Explore the vast range of titles available.

The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault types and locations under various conditions and reduce the fault analysis efforts.

Digital substations have adopted a high amount of information and communication technology (ICT) and cyber–physical systems (CPSs) for monitoring and control. As a result, cyber attacks on substations have been increasing and have become a major concern. An intrusion-detection system (IDS) could be a solution to detect and identify the abnormal behaviors of hackers. In this paper, a Deep Neural Network (DNN)-based IDS is proposed to detect malicious generic object-oriented substation event (GOOSE) communication over the process and station bus network, followed by the multiclassification of the cyber attacks. For training, both the abnormal and the normal substation networks are monitored, captured, and logged, and then the proposed algorithm is applied for distinguishing normal events from abnormal ones within the network communication packets. The designed system is implemented and tested with a real-time IEC 61850 GOOSE message dataset using two different approaches. The experimental results show that the proposed system can successfully detect intrusions with an accuracy of 98%. In addition, a comparison is performed in which the proposed IDS outperforms the support vector machine (SVM)-based IDS.

We determined earthquake locations through re-picking of P- and S-wave arrival times recorded by BMKG network. Earthquake locations were determined using Hypoellipse code that employs a single event determination method. We then relocated the events using hypocenter double-difference method. We also conducted focal mechanism analysis to estimate the type of fault slip. The results indicate improved hypocenter locations, where patterns of seismicity in West Java were delineated clearly. There are several clusters of earthquakes at depths ≤ 30 km, which are probably related to the Cimandiri, Lembang, and Baribis faults. In addition, there is another cluster in Garut trending southwest-northeast, which is possibly related to a local fault. Histograms of travel-time residuals depict good results, in which travel-time residuals are mostly close to zero. Source mechanism throughout the Lembang fault indicates a left-lateral strike slip in agreement with previous studies. The Cimandiri fault also shows a left-lateral slip, but in the south it shows a thrust fault mechanism. While the source mechanisms of the western part of the Baribis fault indicate a thrust fault and the cluster of events in Garut shows a right-lateral slip if they are related to a local fault.

Interconnected transmission systems are increasingly spreading out in HV networks to enhance system efficiency, decrease reserve capacity, and improve service reliability. However, the protection of multi-terminal lines against Broken Conductor Fault (BCF) imposes significant difficulties in such networks as the conventional distance relays cannot detect BCF, as the BCF is not associated with a significant increase in current or reduction in voltage Traditionally, the earth fault relays in transmission lines may detect such fault; Nonetheless, it suffers from a long delay time. Moreover, many of the nearby earth fault relays detect the BCF causing unnecessary trips and badly affecting the system stability. In this article , a novel single-end scheme based on extracting transient features from current signals by discrete wavelet transform (DWT) is proposed for detecting BCFs in interconnected HV transmission systems. The suggested scheme unit (SSU) is capable of accurately detecting all types of BCFs and shunt high impedance faults (SHIFs). It also adaptively calculates the applied threshold values. The accurate selectivity in multi-terminal lines is achieved based on a fault directional element by analyzing transient power polarity. The SSU discriminates between internal/external faults effectively utilizing the time difference observed between the first spikes of aerial and ground modes in the current signals. Different fault scenarios have been simulated on the IEEE 9-Bus, 230 kV interconnected system. The achieved results confirm the effectiveness, robustness, and reliability of SSU in detecting correctly BCFs as well as the SHIFs within only 24.5 ms. The SSU has confirmed its capability to be implemented in interconnected systems without any requirement for communication or synchronization between the SSU installed in multi-terminal lines.

Conventional transmission and transformation line ground fault location technology mainly uses LCC (line combined converter) commutation converter to obtain fault section characteristics, which is vulnerable to short-term load calibration, resulting in wrong location results. Therefore, a transmission and transformation line ground fault location technology based on least square method is proposed. That is to say, the least square method is used to identify the fault location parameters and generate the grounding fault location framework of transmission and transformation lines, thus completing the grounding fault location. The experimental results show that the designed grounding fault location technology for transmission and transformation lines has good positioning effect, no positioning error, low positioning delay, reliability, and certain application value, and has made certain contributions to improving the operation safety of transmission and transformation lines and reducing their operation and maintenance losses.

Kaba volcano is a perilous and currently active volcanic site located near the Sumatran fault, specifically within the Kepahiyang region of Bengkulu. Given the intricate nature of its location, it is crucial to monitor the local fault zone activity in the vicinity of the Kaba volcano. meanwhile, these fault zones are typically associated with high permeability areas and are characterized by high-density contrasts. Therefore, we applied First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) methods to identify the presence of the Musi Kepahiyang segment in Bengkulu, using GGMplus high resolution gravity data. Based on the results of the FHD analysis, clear gravity anomalies are observed along the northwest (NW) and southeast (SE) regions of Kepahiyang, with Bouguer anomaly values reaching 800 mGal. The discernible patterns unveiled through FHD analysis distinctly delineate the Musi fault (NW), Kepahiyang fault (SW), and Garba fault, unveiling a rich tapestry of tectonic activity surrounding the Kepahiyang area in Bengkulu. Complementing these findings, SVD analysis reveals a consistent anomaly distribution, albeit with marginally diminished Bouguer anomaly values, affirming the robustness of the detected features. Through the fusion of FHD and SVD methodologies, our study offers an understanding of the structural complexities pervading the segment in Kaba Stratovolcano, shedding light on its dynamic geological evolution, and fortifying our comprehension of fault dynamics in the Sumatra region.

Though the traditional fault diagnosis method of T-connected transmission lines can identify the faults inside and outside the area, it can not identify the specific branches. To improve the accuracy and reliability of fault diagnosis of T-connection transmission lines, a new method is proposed to identify specific faulty branches of T-connection transmission lines based on multi-scale traveling wave reactive power and random forest. Based on the S-transform, the mean and sum ratios of the corresponding short-time series traveling wave reactive powers of each two traveling wave protection units at multiple frequencies are calculated respectively to form the fault feature vector sample set of the T-connection transmission line. A random forest fault branch identification model is established, and it is trained and tested by the fault feature sample set of T-connection transmission line to identify the fault branch. The simulation results show that the proposed algorithm can identify the branch where the fault is located inside and outside the protection zone of T-connection transmission line quickly and accurately under various working conditions. This method also shows good performance to identify faults even under the situation of CT saturation, noise influence and data loss.

This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity.

Due to their accurate time stamping, phasor measurement units (PMUs) are being used for the modern day power grid protection applications. As PMUs provide synchronous data, so in this paper, a fault location algorithm has been proposed for a three-terminal non-homogeneous transmission network with the help of synchronized data, available at all the three ends. The line parameters are estimated before they are used in solution for identifying the faulted section and fault location. PSCAD ® software is used for obtaining synchronized voltages and currents data of the network by simulating various types of faults. MATLAB is used for developing software code for estimating the fault location and fault section identification. The fault location estimation have been carried out considering transmission line model as frequency distributed transmission line model to evaluate the performance of the proposed algorithm. The fault location algorithm has also been evaluated for effect of fault resistance, source internal angle, source impedance, synchronization errors, change in network configuration, dc offset effect, fault type in other phases and CT saturation.

In modern power systems, accurate and timely detection of faults is crucial for ensuring system stability and reliability. The presence of high resistance in fault path curtails current and causes conventional distance relays to malfunction. These methods often require two-end measurements for accurate assessment of fault resistance necessitates an expensive communication channel. This paper proposes an innovative approach to enhance transmission line protection through an adaptive artificial neural network (ANN)-based relay system. The relay system integrates three ANN units: the fault detection unit, fault classification unit, and fault location unit, each tailored to detect, classify, and locate faults, respectively. By utilizing single-end measurements and employing discrete Fourier transform for feature extraction, the proposed algorithm efficiently diagnoses various fault conditions, including high resistance faults. Additionally, the algorithm dynamically updates its characteristics based on the estimated fault resistance (using one cycle post-fault data and the status of each ANN unit) in real-time, ensuring adaptability to changing system conditions, especially when the fault resistance falls beyond the scope of the training data. Simulation results on a 400-kV, 50-Hz transmission system demonstrate the robustness and effectiveness of the proposed approach in accurately identifying fault events under varying fault parameters, while also accounting for arcing faults and transducer errors. The suitability of the proposed method for real-time operations has been validated using OPAL-RT digital simulator. The adaptability of the proposed method for higher order systems is verified by performing a test case on the modified WSCC 9-bus system. The results support the adaptability and effectiveness of the proposed relaying algorithm in securing the transmission line under various conditions, including high resistance faults.