Fault Inception Angle Research Articles

Robust fault analysis in transmission lines using Synchrophasor measurements

As more electric utilities and transmission system operators move toward the smart grid concept, robust fault analysis has become increasingly complex. This paper proposes a methodology for the detection, classification, and localization of transmission line faults using Synchrophasor measurements. The technique involves the extraction of phasors from the instantaneous three-phase voltages and currents at each bus in the system which are then decomposed into their symmetrical components. These components are sent to the phasor data concentrator (PDC) for real-time fault analysis, which is completed within 2–3 cycles after fault inception. The advantages of this technique are its accuracy and speed, so that fault information may be appropriately communicated to facilitate system restoration. The proposed algorithm is independent of the transmission system topology and displays high accuracy in its results, even with varying parameters such as fault distance, fault inception angle and fault impedance. The proposed algorithm is validated using a three-bus system as well as the Western System Coordinating Council (WSCC) nine bus system. The proposed algorithm is shown to accurately detect the faulted line and classify the fault in all the test cases presented.

A New DC-Offset Removal Method for Distance-Relaying Application Using Intrinsic Time-Scale Decomposition

This paper presents a new adaptive method based on the intrinsic time-scale decomposition (ITD) tool for suppressing the decaying dc component effect on phasor estimation. The ITD decomposes a non-stationary fault signal into a proper rotation component (PRC) and a monotonic trend signal. The PRC is the fundamental frequency component, which is used by the discrete Fourier transform (DFT) to estimate the phasor, and the monotonic trend signal is the decaying dc component. The combination of the ITD and the DFT is a simple accurate method for phasor estimation that is applicable to protection schemes with the minimum sampling rate and also to the off-nominal power system frequency. Three types of data, i.e., mathematical, simulated, and real field fault, are examined to assess the performance of the proposed method. The obtained results confirm that the proposed method not only improves the distance relay operation, but it is not also affected by the change in the fault inception angle, fault type, fault location, power system frequency, and network topology. Furthermore, the method works both with a series compensated transmission line and a parallel transmission line.

An adaptive algorithm to prevent distance relay overreach during CCVT transient

In this paper, the impact of coupling capacitor voltage transformer (CCVT) subsidence transients on the performance of distance relay is investigated. The reach accuracy of distance relay is not maintained during CCVT subsidence transient and for this the responsible factors are system source-impedance-ratio (SIR), CCVT secondary burden, and fault inception angle. Relay generally overreaches during CCVT subsidence transient and in order to mitigate this problem an integrated adaptive logic is proposed. The adaptive zone-1 algorithm uses two criteria: (1) online SIR calculation using prefault and fault positive sequence voltage and current measured at relay location and (2) the maximum value of the transient monitor function obtained from the three-phase voltages to discriminate the in-zone fault from out-of-section fault. For accurate zone discrimination and to avoid relay overreach issue during end zone fault, the importance of adaptive threshold is crucial and can be set based on the online computed SIR value. The simulation study is performed using EMTDC/PSCAD. As the CCVT available in master library (ML) is not able to develop sufficient subsidence transient, a 500kV, 60Hz CCVT is modelled. Distance relay performance is analysed for different critical cases using the developed CCVT model and also the response of proposed method is observed. Results for different critical cases prove that the proposed method is able to maintain proper selectivity between in-zone and end-zone faults and also using the method the time-delay operation can be avoided during subsidence transient in the secondary side of CCVT

Transient- and probabilistic neural network-based fault classification in EHV three-terminal lines

This paper presents a fast and accurate fault classifier for three-terminal transmission circuits. Traditional phasor-based methods fail to meet the high speed requirements of modern power grids and necessitate alternative solutions. The transient-based schemes use advanced signal processing techniques to achieve fast and accurate fault classification. As the three-terminal lines experience very pronounced transients during faults, the proposed method makes use of the fault-generated transients to quickly and correctly classify the fault. Many transient-based schemes fail to give the required accuracy since the transient patterns with relay-measured signals are highly influenced by fault conditions. Therefore, a thorough analysis of transient patterns is carried out in this paper, and based on the typical patterns revealed by the analysis of fault-generated transients an effective classification algorithm is developed. For high-speed classification, only a quarter-cycle of postfault voltage signals measured at the relay points will be processed for feature extraction using wavelet transform. The algorithm includes a hybrid procedure based on a probabilistic neural network for tackling the effects of fault inception angle and fault resistance in transient variations. Particularly, it is designed to overcome the problem with double-line-to-ground fault classification. The technique is simple and extensive simulation studies and comparison substantiate the efficacy of the proposed method under different fault conditions.

Comparison of single and modular ANN based fault detector and classifier for double circuit transmission lines

Comparison of single and modular artificial neural network based techniques for shunt faults detection and classification in double end fed double circuit transmission line is presented in this paper. The proposed method uses the voltages and currents signals available at the local end of line. The model of the power system is developed using MATLAB® software. Effects of variations in pre-fault power flow angle, fault inception angle, source strength, fault resistance, fault type, fault distance and CT saturation have been investigated extensively on the performance of the ANN based protection scheme. Additionally, the effects of network changes: double circuit and single circuit operation with other line switched out and grounded (/ungrounded), intercircuit and cross-country faults have also been investigated. Thus, the present work encompasses practically the entire range of possible operating conditions, which has not been reported earlier. The simulation results are presented to validate the effectiveness of the proposed approach. The main advantage of the proposed technique is that the variation in power system parameters under a variety of operating conditions and single/double circuit operations does not affect its performance. And this technique correctly detects and classifies the intercircuit and cross-country faults which have not been reported so-far.Keywords: Artificial neural network, cross country fault; transmission line protection; intercircuit faults and single circuit operation.

A hybrid method for fault location estimation in a fixed series compensated lines

This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately.

Comparison of Impedance Based and Travelling Waves Based Fault Location Methods for Power Distribution Systems Tested in a Real 205-Nodes Distribution feeder

In this paper, two fault location methods in a real 205-nodes distribution feeder have been compared. The applied fault location algorithms are impedance and travelling based methods. The investigated algorithms have some meaningful differences which researchers find interesting. Most importantly the number and types of measuring devices for logging the voltage/current at a set relay point and at a particular sampling rate. Due to the fundamental component of voltage and current during a post-fault impedance based algorithm, both voltage and current recorders have been installed with a low sampling rate. In a travelling wave based algorithm, they voltage recorder uses high sampling rate. The accuracy of the algorithms have been validated by several simulated fault studies carried out in EMTP-RV software on a 205-nodes 20 kV real radial distribution feeder. In order to show the capabilities of each method, the simulation results have been presented in different cases such as fault type, fault resistance, fault inception angle and existence of distributed generation.

Fault location on high voltage transmission line by applying support vector regression with fault signal amplitudes

This paper proposes a novel high voltage transmission line fault location scheme based on application of support vector regression (SVR). The proposed scheme just uses the amplitudes of the fault voltage waveforms, measured at a single end of the line. Various types of faults at different locations with different fault impedances and a variety of fault inception angles are studied on a 400kV–300km high-voltage transmission line power system. The fault voltages are obtained from 1/8 cycle post-fault signals after the noise has been eliminated using a low-pass filter. The amplitudes of the fault voltage signals are used as features to train the SVR. After training, the SVR is used in the exact location of the fault on the transmission line. When compared with other fault location schemes, the proposed scheme requires less information and a smaller time data window to estimate the fault locations. However, the proposed scheme provides more accurate estimations, irrespective of the fault types, fault inception angles and fault impedances.

A Decentralized Fault Detection Technique for Detecting Single Phase to Ground Faults in Power Distribution Systems With Resonant Grounding

This paper presents a decentralized fault detection technique for power distribution systems with resonant grounding. The aim of this paper is to detect single phase to ground faults and identify the faulty feeder within three cycles of the fault occurrence. In the proposed technique, faults are first detected based on the neutral voltage displacement. The pre-fault and post-fault voltages (phase to ground) are then used to identify the faulty phase. Finally, the faulty feeder (as well as the faulty area of a long feeder) is identified from the relationship between the initial transient of the zero-sequence current and the faulty phase voltage just after the occurrence of faults. A signal processing tool called mathematical morphology is utilized to identify the faulty feeder. To identify the faulty feeder, it is also required to know the fault occurrence time that is estimated using the slope of the neutral voltage. The main feature of the proposed scheme is that this technique only uses voltage and current signals from the corresponding voltage transformers and current transformers. Therefore, it does not require communication among protection devices in the same or different feeders to identify the faulty feeder. The proposed technique also has the ability to distinguish the nature of faults, i.e., whether the faults are permanent or temporary. The effectiveness of the proposed scheme is tested, on an IEEE test system as well as on a practical test system, using MATLAB/SimPowerSystems . Simulation results show that the proposed technique can quickly detect the single phase to ground faults in a resonant grounding power distribution system and identify the faulty feeder. It is also capable of distinguishing faults from other disturbances. Moreover, it works under different compensation levels as well as for different fault inception angles.

New fault location scheme for four-circuit untransposed transmission lines

This paper introduces a new fault location scheme for two-terminal four-circuit untransposed transmission line using synchronized measurements. The current phasors at both line terminals and the voltage phasor at only one terminal are needed to formulate the fault location equation. The proposed scheme is developed based on theory of transmission lines and expansion of Taylor series for the distributed line model parameters. The mutual coupling between all phases and the untransposition of the line are considered to obtain precise results. The faulty phases are determined using Kirchhoff’s current law (KCL) and all fault types including normal shunt and inter-circuit faults can be located. DIgSILENT Power Factory software is used to simulate the power system and the fault location calculations are carried out using MATLAB. The proposed scheme is investigated under different fault locations, fault inception angles, fault types, and fault resistances. The influence of soil resistivity variation, measurement and synchrophasors errors, errors in line parameters, various line lengths, non-linear time-varying arc resistance, and complex fault impedance is also investigated. The simulation results confirm the validity of the fault location scheme for all test cases.

Fault location based on travelling wave identification using an adaptive extended Kalman filter

The fault location in transmission systems remains a challenging problem, primarily due to the fault location near the substation ends or the weak fault signals. In this study, an adaptive extended Kalman filter (EKF) based on the maximum likelihood (ML) is proposed to estimate the instantaneous amplitudes of the travelling waves. In this method, the EKF algorithm is used to estimate the optimal states (the clean travelling waves) with additive white noise while ML is used to adaptively optimise the error covariance matrices and the initial conditions of the EKF algorithm. Using the proposed method, the singularity points of travelling waves can be detected, and the exact arrival time of the initial wave head at the substations M and N can be easily yielded. Thus the fault distance can be calculated precisely. The effectiveness of exacting mutation feature using the proposed method has been demonstrated by a simulated instantaneous pulse. Also, the proposed method has been tested with different types of faults, such as different fault locations, different fault resistances and different fault inception angles using ATP simulation. The accuracy of fault location using the proposed method has been compared with conventional wavelet transformation scheme.

Faulty feeder detection by adjusting the compensation degree of arc‐suppression coil for distribution network

A scheme of single-phase-to-ground faulty feeder detection by adjusting the compensation degree of arc-suppression coil (CDASC) for compensated distribution network is presented in this study. The scheme defines transient variation components (TVCs) of feeders, which use the transient signals of zero-sequence current of each feeder and zero-sequence voltage in the bus before the adjustment and those after the adjustment. The TVCs of sound feeders always keep almost 0, while the TVC of faulty feeder changes remarkably before and after adjusting CDASC. On the basis of TVCs, faulty feeder detection can be achieved sensitively and accurately with simple and practicable procedure without sophisticated algorithms. The performance of the scheme validated by digital simulation with various fault conditions of different transition resistances, fault inception angles, white noises and compensation degrees.

An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

During thunder storms, multilocation faults may occur at different locations in different phases of the 3 phase transmission lines at same or different time. This paper proposes an artificial neural network-based solution to locate the multilocation faults in double-circuit series capacitor compensated transmission lines (SCCTLs), unlike previous works that only locate the fault at single location. Although various fault location schemes have been proposed for normal shunt faults occurring at 1 location in SCCTL, nevertheless, finding the locations of multilocation faults in double circuit SCCTL has not been addressed so far. The proposed artificial neural network-based method determines the location of multilocation faults by using current and voltage signals of 1 end of line only, thus avoiding the need of communication link. The signals are preprocessed by using discrete wavelet transform. A comparative study of various neural networks such as feed-forward back-propagation network with Levenberg-Marquardt algorithm, Elman recurrent neural network with gradient descent algorithm, radial basis function neural network has been carried out. The proposed method is not affected by variation in different parameters viz. fault type, fault location, fault inception angle, fault resistance, degree of series compensation, and location of series capacitor. The key advantage of the method is that it correctly estimates the locations of multilocation faults as well as single-location fault, thus making it more reliable and accurate as compared to conventional fault location schemes.

Wavelet Packet Entropy and RBFNN Based Fault Detection, Classification and Localization on HVAC Transmission Line

The article presents a technique for fast and accurate detection, classification and localization of faults on the high voltage transmission systems considering the alternator's dynamics and the effect of transformers. The systems have been simulated by ATP/EMTP software and three phase fault currents at one end of the transmission line are recorded with a sampling frequency of 50 kHz. The fault signals are decomposed by wavelet packet decomposition (WPD) up to 3rd level with mother wavelet db6 to calculate wavelet packet entropy (WPE) which has the ability to measure the uncertainty of fault signals during feature extraction. A properly designed radial basis function neural network (RBFNN) trained with these features can recognize, classify and locate faults faster as it utilizes only half cycle data after fault initiation. This technique has been verified for different fault categories, fault impedances and fault inception angles (FIA) at different locations for two different transmission systems. The investigated results demonstrate that the wavelet packet entropy is very powerful for extracting the features from the fault signals and RBFNN is very accurate for classification and localization of faults on the transmission line including locations close to the generator's end.

A new FDOST entropy based intelligent digital relaying for detection, classification and localization of faults on the hybrid transmission line

The paper presents a new digital relaying for detection, classification and localization of faults on the hybrid transmission line consisting of an overhead line and an underground cable. The entropy principle together with fast discrete orthogonal S-transform (FDOST) represented by window dependent bases is utilized for feature extraction and the support vector machine (SVM) classifier model & support vector regression (SVR) model are employed for pattern recognitions to predict the types and locations of faults. After modelling and simulation of the transmission system in Electromagnetic Transient Program (EMTP) software, three phase fault current signals are recorded at one end of the line to extract entropy of FDOST coefficients from each of the three current signals of half cycle duration after fault initiation. The proposed relaying technique is tested on a single-junction and a multi-junction hybrid transmission lines under different fault conditions and is found fast and accurate independent of fault type, fault section, fault resistance, fault inception angle (FIA) and load angle. Another important aspect of the method is that it needs no prior identification of the faulty section for the estimation of fault location. The immunity of the proposed method to noise is also established by testing it with fault current signals impregnated with white Gaussian noise of level 30dB signal to noise ratio (SNR).

Fault Detection & Classification in UPFC Integrated Transmission Line Using DWT

Fault detection and classification in UPFC (Unified Power Flow Controller) integrated transmission line using single terminal based DWT (Discrete Wavelet Transform) is proposed. The current is extracted from the sending end bus and processed through wavelet transform to evaluate the spectral energy (SE) using db4 mother wavelet. Three level decomposition is framed to extract the fundamental frequency component from non-stationary signal, considering sampling frequency of 2kHz system. The fundamental frequency component of respective phase currents are used to compute SE at sending end. The SE of individual phase current is the key factor for deciding the fault pattern detection and classification. The advantage of using this it requires less cost and protect entire transmission line with minimal fault detection time. The various types of fault (L-G, L-L, L-L-G, L-L-L) are simulated by considering the parameter like fault resistance, source impedance, fault inception angle, multi-location fault, reverse power flow and UPFC system parameter. The scheme works reliable and efficient to detect and classify the fault within a cycle of sample period 40 or a cycle of time period 20ms compared to other conventional relaying scheme.

A Time-Frequency Transform Based Fault Detectionand Classificationof STATCOM Integrated Single Circuit Transmission Line

<p align="center"> </p><p align="center"> </p><table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>This paper discusses the time-frequency transform based fault detection and classification of STATCOM (Static synchronous compensator) integrated single circuit transmission line. Here, fast-discrete S-Transform (FDST) based time-frequency transformation is proposed for evaluation of fault detection and classification including STATCOM in transmission line. The STATCOM is placed at mid-point of transmission line. The system starts processing by extracting the current signals from both end of current transformer (CT) connected in transmission line. The current signals from CT’s are fed to FDST to compute the spectral energy (SE) of phase current at both end of the line. The differential spectral energy (DSE) is evaluated by subtracting the SE obtained from sending end and SE obtained from receiving end of the line. The DSE is the key indicator for deciding the fault pattern detection and classification of transmission line. This proposed scheme is simulated using MATLAB simulink R2010a version and successfully tested under various parameter condition such as fault resistance (Rf),source impedance (SI), fault inception angle (FIA) and reverse flow of current. The proposed approach is simple, reliable and efficient as the processing speed is very fast to detect the fault within a cycle period of FDT (fault detection time).</p></td></tr></tbody></table>

An enhanced fault detection and location estimation method for TCSC compensated line connecting wind farm

The paper presents a new approach to enhance the fault detection, and location determination based on travelling waves using Fast Discrete S-transform (FDST) for TCSC compensated lines connecting to wind farm. The FDST is applied to the modal components of measured currents at each terminal to detect the arrival time of the first travelling wave (transient) produced by the fault. The proposed method includes detecting the terminal with fastest arrival time of wave to identify the faulted section and estimate the fault location by using proper distance index. The simulation results have demonstrated good performance of the proposed scheme under different fault locations, fault resistances, fault inception angles, fault types, faulted sections, variations in TCSC parameters and changing wind speeds. The performance validation on the real-time digital simulator (RTDS) platform enhances the applicability of the proposed protection scheme for the TCSC based compensated line integrated with wind farm. The performance comparison with the conventional travelling wave fault location algorithms using Continuous Wavelet Transform (CWT) shows potential ability of the proposed method.

New fault location scheme for three-terminal untransposed parallel transmission lines

This paper proposes a new fault location scheme in the phase-domain for three-terminal untransposed double-circuit transmission lines utilizing synchronized voltage and current measurements obtained by GPS technique. The proposed scheme is derived taking into consideration the distributed line model and the mutual couplings effect between the parallel lines to obtain accurate results. The proposed scheme is derived based on the transmission line theory and Taylor series expansion of the distributed line model parameters. All fault types including normal shunt faults, evolving faults, and cross-country faults can be discriminated from each other and the fault location can be obtained for all fault types. The evolving faults include earth faults occurring at the same location in two phases of one circuit or two phases of different circuits at different fault inception time. While cross-country faults include earth faults occurring at different locations in two phases of one circuit or two phases of different circuits at same or different fault inception time. The proposed scheme is tested under different fault locations, different fault resistances, different fault inception angles, and all fault types including cross-country and evolving faults. Also, the effect of different sampling rate, measurement and synchronization errors, earth resistivity variations, and transmission line parameters errors on the proposed scheme is considered. Simulations studies conducted by MATLAB software demonstrate that the maximum estimation error in fault location does not exceed 3.06%.

K-NN based fault detection and classification methods for power transmission systems

This paper deals with two new methods, based on k-NN algorithm, for fault detection and classification in distance protection. In these methods, by finding the distance between each sample and its fifth nearest neighbor in a pre-default window, the fault occurrence time and the faulty phases are determined. The maximum value of the distances in case of detection and classification procedures is compared with pre-defined threshold values. The main advantages of these methods are: simplicity, low calculation burden, acceptable accuracy, and speed. The performance of the proposed scheme is tested on a typical system in MATLAB Simulink. Various possible fault types in different fault resistances, fault inception angles, fault locations, short circuit levels, X/R ratios, source load angles are simulated. In addition, the performance of similar six well-known classification techniques is compared with the proposed classification method using plenty of simulation data.