Abstract
Transient stability is very important in power system. Large disturbances like fault in a transmission line are a concern which needs to be disconnected as quickly as possible in order to restore the transient stability. Faulty current and voltage signals are used for location, detection and classification of faults in a transmission network. Relay detects an abnormal signal, and then the circuit breaker disconnects the unhealthy transmission line from the rest of the health system. This paper discusses various signal processing techniques, impedance-based measurement method, travelling wave phenomenon-based method, artificial intelligence-based method and some special technique for the detection, location and classification of various faults in a transmission network. In this survey, paper signifies all method and techniques till August 2017. This compact and effective survey helps the researcher to understand different techniques and methods.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
1 Introduction
To supply uninterrupted electric power to the end users is a challenging task for the power system engineers. The cause of the fault may be beyond human control, but it is essential to detect the type of the fault and accurately locate it. Conductors contact with each other or ground, and then fault is generated. Different kinds of faults are single line-to-ground fault (SLG), line-to-line fault (LL), double line-to-ground fault (LLG) and triple line fault (LLL). SLG, LL and LLG faults are unbalanced faults, whereas LLL fault is balanced one. High fault current flows in the power system network due to short circuit, and it causes overheating and mechanical stress on the equipment of the power system [1,2,3,4,5].
Open circuit occurs when any one of the situation aries such as disconnection of one or more phases; or circuit breakers/isolators opens; or joins of cable or jumper break occurs at the tower tension point. Due to open circuit one or two phase, produce unbalance current in the system, so it causes heating of rotating machine. Such abnormal condition should be protected by protective scheme.
Information is collected from journals, books, conference papers, articles online libraries, and databases like IEEE, IET, ELSEVIER, Taylor & Francis, Google Scholar, Scopus, EBSCO and many more relevant websites.
The remaining part of the paper is systematized as follows. Section 2 presents conventional methods that are used for transmission line protection, Sect. 3 is about signal processing technique, Sects. 4 and 5 explain various methods of artificial intelligence (AI)-based techniques and some special techniques, Sect. 6 explains the strength and weakness of all the technique, Sect. 7 is about comparative study of fault classification, location and detection of transmission line, Sect. 8 presents practical case study and comparison of fault detection, classification and location methods, and Sect. 9 gives the conclusion drawn from the survey followed by references.
2 Conventional methods used for transmission line protection
Impedance measurement-based method and travelling wave method are the conventional methods broadly used for detection, classification and localization of the fault in a transmission line [6].
In impedance-based methods, the distance relay operation is accurate and reliable on low value of fault impedance, but did not rely for high fault impedance [7]. Based on a number of current and voltage signals collected from a terminal of transmission line, single-end or two-end impedance methods are proposed. The concept of single-ended impedance-based method is to identify the location of the fault by calculating the apparent impedance seen from one termination of the line. Impedance-based method fault position error is high due to high fault path impedance, load on the line, source parameters and shunt capacitance [8,9,10,11].
Two-ended impedance-based method is implemented to locate the fault to eliminate the above-said problems. The disadvantage of this method is a high computational burden due to measurement of current and voltage signals at two ends of the line. However, improve the accuracy to locate the fault [12,13,14].
Travelling wave-based methods are used to determine the distance of fault by using correlation of forward and backward waves travelling in a transmission line. This method has less error to locate faults in high resistance faults. But the main difficulties are computational burden, expensive and high sampling frequency, difficult for practical application [15,16,17].
3 Signal processing technique
3.1 Discrete wavelet transform
Time scale decomposition of DWT is done by a digital filtering process up to level 8 as displayed in Fig. 1. The fault signal is fed to the low pass filter (LPF) and high pass filter (HPF) where factor 2 is down-sampled. Detail coefficient (d1) is the production of HPF at level one. Approximation coefficient (a1) is the production of LPF at level one. Similarly, the process is continued to decompose the signal until and unless only two samples are left for decomposition. Due to less computational burden, DWT is used in fault analysis in a transmission line [18,19,20,21].
The DWT of a signal x(t) is defined as
where K, m and n are integer. \(a_{0}^{m}\) and \(nb_{0} a_{0}^{m}\) are represented as dilation (scale) and translation (time shift) parameter. \(b_{0}\) and \(a_{0}\) are constant and taken as 1 and 2, respectively [89].
3.2 Wavelet transform
Wavelet transform has flexible resizing of the window for use of frequency–time information. It is applicable for non-stationary signal. But detailed information can be obtained in WT at a higher sampling frequency [22,23,24].
Mathematical expression of a signal x(t) in WT is given below
Translation factor and scale factors are denoted as \(\tau\) and m, respectively. \(\psi (t)\) is the mother wavelet [25].
3.3 Wavelet packet transform (WPT)
To get the important data on high frequency, WPT is implemented. So both approximation coefficient (a1) and detail coefficient (d1) are decomposed to get full frequency band. Due to calculation burden, it is decomposed up to 4 levels as shown in Fig. 2. So WPT gives an excellent frequency resolution and maximum number of features than DWT [26,27,28].
WPT of a signal x(t) is
Wavelet position and scale are denoted by b and a. Mother wavelet is \(\psi_{n}\). The nth and (n + 1)th level decomposition are related as
where wavelet quadrature mirror filter coefficients are h(i)and g(i) [94].
3.4 S Transform
S transform is the combined properties of wavelet transform and short-time Fourier transform (STFT). It is implemented for non-stationary signals where the window width changes inversely with frequency. The main advantage of the ST than other signal processing tool is that it provides information on time, frequency and phase angle of signal. S transform is protected to noise. So it is widely used in fault studies in power system [29,30,31].
The mathematical expression for S transform [32] for signal x(t) is specified as:
Time and frequency signify t and f, respectively. τ is the control parameter for adjusting the Gaussian window. Frequency (f) and phase (ϕ) [32] of the signal is well defined in (7) and (8).
4 Artificial intelligence (AI)-based techniques
Artificial intelligence (AI)-based methods are used for detection, classification and position of the fault in a transmission network. Support vector machine (SVM), decision tree (DT) classifier, extremely learning machine (ELM)-based method, artificial immune system (AIS), self-organizing map (SOM), auto-regressive neural network (ARNN), artificial neural network (ANN)-based technique, adaptive neuro-fuzzy inference system (ANFIS), adaptive resonance theory (ARP), fuzzy logic control (FLC) and expert system technique and many more are AI-based techniques used in power system. To find the solution of complex multiobjective nonlinear systems, the above-said methods are used to get faster solution and less error. The paper focuses on signal processing techniques in combination with artificial intelligence methods to accurately detect, locate and classify the faults in a transmission network.
4.1 Artificial neural network (ANN)
Due to simple, better generalization property, adaptive nature, ANN is widely used for fault location, classification and detection in power system transmission line in both real-time and offline application. Faulty signal is trained by ANN as an input and to diagnose fault condition as an output [33].
4.2 Back-propagation neural network (BPNN)
For pattern recognition, BPNN is effectively used. To adjust the feedback of network, error is reduced. The main problem is selecting the number of neurons and hidden layers for each layer. Using large number of neurons and hidden layers makes the training process slow. On the other hand, less number of neurons and hidden layers make divergent of the training process [34] BPNN is used to identify the fault in the transmission network. BPNN and PNN (probabilistic neural network classifier) with S transform are used to detection and classification of fault is proposed in [35]. Six statics features are imported from current or voltage signals by S transform and then classified by probabilistic neural network (PNN). But under noise condition, the accuracy of fault classification is reduced.
4.3 Probabilistic neural network (PNN)
The training examples are classified allowing to their distribution values of probability density function (PDF) in PNN algorithms. Mathematically, the PDF is explained below [36]
The output vector of the hidden layer H is modified as
\(net_{j} = \max_{k} (net_{k} )\) then \(y_{j} = 1\) else \(y_{j} = 0\). Number of input, hidden units, outputs, training examples and clusters are denoted as i, h, j, k and N, respectively. Smoothing parameter (standard deviation) and the input vector are presented as r and X, respectively. The Euclidean distance between the vectors X and \(X_{kj}\) is given below \(\left\| {X - X_{kj} } \right\| = \sum\nolimits_{i} {(X - X_{kj} )^{2} }\). The connection weight between the input layer X and the hidden layer H is \(W_{ih}^{xh}\) and hidden layer to the output layer Y is \(W_{hj}^{hy}\) as shown in Fig. 3 [86].
Input vector is classified into two classes in a Bayesian optimal manner. To calculate the PDF, Bayes decision rule is applied. All PDF is positive and equal to one after integration over all values [86].
4.4 Feedforward neural network (FFNN)
FFNN made with input, hidden and an output layer with multilayer perceptron and back-propagation learning algorithm. The error produced by this method is minimized by adjusting weight and biases of the network. FFNN structure is shown in Fig. 4 [37].
If x 1, x 2,…., x i ,…x n are the input variable of neuron j. The output u j is given below
where \(\varphi\) is the activation function and the bias of neuron j is b j . w ij is the weight factor connecting ith input and jth neuron [168].
4.5 Radial basis function neural network (RBFNN)
RBFNN contains 3 layers, and they are characterized by input, hidden and output layer. The input layer signals are given to the hidden layer where nonlinear radial basis function neuron action will take place, and linear neurons contain the output layer. Figure 5 shows the RBFNN architecture. The output Y is expressed as below
where the x = input vector, bias = w 0, weight parameter = W i , number of nodes in hidden layer = m, radial basic function (D i ) is a Gaussian function.
where \(\sigma\) is the cluster radius. RBFNN locates the fault in transmission line better than BPNN [38, 39].
4.6 Fuzzy logic-based methods
Fuzzy logic works on the principle of ‘if–then’ relationship. It is used for classification, location and detection of fault in a transmission network. The computational burden of this method is less, but accuracy is affected due to the resistance of the fault and the inception angle of the fault [40, 41].
A simple overall organization of a fuzzy scheme consists of fuzzification, fuzzy inference system, fuzzy rule base and defuzzification as displayed in Fig. 6 for fault classification. In the fuzzification stage, crisp numbers are mapped into fuzzy set. After fuzzification, the fuzzified inputs are given to the fuzzy inference system, and following the given fuzzy rule base, it gives the type of fault in its output. Finally, in the defuzzification stage, the fuzzy output set is mapped into crisp fault type [42].
4.7 Adaptive neuro-fuzzy inference system (ANFIS)
Adaptive network means multilayer network, where every node operates a particular function of the applied data set. The function of the node varies node to node. It is similar to neural network, and the function is same as a fuzzy inference system. ANFIS is used for location and classification fault in a transmission line. Accuracy of this method is better. Due to fuzzy logic, it will take more time to train the data set. A method uses wavelet multiresolution analysis (MRA) to extract the important features, then applying the ANFIS to locate the fault in transmission line [43]. In [44], ANFIS method is compared with the fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and artificial neural network (ANN) to locate the fault in the system. Error analysis by Monte Carlo simulation presents that the ANFIS algorithm is better reliable and precise than FIS and ANN methods in the circumstance of different simulations of various faults. But in this proposed method, computational efficiency is affected during processing of the data and more memory space is required for the calculation.
For fuzzy inference system, x and y are two inputs and fi is the output. Mathematically, 2 fuzzy if–then rules of Takagi–Sugeno’s are given below
-
Rule 1: if x is A 1 and y is B 1, then \(f_{1} = p_{1} x + q_{1} y + r_{1}\)
-
Rule 2: if x is A 2 and y is B 2, then \(f_{2} = p_{2} x + q_{2} y + r_{2}\)
where fuzzy sets are denoted by Ai and Bi and design parameters are pi, qi and ri.
The architecture of ANFIS consists of 5 layers as shown in Fig. 7 [21].
Layer 1: Every node in these layers is an adaptive node with a node function.
where input to the node are x and y and \(o_{i}^{1}\) is the membership function of Ai and Bi. Ai and Bi are the linguistic labels related to node function. So \(\mu_{Ai} (x)\) can adopt any bell-shaped function as follows
Label 2: Every node is fixed and multiplies with the incoming signal. Firing strength is the weight degree of the if–then rules. The output is
Layer 3: It is the normalized layer and normalized the firing strength.
Layer 4: All the nodes in these layers are square node with a node function.
Layer 5: The summation of all incoming signal output is
4.8 Decision tree (DT)
DT is a data miming classification technique. For high-dimensional pattern classification, DT is applied based on selection of attribute that maximizes and fixes data division. Attributes are split into several branches recursively until the termination and the classification are achieved. The mathematically DT technique is
The available observation number is m, the independent variable number is n, m dimension vector S is having the variable predicated from \(\overline{X}\) and\(X_{i}\). T is the vector transpose. The ith component of n dimension independent variable \(x_{i1} ,x_{i2} , \ldots x_{ij} , \ldots \ldots x_{in}\) is autonomous variable of the pattern vector \(X_{i}\).
The target of DT is to predict S based on the observation of \(\overline{X}\). DTs shows differnt level of accuracy when developed from different \(\overline{X}\). To get the optimal tree is a difficult task because of the large size of the search space. This algorithm develops a DT by a sequence of local optimal decision about which features can be used to partition data set \(\overline{X}\). The optimal size DT \(T_{k0}\) is generated according to the below optimization problem
The misclassification error of the tree T k is presented by \(R(T_{k} )\), where the optimal DT model \(T_{k0}\) is used to reduce the misclassification error \(R(T_{k} )\). Binary tree (T) is \(T \in \{ T_{1} ,T_{2} ,T_{3} , \ldots \ldots ,T_{k} ,t_{1} \}\), where the index number of the tree is K, tree node is t, root node is t 1. Re-substitution estimation of error in misclassification of the node t is r(t) and probability drop into node t is p(t). \(T^{L}\) and \(T^{R}\) are denoted as subtrees and define the left and right set of partition. Figure 8 shows the lattice L binary partition into conjointly left/right sets. Two-dimensional binary classifications are shown in Fig. 9. The left set gets the lattice elements with feature q having less value than threshold. In right set, feature q value of lattice components is more than the threshold value [110].
4.9 Support vector machine
SVM is a statistical method used for the computational learning purpose [45]. Sequential minimal optimization (SMO) for kernel support vector machine is implemented in LIBSVM to support the regression (nu-SVR). It has always given a global solution rather than local minima. The error bound is controlled with cost parameter, and width of hyper axis is controlled by gamma parameter. SVM is used for better accuracy in the location and classification of fault in a transmission line. SVM structure algorithms are shown in Fig. 10, where K(·) = kernel function, M = number of support vectors, F(x) = decision function, W = weights and b = bias [46,47,48].
For n dimension inputs Si(i = 1, 2,….M), where M is the sample number. Output Oi = 1 for class 1 and Oi = − 1 for class 2.
Mathematically, the hyperplane is
where n dimension vector is w and b is a parameter. The position of hyperplane is decided by w and b magnitude as shown in Fig. 11.
If Oi = 1, then the constraints is \(f(s_{i} ) \ge 1\) and if Oi = − 1, then \(f(s_{i} ) \ge - 1\), so
\(\left\| w \right\|^{ - 2}\) is the geometrical distance. Then the optimization problem of the optimal hyperplane is [45]
The optimal bias of b * is
For class 1 and class 2, v 1 and v 2 are random SVM.
The decision function is
Data samples have classified as
4.10 Random forest
Biggest grouping de-correlated tree interpreters are called as random forest, and every tree independently depends on the random vector sample. Instability and noise are the major disadvantage of a singular tree, but when developed suitably deep, they have a comparatively small bias. So there are perfect candidates for collaborative rising as they can apprehend complex interactions and totally benefit from a combination-based variance decrease [49]. Random selection of features to divide each node and resampling the training set to propagate each tree yield error rates that are de-correlated and noise tolerant. The errors of the forests are converged to a perimeter as the number of trees in the forest is huge [50].
The main concept of the collaborative tree growing processes is in the nth tree (n ≤ T tree, total number of tree ensemble). \(\theta n\) is produced as a random vector and independent of \(\theta n, \ldots \ldots \theta n - 1\) previous random vector in the same distribution. From the training set M, single tree grows and the attribute set \(\theta n\), and output classify Sn (Y, \(\theta n\)), here input vector is Y. In the random riven selection, \(\theta\) contains the number of Ttree, the no of attributes Ta > T try in the training set M.
The assemblage of tree-structured classifiers {Sn(Y, \(\theta n\)), n = 1,…T tree} contained in random forest, where \(\theta n\) is liberated indistinguishable distributed random vectors and tree performs a unit vote for the utmost popular class at input Y, respectively.
All distinct trees are united to predictions for ensemble of trees. For the class that most trees vote is reverted as the extrapolation of the ensemble to classify.
\(\hat{C}_{n} (Y)\) is the class prediction of the nth RF tree.For classification, the class that most trees vote is returned as the prediction of the ensemble. In relatively class frequency, i.e. for prediction probability single tree average is
where \(P_{{{\text{Sn(}}\theta n,T )}}\) is the probability associated with Y by the RF tree Sn(Y, \(\theta n\)). A old-fashioned decision tree basically signifies an overt decision boundary, and a case E is classified into class c if E falls into the decision area consistent to c. The class probability p(c|E) is normally projected by the portion of occurrences of class c in the leaf into which E falls [51, 52].
4.11 Extreme learning machine (ELM)
Extreme learning machine has only one optimize hidden layer. The main advantage of ELM is that there is no requirement of tuning of the hidden layer. Figure 12 shows the structure of ELM. Kernel function and nonlinear activation function are applied to scale the data for a definite range. Weight and bias value adjustment is not required in ELM methods. It is faster and gives better performance than conventional function ELM used for fault location and classification in the power system network [53, 54].
ELM technique is explained by using a training data set of \(\left\{ {x_{i} {\kern 1pt} ,} \right.\left. {y_{i} } \right\}\) where \(x_{i} \in \Re^{p}\) and \(t_{i} \in \Re^{q}\), i = 1… n. n is the number of samples. Mathematically, single hidden layer feedforward neural network expressed as
where f(x) is the activation function. w i is the weight that connects ith input neuron to hidden neuron and β i is the weight that connects ith hidden neuron to output neuron. ‘b i ’ is the bias represented by the threshold of the ith hidden neuron, and output of jth input is denoted by O j.
For n sample and (L) hidden layer of the ELM is given below [55]
So, (33) turns out to be:
(35) is stated as:
where
H is the hidden layer matrix, and input weight w and biases b are randomly chosen. So least-square solution
The result of (36) is stated as:
where H † signifies the Moore–Penrose general inverse of matrix H, hidden layer output matrix is symbolized by β and target matrix is symbolized by T.
5 Emerging computational intelligence techniques
5.1 Stationary wavelet transform (SWT)
SWT is alike as WPT and also called as non-decimated wavelet transform. The main change in SWT is up-sampling of the decomposed coefficients. So the filter coefficient at every level holds the same no. of samples as the original signal. DWT does not get the equivalent shift of the output, but SWT has this property due to shifting of the original signal [128]. Filtering and feature extraction by SWT is applied in [130]. Decaying DC offset current due to current transformer, high-order harmonics and noise are removed by SWT.
5.2 Principal component analysis (PCA)
The main advantage of PCA is to map the data from the original high-dimensional space to low-dimensional subspace, so the dimension of the data is reduced, where the best outcome is the variance of the data [145]. In [140] wavelet transform (WT) and principal component analysis (PCA), techniques are used for location and classification faults in Taipower 345 kV power transmission network. For feature extraction, PCA is used in [146].
5.3 Wide-area fault location methods
Location of faults in power network PMU plays a major role, but failure occurs to locate the fault if the end terminal PMU fails to record the faulty signal. It is not economical to locate PMU at every bus of the network due to communication problem and high cost. But optimal PMU placement overcomes this problem [147]. In [148, 149], location of fault in transmission grid was determined using wide-area synchronized voltage measurements with the help of global positioning system (GPS) receivers. The main advantage of the proposed algorithm is, it requires less synchronize measuring devices. The outcomes of the technique give closed-form expression solution. Location of the fault in transmission line by using a non-iterative wide-area technique was proposed in [150]. Impedance matrix was developed by the help of pre-fault positive-sequence and negative-sequence network topology. The location of the fault in the transmission line is determined by using linear least-squares method. The accuracy of the technique is not affected by the high resistance fault. In [151], PMU was used to synchronize the voltage and current signals for the localization of the fault in the transmission grid and successfully diagnose the fault in a hierarchical manner.
5.4 Modal transformation
The phase signal of three-phase systems is decomposed into their modal components by means of the modal transformation matrices. For the un-transposed multiphase lines, eigenvector-based transformation matrix is applied to the phase impedance and admittance matrices to decide the current and voltage transformation matrices. Wedepohl, Karrenbauer and Clarke transformations are non-identical real-value matrices, which are selected for balanced (equally transposed) multiphase lines [135]. In [152, 153], Clarke transformation was implemented to decouple three-phase quantity to α, β (two stationary phase components) and 0 (zero-sequence component) on the basis of characteristics of fault.
5.5 Independent component analysis (ICA)
ICA is defined as given below.
Let random vectors be X and S, where \(X = \{ x_{1} ,x_{2} , \ldots \ldots \ldots x_{n} \}\) and \(S = \{ s_{1} ,s_{2} , \ldots \ldots \ldots s_{n} \}\). The matrix A has element a ij . The X T is the transpose of X a row vector, as all vectors are taken as column vectors. The mixing model is
If A is the columns matrix, then A is denoted by a j . The modified model matrix is
The ICA is the statistical model in Eq (40). So ICA is also called generative model [154, 155]. In [156], a combination of ICA, travelling wave and SVM in high-voltage (HV) transmission lines for the location and classification of fault is proposed. The results of the technique give 100 and 99% classification and location accuracy in a real transmission line of a noise faulty signal environment. The main advantage ICA technique overcomes the noise problem in the signal. ICA works on the principle of blind source separation problem [157] and applicable for the separation of the Gaussian signals from non-Gaussian signals [158].
5.6 Pencil matrix method
To extract the parameter from the exponentially damped/undamped signal, PMM is applied in [159]. PMM is less affected by noise and has better computational efficiency [160]. PMM is also used to extract the fundamental frequency component of transmission line and eliminating the DC offset and higher-order harmonic components of the faulty signal [161]. The algorithm of matrix pencil is explained in [162, 163].
6 Strength and weakness of all the technique
Generalized strength and weakness of all the technique are explained in Table 1 as given below.
7 Comparative studies of fault classification, location and detection of transmission line
To sustain the stability of power networks, it is required to detect the fault and locate the fault in a transmission line. So many methods and techniques are used to detect the faults. Different circumstances like the fault inception angle, loading condition, fault resistance, harmonics and DC offset in the fault signal result in unsatisfactory output. Researchers have implemented various methods and algorithms in both online and offline to identify, locate and classify the faults on transmission network, so that the system operates effectively and efficiently. Comparative analysis of different methods that are used for classification, location and detection of fault in transmission line is shown in the table below. The purpose of the system, input used for algorithm, features and numerical result of the various methods are highlighted in Tables 2, 3, 4, 5 and 6. Table 2 represents the comparative study of fault location of a transmission line, and Table 3 shows the comparative study of fault classification of a transmission line. Fault classification and detection of transmission line is presented in Table 4, and fault classification and location of transmission lines is compared in Table 5. Comparative study of fault classification, location and detection of transmission line is presented in Table 6.
8 Practical case study and comparison of fault detection, classification and location methods
Travelling wave-based technique is implemented in [164] to locate the fault in a 230 kV, 200-km transmission line using the real-time digital simulator (RTDS). The main advantage of this technique is synchronization of data from both the terminal is not required. So this method is applicable for real-time application for synchronized or unsynchronized two-terminal data. The outcome of this method is acceptable. In [165], PMU-based state estimation technique is implemented in a real 18-bus distribution network for the detection and location of faults and faulty line. The outcomes of this method are not affected by noise and the nature of load/generators. But this technique is more costly, as PMU is placed at every bus of the system. Current and voltage signals of both ends are used to locate the faults at CEMIG (Energetic Company of Minas Gerais—Brazil) transmission lines in [166]. Digital event recorders are installed for collection of signal. The proposed algorithms mainly depend on the fault point voltage magnitude and do not require the phase angle and synchronized data set. So this technique is robust, accurate and easy to apply in real short-circuit cases. The fault location error is only 0.03%. The maximal overlap discrete wavelet transform (MODWT) [167] is applied in real-time detection of fault, where faults are produced by the real-time digital simulator. MODWT has the same characteristic as DWT but up-sampling take place there. The current and voltage signals are decomposed by MODWT and then computed for detection of fault in real time. But this technique’s accuracy is affected by the saturation of the transducer. In [169], maximal overlap discrete wavelet transform (MODWT) and discrete wavelet transform (DWT) are implemented in real time for fault detection and location 500 kV, 400-km-long transmission lines. The MODWT gives acceptable accuracy (mean error is 0.63%) as comparable to DWT. The technique is executed with the help of real-time digital simulator (RTDS). Real-time and offline fault classification is done in [170] by using the MODWT technique in 230 kV transmission line. Offline and real-time classification are evaluated by using actual oscillographic records and real-time digital simulator (RTDS), respectively. For line-to-ground and line-to-line faults, the classification accuracy in real time is 100%. But in the wavelet coefficient energy investigation, the misclassification problem occurs for the double line-to-ground fault. In [136], hardware arrangement is done for analysis of faults. The high-speed communication action is done by fibre-optic links/Etherne to locate the fault quickly, where PMU/digital fault recorder (DFR) is used as sampling unit.
9 Conclusions
Conventional methods are used for detection, classification and location of the fault in the transmission network, but to overcome the limitation of these methods, signal processing technique and artificial intelligence (AI)-based methods are widely applied in power system protection. Some of the selective and important papers are analysed to compare the system use, techniques, methods, input signal, features and numerical results, where artificial Intelligence (AI)-based method is the efficient, fast, accurate and robust for detection, classification and location of the fault in a transmission line. This paper helps the researcher for development and further study in this field.
References
Lewis WA (1943) Principles of high-speed relaying. Westinghouse Eng 3:131–134
Crary SB (1947) Power system stability, vol 2. Wiley, New York
Hawary ME (1995) Electrical power systems. IEEE Press, New York, pp 469–536
IEEE guide for determining fault location on AC transmission and distribution lines (2005) IEEE Power Engineering Society Pub., New York, IEEE Std C 37.114
Magnago FH, Abur A (1999) Advanced techniques for transmission and distribution system fault location. In: Proceedings of CIGRE—Study committee 34 colloquium and meeting, Florence, paper 215
Capar A, Arsoy AB (2015) A performance oriented impedance based fault location algorithm for series compensated transmission lines. Electr Power Energy Syst 71:209–214
Eriksson L, Saha MM, Rockefeller GD (1985) An accurate fault locator with compensation for apparent reactance in the fault resistance resulting from the remote—end feed. IEEE Trans Power Appar Syst 104:424–436
Takagi T, Yamakoshi Y, Yamaura M, Kondou R, Matsushima T (1982) Development of a new type fault locator using the one-terminal voltage and current data. IEEE Trans Power Appar Syst PAS-101(8):2892–2898
Novosel D, Bachmann B, Hart DG, Hu Y, Saha MM (1996) Algorithms for locating faults on series compensated lines using neural network and deterministic methods. IEEE Trans Power Deliv 11(4):1728–1736
Adu T (2001) A new transmission line fault locating system. IEEE Trans Power Deliv 16(4):498–503
Guobing S, Jiale S, Yaozhong G (2009) An accurate fault location algorithm for parallel transmission lines using one terminal data. Int J Electr Power Energy Syst 31(2–3):124–129
Sachdev M, Agarwal R (1988) A technique for estimating transmission line fault locations from digital impedance relay measurements. IEEE Trans Power Deliv 3(1):121–129
Girgis AA, Hart DG, Peterson WL (1992) A new fault location technique for two- and three-terminal lines. IEEE Trans Power Deliv 7(7):98–107
Dabbagh MA, Kapuduwage SK (2005) Using instantaneous values for estimating fault locations on series compensated transmission lines. Electr Power Syst Res 76(1–3):25–32
Dong X, Kong W, Cui T (2009) Fault classification and faulted phase selection based on the initial current travelling wave. IEEE Trans Power Deliv 24(2):552–559
Shehab-Eldin EH, McLaren PG (1998) Travelling wave distance protection-problem areas and solutions. IEEE Trans Power Deliv 3(3):894–902
Ngu EE, Ramar K (2011) A combined impedance and travelling wave based fault location method for multi-terminal transmission lines. Int J Electr Power Energy Syst 33(10):1767–1775
Yadav A, Swetapadma A (2015) A novel transmission line relaying scheme for fault detection and classification using wavelet transform and linear discriminant analysis. Ain Shams Eng J 6:199–209
Swetapadma A, Yadav A (2015) All shunt fault location including cross-country and evolving faults in transmission lines without fault type classification. Electr Power Syst Res 123:1–12
Silva KM, Souza BA, Brito NSD (2006) Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Trans Power Deliv 21(4):2058–2063
Jung CK, Kim KH, Lee JB, Klockl B (2007) Wavelet and neuro-fuzzy based fault location for combined transmission systems. Electr Power Energy Syst 29:445–454
Valsan SP, Swarup KS (2008) Fault detection and classification logic for transmission lines using multi-resolution wavelet analysis. Electr Power Compon Syst 36:321–344
Shaik AG, Pulipaka RRV (2015) A new wavelet based fault detection, classification and location in transmission lines. Electr Power Energy Syst 64:35–40
Pérez FE, Orduña E, Guidi G (2011) Adaptive wavelets applied to fault classification on transmission lines. IET Gener Transm Distrib 5(7):694–702
Burrus CS, Gopinath RA (1998) Introduction to wavelets and wavelet transform: a primer. Prentice-Hall, Upper Saddle River
Dasgupta A, Nath S, Das A (2012) Transmission line fault classification and location using wavelet entropy and neural network. Electr Power Compon Syst 40:1676–1689
Mahari A, Seyedi H (2015) High impedance fault protection in transmission lines using a WPT-based algorithm. Electr Power Energy Syst 67:537–545
Ray P, Panigrahi BK, Senroy N (2013) Hybrid methodology for fault distance estimation in series compensated transmission line. IET Gener Transm Distrib 7(5):431–439
Moravej Z, Ashkezari JD, Pazok M (2015) An effective combined method for symmetrical faults identification during power swing. Electr Power Energy Syst 64:24–34
Krishnanand KR, Dash PK, Naeem MH (2015) Detection, classification, and location of faults in power transmission lines. Electr Power Energy Syst 67:76–86
Yadav A, Swetapadma A (2015) A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach. Electr Power Energy Syst 69:27–33
Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44:998–1001
Ekici S, Yildirim S, Poyraz M (2008) Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Syst Appl 34:2937–2944
Bhowmik PS, Purkait P, Bhattacharya K (2009) A novel wavelet transform aided neural network based transmission line fault analysis method. Electr Power Energy Syst 31:213–219
Roy N, Bhattacharya K (2015) Detection, classification, and estimation of fault location on an overhead transmission line using S-transform and neural network. Electr Power Compon Syst 43(4):461–472
Mao KZ, Tan KC, Ser W (2000) Probabilistic neural network structure determination for pattern classification. IEEE Trans Neural Netw 11(4):1009–1016
Zhanga Jing-Ru, Zhanga Jun, Lokc Tat-Ming, Lyud Michael R (2007) A hybrid particle swarm optimization–back-propagation algorithm for feed forward neural network training. Appl Math Comput 185(2):1026–1037
Joorabian M, Asl SMAT, Aggarwal RK (2004) Accurate fault locator for EHV transmission lines based on radial basis function neural networks. Electr Power Syst Res 71:195–202
Gayathri K, Kumarappan N (2015) Double circuit EHV transmission lines fault location with RBF based support vector machine and reconstructed input scaled conjugate gradient based neural network. Int J Comput Intell Syst 8(1):95–105
Dash PK, Pradhan AK, Panda G (2000) A novel fuzzy neural network based distance relay scheme. IEEE Trans Power Deliv 15(3):902–907
Pradhan AK, Routray A, Pati S, Pradhan DK (2004) Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line. IEEE Trans Power Deliv 19(4):1612–1618
Mendal JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377
Sadeh J, Afradi H (2009) A new and accurate fault location algorithm for combined transmission lines using adaptive network-based fuzzy inference system. Electr Power Syst Res 79:1538–1545
Reddy MJ, Mohanta DK (2008) Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings. IET Gener Transm Distrib 2(2):235–244
Ekici S (2012) Support vector machines for classification and locating faults on transmission lines. Appl Soft Comput 12:1650–1658
Ray P, Mishra D (2016) Support vector machine based fault classification and location of a long transmission line. Eng Sci Technol Int J 19:1368–1380
Moravej Z, Khederzadeh M, Pazoki M (2012) New combined method for fault detection, classification, and location in series-compensated transmission line. Electr Power Compon Syst 40:1050–1071
Jiang Joe-Air, Chuang Cheng-Long, Wang Yung-Chung, Hung Chih-Hung, Wang Jiing-Yi, Lee Chien-Hsing, Hsiao Ying-Tung (2011) A hybrid framework for fault detection, classification, and location—Part I: concept, structure, and methodology. IEEE Trans Power Deliv 26(3):1988–1998
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
Esposito F, Malerba D, Semeraro GA (1997) A comparative analysis of methods for pruning decision trees. IEEE Trans Pattern Anal Mach Intell 19(5):476–491
Provost FJ, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52(30):199–215
Liang H, Zhang H, Yan Y (2006) Decision trees for probability estimation: an empirical study. In: Proceedings of 18th IEEE international conference on tools with, artificial intelligence (ICTAI’06), pp 1–9
Malathi V, Marimuthu NS, Baskar S (2010) Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. J Neuro Comput 73(10–12):2160–2167
Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107–122
Ray P, Mishra D (2014) Application of extreme learning machine for underground cable fault location. Int Trans Electr Energ. Syst. Published Online in Wiley Online Library, Dec. 2014
Mazon AJ, Zamora I, Miñambres JF, Zorrozua MA, Barandiaran JJ, Sagastabeitia K (2000) A new approach to fault location in two-terminal transmission lines using artificial neural networks. Electr Power Syst Res 56:261–266
Radojević ZM, Terzija VV, Djuric MB (2000) Numerical algorithm for overhead lines arcing faults detection and distance and directional protection. IEEE Trans Power Deliv 15(1):31–37
Chen Z, Maun Jean-Claud (2000) Artificial neural network approach to single-ended fault locator for transmission lines. IEEE Trans Power Syst 15(1):370–375
Funabashi T, Otoguro H, Mizuma Y, Dube L, Ametani A (2000) Digital fault location for parallel double-circuit multi-terminal transmission lines. IEEE Trans Power Deliv 15(2):531–537
de Morais Pereira CE, Zanetta LC (2004) Fault location in transmission lines using one-terminal post fault voltage data. IEEE Trans Power Deliv 19(2):570–575
Liao Y (2006) Fault location utilizing unsynchronized voltage measurements during fault. Electr Power Compon Syst 34:1283–1293
Jung H, Park Y, Han M, Lee C, Park H, Shin M (2007) Novel technique for fault location estimation on parallel transmission lines using wavelet. Electr Power Energy Syst 29:76–82
Reddy MJB, Mohanta DK (2008) Performance evaluation of an adaptive-network-based fuzzy inference system approach for location of faults on transmission lines using Monte Carlo simulation. IEEE Trans Fuzzy Syst 16(4):909–919
Perera N, Rajapakse AD (2008) Fast isolation of faults in transmission systems using current transients. Electr Power Syst Res 78:1568–1578
Gayathri K, Kumarappan N (2010) Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network. Expert Syst Appl 37:8822–8830
Sadeh J, Adinehzadeh A (2010) Accurate fault location algorithm for transmission line in the presence of series connected FACTS devices. Electr Power Energy Syst 32:323–328
Ezquerra J, Valverde V, Mazoń AJ, Zamora I, Zamora JJ (2011) Field programmable gate array implementation of a fault location system in transmission lines based on artificial neural networks. IET Gener Transm Distrib 5(2):191–198
da Silva PRN, Negrão MMLC, Junior PV, Sanz-Bobi Miguel A (2012) A new methodology of fault location for predictive maintenance of transmission lines. Electr Power Energy Syst 42:568–574
Zhang Y, Wang Z, Zhang J, Ma J (2011) Fault localization in electrical power systems: a pattern recognition approach. Electr Power Energy Syst 33:791–798
Montañés M, García-Gracia A, El Halabi N, Comech MP (2012) High resistive zero-crossing instant faults detection and location scheme based on wavelet analysis. Electr Power Syst Res 92:138–144
Jiang Q, Li X, Wang B, Wang H (2012) PMU-based fault location using voltage measurements in large transmission networks. IEEE Trans Power Deliv 27(3):1644–1652
Mamis MS, Arkan M, Keles C (2013) Transmission lines fault location using transient signal spectrum. Electr Power Energy Syst 53:714–718
Mahamedi B, Zhu JG (2014) Unsynchronized fault location based on the negative-sequence voltage magnitude for double-circuit transmission lines. IEEE Trans Power Deliv 29(4):1901–1908
Dobakhshari AS, Ranjbar AM (2015) A novel method for fault location of transmission lines by wide-area voltage measurements considering measurement errors. IEEE Trans Smart Grid 6(2):874–884
Dalstein T, Kulicke B (1995) Neural network approach to fault classification for high speed protective relaying. IEEE Trans Power Deliv 10(2):1002–1011
Song YH, Xuan QX, Johns AT (1997) Comparison studies of five neural network based fault classifiers for complex transmission lines. Electr Power Syst Res 43:125–132
Aggarwal RK, Xuan QY, Dunn RW, Johns AT, Bennett A (1999) A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network. IEEE Trans Power Deliv 14(4):1250–1256
Lin W-M, Yang C-D, Lin J-H, Tsay M-T (2001) A fault classification method by RBF neural network with OLS learning procedure. IEEE Trans Power Deliv 16(4):473–477
Adu T (2002) An accurate fault classification technique for power system monitoring devices. IEEE Trans Power Deliv 17(3):684–690
Youssef OAS (2004) Combined fuzzy-logic wavelet-based fault classification technique for power system relaying. IEEE Trans Power Deliv 19(2):582–589
Das B, Reddy JV (2005) Fuzzy-logic-based fault classification scheme for digital distance protection. IEEE Trans Power Deliv 20(2):609–616
Megahed AI, Moussa AM, Bayoumy AE (2006) Usage of wavelet transform in the protection of series-compensated transmission lines. IEEE Trans Power Deliv 21(3):1213–1221
Mahanty RN, Gupta PBD (2006) Comparison of fault classification methods based on wavelet analysis and ANN. Electr Power Compon Syst 34:47–60
Mahanty RN, Dutta Gupta PB (2007) A fuzzy logic based fault classification approach using current samples only. Electr Power Syst Res 77:501–507
Samantaray SR, Dash PK (2008) Transmission line distance relaying using machine intelligence technique. IET Gener Transm Distrib 2(1):53–61
Samantaray SR, Dash PK (2008) Pattern recognition based digital relaying for advanced series compensated line. Electr Power Energy Syst 30:102–112
Valsan SP, Swarup KS (2009) High-speed fault classification in power lines: theory and FPGA-based implementation. IEEE Trans Ind Electron 56(5):1793–1800
Nguyen T, Liao Y (2010) Transmission line fault type classification based on novel features and neuro-fuzzy system. Electr Power Compon Syst 38:695–709
Upendar J, Gupta CP, Singh GK, Ramakrishna G (2010) PSO and ANN-based fault classification for protective relaying. IET Gener Transm Distrib 4(10):1197–1212
Chothani NG, Bhalja BR, Parikh UB (2011) New fault zone identification scheme for busbar using support vector machine. IET Gener Transm Distrib 5(10):1073–1079
Seyedtabaii S (2012) Improvement in the performance of neural network-based power transmission line fault classifiers. IET Gener Transm Distrib 6(8):731–737
Beg MA, Khedkar MK, Paraskar SR, Dhole GM (2013) Feed-forward artificial neural network-discrete wavelet transform approach to classify power system transients. Electr Power Compon Syst 41:586–604
Jafarian P, Sanaye-Pasand M (2013) High-frequency transients-based protection of multiterminal transmission lines using the SVM technique. IEEE Trans Power Deliv 28(1):188–196
Vyas B, Maheshwari RP, Das B (2014) Investigation for improved artificial intelligence techniques for thyristor-controlled series compensated transmission line fault classification with discrete wavelet packet entropy measures. Electr Power Compon Syst 42(6):554–566
He Z, Lin S, Deng Y, Li X, Qian Q (2014) A rough membership neural network approach for fault classification in transmission lines. Electr Power Energy Syst 61:429–439
Vyas BY, Maheshwari RP, Das B (2014) Improved fault analysis technique for protection of Thyristor controlled series compensated transmission line. Electr Power Energy Syst 55:321–330
Gao F, Thorp James S, Gao S, Pal A, Vance KA (2015) A voltage phasor based fault-classification method for phasor measurement unit only state estimator output. Electr Power Compon Syst 43:22–31
Barros J, Drake JM (1994) Realtime fault detection and classification in power systems using microprocessors. IEE Proc Gem Transm Distrib 141(4):315–322
Liang J, Elangovan S, Devotta JBX (1998) A wavelet multi resolution analysis approach to fault detection and classification in transmission lines. Electr power Energy Syst 20(5):327–332
Chowdhury FN, Aravena JL (1998) A modular methodology for fast fault detection and classification in power systems. IEEE Trans Control Syst Technol 6(5):623–634
Wang H, Keerthipala WWL (1998) Fuzzy-neuro approach to fault classification for transmission line protection. IEEE Trans Power Deliv 13(4):1093–1104
Hong C, Elangovan S (2000) A B-spline wavelet based fault classification scheme for high speed protection relaying. Electr Mach Power Syst 28:313–324
Dash PK, Pradhan AK, Panda G (2001) Application of minimal radial basis function neural network to distance protection. IEEE Trans Power Deliv 16(1):68–74
Martín F, Aguado JA (2003) Wavelet-based ANN approach for transmission line protection. IEEE Trans Power Deliv 18(4):1572–1574
Yeo SM, Kim CH, Hong KS, Lim YB, Aggarwal RK, Johns AT, Choi MS (2003) A novel algorithm for fault classification in transmission lines using a combined adaptive network and fuzzy inference system. Electr Power Energy Syst 25:747–758
Chanda D, Kishore NK, Sinha AK Identification and classification of faults on transmission lines using wavelet multiresolution analysis. Electr Power Compon Syst 32:391–405
Chanda D, Kishore NK, Sinha AK (2005) Application of wavelet multiresolution analysis for identification and classification of faults on transmission lines. Electr Power Syst Res 73:323–333
Aguilera C, Orduna E, Ratta G (2006) Fault detection, classification and faulted phase selection approach based on high-frequency voltage signals applied to a series-compensated line. IEE Proc-Gener Transm Distrib 153(4):469–475
Zhang N, Kezunovic M (2007) Transmission line boundary protection using wavelet transform and neural network. IEEE Trans Power Deliv 22(2):859–869
Samantaray SR (2009) Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line. IET Gener Transm Distrib 3(5):425–436
He Z, Fu L, Lin S, Bo Z (2010) Fault detection and classification in EHV transmission line based on wavelet singular entropy. IEEE Trans Power Deliv 25(4):2156–2163
Yusuff AA, Jimoh AA, Munda JL (2011) Determinant-based feature extraction for fault detection and classification for power transmission lines. IET Gener Transm Distrib 5(12):1259–1267
Ibrahim AM, Marei MI, Mekhamer SF, Mansour MM (2011) An artificial neural network based protection approach using total least square estimation of signal parameters via the rotational invariance technique for flexible AC transmission system compensated transmission lines. Electr Power Compon Syst 39:64–79
Dash PK, Moirangthem J, Das S (2014) A new time–frequency approach for distance protection in parallel transmission lines operating with STATCOM. Electr Power Energy Syst 61:606–619
Gupta OH, Tripathy M (2015) An innovative pilot relaying scheme for shunt-compensated line. IEEE Trans Power Deliv 30(3)
Gopakumar P, Reddy MJB, Mohanta DK (2015) Adaptive fault identification and classification methodology for smart power grids using synchronous phasor angle measurements. IET Gener Transm Distrib 9(2):133–145
Swetapadma A, Yadav A (2016) Data-mining-based fault during power swing identification in power transmission system. IET Sci Meas Technol 10(2):130–139
Dash PK, Pradhan AK, Panda G (2003) Application of artificial intelligence techniques for classification and location of faults on thyristor-controlled series-compensated line. Electr Power Compon Syst 31:241–260
Mahanty RN, Dutta Gupta PB (2004) Application of RBF neural network to fault classification and location in transmission lines. IEE Proc. Gener Transm Distrib 151(2):201–2012
Gracia J, Mazón AJ, Zamora I (2005) Best ANN structures for fault location in single and double-circuit transmission lines. IEEE Trans Power Deliv 20(4):2389–2395
Samantaray SR, Dash PK, Panda G (2006) Fault classification and location using HS-transform and radial basis function neural network. Electr Power Syst Res 76:897–905
Reddy MJ, Mohanta DK (2007) A wavelet-neuro-fuzzy combined approach for digital relaying of transmission line faults. Electr Power Compon Syst 35:1385–1407
Samantaray SR, Dash PK, Panda G (2007) Distance relaying for transmission line using support vector machine and radial basis function neural network. Electr Power Energy Syst 29:551–556
Bhalja B, Maheshwari RP (2008) Wavelet-based fault classification scheme for a transmission line using a support vector machine. Electr Power Compon Syst 36:1017–1030
Valsan SP, Swarup KS (2009) Wavelet transform based digital protection for transmission lines. Electr Power Energy Syst 31:379–388
Upendar J, Gupta CP, Singh GK (2010) Fault classification scheme based on the adaptive resonance theory neural network for protection of transmission lines. Electr Power Compon Syst 38:424–444
Upendar J, Gupta CP, Singh GK (2012) Statistical decision-tree based fault classification scheme for protection of power transmission lines. Electr Power Energy Syst 36:1–12
da Silva APA, Lima ACS, Souza SM (2012) Fault location on transmission lines using complex-domain neural networks. Electr Power Energy Syst 43:720–727
Dutta P, Esmaeilian A, Kezunovic M (2014) Transmission-line fault analysis using synchronized sampling. IEEE Trans Power Deliv 29(2):942–950
Yusuff AA, Jimoh AA, Munda JL (2014) Fault location in transmission lines based on stationary wavelet transform, determinant function feature and support vector regression. Electr Power Syst Res 110:73–83
Yadav A, Swetapadma A (2015) Enhancing the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy inference system. IET Gener Transm Distrib 9(6):580–591
Girgis AA, Johns MB (1989) A hybrid expert system for faulted section identification, fault type classification and selection of fault location algorithms. IEEE Trans Power Deliv 4(2):978–985
KezunoviC M, PeruniEiC B (1996) Automated transmission line fault analysis using synchronized sampling at two ends. IEEE Trans Power Syst 11(1):441–447
Coury DV, Oleskovicz M, Aggarwal RK (2002) An ANN routine for fault detection, classification, and location in transmission lines. Electr Power Compon Syst 30:1137–1149
Jiang Joe-Air, Chen Ching-Shan, Liu Chih-Wen (2003) A new protection scheme for fault detection, direction discrimination, classification, and location in transmission lines. IEEE Trans Power Deliv 18(1):34–42
Zhang N, Kezunovic M (2007) A real time fault analysis tool for monitoring operation of transmission line protective relay. Electr Power Syst Res 77:361–370
Roy DS, Mohanta DK, Panda AK (2008) Software reliability allocation of digital relay for transmission line protection using a combined system hierarchy and fault tree approach. IET Softw 2(5):437–445
Mohamed EA, Talaat HA, Khamis EA (2010) Fault diagnosis system for tapped power transmission lines. Electr Power Syst Res 80:599–613
Ibrahim DK, Saleh SM (2011) Unsymmetrical high-impedance earth fault central relay for transmission networks. Electr Power Compon Syst 39:1469–1492
Jiang J, Chuang C, Wang Y, Hung C, Wang J, Lee C, Hsiao Y (2011) A hybrid framework for fault detection, classification, and location—Part II: implementation and test results. IEEE Trans Power Deliv 26(3):1999–2008
Eristi H (2013) Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive Neuro-fuzzy inference system. Measurement 46:393–401
Dash PK, Das S, Moirangthem J (2015) Distance protection of shunt compensated transmission line using a sparse S-transform. IET Gener Transm Distrib 9(12):1264–1274
Esmaeilian A, Popovic T, Kezunovic M (2015) Transmission line relay mis-operation detection based on time-synchronized field data. Electr Power Syst Res 125:174–183
Hasheminejad S, Seifossadat SG, Razaz M, Joorabian M (2016) Traveling-wave-based protection of parallel transmission lines using Teager energy operator and fuzzy systems. IET Gener Transm Distrib 10(4):1067–1074
Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23:228–233
Thukaram D, Khincha HP, Vijaynarasimha HP (2005) Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans Power Deliv 20:710–721
Nazari-Heris M, Mohammadi-Ivatloo B (2015) Application of heuristic algorithms to optimal PMU placement in electric power systems: an updated review. Renew Sustain Energy Rev 50:214–228
Korkali M, Lev-Ari H, Abur A (2012) Traveling-wave-based fault-location technique for transmission grids via wide-area synchronized voltage measurement. IEEE Trans Power Syst 27:1003–1011
Korkali M, Abur A (2013) Optimal deployment of wide-area synchronized measurements for fault-location observability. IEEE Trans Power Syst 28:482–489
Azizi S, Sanaye-Pasand M (2015) A straightforward method for wide-area fault location on transmission networks. IEEE Trans Power Deliv 30:264–272
Salehi-Dobakhshari A, Ranjbar AM (2014) Application of synchronised phasor measurements to wide-area fault diagnosis and location. IET Gener Transm Distrib 8:716–729
Jiang JA, Chen CS, Liu CW (2003) A new protection scheme for fault detection, direction discrimination, classification, and location in transmission lines. IEEE Trans Power Deliv 18:34–42
Asuhaimi Mohd Zin A, Saini M et al (2015) New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation. Neurocomputing 168:983–993
Jutten C, Herault J (1991) Blind separation of sources, part I: AN adaptive algorithm based on neuromimetic architecture. Signal Process 24:1–10
Comon P (1994) Independent component analysis a new concept. Signal Process 36:287–314
Almeidaa AR, Almeidaa OM, Juniora BFS, Barretob LHSC, Barros AK (2017) ICA feature extraction for the location and classification of faults in high-voltage transmission lines. Electr Power Syst Res 148:254–263
Cichocki A, Amari S (2003) Adaptive blind signal and image processing. Wiley, New York
Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley-Interscience, New York
Hua Y, Sarkar TK (1990) Matrix Pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise. IEEE Trans Acoust Speech Signal Process 38(5):814–824
Hua Y, Sarkar TK (1991) On SVD for estimating generalized eigenvalues of singular matrix Pencil in noise. IEEE Trans Signal Process 39(4):892–900
Wang L, Suonan J, Jiao Z (2013) A fast extraction method in the application of UHV transmission line fault location. Energy Power Eng 5:1277–1283
Sarkar TK, Pereira O (1995) Using the matrix pencil method to estimate the parameters of a sum of complex exponentials. IEEE Antennas Propag Mag 37(1):48–55
Hua Y, Sarker TK (1988) Matrix pencil method and its performance. Acoust Speech Signal Process 4:2476–2479
Lopes FV, Silva KM, Costa FB, Neves WLA, Fernandes D (2015) Real-time traveling-wave-based fault location using two-terminal unsynchronized data. IEEE Trans Power Deliv 30(3):1067–1076
Pignati M, Zanni L, Romano P, Cherkaoui R, Paolone M (2017) Fault detection and faulted line identification in active distribution networks using synchrophasors-based real-time state estimation. IEEE Trans Power Deliv 32(1):381–392
Silveira EG, Pereira C (2007) Transmission line fault location using two-terminal data without time synchronization. IEEE Trans Power Syst 22(1):498–499
Costa FB, Souza BA, Brito NSD (2010) Real-time detection of fault-induced transients in transmission lines. Electron Lett 46(11)
Bouthiba T (2004) Fault location in EHV transmission lines using artificial neural networks. Int J Appl Math Comput Sci 14(1):69–78
Costa FB, Souza BA (2011) Fault-induced transient analysis for real-time fault detection and location in transmission lines. In: International conference on power systems transients (IPST’11) in Delft, Netherlands, June 2011, pp 1–6
Costa FB, Souza BA, Brito NSD (2012) Real-time classification of transmission line faults based on maximal overlap discrete wavelet transform. PES T&D 2012, pp 1–8
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
Rights and permissions
About this article
Cite this article
Mishra, D.P., Ray, P. Fault detection, location and classification of a transmission line. Neural Comput & Applic 30, 1377–1424 (2018). https://doi.org/10.1007/s00521-017-3295-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-017-3295-y