Equivalent circuit and calculation of unbalanced power in three‐wire three‐phase linear networks

Lee

et al. 2023

Sensors

Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early diagnosis of cable faults is the most effective approach to prevent system downtime and maximize productivity. We focused on a “soft fault state”, which is a transient state that eventually becomes a permanent fault —open-circuit and short-circuit. However, the issue of soft fault diagnosis has not been considered enough in previous research, which could not provide crucial information, such as fault severity, to support maintenance. In this study, we focused on solving soft fault problem by estimating fault severity to diagnose early-stage faults. The proposed diagnosis method comprised a novelty detection and severity estimation network. The novelty detection part is specially designed to deal with varying operating conditions of industrial applications. First, an autoencoder calculates anomaly scores to detect faults using three-phase currents. If a fault is detected, a fault severity estimation network, wherein long short-term memory and attention mechanisms are integrated, estimates the fault severity based on the time-dependent information of the input. Accordingly, no additional equipment, such as voltage sensors and signal generators, is required. The conducted experiments demonstrated that the proposed method successfully distinguishes seven different soft fault degrees.

Section: Soft Fault In Cablementioning

confidence: 99%

Attention Recurrent Neural Network-Based Severity Estimation Method for Early-Stage Fault Diagnosis in Robot Harness Cable

Lee

et al. 2023

Sensors

“…Consequently, it reflects the faulty power network having a fault resistance R f . The shrunk cross-sectional area S f in a single phase causes a phase imbalance and has harmful effects, such as power loss, additional heating, and energy decrease of the equipment [24]. The asymmetry in phase impedance leads to the imbalance in the phase, leading to the imbalance of the three-phase currents.…”

Section: Soft Faults and Features In Control Cablementioning

confidence: 99%

Current Only-Based Fault Diagnosis Method for Industrial Robot Control Cables

Lee

2022

Sensors

With the growth of factory automation, deep learning-based methods have become popular diagnostic tools because they can extract features automatically and diagnose faults under various fault conditions. Among these methods, a novelty detection approach is useful if the fault dataset is imbalanced and impossible reproduce perfectly in a laboratory. This study proposes a novelty detection-based soft fault-diagnosis method for control cables using only currents flowing through the cables. The proposed algorithm uses three-phase currents to calculate the sum and ratios of currents, which are used as inputs to the diagnosis network to detect novelties caused by soft faults. Autoencoder architecture is adopted to detect novelties and calculate anomaly scores for the inputs. Applying a moving average filter to anomaly scores, a threshold is defined, by which soft faults can be properly diagnosed under environmental disturbances. The proposed method is evaluated in 11 fault scenarios. The datasets for each scenario are collected when an industrial robot is working. To induce soft fault conditions, the conductor and its insulator in the cable are damaged gradually according to the scenarios. Experiments demonstrate that the proposed method accurately diagnoses soft faults under various operating conditions and degrees of fault severity.

“…Therefore, it is necessary to establish a new method to detect faults in capacitor banks. Based on a literature review , several methods have been proposed for detecting faults in capacitor banks based on parameters such as the phase voltage unbalance phasor [7][8][9][10], neutral voltage unbalance phasor [11], phase current unbalance phasor [12], and neutral current unbalance phasor [13,[14][15][16][17][18][19]. The method used in [20][21] employed the superimposed reactance method considering voltage and current; the results proved that the relay algorithm based on measuring unbalance factors was effective and capable of operating even when faults occurred within a short processing time.…”

Section: Introductionmentioning

confidence: 99%

Discriminating Between Capacitor Bank Faults and External Faults for an Unbalanced Current Protection Relay Using DWT

Lertwanitrot

Ngaopitakkul

2020

IEEE Access

Capacitor faults are a common issue in modern-day power systems. Such systems employ a traditional mechanism that solely relies on unbalanced relays as an indicator of faults in capacitor banks; however, in the case of a relay trip, operators may find it difficult to identify the cause of these faults. To address this issue, this study aims to detect and discriminate between faults in the capacitor bank (i.e., internal faults) and those in the transmission line (i.e., external faults) by employing discrete wavelet transform (DWT). For this purpose, simulations were conducted using PSCAD software and modelled according to the 115-kV system from the Electricity Generating Authority of Thailand (EGAT). Furthermore, case studies involving faults with different phases, side and branch connections, row connections, inception angles, and blown fuses were considered. Moreover, the phase current, unbalanced current, and unbalanced voltage were analyzed to elucidate fault behaviors and the variation in the coefficient obtained via DWT; the unbalanced current and unbalanced voltage were used as design criteria for the proposed detection and relay operation algorithm. Real-world signals were employed to verify the accuracy of the proposed model. The results indicate that the proposed algorithm achieves satisfactory accuracy in terms of fault detection and the operation of relays in capacitor banks. The proposed algorithm is expected to enable an improvement in the performance of protection systems for capacitor banks in the near future.