Power Transmission Line Fault Research Articles

Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data.

The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Transmission Line Fault Classification Using Hidden Markov Models

The maintenance of power quality in electrical power systems depends on addressing the major disturbances that may arise during generation, transmission and distribution. Many studies aim to investigate these disturbances by analyzing the behavior of the electrical signal through the classification of short circuit faults in power transmission lines as a way to assist the administration and maintenance of the electrical system. However, most fault classification methods generate a high computational cost that do not always yield satisfactory results; these methods utilize front ends in data processing before being processed by conventional classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) that are adopted into the Frame Based Sequence Classification (FBSC) architecture that uses the front ends Wavelet energy, Waveletconcat, RAW, Root Mean Square front ends (RMS) and Con cat Front End. An alternative method for classifying faults without having to use front ends employs the UFPAFaults database and the Hidden Markov Model (HMM) algorithm that directly treats the electrical signal in the form of multivariate time series. The results indicate the HMM algorithm as a potential classifier because its comparatively low error rate of 0.03% exceeds the performance of the conventional classifiers ANN, SVM, KNN and RF as used with the FBSC architecture. When the statistical test with a significance of α =5 % is applied, only the ANN and RF classifiers present a result close to what the HMM algorithm provides. Another relevant factor is that the HMM algorithm considerably decreases the computational cost by more than 90% of processing time as compared to the conventional classifiers of the FBSC architecture, thereby validating its potential in the direct classification of faults in electric power system transmission lines.

Method for estimation of location of the asymmetrical phase‐to‐ground faults existing during an overhead line energisation

Fast and precise location of faults in power transmission lines is essentially important for fast line restoration. Widely used fault location methods based on impedance measurement and travelling wave analysis have some drawbacks leaving a lot of room for further research. This study proposes a novel method for estimation of the location of the asymmetrical phase-to-ground faults that exist during line energisation. The method is based on the empirical mode decomposition and its application relies on knowing the fundamental system parameters and frequency estimation of the analysed signal. It is simple to use while providing highly accurate results for overhead lines up to 80 km length.

Location of Faults in Power Transmission Lines Using the ARIMA Method

One of the major problems in transmission lines is the occurrence of failures that affect the quality of the electric power supplied, as the exact localization of the fault must be known for correction. In order to streamline the work of maintenance teams and standardize services, this paper proposes a method of locating faults in power transmission lines by analyzing the voltage oscillographic signals extracted at the line monitoring terminals. The developed method relates time series models obtained specifically for each failure pattern. The parameters of the autoregressive integrated moving average (ARIMA) model are estimated in order to adjust the voltage curves and calculate the distance from the initial fault localization to the terminals. Simulations of the failures are performed through the ATPDraw ® (5.5) software and the analyses were completed using the RStudio ® (1.0.143) software. The results obtained with respect to the failures, which did not involve earth return, were satisfactory when compared with widely used techniques in the literature, particularly when the fault distance became larger in relation to the beginning of the transmission line.

電力系統における送電線故障点標定に関する技術動向

This article introduces the survey results of fault location technique in power systems. The power transmission line Fault Locator (FL) is an indispensable device for early determination of fault location on power transmission line. Several types of FL have been developed to date, including the pulse radar type, surge type and impedance type. The purpose of this article are to pick up overview of FL of the three types. Furthermore this article introduces about applied FL in field and the research trend of FL.

NOVEL APPROACH OF DETECTING FAULT IN POWER LINE USING WIRELESS SENSOR NETWORK

Fault occurring in power line are mostly asymmetrical in nature thereby detecting and locating fault is very necessary for healthy operation of distribution system. Fault which occurs in system makes the working of system untrustworthy. In this paper a novel concept using wireless sensor for detecting fault which includes phase to phase, short circuit and mainly line to ground fault is analysed. In the proposed concept power line is divided by WSN (wireless sensor network) nodes which will sense the faulty condition in power line send command to ARDUINO board and via ATmega328 microcontroller, displays to operator and send SMS through GSM modem to service engineer. This is well demonstrated with the help of hardware model and encouraging results are obtained which can make WSN an attractive instrumentation solution in electrical distribution networks and also a viable tool for detecting fault in power transmission line for its accurate fault detection

Classification of Faults in Power Transmission Lines using Fuzzy-Logic Technique

Classification of Faults in Power Transmission Lines using Fuzzy-Logic Technique

Novel filter based ANN approach for short-circuit faults detection, classification and location in power transmission lines

Abstract This paper presents a novel approach for detecting, classifying and locating short-circuit faults in power transmission lines. Based on the proposed approach, a hybrid framework consisting of a proposed two-stage finite impulse response (FIR) filter, four support vector machines (SVMs), and eleven support vector regressions (SVRs) is implemented in Proteus 6/MATLAB environments. The proposed two-stage FIR filter together with the SVMs are used to detect and classify short-circuit faults while the SVRs are utilized to locate short-circuit faults. The implemented framework needs few training samples for training the SVMs and SVRs. As will be shown, for a power transmission line with the length of 50 km, only 6 training samples are needed to train each SVR. The trained hybrid framework carries out the processes of fault detection, classification and location only during 1 cycle which is strictly shorter than the faults clearing time. It means that the proposed hybrid framework can rapidly detect, classify and locate short-circuit faults in power transmission lines before power outage carried out by protection relays. An actual three-phase 230 kV, 50 Hz power transmission line with the length of 50 km is simulated to validate the theoretical results and to verify the proposed technique accuracy.

Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

In this study, two approaches are presented to detect short-circuit faults in power transmission lines. The two proposed methods are completely novel from both theoretical and technical aspects. The first approach is a soft computing method that uses discrete wavelet transform with Daubechies mother wavelets db1, db2, db3, and db4. The second approach is a hardware based method that utilizes a novel proposed two-stage finite impulse response filter with a sampling frequency of 32kHz, and a very short process time about three samples time. The two approaches are analyzed by presenting theoretical results. Simulated results obtained by simulating a three-phase 230kV, 50Hz power transmission line are given that validate the theoretical results, and explicitly verify that the filter based approach has an accuracy of 100% in presence of 10% disturbance while the accuracy of the wavelet transform based approach is maximally 97%, but it has less complication and implementation cost. Another comparative study between this work and other works shows that the two proposed methods have higher accuracy and very shorter process time compared to the other methods, especially in presence of 10% disturbance that actually occurs in power transmission lines.

Classification of Faults in Power Transmission Lines using Fuzzy-Logic Technique

Transmission lines safeguard against exposed fault is the most critical task in the protection of power system. The purpose of a protective relaying is to identify the abnormal signals representing faults on a power transmission system. So fault classification is necessary for reliable and high speed protective relaying. This paper uses fuzzy logic technique for fault classification and this study describes a new approach to distinctly identify and classify ground and phase faults by using two separate fuzzy classifiers. Samples of post fault currents from all three phases at one end of the transmission system are being used to classify the nature of the faults. To demonstrate the effectiveness of this method, simulations considering various operating conditions have been performed on MATLAB. The simulation studies of the proposed technique indicate that the accuracy in fault classification increases because of two fuzzy classifiers is used for fault analysis.

Detection, classification, and location of faults in power transmission lines

Abstract This paper presents a pattern recognition approach for current differential relaying of power transmission lines. The current differential method uses spectral energy information provided through a new Fast Discrete S-Transform (FDST). Unlike the conventional S-Transform (ST) technique the new one uses different types of frequency scaling, band pass filtering, and interpolation techniques to reduce the computational cost and remove redundant information. Further due to its low computational complexity, the new algorithm is suitable for real-time implementation. The proposed scheme is evaluated for current differential protection of a transmission line fed from both ends for a variety of faults, fault resistance, inception angles, and significant noise in the signal using computer simulation studies. Also the fundamental amplitude and phase angle of the two end currents and one end voltage are computed with the help of the new formulation to provide fault location with significant accuracy. The results obtained from the exhaustive computation show the feasibility of the new approach.

Fault Location Scheme for Transmission Lines Using Kaiser Self-Convolution Windowed FFT Phase Comparison

In order to improve the fault location accuracy, and eliminate the influence of spectral leakage in power transmission line fault location, we proposed a novel fault location scheme for transmission lines based on multi-measurements and phase comparison with Kaiser self-convolution windows FFT, which can locate the fault between two adjacent towers in the fault-prone segment. It is based Kaiser self-convolution window's excellent performance of leakage suppression, which can effectively solve the phase current fault component is relatively large errors due to spectral leakage problem. Firstly, this paper analyzes the causes of the phase spectrum leakage, then elaborates the sidelobe characteristics of Kaiser self-convolution window, and lists phase comparative fault location using steps, finally, verifies the correctness of the proposed method by simulation. The results show that the proposed method can effectively suppress spectral leakage, has a high location accuracy, a wide applicability and its solving of phase difference is precise.

Adaptive Equalizers Applied to Faults Location in Power Transmission Lines

Time-domain reflectometry is a technique widely used to detect faults in power transmission lines. Based on this technique, this article presents a new approach in which an adaptive equalizer filter of the decision feedback equalizer type is applied to estimate the reflections generated by impedance mismatch in transmission lines and, hence, detect and locate faults. Adaptive equalizers are digital filters used in digital communication systems to minimize reflections by estimating the transmission line characteristic, and it is this property on which this work is founded. A description of the proposed scheme and its characteristics are presented together with results obtained from simulations of various fault situations.

Detection and Classification of Faults in Power Transmission Lines Using Functional Analysis and Computational Intelligence

The transmission line is the most vulnerable element of any electrical power system due to its large physical dimension. As a consequence, many fault diagnosis algorithms have been proposed in the literature. In general, most proposals use signal-processing analysis and computational intelligence. In this paper, a new model to functionally represent the phases of a transmission line is proposed. The detection and classification strategy are developed from the analysis of the model's parameters and were evaluated using a set of simulated faults and a real database. The results show that the proposed model detects faults very quickly, using a vastly simplified mathematical process, and is able to classify faults accurately.

Fault diagnosis of transmission system based on Wavelet Transform and Neural network

One of the most important components of power systems are power transmission lines. Different types of faults in power transmission lines may cause disruption of power transmission or damage power system equipment, as well as it can effect on the power quality of the entire network. Therefore accurate estimation of fault location in power transmission for restoring power transmission at the shortest possible time with the lowest disruption at power transmission is vital. On the other hand accurate estimation of type and location of faults in transmission lines can save time and maintenance cost of power system equipment. In this paper, EMTP software is used to simulate a real power grid model with 100 km transmission line for different fault locations and fault resistances. Then Discrete Wavelet Transform DWT, which is anadvance signal processing tool, is applied to acquire fundamental harmonics of three phase voltage and current signals at the end of transmission line. To classify type of faults and their locations, artificial neural network is utilizedat transmission line. The obtained results show that the error percentage in both location and fault typediagnosis is so low.

Improvement in the performance of neural network-based power transmission line fault classifiers

A power line expert can easily pinpoint the type of fault that may have been occurred in a power transmission line. Transferring the experts intelligence to an artificial neural network (NN) makes the classification process fast and available online. Often the phase currents are used as NN inputs for this purpose. Lack of a somehow one-to-one relationship between the type of fault and the phases faulty currents prohibits the underlying network from being adequately trained. In a search for finding a type of feature that establishes a relatively unique link between the type of faults and the phase currents, it is noticed and mathematically proved that the ratios of the phase current jumps enjoy such a valuable advantage to be a prime choice as NN inputs. The inputs let a multi-layer perceptron (MLP) NN with about one node per phase to identify the faults accurately. The scheme works well in the presence of a various number of fault items. The superiority of the method is well realised when it is compared with the results of similar investigations using wavelet, fuzzy and others. The reference data are generated using MATLAB Power System Toolbox. The test samples are more general than those previously used in other investigations.

A Novel Algorithm for Fault Type Fast Diagnosis in Overhead Transmission Lines Using Hidden Markov Models

Power transmission lines are one of the most important components of electric power system. Failures in the operation of power transmission lines can result in serious power system problems. Hence, fault diagnosis (transient or permanent) in power transmission lines is very important to ensure the reliable operation of the power system. A hidden Markov model (HMM), a powerful pattern recognizer, classifies events in a probabilistic manner based on fault signal waveform and characteristics. This paper presents application of HMM to classify faults in overhead power transmission lines. The algorithm uses voltage samples of one-fourth cycle from the inception of the fault. The simulation performed in EMTPWorks and MATLAB environments validates the fast response of the classifier, which provides fast and accurate protection scheme for power transmission lines.

A New Technique for Location of Fault Location on Transmission Lines

Magnetic field sensors can be used to detect fault detection and location effectively. Quick fault detection can help protect equipment by allowing the disconnection of faulted lines before any significant damage is done. A variety of algorithms continue to be developed to perform this task more accurately and more effectively. Particularly fault impedance (based algorithms) require both current and voltage information. However, it is possible to monitor a transmission system without using current or voltage transformers through the analysis of the magnetic field near the conductors. Since each conductor in a transmission line creates a magnetic field due to the current pass through it. Magnetic field sensors are used and tested using MATLAB through analyzing wave fault detection and location as there is a possibility of analyzing the transmission line system based on the resultant magnetic field produced. The results show that the magnetic field sensors are a viable tool for power transmission line fault detection, so this method is highly recommended to be used based on its efficient and accuracy.

A Novel Approach to Adaptive Single Phase Autoreclosure Scheme for EHV Power Transmission Lines Based on Learning Error Function of ADALINE

In modern interconnected power systems, almost 70—90% of faults in high voltage Power Transmission Lines (PTLs) are intrinsically transient. The necessity of rapid fault clearing results in fast developing of protection equipments. Moreover, need for reliable supplying of loads, lead to improvements in single-phase autoreclosure (SPAR) equipments. An ADAptive LInear NEuron (ADALINE) is suitable for important applications such as protection of power systems and digital relays. In this paper, a novel simple adaptive SPAR algorithm is introduced. This algorithm is based on learning error function of an ADALINE. It can be distinguished by fault type (transient fault or a permanent fault), and if the fault is permanent, autoreclosure should be blocked. This leads to improve the performance and efficiency of SPAR. Electromagnetic transients program-based simulation results show that the autoreclosure scheme based on learning error function of ADALINE on a typical 400 kV circuit for various system and fault conditions improves the reliability of fault discrimination.

Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach

The paper presents a new approach to the location of fault in the high-voltage power transmission line, relying on the application of the support vector machine and frequency characteristics of the measured one-terminal voltage and current transient signals of the system. The extensive numerical experiments performed for location of different kinds of faults of the transmission line have proved very good accuracy of fault location algorithm. The average error of fault location in a 200-km transmission line is below 100 m and the maximum error did not exceed 2 km.