A nuclear power plant (NPP) depends on instrumentation and control (I&C) systems to ensure its safe and efficient operation. In particular, I&C cables take on the pivotal role of measuring and controlling the critical equipment of the NPP. Thus, it is indubitable that the diagnostic technology of I&C cables for detecting faults and accurately assessing their health status is required for ensuring the safety and reliability of the NPP operation. We propose a diagnostic method that combines fault detection and evaluation algorithm for the I&C cables with stepped-frequency waveform reflectometry with signal propagation and reflection modeling. The signal modeling allows the assessment of the fault with an estimated reflection coefficient by separating the propagation and reflection effects of the measured signal. In short, cable faults are differentiated and quantified regardless of distance. The proposed algorithm is verified by characteristic impedance measurement, various fault detection/evaluation experiments, and the fault evaluation of local accelerated thermal aging cable.

Fault diagnosis has been studied actively across the electrical industry to help maintain the stability of electrical equipment. Among these equipment, shielded cables, which are widely used in various industrial sectors, require careful and periodic diagnosis, owing to their poor installation environments and potential for creating huge economic losses. Reflectometry is a representative solution to locate the cable faults; however, conventional reflectometry techniques require prior knowledge about the cable under test, such as the reference wave velocity, total length of the cable, etc. Moreover, the degree of failure cannot be determined using conventional methods. In this paper, a novel reflectometry technique is proposed to locate and evaluate the faults in a cable, without requiring any prior knowledge. General regression neural network (GRNN) based on kernel density estimation are utilized with special feature extraction procedures. The proposed method is tested in an actual test bed with two types of emulated faults, and is found to estimate both the fault location and reflection coefficient successfully. It is expected that the proposed method can improve the stability of industrial equipment.

The maintenance of control and instrumentation (C&I) cables is crucial to safety of operating nuclear power plants. Therefore, when an accident occurs, there is a need for an accurate assessment of the impact on the cable's integrity. Unfortunately, most cable diagnostic methods are destructive and real-time assessment of the effect of accidents is not possible. Thus, in this paper, we present an analysis of a specific type of accident, a loss of coolant accident (LOCA), on C&I cables in real-time, based on time-frequency domain reflectometry (TFDR). Because the TFDR is sensitive to the signal-to-noise ratio and distortion of a reflected signal, we apply postprocessing techniques that compensate the dispersion based on the estimated propagation constant and a denoising method using singular value decomposition. The approach is verified by experimentally monitoring condition changes of localized LOCA hot spot in different C&I cables. The results are also validated by comparing with elongation at break test results.

In this paper, we propose a fault detection and assessment technique of an instrumentation and control (I&C) cable based on time-frequency image classification using the Faster region based convolutional neural network (R-CNN). For the training of the Faster R-CNN and multiple reflection compensation, an estimation of the reflected signal is utilized, which enables the reflected signal to segregate into two: the signal propagation and the reflection from an impedance discontinuity point. Experimental results on the location and assessment of two fault scenarios specifically, under multiple faults detection and branch networks have demonstrated the effectiveness of the proposed method.

While the world’s power distribution system resembles an intricate web-like structure, the most conventionally implemented distribution mechanism is the radial distribution system (RDS) where connection points for each distribution line are normally kept open. However, disadvantages regarding the traditional system have led to active research on establishing the networked distribution system (NDS), in which multiple circuits are interconnected with electricity as well as high-speed communication systems. The NDS offers multiple advantages including increased facility utilization, increased hosting capacity, and higher terminal voltage. Conversely, an unsolved issue prevails as the existing protection coordination method designed for the RDS is inadequate for fault occurrences in the NDS due to inaccurate fault direction identification. Hence, there is an urgent need for an alternative protection coordination method that allows high precision fault direction identification through communication. Moreover, the application of various existing technologies is hindered as the distance relay protection coordination algorithm malfunctions in situations where distribution lines are of short length, loads are dispersed onto multiple lines, and integration of distributed generation (DG) is frequent. Therefore, this paper presents a fault direction identification method that uses the waveform of the fault current based on long short-term memory (LSTM) neural network and a communication-based protection coordination scheme that can be applied in fault situations within an NDS.

The blind spot has been an important issue for fault location/diagnosis methods, especially reflectometry. In this paper, a time domain reflectometry (TDR) based on a multi-level wavelet analysis is proposed to accurately localize a fault within a blind spot. The proposed method consists of a parallel fault scanning process which depends on the wavelet decomposition level, (1) time cross-correlation and (2) coefficient feature-based pattern matching. To locate a fault within a wire, the pattern of the TDR reference signal is decomposed to an approximation (low pass) and a detail (high pass) coefficient through a wavelet multi-level analysis. For the low-level wavelet decomposition, the smoothing spline fitted decomposed signal is cross-correlated to detect possible fault locations by comparing the reference signal and the reflected signal. For high-level wavelet decomposed data points, coefficient features are matched to specify the exact location of the fault. In fault scenarios using automotive wires, the experimental results of the fault location and velocity of propagation estimation demonstrates the effectiveness of the proposed method.