- Open Access
A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC
© The Author(s) 2016
- Received: 8 June 2016
- Accepted: 13 October 2016
- Published: 21 October 2016
In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg–Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha–Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.
- Artificial neural network
- Transmission lines
- Power system faults
- Fault location
Modern power systems with ever increasing power demands require power with satisfactory standards of quality and economy with feasible cost cutting measures (Hor et al. 2010; Nurdin et al. 2012). In developing countries, the optimized use of transmission system investments is very important. Practical application of FSC has been found it as the suitable choice not only to achieve the desired power increment, but also to stabilize the two interconnected strong networks by reducing the connecting line impedance of the given corridor’s transmission capacity (Oliveira 2008). In addition to this, FSC is the simplest and cost-effective solution of increasing the power transfer capability of a power system that has long (250 km or more) transmission lines (Oliveira 2008).
Estimation of the impedance to the fault point is influenced by the series compensation (Vyas et al. 2014a, b). Series compensation affects the fault location estimation in an unpredictable manner. Most of the reactance-based topologies suffer from mal-operation under reach due to DC offset current. Also, these schemes require measurement from both ends if the transmission line is having two end sources. In addition, the problem gets worse if the overvoltage protection of a series capacitor starts to operate due to high fault currents introducing nonlinearities in the measurement (Hosny and Safiuddin 2009). All of these nonlinear behavior due to series capacitor and its protection contributes to the distortion in phase voltage and line current waveforms. So, false tripping and mal-operation of circuit breakers is dominant due to series compensation. Hence conventional impedance based protection systems are most likely to malfunction (Hosny and Safiuddin 2009).
Fault classification and fault location estimation increases system stability, reliability and availability. Accurate location of faults on overhead transmission lines for inspection maintenance purposes is of vital importance for expediting service restoration to reduce service outage time and operating costs. Accurate fault analysis with location estimation will surely improve overall transient stability and reduce the switching overvoltage in the power system. Therefore, the fault type classification and fault distance estimation have become very important aspects of protection of series compensated transmission line.
MOV energy signals are never implemented before in any of the earlier conventional fault classification and location estimation algorithms. In order to find some new solution, authors have considered a new measurement scheme on FSC side rather than conventional line side voltage and current measurement.
A bibliographical survey of relevant background, effect of series compensation on transmission line protection and protection efforts for series compensated line is presented in Vyas et al. (2014a). Various fault-location algorithms for series-compensated lines have been developed so far. They apply one-end (Hosny and Safiuddin 2009; Ray 2014; Ray et al. 2013; Parikh et al. 2008; Abdelaziz et al. 2005; Vyas et al. 2014b; Moravej et al. 2012) and two-end measurements (Izykowski et al. 2011; Rubeena et al. 2014; Ahsaee and Sadeh 2011; Kang et al. 2015; Eldin 2010; Hussain and Osman 2014; Al-Dabbagh and Kapuduwage 2005; Yusuff et al. 2011; Ma et al. 2015; Abdelaziz et al. 2013) for two-terminal lines. In general, the impedance-based approach is the mostly applied. Multilayer perceptron neural networks (MLPNN) based scheme with two neural networks to address fault classification and location is proposed in Hosny and Safiuddin (2009). Fault location by extreme learning machine with genetic algorithm based feature selection method is depicted in Ray (2014). Wavelet transform (WT) and wavelet packet transform (WPT) combining ANN method for fault distance estimation has been considered in Ray et al. (2013) while a combined wavelet-support vector machine (SVM) technique for fault zone identification in series compensated transmission line has been investigated in Parikh et al. (2008), Yusuff et al. (2011). Two approaches based on travelling waves and two level ANN for fault type classification and faulted phase selection of series compensated transmission lines have been proposed in Abdelaziz et al. (2005). A new approach based on hyperbolic S-transform for extracting useful features from the input signals and support vector regression for fault location is presented in Moravej et al. (2012). A more general case of unsynchronized measurements utilizing two subroutines for locating a fault has been presented in Izykowski et al. (2011). Direct Prony analysis and four-cycle discrete Fourier transform algorithm based new offline technique is proposed for accurate estimation of fault current and voltage phasors of the series compensated transmission system in Rubeena et al. (2014). In Eldin (2010), a new system with wavelet MRA coefficients for fault detection and classification and adaptive neuro-fuzzy inference system (ANFIS) to obtain accurate fault location has been studied. A generalized fault loop method using pre-fault PMU measurements is considered in algorithm (Al-Mohammed and Abido 2014). A fault direction estimation technique for a series compensated line using phase change in positive-sequence current and magnitude change in the positive-sequence voltage at fault is provided in Jena and Pradhan (2010). Detailed analysis of a new method for fault event detection and optimum relay coordination in wind farm using genetic algorithm is given in Perven et al. (2015). A new concept of integrated impedance based intelligent relaying for transmission line with TCSC is introduced in Jena and Samantaray (2015). The technique in Moravej et al. (2011) presents a new combined S-Transform and Logistic Model Tree techniques for fault classification and fault section identification in transmission system with TCSC. Both schemes in Jena and Samantaray (2015), Moravej et al. (2011) lack to give fault distance estimation. In Ray (2014), Ray et al. (2013), post fault one cycle voltage and current signals have been taken while in Parikh et al. (2008), Ma et al. (2015) post fault one cycle current signals have been considered. Post fault half cycle window signals are used in Vyas et al. (2014a, b), Yusuff et al. (2011).
ANN is powerful in pattern recognition, classification and generalization tool (Vyas et al. 2014a, b). Off-line data training is very useful feature of ANN. Immunity to noise, robustness and tolerance to fault are a few of the advantages of ANN over other pattern recognition tools. Nonlinearity and variations in system parameters will not seriously affect an ANN-based relay decision. So, various ANN-based algorithms have been investigated and implemented in power systems in recent years Jain et al. (2009).
This paper presents the application of ANN for fault distance location in a double end fed single circuit transmission line. All the ten types of internal shunt faults using only one terminal data, i.e. mainly MOV energy signals from relaying end have been considered. These MOV energy signals are taken over only one cycle window from the inception of fault which makes this scheme rapid. The effects of varying fault type, fault location and fault inception angle have been studied.
At present, quadrilateral characteristic distance relays are used for distance protection of 400 kV fixed series compensated transmission line and is reported to perform less accurate in estimating fault location distance. Line side voltage and current measurement get affected abruptly in series compensated line, whereas, MOV energy signals are not affected as compared to impedance measurement signals used in other distance protection schemes due to fixed series compensation.
The performance of the proposed scheme has been investigated by a number of offline tests for internal faults. Percent absolute error criterion is chosen to evaluate the proposed method performance. The simulation results show that all the ten types of internal faults can be correctly classified and located after one cycle from the inception of fault. In addition, the proposed scheme does not require a communication link to recover the remote end data. Such comprehensive work has not been reported earlier for fault classification and fault location estimation of fixed series compensated line using MOV energy signals of fixed series capacitors (FSC) as input to train the ANN with soft computing paradigm.
This paper is structured into 7 main sections; the first section is an introduction. In the “Working philosophy of FSC” section we discuss the basic protection scheme of FSC and in “System configuration and modeling” section system configuration and modeling in Matlab/Simulink with above mentioned real time system parameters is explained. Section “Implementation of ANN” focuses on ANN and learning rule implemented in this novel scheme while “Proposed algorithm” section introduces the detailed explanation of the proposed scheme of fault classification and fault location estimation with complete algorithm. Also, the details of training the ANN with MOV energy signals at various fault conditions to get the minimum error is explained in “Proposed algorithm” section. Section “Performance evaluation and results” presents the performance evaluation and simulation results of the proposed scheme in terms of percent absolute error. Finally, “Conclusions” section clarifies the extracted conclusions with this new superior proposed protection scheme of series compensated transmission line.
In case of external fault (fault outside the line section, where the series capacitor is located), the MOV is so designed that it will limit the voltage to the protective voltage level, keeping capacitor banks in circuit until the line circuit breakers in the external line will clear the fault.
In case of internal fault (fault inside the same line section, where the series capacitor bank is located), the forced triggered spark gap will bypass the MOV and the capacitor bank only when the fault current crosses the maximum current rating of the capacitor units (i.e. 3000 A in this case) and MOV dissipation energy level crosses its threshold limit (i.e. 15 MJ) thus protecting them. At the same time, the closing command is given to the bypass circuit breaker (BPCB) in order to protect and extinguish the spark gap.
The damping circuit helps to limit and dampen the discharge current of the capacitor bank in case the spark gap operates or the BPCB is closed. Current transformers (CTs) are provided at different locations to measure the current for different protection systems. The two Isolators are used to connect the platform and platform equipments to the line or to isolate the platform and platform equipments from the line. The earth switches are used for earthing the platform and platform equipments, as soon as the platform is completely isolated from the line.
Ratings of various components of FSC
Fixed compensation capacitor bank
43.04 Ω, 73.96 μF, 3000 A, 387.36 MVAR/phase
MOV for capacitor bank
MCOV = 130 kV rms, E = 15 MJ/phase
FOV = 400 kVp
700 μH, 3000 A
By-pass circuit breaker
400 kV, 3 phase, SF6, 3150 A
Total line reactance is X l = 107.6 Ω. The capacitance of FSC, C = 73.96 μF and capacitive reactance of FSC is Xc = 43.04 Ω which is 40 % of Wardha–Aurangabad total line reactance. The capacitor bank is designed for maximum current rating of 3000 A. The MCOV of MOV is kept 130 kV in this case. Since the MOV is a non-linear resistive element and it has an energy dissipation limit, it is protected against excessive heat by an overload protection. The overload protection calculates the energy absorbed by the MOV and triggers a parallel air gap if the energy exceeds a threshold value i.e. 15 MJ used in this study.
In practice, these measurements are done with 0.2 accuracy class current and capacitive voltage transformers (CVT) which gives highly accurate measurements and saturation effects of measuring current and voltage transformers can be avoided.
ANN is a family of models motivated by biological neural networks which can be used to approximate the functions that can depend on a large number of inputs which are generally unknown and random. ANN is an efficient random function approximation tool (Vyas et al. 2014a, b). ANN can be powerfully used in online learning and large data set base applications. After appropriate mapping of inputs and outputs in ANN, the connections will enclose the non-linearity of the desired mapping.
Advantages of ANN over other classifiers are (1) it can be easily implemented in parallel architectures which reduces the processing time compared to other kind of algorithms, attaining comparable results, (2) it is able to obtain non-linear and complex relationships, (3) response is better and (4) it can handle large amount of data sets resulting in easy implementation in a digital relaying system.
It has been proven that approximation of any nonlinear function to arbitrary accuracy can be achieved by backpropagation learning with sufficient hidden layers. This makes backpropagation learning neural network a good investment for system modeling and signal prediction. The back-propagation learning rule is therefore used in perhaps 80–90 % of practical applications. The most suitable training method for the algorithm of this research work is carried on with the Levenberg–Marquardt optimization technique. It is fast and has stable convergence. It has become a standard method for non-linear least-squared problems and is widely adopted in various applications and streams for tackling with real time data-fitting problems.
Series compensation affects the impedance estimation due to the fault and causes distortion of phase voltage and line current waveforms resulting in the fault location estimation in an unpredictable manner. Whereas, MOV energy signals are not affected as compared to impedance measurement signals used in protection schemes due to fixed series compensation. MOV energy signals are never implemented before in any of the earlier conventional fault classification and location estimation algorithms which make this online scheme distinctive. The use of online MOV energy signals with relaying end neutral current makes this scheme very easy and unique.
Fault detection and classification
If the magnitudes of MOV energy in only one phase and Ig are greater than the threshold, then the fault is classified as single phase to ground (L–G) fault.
If the magnitudes of MOV energy in any two phases and Ig are greater than the threshold while the magnitude of MOV energy in third phase is negligible, then the fault is classified as a double phase to ground (L–L–G) fault.
If the magnitudes of MOV energy in any two phases are greater than the threshold while the magnitude of MOV energy in third phase is negligible and Ig is less than the threshold, then the fault is classified as phase to phase (L–L) fault.
If the magnitudes of MOV energy in all the three phases are greater than the threshold simultaneously, then the fault is classified as 3-phase fault.
Fault location estimation
In this stage, the output of fault classification stage and MOV energy signals are used as input to the respective fault ANN to estimate accurate fault distance from relaying end. Four efficiently trained ANN networks for L–G, L–L, L–L–G and 3-Phase faults respectively are considered. Respective ANN is then activated according to the type of fault which then detects accurate fault location as the output of the proposed scheme as shown in algorithm Fig. 4. If there is an external fault, i.e. fault in another line behind the FSC then the main relay will recognize it as a reverse fault (as followed in industry at present) and the main relay will not let the proposed relay operate for such external fault proper logical coordination assigned. Further the fault point outside the line, it will have less impact on MOV energy and following this convention, the change in the MOV energy will not cross the set threshold value of the proposed algorithm and this relay will not operate for any external fault.
To design the best ANN, it is crucial to train it efficiently and correctly. The training sets are carefully chosen so that all fault conditions, i.e. different FIA and fault locations, are considered. The performance of this soft computation ANN scheme is then tested using random fault conditions in the training set. The approach adopted here is based on the Levenberg–Marquardt (Trainlm) optimization technique. To find the optimum values of the ANN parameters, input signals of Ea, Eb and Ec corresponding to a particular fault condition are fed to the ANN and its output is compared with the desired output corresponding to that fault condition. The ANN is re-trained after each set of new fault conditions.
The expert system database is obtained by extensively simulating the system under normal and fault conditions of a transmission line during the investigation. The inputs are combined and also linked with the output, based on the expert system database to find the accurate ANN output. Accuracy of ANN outputs was also tested with various numbers of hidden layer neurons (up to 50 neurons). Best accuracy is found with 3 neurons in hidden layer for L–G fault, 4 neurons in hidden layer for L–L–G fault, 5 neurons in hidden layer for L–L fault and 7 neurons in hidden layer for 3-phase fault. The various extensive fault conditions considered to train the ANN are explained in Performance evaluation and result section.
FSC data generation for fault cases
Type of faults
a–g, b–g, c–g, a–b–g, b–c–g, a–c–g, a–b, b–c, a–c, a–b–c
All 3 phases
10–390 km (in step of 10 km)
Fault inception angle
0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°
Total cases = 10 × 3 × 39 × 8 = 9360
MOV energy for L–G fault
MOV energy for L–L fault
MOV energy for L–L–G fault
MOV energy for 3-phase fault
Ig for L–G fault
Ig for L–L fault
Ig for L–L–G fault
These threshold values are flexible and can be varied according to various system parameters and requirements of the particular transmission line. Even if there is involvement of ground due to another reason for the short time, threshold MOV energy value will not be crossed and proposed scheme will not treat it as a fault and the same has been checked.
Test results of ANN-based fault locator for a–g (L–G) fault
MOV energy a-ph (MJ)
Actual fault location (km)
ANN fault location (km)
Abs. error (%)
Test results of ANN-based fault locator for a–b (L–L) fault
MOV energy a-ph (MJ)
MOV energy b-ph (MJ)
Actual fault location (km)
ANN fault location (km)
Abs. error (%)
Test results of ANN-based fault locator for a–b–g (L–L–G) fault
MOV energy a-ph (MJ)
MOV energy b-ph (MJ)
Actual fault location (km)
ANN fault location (km)
Abs. error (%)
Test results of ANN-based fault locator for a–b–c (3-phase) fault
MOV energy a-ph (MJ)
MOV energy b-ph (MJ)
MOV energy c-ph (MJ)
Actual fault location (km)
ANN fault location (km)
Abs. error (%)
MOV energy measured at various locations at different inception angles was given as input to the trained ANN and output was given as soft computation estimated fault location by ANN. Outputs at randomly selected fault inception angles were checked and presented so as to get further clarified vision and more accuracy. The faults were created at 8th cycle. A realistic fault resistance of 0.1 Ω was utilized considering the high severity of the faults and previously recorded real fault resistance values at the Wardha Substation.
Since the proposed scheme employed in this analysis is based on a distributed line model, exact transmission line parameters and results are accurate, this scheme can be implemented in a real fixed series compensated line.
The FSC protection scheme was found working effectively for all the fault simulations performed. Evidently, the design settings for FSC were found correct. At various locations, different types of faults were tested to find out the maximum deviation of the ANN estimated distance measured from the relay location, from the actual fault location. All the absolute errors were found to be well within 2 %. Tabulated results show that the soft computation technique of ANN captures the nonlinear relationship of input signals properly as evidenced by absolute error calculations. It can easily be seen from all the tables that the scheme is robust and accurate for varying fault conditions.
Fault classification comparision
This algorithm requires only local per phase MOV energy measurement which is easily available at substation end.
It does not need any feature extraction process from input signals which is mostly used in other algorithms in literature and hence the computational burden is reduced.
No need of other end measurement, i.e. synchronized or unsynchronized data. So, the two end communication delay problems are avoided and cost is saved.
MOV energy signals are not much affected as compared to impedance measurement signals used in other distance protection schemes due to fixed series compensation.
The system considered in the proposed algorithm is modeled using real 400 kV grid parameters.
A new online ANN–MOV energy based accurate scheme for fault detection, classification and fault location estimation in a series compensated transmission line has been proposed. The proposed algorithm provides a novel method for accurately estimating the fault location using only one end measurement where the FSC is installed. This novel scheme proves that it suits well in such fixed series compensated transmission system for complete protection application. The proposed algorithm has been suggested to the competent testing authority at the substation and the same has agreed to test run the algorithm with real time fault data during test commissioning.
The proposed scheme requires measurement data of MOV energy over only a short duration of post fault to classify and estimate the location of fault accurately. This fault location scheme gives estimates for fault distances which are well within the 2 % error margin with proposed high accuracy 0.2 class measuring devices mentioned. The proposed online scheme, with simplicity, high accuracy and fast performance, is a smart investment for power system protection application. Thus, this ANN based scheme is proposed as a potential solution to detect, classify and locate faults accurately in 400 kV series compensated line.
PK conceived the idea behind the proposed technique. AS participated in its design and modeling. GS and NP provided the real system parameters and testing platform. All the authors helped in drafting the manuscript. All authors read and approved the final manuscript.
The authors would like to thank Electrical Engineering Department., Visvesvaraya National Institute of Technology, Nagpur (India) for providing required research infrastructure for this project and Power Grid Corporation of India Ltd., WR—I for providing the necessary data as well as technical expertise for the same.
The authors declare that they have no competing interests.
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