Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

In power system reliability evaluation, alternating renewal process is generally used to model the failure and repair cycle of a component. This means that after repair, the component is assumed to be restored to as good as new condition from the reliability perspective. However, in practice, there may exist an aging trend in some components as they grow old. This paper describes how aging characteristics of components may impact the calculation of commonly used reliability indices such as loss of load expectation (LOLE). In order to construct the system failure and repair history of components, sequential Monte Carlo simulation method using stochastic point process modeling is introduced. Three methods are described for this purpose and this methodology is applied to the single area IEEE reliability test system. The results are analyzed and compared.

Reliability Modeling and Simulation in Power Systems With Aging Characteristics

Smart grid technology places greater demands for reliability on communications infrastructure. This work focuses on identifying requirements for distribution feeder level communications. Due to the large number of distribution components connected to the distribution level feeders, a massively deployed wireless communication network is identified as the potential technology for this application. This network would allow prioritized communication: high priority for abnormal events and system control operations, and low priority communication for asset management tasks. A three-layer wireless communication architecture is proposed in this work to increase the reliability and reduce the latency of event notification. Fault location is considered as an example application to illustrate the proposed architecture.

Wireless communication for smart grid applications at distribution level — Feasibility and requirements

Many mine power systems operate with a floating neutral or are high-resistance grounded, and earth fault current is no more than a few tens of amperes. Traditional earth-fault-detection methods based on zero-sequence current have poor sensitivity in this case. For improvement, a fault-detection scheme with fault-current and fault-resistance measurement is presented in this paper, which is capable of detecting high-impedance ground faults. The scheme has been verified by electromagnetic-transient-program simulation, and a fault-detection prototype has been developed and tested in the laboratory. Results show that the scheme has high sensitivity and robustness.

Ground-Fault Feeder Detection With Fault-Current and Fault-Resistance Measurement in Mine Power Systems

This paper deals with the subject of incipient faults in underground distribution systems and their progression over time leading to an eventual permanent failure. Field data recorded from an underground distribution feeder were analyzed in both time and frequency domain to identify the symptom parameters and characterize the observed incipient behavior. This exploratory data analysis sheds additional light on the progressive characteristics and nature of these self-clearing faults and advances the fundamental knowledge in an effort to detect such pre-mature failures before developing into a full-blown fault.

Field investigation and analysis of incipient faults leading to a catastrophic failure in an underground distribution feeder

This paper focuses on a systematic and cumulative statistical approach for identifying symptom parameters of incipient faults in distribution feeders. The proposed method aims at providing a tool for evaluating and identifying the best and highly correlated parameters to the faults so that they could be used for incipient fault detection and feeder condition monitoring. The Laplace test statistic is adopted for trend analysis of the event log of candidate parameters and applied to actual feeder event data for online detection and monitoring purposes.

Cheon-Ho Kim

Papers

Evaluation of feeder monitoring parameters for incipient fault detection using Laplace trend statistic