Customized Multi Layer ANN Based Pattern Recognition Technique For Differential Protection Of Power Transformer Harish Balaga, D. N. Vishwakarma, Senior Member, IEEE Abstract: This paper presents the use of Customized Multi Layer ANN based Pattern Recognition Technique for the differential protection of a two winding three-phase power transformer. It proposes an efficient Resilient Back Propagation neural network (RPNN) model, which makes the discrimination among normal, magnetizing inrush, over-excitation and internal fault currents. Fault conditions of the transformer are simulated using MATLAB/SIMULINK in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB. Index Terms-- Artificial neural network, differential protection, Harmonic restraint relay, Pattern Recognition, Power transformer.
I. INTRODUCTION Now-a-days, research on artificial intelligence and its applications has been in full swing, particularly in the field of pattern recognition [1, 4, 15]. The ANN-based algorithms have been successfully implemented in many pattern or signature recognition problems. Because this particular protection problem can also be considered as a current waveform recognition problem, the use of ANN seems to be a good choice. The ANN-based approach can detect healthy operating based on recognizing their wave shapes, more precisely, by differentiating them from the fault current wave shapes. It gives a trip signal in the case of internal fault only and exercises restraint under healthy, magnetizing inrush and overexcitation conditions. In power systems, transformer is one of the essential elements and thus transformer protection is of critical importance. There are a variety of protective relays to provide reliable and secure transformer protection. Although using the second harmonic restrain/blocking approach that was used in the past may prevent false tripping during inrush conditions, practice has shown that it may sometimes increase fault clearance time for heavy internal faults followed by CT saturation. Since the CT iron-core has a non-linear characteristic it saturates at high currents, or when DC is present in the primary current. Now-a-days, differential relays are widely used to detect the internal faults of a transformer which involves converting the primary current and secondary currents in a common base and comparing them. In principle, this protection scheme makes use of current difference flowing through the different terminals of transformer so as to distinguish between internal The authors are with Department of Electrical Engineering, Institute of Technology, Banaras Hindu University, Varanasi-221005,UP, India(e-mail:
[email protected],
[email protected])
fault and healthy operating conditions. The transformer differential relay should be designed such that it does not maloperate during magnetizing inrush and over excitation conditions which fools differential relay [1]. Some researchers have investigated the use of ANN methods for the same purpose [5, 6, 9, 10]. In this work, an attempt has been made to design a novel relay using ANN which can effectively differentiate faulty operating condition from other healthy operating conditions based on the shape or pattern of the differential current signal. A multi-layered resilient back propagation network has been developed to realise the relay. This network consists of 48 inputs, 6 outputs. It also consists of two parallel operated independent hidden layers. After experimenting with different number of hidden neurons, a network with 40 hidden neurons on each layer is finalised. Detailed description about these inputs and outputs is given in later sections. The ANN-based pattern recognition algorithm has been tested to evaluate the performance of the proposed method in terms of accuracy and speed, and the encouraging results were obtained. The proposed network has shown excellent results by perfectly discriminating the faulty condition from other operating conditions. II. POWER SYSTEM SIMULATION FOR PATTERN GENERATION A three-phase 220/40 kV power system included a 150 km transmission line, has been used to produce the required test and training patterns. The simulation was done by means of Simulink (MATLAB version R2010b) software. The condition system has been produced, as shown in Table I, using this system to train the ANN. Faults are located at different points of transmission line. Also, they involve inrush current and over excitation condition with different voltage angles and with different loads. Breakers are connected at different positions for creating different operating conditions. TABLE I.
CONDITION SYSTEM FOR PATTERNS DATA GENERATION
Normal: Internal fault: Condition system
Load(MW)
When there is no fault. 3-phase fault is connected to Transformer secondary. Inrush: Breaker is closed at different voltage angles Over-excitation: At different over voltages 200, 400, 600, 400 and 1000
2
Figure 1.
Simulated three-phase power system model
III. NEURAL NETWORK DESIGN AND SIMULATION The developed ANN is one of the ANNs that are biologically inspired, that map sets of inputs into sets of outputs through a neural network. Each neuron in each layer generates one normalize output, as a function of its normalized inputs. The used network architecture is multi-layered with one input layer, two independent hidden layers, and one output layer. The outputs are nonlinear functions of the input, and are controlled by weights that are computed during learning process. The used learning process is the supervised type, and the used learning paradigm is the Resilient Back Propagation A. Resilient Back Propagation Training Algorithm To overcome the inherent disadvantages of pure gradient-descent, Resilient Back Propagation (RPROP) Algorithm performs a local adaptation of the weight-updates according to the behavior of the error function. In substantial difference to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforeseeable influence of the size of the derivative but only dependent on the temporal behavior of its sign. This leads to an efficient and transparent adaptation process. RPROP is generally much faster than the standard steepest descent algorithm. It also has the nice property that it requires only a modest increase in memory requirements. We do need to store the update values for each weight and bias, which is equivalent to storage of the gradient. B. Network Architecture and Training The first step to formulate the problem is identification of proper input and output set. Various architectures and combination of input sets were attempted to arrive at the final configuration with a goal of maximum accuracy. Keeping the number of outputs fixed at 6, the number of input neurons and the number of hidden neurons are varied, on trial and error basis, until it produced minimum error. During this process, trials were made considering both primary and secondary currents individually also. But the results are better when differential current samples are used as inputs. Each of the differential currents (of each phase) is typically represented in discrete form as a set of 32 uniformly spaced (in time) samples obtained over a data window of one cycle i.e. at the sampling rate of 32 samples per cycle or 1600Hz. These samples are used for training and testing the developed NNs. A total of 2142 training sets of samples (918 sets for healthy conditions and remaining sets for different types of faults, by simulating for a period of 200mSec, i.e. 10 cycles) generated by SIMULINK in MATLAB, have been used to train and test the neural network. Out of these 2142 training
Figure 2. ANN architecture TABLE II.
Adaption learning function Hidden layer transfer function Epochs Goal
TRAINING FUNCTION AND PARAMETERS
LEARNGDM
Tansig
1000 0
Training function Output layer transfer function Performance function Max-fail
TRAINRP
logsig
MSE 100
LEARNGDM- Gradient descent with momentum weight and bias learning function; TRAINRP- Resilient back-propagation training function.
sets of samples, 10% samples are used for validation and another 10% are used for testing purpose during training process. The network consists of 48 inputs. These 48 input values are combination of 3 sets of data, each set comprising of 16 differential current samples from each phase. For each case, signals are sampled at the sampling rate of 32 samples per cycle (over a data window of one cycle). This data is fed to the ANN in moving window format, i.e. first set of input consists of 1 to 16 samples, 2nd set consists of 2 to 17 samples and so on. Hence, at any instant of time, the ANN will have the latest half cycle data from each phase. The simulated waveform patterns that are used to train the ANN are presented in Fig.3. On the other side, the ANN will generate 6 outputs, each for 6 different operating conditions, namely, “Normal”, “Inrush”, “Over excitation”, “Fault @ Phase-A”, “Fault @ Phase-B” and “Fault @ Phase-C”. The values of these outputs are always either „1‟ or „0‟, ideally. Hence the outputs of the network have a unique set (e.g., 100000 = normal, 010000 = inrush, 001000 = over-excitation, 000100 = L-G Fault at phaseA, 000011 = 2L-G fault at phases B and C). This network
3
monitors all the conditions occurring in the power transformer and it issues the trip signal only in the case of internal fault condition i.e. when output is anyone from „000100‟ to „000111‟.
Figure 3(a). Normal condition
Figure 3(b). Inrush condition
Next comes the most important part of the architecture, i.e. the hidden layer. The proposed architecture consists of two hidden layers. But unlike the traditional series connected layers, the two hidden layers in the proposed in the network are operated in parallel, independently. That means the output of anyone of the hidden layer doesn‟t affect either the outputs or inputs of the other. Figure 2 illustrates the interconnections between different layers of the network. C. Testing algorithm Once the training process is completed the network is ready for testing. The network is then fed with new samples that are not used for training. The proposed fault detection algorithm is shown below in the form of flowchart. Before confirming the operating condition as faulty condition and issuing a trip signal, the algorithm first checks for any possible occurrence of inrush and over excitation conditions i.e., for cases that produce an output like „010010‟ (inrush and fault @ phase B) or „00400‟ (Over excitation and Fault @ phase A). This verification process is only to improve the reliability of the system. In the actual case, the above mentioned cases have very less probability to occur, as the proposed algorithm successfully differentiates the fault conditions from other operating conditions.
Figure 3(c). Over-excitation condition
Figure 3(d). Internal fault condition (L-G Fault)
Figure 3(e). Internal fault condition (3L-G fault) Figure 4. Fault detection algorithm
4
IV. NETWORK PERFORMANCE AND NUMERICAL RESULTS Concerning the ANN architecture, parameters such as the number of inputs to the network and the number of neurons in the hidden layer were decided empirically. This process involves experimentation with various network configurations. The best validation performance was 0.0083299 for the proposed network and was within acceptable limit. The proposed network responds in a very adequate way, performing the discrimination among normal, inrush, over-excitation, and internal fault currents correctly for all cases. Fig.5 shows the performance graphs and Table III shows the performance results of different topologies.
repeatability and accuracy of the proposed method into consideration, a value more than 0.999 can be considered as acceptable value to issue the trip signal. This is produced when 12th or 13th sample of the fault pattern is detected, which is less than a half cycle period. The tested output results for different operating conditions when 12th sample of fault or inrush or over excitation pattern is detected is shown in Table IV. Though one cannot find much difference in the tested output when trained the network with different architecture, one can definitely find some difference in the accuracy of the proposed system while discriminating the faulty condition from other conditions. TABLE IV.
TESTED OUTPUT FOR THE PROPOSED ARCHITECTURES Outputs
Operating condition
48_20_6
0.0191
48_30_6
0.0153
48_40_6
0.0083
48_60_6
0.0109
48_80_6
0.0126
48_ 96_6
0.0141
48_ 120_6
0.0173
The neural network is created using MATLAB nprtool, which is designed particularly for pattern recognition method based applications, rather than the generally used nntool. The training process takes just above 2 minutes for 1000 epochs. This is far better when compared to using nntool for network creation and training which takes more than an hour for 1000 epochs. Another important thing in protection scheme is its speed of issuing trip signal with maximum accuracy. The proposed network produced an output of „1‟ when the 10th sample of the fault pattern is detected. That means it takes just above half cycle period to detect the fault. However, taking the
4/5/6
O
T
O
T
O
T
Normal
0.99938
1
0.00017
0
0.00036
0
0.01952
0
Inrush
0.00005
0
1
1
0.00003
0
0.00046
0
0.00526
0
0.00011
0
0.99994
1
0.00772
0
0.00916
0
0.00018
0
0.00392
0
0.99921
1
T = Target ; O = actual output;
PERFORMANCE OF ANN WITH 48 INPUTS, 6 OUTPUTS, AND VARIABLE NEURONS IN THE HIDDEN LAYER (TRAINING EPOCHS: 1000) Best validation performance
3
T
TABLE III.
ANN topology
2
O
Overexcitation Internal fault (any phase)
Figure 5. Learning performance
1
V. CONCLUSION In this paper a feed forward back propagation neural network model for protection of three-phase power transformer have been proposed. The 48_40-40_6 architecture could correctly discriminate among the different conditions in power transformer such as normal, magnetizing inrush, overexcitation and internal fault conditions and issues trip signal only when any one or more of the last three outputs (4,5,6) crosses the threshold value. The FFBPNN based pattern recognition method is efficient in solving classification problems and a differential relay can be considered as a classifier which identifies what kind of event occurs on the network. The ANN has been trained for all the possible sets of simulated data under different operating conditions of transformer. ANN based differential relaying for power transformer shows promising security, accuracy and speed.
VI. REFERENCES [1]
[2]
[3]
[4]
A.J. Degens, “Microprocessor Implemented Digital Filters for Inrush Current Detection”, Electrical Power and Energy Systems, vol.4,no.3, pp.196-205, July 1982. H. K. Verma and A. M. Bosh, “A microprocessor-based inrush restrained differential relay for transformer protection,” Journal of Microcomputer Applications, no. 9, pp-313-318, 1986 Michel Habib, Miguel A. Marin, “ A comparative Analysis of Digital Relay in Algorithms for the differential protection of 3 phase Transformers”, IEEE Transactions on power systems, Vol.3, No.3 pp.1376-1384 August 1988 Keizo Inagaki, Masaru Higaki, Yoshiaki Matsui and Kentaro Kurita, “Digital Protection Method for Power Transformer Based on an
5 Equivalent Circuit Composed of Inverse Inductance”, IEEE Transactions on Power Delivery, vol.3, no.4, pp.1501, Oct 1988 [5] J. C. Maim, “Hardware and software design of a digital protection relay for power transformers,” Conference on Computer RelayingFuture Directions and Impacts, pp. 1- 19, 1988. [6] M. s. Sachdev, and M. Nagpal, “A Recursive Least Error Squares Algorithm for Power system Relaying and Measurement Application”, IEEE Trans. On Power Delivery, vol.6, no.3, pp-1008-1015, July 1991. [7] Limin Fu. „Neural Network in Computer Intelligence.‟ McGrew-Hill, Inc, New Delhi 1994 [8] SimPowerSystems and MATLAB ( 7.0.1) Simulink help manual Guide. [9] T.S. Sidhu, M.S. Sachdev, H.C. Wood, M. Nagpal, “Design Implementation and Testing of Microprocessor Based High Speed Relay for Detecting Transformer Winding Faults”, IEEE Tran. On Power Delivery, vol.7, no.1, pp.108-117, January 1992. [10] J.Pihler, D. Dolinar, “Improved Operation of Power Transformer Protection Using Artificial Neural Network”, IEEE Transctions on Power Delivery, vol.12, no.3, July 1997. [11] BKasztenny and E.Rosolowski, “Multi-Objective Optimization of a Neural Network based Differential Relay for Power Transformers” IEEE conference on Transmission and Distribution, 1999, pp 476 481 vol.2.
[12] M.R. Zaman and M.A. Rahman , “Experimental Testing of the Artificial Neural Network Based Protection of Power Transformers”, IEEE Transactions on Power Delivery, vol.13, no.2, April 1998 [13] Zahra Moravej, D. N. Vishwakarma and S. P. Singh, “ANN Based protection Scheme for Power Transformer”, Electric Machine and Power Systems, 28:875-884, 2000. [14] Zahra Moravej, and D. N. Vishwakarma, “ ANN Based Harmonic Restraint Differential Protection of Power Transformer”, IE(I) Journal-EL, Vol 84, June 2003 [15] H. Khorashadi-Zadeh “Power Transformer Differential Protection Scheme Based on Symmetrical Component and Artificial Neural Network”, 7th Seminar on Neural Network Applications in Electrical Engineering, IEEE Sep-23-25, 2004 [16] Enio C. Segatto and Denish V. Coury “A Differential Relay For Power Transformers Using Intelligent Tools”, IEEE Transactions on power system, vol. 21, No. 3, August 2006 [17] B. Ram and D. N. Vishwakarms, “Power System Protection and Switch Gear”, Tata Mcgrew-Hill Publishing Company Limited, New Delhi,1995 [18] C. L. Wadhwa, “Electrical Power Systems”, New Age International(P) Limited, Publishers [19] T.S. Madhava Rao, “Power System Protection Static Relays With Microprocessor Applications”, Tata Mcgrew-Hill Publishing Company Limited New Delhi,1989.
