs is an essential issue for power systems. In this paper, a novel approach based on an adaptive kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed for real-time fault diagnosis of HVCBs. In the proposed approach, a sample reduction algorithm based on a similarity degree function is proposed to analyze the similarity between the samples, and the redundant data can be eliminated. An adaptive KPCA method is used for the fault detection of HVCBs based on squared prediction error statistics. An SVM is used to carry out the fault recognition. Two spare data areas are set up for fault detection and recognition modeling. The data in the spare date areas are updated continuously, and the detection and recognition models are updated subsequently to improve the adaptivity of the diagnosis models and reduce the diagnosis error. The proposed approach can deal with various situations of the fault diagnosis for HVCBs. The experimental results show that the proposed approach is capable of detecting and recognizing the faults efficiently.

An Adaptive Approach Based on KPCA and SVM for Real-Time Fault Diagnosis of HVCBs

Prognostics predictions estimate the remaining useful life (RUL) of assets. This information enables the implementation of condition-based maintenance strategies by scheduling intervention when failure is imminent. Circuit breakers (CBs) are key assets for the correct operation of the power network, fulfilling both a protection and a network reconfiguration role. Certain breakers will perform switching on a deterministic schedule, while operating stochastically in response to network faults. Both types of operation increase wear on the main contact, with high fault currents leading to more rapid aging. This paper presents a hybrid approach for prognostics of CBs, which integrates deterministic and stochastic operation through piecewise deterministic Markov processes. The main contributions of this paper are: 1) the integration of hybrid prognostics models with dynamic reliability concepts for a more accurate RUL forecasting and 2) the uncertain failure threshold modeling to integrate and propagate uncertain failure evaluation levels in the prognostics estimation process. Results show the effect of dynamic operation conditions on prognostics predictions and confirm the potential for its use within a condition-based maintenance strategy.

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A Model-Based Hybrid Approach for Circuit Breaker Prognostics Encompassing Dynamic Reliability and Uncertainty

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 SF6 insulation medium, and are equipped with a spring-drive mechanism.

Circuit-Breaker Automated Failure Tracking Based on Coil Current Signature

The focus of this paper centers on the condition assessment of 11-kV-33-kV distribution circuit breakers (CBs) from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a CB's operation may be defined and used to characterize varying states of the CB condition. The knowledge and understanding of the satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting CB maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of Scottish Power - Energy Networks' in-service CBs. This knowledge then forms the basis of a decision support system for the condition assessment of these CBs during routine trip testing

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Providing Decision Support for the Condition-Based Maintenance of Circuit Breakers Through Data Mining of Trip Coil Current Signatures

This paper describes a solution for automated analysis of circuit breaker operation. The analysis is based on a record of waveforms taken from the circuit breaker control circuit by using a portable recorder and manually forcing an operation of the breaker. This solution was driven by a need to perform the analysis in a more timely and consistent manner than what is available with existing technology. The solution is implemented using advanced wavelet transforms for waveform feature extraction and an expert system for decision making. A web-based database solution for storing and retrieving both the field-recorded and processed data is also implemented. The software is developed in two versions: for field (substation) as well as off-line (office) applications.

Automated monitoring and analysis of circuit breaker operation

The automated circuit breaker diagnostic system described in this paper is an extension of the widely used portable circuit breaker testing device concept. The testing devices can be connected to the circuit breaker's control circuit to record analog and digital signals. The automated system consists of a signal processing module and an expert system module. The two modules process the voltage and current signals recorded by the testing device, diagnose and report any abnormalities that are discovered. The paper describes the system requirements, the implementation of the two system modules, and provides an example that demonstrates the entire process. Real data recorded in a substation has been used to test the system.

J.S. Lucey

Papers

Automated monitoring and analysis of circuit breaker operation

Automated circuit breaker monitoring and analysis