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International Colloquium on Lightning and Power Systems
Ljubljana 2017
Detection of high impedance faults in power transmission network with nonlinear loads using artificial neural networks Ljupko Teklić1, Božidar Filipović-Grčić2, Ivica Pavić2, Roko Jerčić1 1Croatian Transmission System Operator Ltd., Zagreb, Croatia 2University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia
SUMMARY High impedance faults (HIF) represent one of the most difficult problems for fault detection in the power transmission networks. The main difficulty during the detection of HIFs is that a fault current is very low and therefore it is difficult to detect a fault through conventional protection devices such as distance or overcurrent relays. In case when transmission network contains a variety of connected nonlinear loads the detection of fault is even more challenging, because HIF currents and nonlinear load currents may have similar RMS values. Therefore, it is important to make a distinction between HIF current and rated current of nonlinear loads. This paper presents an approach for distinguishing HIFs from nonlinear load operation using artificial neural networks (ANNs). A part of the 110 kV transmission network is modelled in MATLAB Simulink to perform simulations of HIFs and nonlinear load operation. As input values ANNs use voltage and current waveforms in the frequency domain from both ends of the line. Pattern recognition neural network is used to make a distinction between HIF current and nonlinear load current in case of similar RMS values. Two cases of ANNs with different network sizes are compared considering the number of neurons in hidden layer. The proposed approach is successfully tested with actual measured current of single-phase electric locomotive with diode converters.
KEYWORDS High impedance faults, nonlinear loads, artificial neural networks, power transmission system, transmission lines, relay protection.
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1. INTRODUCTION A high impedance ground fault occurs when energized phase conductor makes unwanted contact with objects of low conductance such as a road surface, sidewalk, lawn or tree branch, limiting a fault current to very low values. These faults cannot be easily detected by conventional over-current protection devices [1] and they often exhibit arcing phenomena when no solid return path for current is available, resulting in fault currents with noticeable high-frequency components. Various methods for HIF detection have been previously proposed by other researchers. These methods concerning used algorithms can be divided into time domain and frequency domain. In time domain, ratio ground relay, proportional relay algorithm [2], and a smart relay [3], fault current flicker and half-cycle asymmetry [4] has been proposed. In frequency domain, several articles have been published based on Kalman filtering approach [5], the fractal theory [6] and neural networks [7,8]. Recently wavelet transform has been proposed to achieve better results [9]. Transmission line fault location techniques can be classified into two categories: 1) methods using data from one terminal and 2) methods using data from two terminals of transmission line. It is well known that these techniques based on two terminals data require communication links and synchronized sampling equipment [10]. In [11], a protection scheme has been proposed for discriminating HIF and the normal system operation events in distribution system based on wavelet packet transform and ANN. However, not much research was done regarding distinction between HIFs and nonlinear load operation in high voltage transmission networks. This paper presents an approach for distinguishing HIFs from nonlinear load operation using ANNs. ANNs [12] are powerful in pattern recognition, classification and generalization and they have excellent features such as noise immunity, robustness and fault tolerance. Therefore, ANNs are widely used for a variety of power system applications because they can be trained with off-line data. A part of 110 kV transmission system is modelled in MATLAB Simulink to perform simulations of HIFs and nonlinear load operation. As an input values ANNs use the calculated current and voltage waveforms in frequency domain from both ends of the line. Fast Fourier transform is used for harmonic analysis of the current and voltage waveforms. The performance of the proposed approach is tested with actual measurements from the transmission power system. 2. THREE-PHASE TRANSMISSION SYSTEM MODELING A part of 110 kV transmission network is modelled in MATLAB Simulink using three-phase models of nonlinear loads, substations and overhead lines (OHL) as shown in Figure 1. HIFs are simulated using two different models represented by Subsystems 1 and 2: model including ideal switch and fault resistance (without arcing phenomena) and arcing model proposed by Keyhani [14].
Figure 1 Three-phase model of transmission system
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All OHLs are modelled as distributed parameter lines except in case of very short lines which are modelled as π sections. Simulation time step Ts=5 µs is selected according to the shortest line length and it is sufficient for an adequate representation of frequency behaviour of connected nonlinear loads. Some parts of the network are represented as subsystems to get a better overview of the complete transmission network. Network model is based on input data from Croatian transmission system. Characteristic part of the network which is observed is a railway substation (Subsystem3) which is connected to neighbouring substations SS 1 and SS 2 by OHL3 on one side, and OHL1 and OHL2 on the other side. Electric railway system is modelled in detail including 110/25 kV substation and contact line with connected thyristor and diode locomotives as described in [13]. OHL1 and OHL2 represent one physical OHL which is divided into two parts so the fault simulations could be conducted on various locations along the line. The rest of 110 kV network is represented by Subsystem 4. Waveforms of currents in SS 1 during HIFs and nonlinear load operation are shown in Figure 2. Double phase (B and C) to ground faults (BCG) are simulated with a fault resistance of 10 kΩ to obtain RMS values of HIF currents like ones corresponding to nonlinear load operation (electric locomotive connected between phases B and C). Current waveforms are represented in per unit (p.u.) values, where 1 p.u. corresponds to base voltage 110 kV and base power 100 MVA (1 p.u.=742 A).
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Figure 2 Simulated current waveforms (phase A – blue, phase B – green, phase C – red): a) BCG fault; b) BCG arcing fault; c) diode rectifier locomotive; d) thyristor rectifier locomotive
3. ARTIFICIAL NEURAL NETWORKS FOR PATTERN RECOGNITION ANNs have broad applications in electrical engineering such as load prediction, fault detection and locating faults. In this paper detection of HIFs and their distinction from nonlinear load operation are approached as a classification problem, and solved by pattern recognition networks. These are feedforward networks that can be trained to classify inputs according to target classes. Main classes in this paper are normal operation of the power system with nonlinear load and HIF. ANNs represent a mathematical tool which mimics functions of biological neural networks. Neuron is the basic element 2
of neural network as shown in Figure 3. It consists of input elements (x1 - xp) which are multiplied by neural connection weights (wk1 – wkp), added together and then applied to transfer function φ together with bias threshold Θk. Result is an output of neuron yk. activation function
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Mathematical interpretation is represented by the following equations:
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Connecting artificial neurons in a way that output of one neuron makes input for other creates an ANN. 4. DISTINCTION BETWEEN NON-LINEAR LOAD OPERATION AND HIF Main concern for HIF detection in the system shown in Figure 1 is a case of double phase (B and C) to ground fault (BCG) where it can be misinterpreted as normal operation of electric railway. The ANN as inputs takes simulated voltage and current waveforms in SS 1 and SS 2. Simulated waveforms are processed by Fast Fourier Transform (FFT) to obtain amplitudes of the first 29 harmonics of currents and voltages which make an input vector containing 360 elements. Example of such input vector is shown in Figure 4 where every voltage and current group consists of first 29 harmonic amplitudes. Current and voltage amplitudes are represented in per unit (p.u.) values, where 1 p.u. corresponds to 742 A and 110/√3·√2 kV, respectively.
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Figure 4 Input vector for ANN
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A total of 3975 simulations are conducted including 990 BCG faults, 990 BCG arcing faults, 399 diode rectifier locomotive operations and 1596 thyristor rectifier locomotive operations. In HIF simulations, the following parameters are varied: load flow (active power from -100 MW to 100 MW in SS 1; reactive power 30% of active power), fault resistance (0 Ω - 10 kΩ), fault inception time (5 ms, 10 ms and 12.5 ms) and fault location (each 2 km along the OHL1-2). In simulations of nonlinear load operation, a back electromotive force of DC motors is varied in the range 100 V - 1100 V, for both diode and thyristor locomotive. The variation of thyristor conduction angle is considered for thyristor locomotive. Training of the ANN gives a confusion matrix as shown in Figure 5 which displays the classification confusion grid for cases with 25 and 50 neurons in hidden layer. Complete input vector set consists of 990 BCG fault vectors (element 1,1), 990 BCG arcing fault vectors (element 2,2), 399 diode locomotive operation vectors (element 3,3) and 1596 thyristor locomotive operation vectors (element 4,4).
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All Confusion Matrix 990 0 0 0 100.0% 24.9% 0.0% 0.0% 0.0% 0.0% 0 0%
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Figure 5 ANN confusion matrix: a) 25 neurons in hidden layer; b) 50 neurons in hidden layer
On the confusion matrix plot, the rows correspond to the predicted class (Output Class), and the columns show the true class (Target Class). The diagonal cells show for how many (and what percentage) of the examples the trained network correctly estimates the classes of observations. That is, it shows what percentage of the true and predicted classes match. The off diagonal cells show where the classifier has made mistakes. The column on the far right of the plot shows the accuracy for each predicted class, while the row at the bottom of the plot shows the accuracy for each true class. The cell in the bottom right of the plot shows the overall accuracy. As can be seen from Figure 5, ANN with 25 neurons has more misclassifications than the one with 50 neurons. ANN with 25 neurons misclassified all BCG faults as BCG arcing faults, therefore returning accuracy as a NaN% (top right element of confusion matrix in Figure 5 a), corresponding to division by zero, and 45 cases of diode locomotive operation as thyristor locomotive operation. ANN with 50 neurons misclassified only 1 thyristor locomotive operation as a diode locomotive operation. In both ANN cases, HIF is successfully distinguished from nonlinear load operation. Four output classes are defined during training of ANN, one for each type of input class in the form shown in Table 1. Table 1 – ANN output classes
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5. VERIFICATION OF THE PROPOSED APPROACH Verification is conducted by injecting measured current waveform shown in Figure 6 obtained during the constant drive of diode locomotive. In simulations, each current harmonic of measured 4
Current (A)
waveform is represented by separate current source with corresponding phase angle and amplitude. Voltage and current waveforms obtained at SS1 and SS2 during current injection are used as an input vector for verification of the proposed approach.
Time (s) Figure 6 Measured current waveform on 25 kV side of railway substation transformer 110/25 kV
By applying the input vector to ANN trained in chapter 4, the ANN verification output class vector shown in Table 2 is obtained, corresponding to diode locomotive operation. Table 2 – ANN verification output class
1.3e-10 5.7e-9 0.999 2.7e-08
6. CONCLUSION This paper presents a method for detection of HIFs in power transmission network with connected nonlinear loads using ANNs. A part of 110 kV transmission network is modelled in MATLAB Simulink and numerous simulations are conducted including different cases of double phase to ground faults and nonlinear load operation (diode rectifier locomotive and thyristor rectifier locomotive). The following parameters are varied in HIF simulations: load flow, fault resistance, fault inception time and fault location. In simulations of nonlinear load operation, a back electromotive force of DC motors is varied and variation of thyristor conduction angle is considered for thyristor locomotive. As an input values ANNs use the calculated current and voltage waveforms in frequency domain from both ends of the transmission line. Pattern recognition ANN is used to make a distinction between HIF and nonlinear load operation in case of similar RMS values of currents. In all cases, ANN successfully distinguished HIFs from nonlinear load operation. Case with higher number of neurons in hidden layer significantly improved ANN classification performance. Verification of the presented approach is conducted by testing ANN with injected measured current waveform obtained during the constant drive of diode locomotive.
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ACKNOWLEDGEMENTS This work has been supported in part by the Croatian Science Foundation under the project “Development of advanced high voltage systems by application of new information and communication technologies” (DAHVAT).
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