Exact Fault Location Research Articles

A novel phaselet-based approach for fault detection and location in HVDC lines

Recently, the popularity of direct current (DC) transmission has surged due to a 30 % reduction in power electronics equipment costs over the last decade. High-Voltage Direct Current (HVDC) transmission lines experience fluctuations in both nominal and faulty states, posing challenges for fault location methods, particularly those based on traveling waves, resulting in imprecise outcomes with an average error margin of ±2 %. This paper investigates a novel fault detection and location approach utilizing wavelet and phaselet transforms. The proposed method focuses on traveling waves in HVDC transmission lines, where the phaselet transform effectively eliminates undesired fluctuations in line currents, improving fault detection accuracy by up to 15 %. By leveraging the traveling time in the faulty line, the exact fault location is calculated with an accuracy of 98.5 %, independent of transmission line parameters, making it applicable to any DC line. Additionally, the method's precision is minimally affected by fault resistance, with <0.5 % deviation even in nonlinear scenarios. Sensitivity analysis conducted on various parameters, including mother wavelet patterns, suggests the optimal solution for each fault type, enhancing detection speed by 20 %. The proposed method is validated using the Cigre HVDC benchmark, confirming its accuracy, speed, and robustness in fault detection and location in HVDC lines. The main contribution of this paper is an accurate, fast, and robust phaselet-based algorithm that reduces fault location errors to <1 % and is applicable to all types of bipolar and monopolar HVDC systems, delivering reliable results for both ground and line faults.

Underground Cable Fault Detection Using IOT

Abstract: The objective of this project is to determine the distance of underground cable fault from base station in kilo meters using an Arduino. The underground cable system is a common practice followed in many urban areas. An issue may happen in an underground cable just because of earth tremors or any burrowing procedure. Since the area of the issue happened is obscure it is very hard for the fixing procedure. This hindrance is handled with the assistance of optical fibre framework. A lot of optical strands is put alongside the force cables. While a fault occurs for some reason, at that time the repairing process related to that particular cable is difficult due to not knowing the exact location of the cable fault. The proposed system is to find the exact location of the fault. The project uses the standard concept of Ohms law i.e., when a low DC voltage is applied at the feeder end through a series resistor (Cable lines), then current would vary depending upon the location of fault in the cable. In case there is a short circuit (Line to Ground), the voltage across series resistors changes accordingly, which is then fed to inbuilt ADC of Arduino to develop precise digital data for display in kilometres. The project is assembled with a set of resistors representing the cable length in km and the fault creation is made by a set of switches at every known km to cross check the accuracy of the same. The fault occurring at a particular distance, the respective phase along with the distance is displayed on the LCD. The same information is also sent over IOT, interfaced to the Arduino. Further this project can be enhanced by using capacitor in an ac circuit to measure the impedance which can even locate the open circuited cable, unlike the short circuited fault only using resistors in DC circuit as followed in the above proposed project.

Precise PMU-Based Localization and Classification of Short-Circuit Faults in Power Distribution Systems

Knowing the exact location of a short-circuit fault in a power distribution system (PDS) is essential for rapid restoration of service to customers and has a direct impact on the operational costs and reliability of the system. In this paper a phasor measurement unit (PMU) based method for fault localization and classification is presented. By introducing the concept of a virtual bus, the exact fault location on the line is determined rather than just a bus closest to the fault. Moreover, after determining the fault location, a generic fault model (GFM) is introduced to classify the type of fault by solving a minimization problem, which also provides fault impedances as a result. These can then be further used to determine if the fault occurred on the lateral instead of the main feeder without using additional PMU at the end of that lateral. The effectiveness of the proposed approach is verified by simulating various fault scenarios in two test systems based on real PDSs using a real-time digital simulator. In addition, a sensitivity analysis of the method is performed for different noise levels in the input parameters to verify its applicability to the real system.

An Intelligent Method for Fault Location Estimation in HVDC Cable Systems Connected to Offshore Wind Farms

Large and remote offshore wind farms (OWFs) usually use voltage source converter (VSC) systems to transmit electrical power to the main network. Submarine high-voltage direct current (HVDC) cables are commonly used as transmission links. As they are liable to insulation breakdown, fault location in the HVDC cables is a major issue in these systems. Exact fault location can significantly reduce the high cost of submarine HVDC cable repair in multi-terminal networks. In this paper, a novel method is presented to find the exact location of the DC faults. The fault location is calculated using extraction of new features from voltage signals of cables’ sheaths and a trained artificial neural network (ANN). The results obtained from a simulation of a three-terminal HVDC system in power systems computer-aided design (PSCAD) environment show that the maximum percentage error of the proposed method is less than 1%.

A novel SVM based adaptive scheme for accurate fault identification in microgrid

Microgrids, which include distributed energy resources, provide a more reliable power supply. However, despite the fast advancements in microgrid protection, the microgrid’s dynamic behaviour challenges the protection. The large-scale integration of renewable energy sources (RES) and the addition of electric vehicle (EV) charging loads cause additional protection issues on the grid due to its volatile nature. Conventional fault prediction schemes do not provide reliable protection with the dynamic nature of microgrids. This article proposes an adaptive scheme that accurately detects the volatile changes in the microgrid under normal and fault conditions. The novelty of the proposed approach is that the general-purpose adaptive scheme is based on a two-level SVM classifier model to ensure accuracy and speed of operation. The SVM-1 model identifies the mode of operation, the variations in RES generation, the random addition of EV charging load, and the occurrence of a fault in the microgrid. SVM-2 identifies the exact type and location of fault that occurred. The SVM model uses RMS values of three-phase voltage and current measurements as data inputs. The efficacy of the proposed scheme is tested on an IEEE 9-bus microgrid test bed by simulation experiments using MATLAB/SIMULINK. The sensitivity analysis results prove the robustness of the proposed scheme. Moreover, the proposed method is validated using real-time experiments on Hardware-In-Loop (HIL) real-time simulator, OPAL-RT, and Raspberry Pi microcontroller. Accurate online detection of faulted lines and fault types is obtained within a cycle of AC in grid-connected and islanded modes of operation.

Fault Detection and Localization in Industrial IoT Systems using Deep Learning

In the ever-evolving landscape of Industrial Internet of Things (IoT) systems, the need for robust fault detection and localization has become paramount. Existing methods, while commendable, have faced certain limitations that necessitate the development of more sophisticated solutions. This work embarks on a journey to address these challenges and presents a novel approach to fault detection and localization, leveraging the formidable power of Deep Learning process. The limitations of existing methodologies primarily revolve around their inability to provide the required precision and accuracy in detecting and localizing faults within complex IoT deployments. These methods often struggle to handle the diverse contextual datasets that characterize modern industrial settings. Additionally, their computational efficiency often leaves much to be desired, hindering real-time fault response mechanisms. In response to these limitations, our proposed model introduces a hybrid framework. It combines Vector AutoRegressive Moving Average with Exogenous Variables (VARMAx) for precise fault localization and Convolutional Neural Networks (CNN) for robust fault detection. This fusion harnesses the strengths of both approaches, providing a comprehensive solution for fault management in industrial IoT systems. The advantages of this model are twofold. First, VARMAx offers exceptional accuracy in pinpointing the exact location of faults within the system, facilitating swift corrective actions. Second, the integration of CNN enhances fault detection by effectively capturing patterns and anomalies in the data, resulting in a highly responsive fault detection system. Notably, this model not only outperforms existing methods with a 3.9% boost in precision, 4.5% increase in accuracy, 4.9% higher recall, 8.5% improved AUC, but also boasts a remarkable 5.9% enhancement in computational speed, ensuring real-time responsiveness. The profound impact of this work extends to the realms of industrial automation and IoT system reliability. By addressing the limitations of existing approaches and introducing a robust fault detection and localization model, this paper paves the way for safer and more efficient industrial operations. The improved precision, accuracy, and speed of fault detection will minimize downtime, reduce maintenance costs, and ultimately elevate the performance and reliability of industrial IoT systems to new heights.

Underground Cable Fault Monitoring System

This paper is proposing a method to find the faults in the underground cable using microcontroller(esp32). The aim of this paper is to determine the exact location of fault with latitude and longitude. When any fault occurs in underground cable the voltage will be fluctuate because current at that point will varies. This paper presents the working model of the device that can help humans to find faults in underground cable where human presence is dangerous due to environmental conditions. Hence we can inspect the faults under such circumstances. Though there were several approaches are there to find faults in underground cable ,this paper is going to find it in a simpler way and more efficiently using voltage sensors.

Distance protection with fault resistance compensation for lines connected to PV plant

The output power of the inverter-interfaced photovoltaic (PV) plant has the continuous variations due to the properties of PV modules and environmental conditions. These variations fundamentally affect the performance of the conventional distance protection during nonmetallic faults. This paper proposes a positive sequence network-based distance protection scheme for the transmission lines connected to the PV plants. Different from the adaptive mho and quadrilateral characteristics, the proposed scheme is independent of PV plant parameters and fault resistance. In this method, the Thevenin equivalent parameters of the grid seen from the PV plant are calculated. Thevenin equivalent parameters are calculated using prefault current and voltage data at the local terminal, without additional costs related to the requirements of the communication channel. The obtained parameters are used in the fault resistance compensation. Furthermore, by eliminating the fault resistance effect on the relay impedance, the exact fault location is also calculated. Since the fault line impedance formula is derived only from the positive sequence network, the proposed method is applicable to different grid codes. The efficiency of the proposed scheme is evaluated on the Western System Coordinating Council (WSCC) 9-bus and IEEE 30-bus test systems. The obtained simulation results verify the validity of the proposed scheme under various fault and PV plant conditions.

Efficient Fault Detection Algorithm in Fiber Optic Communication Systems

Primary concern of optical communication system is the signal loss, which need to be traced precisely to enhance the efficiency of the system. Among numerous available techniques, optical time domain reflectometer (OTDR) is the most suitable one for characterizing and tracing the losses. It measures the fraction of a probe pulse that scatters back from the fiber optic cable. Due to the presence of small levels of backscattering in single-mode fiber at long wavelengths, very sensitive optical detector is necessary to achieve sufficient range performance. In the present research, a novel yet simple approach has been demonstrated to understand the range of optical fiber cable feasibility on fault detection and rectification technique. The actual place of fault is marked with the variation in the measured values of OTDR. Thus, it becomes a difficult task to trace and locate the actual place of fault detection, which leads to an inefficient rectification method. The observed values of OTDR shows a gradual decrease of accuracy in locating the actual place of fault. To resolve these issues, here we try to develop an algorithm which is able to easily and efficiently trace the exact location of fault on earth.

Automated Transmission Line Fault Detection using Distance Locator

Faults play an important role in affecting the reliability of power system. More than 80% of the power system faults occur in transmission sector which badly effect the reliability of supply and causes damage to the system. So, it is compulsory to monitor the transmission lines in normal and faulty conditions and clear the fault as soon as identified. The main aim of this proposed research is to design a circuit which will have a capability of determining the type of fault and exact location of fault. From the results of simulations, it has been concluded that the four types of faults such as L-G, L-L and L- L-G have been detected with their location remotely. Afterwards, transfer its data to the utility mobile phone of the user with the help of GPS and NodeMCU.

Double Line-to-Ground Faults Detection Method in DC-AC Converters

Numerous electrical machines use power converters for their efficient operation. However, their insulations experience a fast deterioration due to high frequency electrical stresses. This loss of insulation can lead into ground faults, which are the most common electrical faults. Convectional protection relays are not specifically designed to protect electrical drives. Moreover, relays’ malfunction could be provoked because of commutation noises, or DC and AC fault current's components influence in their measurement. Therefore, if the fault is not detected in time, it can be developed into a more severe fault. This paper proposes a detection method for high resistance double line-to-ground faults in electrical drives based on a grounding resistor placed in the midpoint of the DC side. The method has been developed for DC/AC systems and it is based on the voltage measurement in the grounding resistor and in the AC phases. Afterwards, performing a frequency domain analysis of the grounding resistor voltage and comparing its fundamental harmonic phase differences with the AC voltage phasors, the double line-to-ground fault incipient zone (DC and/or AC) can be detected, and also, the DC faulty pole or AC faulty phase can be discerned. The method has been verified through computer numerous simulations and experimental tests in a 140 kW power converter, obtaining satisfactory results in the fault detection. However, the exact location of each ground fault still being incognita.

Power quality improvement in power electronics systems using machine learning

This study focuses on improving power electronic components and devices, which have gained popularity due to their compact size and precise control over the output voltage and current. They are widely used in renewable energy systems, such as converters and inverters, and in industrial drives as control devices. However, the switching process in power electronic components introduces nonlinear behavior, leading to the generation of harmonics and power quality problems. To address these issues, various methods and techniques recommended by industry standards have been proposed to mitigate or eliminate harmonics and ensure the power quality. These standards provide limits and guidelines for assisting customers and manufacturers in maintaining acceptable power quality levels. Machine-learning algorithms offer a promising approach for improving power quality by leveraging data from a system to make predictions or classifications. In the context of power electronics, machine-learning algorithms can be trained to classify fault types, identify the exact location of faults, predict the remaining life of power electronic components, and detect voltage disturbances. Different machine learning techniques can be employed, depending on the specific application. For example, fault classification and fault location identification can be achieved through supervised learning algorithms, whereas predicting component life and detecting voltage disturbances can utilize techniques such as regression or anomaly detection. By leveraging the power of machine learning, enhanced reliability, performance, and efficiency can be achieved in various applications, thereby contributing to the overall advancement of power electronics technology.

Empirical Wavelet Transform-Based Intelligent Protection Scheme for Microgrids

Recently, the concept of the microgrid (MG) has been developed to assist the penetration of large numbers of distributed energy resources (DERs) into distribution networks. However, the integration of DERs in the form of MGs disturbs the operating codes of traditional distribution networks. Consequently, traditional protection strategies cannot be applied to MG against short-circuit faults. This paper presents a novel intelligent protection strategy (NIPS) for MGs based on empirical wavelet transform (EWT) and long short-term memory (LSTM) networks. In the proposed NIPS, firstly, the three-phase current signals measured by protective relays are decomposed into empirical modes (EMs). Then, various statistical features are extracted from the obtained EMs. Afterwards, the extracted features along with the three-phase current measurement are input to three different LSTM network to obtain exact fault type, phase, and location information. Finally, a trip signal based on the obtained fault information is generated to disconnect the faulty portion from the rest of the MG. The significant feature of the proposed NIPS is that it does not need adaptive relaying and communication networks. Moreover, it is independent of the operating scenario and hence fault current magnitude. To evaluate the efficacy of the proposed NIPS, exhaustive simulations are performed on an international electro-technical commission (IEC) MG. The simulation results confirm the efficiency of the proposed NIPs in terms of accuracy, dependability, and security. Moreover, comparisons with existing intelligent protection schemes validate that the proposed NIPS is highly accurate, secure, and dependable.

Fast Low Frequency Fault Location and Section Identification Scheme for VSC-Based Multi-Terminal HVDC Systems

Penetration of DC networks is rapidly increasing in modern power systems. As transient behavior of DC grids is different from that of AC ones, AC grid protection schemes might not be applicable to protect DC networks. Exact fault location is vital for protection of DC networks to reduce repair cost and achieve fast power recovery. This paper proposes a novel topology independent multi-terminal DC scheme to identify fault section and location for DC transmission systems. Voltage and current signals measured at the terminals are analyzed by the least squares error (LSE) algorithm. Then, the proposed scheme uses low frequency components of the signals to locate DC faults offline. Unlike some other methods, it does not require sophisticated sensors and hardware to capture very high frequency content of the signals. Hence, the proposed scheme can be implemented in practice without additional measuring infrastructure. The performance of the proposed scheme is evaluated under various fault conditions for different topologies of DC networks in both overhead lines and cables. Numerous simulations carried out demonstrate that the proposed scheme is quite accurate and provides excellent performance under different fault resistances, sections, and locations.

Approach for Identification of Inter-Turn Fault Location in Transformer Windings Using Sweep Frequency Response Analysis

As inter-turn faults in transformer windings are the most severe, their delayed detection and localization may create extensive damage to the winding. Therefore, a new inter-turn fault localization methodology based on sweep frequency response analysis (SFRA) through calculation of Pre-fault Inter-Turn Fault Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_pre</i> ) for all possible fault locations by estimating the values of series and shunt capacitance of the windings during healthy conditions is presented. For a faulty transformer, Post-fault Inter-Turn Fault Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_post</i> ) is calculated by determining the value of equivalent capacitance using SFRA. The exact fault location in the faulty transformer is obtained by comparing the single calculated value of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_post</i> of the faulty transformer with the values stored in the lookup table. The efficacy of the suggested approach is verified by performing faults at various locations on the transformer model developed in the laboratory. Its authenticity is also verified on an existing 400 kVA, 11 kV/440 V distribution transformer installed in the real field. The results indicate that the suggested method is capable to identify inter-turn fault location with the minimum ladder network parameters. Finally, a relative assessment of the suggested approach with numerous other methods confirms the superiority in terms of inter-turn fault localization.

A hybrid approach for fault location in power distributed networks: Impedance-based and machine learning technique

Fault location (FL) is one of the challenges in distribution networks. Many impedance-based methods have been developed to address this challenge. However, these impedance-based methods are not yet able to provide a unique answer, and they only can approximate the distance of the fault from the reference point, which creates challenges in systems that have a large number of separate lines. In this paper, using the impedance method, as well as a deep neural network, a new method for FL is suggested, which can provide a unique answer. In this approach, the FL is determined in less than 6 s, and the accuracy of 99 percent. The deep memory neural network is then used to accurately detect the exact fault location between candidate points. This method also identifies the line where the fault occurred. The performed simulations confirm the validity of the proposed method.

Data driven approach for fault detection and Gaussian process regression based location prognosis in smart AC microgrid

Modern intelligent digital relays embedded with phasor measurement units have facilitated easy access and collection of electrical signals in a microgrid. In this paper, a model for detection, location and isolation of faults in AC microgrid is presented. The real time voltage, frequency and current data are processed via simple formulations to detect the fault. The tripping command is promptly communicated to the incumbent line relays within fault tolerance time, after fault detection. The proposed scheme assures reliable protection in grid connected and islanded mode of operation with added security in case of primary protection failure. Post tripping, the exact location of fault in any distribution line from utility grid is predicted using a fault locator module. This module is designed and developed by gaussian process regression technique. Further, various machine learning techniques used for fault location has been explored and compared to establish the superiority of GPR method over others. Also the features extracted for model training is quite simpler, which imposes less computational burden on central protection computer. An IEEE 15 bus distribution system with synchronous and solar photo voltaic based DG is simulated in EMTP-RV platform to generate the electrical data. Further the required calculations are performed in MATLAB 2020a

GSM Based Underground Cable Open and Short Circuit Fault Detection

The underground cable system is a common practice followed in many urban areas. While a fault occurs for some reason, at that time the repairing process related to that particular cable is difficult due to not knowing the exact location of the cable fault. Hence, the main objective of this project is to determine the distance of underground cable fault from base station in kilometers using an Arduino board and GSM. The project uses the standard concept of Ohms law and voltage division rule to detect and locate the fault. The fault occurring at a particular distance and the respective phase is displayed on a LCD interfaced to the Arduino board and information regarding fault occurrence is sent to control areas using GSM modules.

A Hybrid scheme for fault location in unbalanced multi-lateral distribution network with distributed generation

Fault location in an unbalancend multi-lateral distribution network having several branches, laterals, load taps and distributed generation is a major challenge. In case of multi-lateral distribution network, the fault location scheme needs to accurately locate the faulted line segment to avoid the problem of multiple fault location candidates. Traditional fault location schemes in such network employs additional measures to filter out the exact fault location out of the multiple candidates. To overcome the above mentioned problem, a new hybrid method combining high-frequency transient method and impedance based method is proposed in this work for fault location in a unbalacened multi-lateral distribution network. This method first identifies the line section with fault after that the impedance based method is utilized to locate the exact fault location. The simulation results obtained demonstrate that the proposed scheme for fault location is capable of locating all shunt fault type with good accuracy.

Differential fault location identification by machine learning

As the fault-based attacks are becoming a more pertinent threat in today's era of edge computing/internet-of-things, there is a need to streamline the existing tools for better accuracy and ease of use, so that we can gauge the attacker's power and a proper countermeasure can be devised in the long run. In this regard, we propose a machine learning (ML) assisted tool that can be used in the context of a differential fault attack. In particular, finding the exact fault location by analysing the output difference (typically the XOR of the nonfaulty and the faulty ciphertexts) is somewhat nontrivial. During the literature survey, we notice that the Pearson's correlation coefficient dominantly is used for this purpose, and has almost become the defacto standard. While this method can yield good accuracy for certain cases, we argue that an ML-based method is more powerful in all the situations we experiment with. We substantiate our claim by showing the relative performances (we choose the commonly used multilayer perceptron as our ML tool) with two variants of Grain-128a (a stream cipher, and a stream cipher with authentication), the lightweight stream cipher LIZARD and the lightweight block cipher SIMON-32 (where the faults are injected at the fifth last rounds). Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing data. We believe that our work extends the state-of-the-art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.