Identification of Overcurrent Relay Faults Using Backpropagation Neural Network

Transcription

1 Identification of Overcurrent Relay Faults Using Backpropagation Neural Network Tey Ching Li College of Graduate Studies, Universiti Tenaga Nasional, Malaysia. Farah Hani Nordin Department of Electronics & Communication Engineering, University Tenaga Nasional, Malaysia. Tuan Ab Rashid Bin Tuan Abdullah Department of Electrical Power Engineering, University Tenaga Nasional, Malaysia. Abstract Overcurrent relays are considered as the backbone for any protection system and any faults in overcurrent relays may lead to power system breakdown. However, the actual fault of a relay is usually known after the faulty relay is sent for a Root Cause Analysis which is time consuming. Thus, the aim of this paper is to identify the overcurrent relay faults using Backpropogation Neural Network based on the general faults obtained by visual inspection without going through the Root Cause Analysis. A systematic system is created using neural network and Graphical User Interface (GUI) so that an inexperienced user will be able to predict the actual fault without sending the relay to the manufacturer. This will not only expedite repairing time, improve system availability but also will be able to reduce the operating cost. Keywords - Overcurrent Relay; Backpropagation Neural Network; Graphical User Interface (GUI) I. INTRODUCTI Power system protection plays an important role in maintaining a high degree of service reliability and security in present day. Nowadays, electricity supply is an essential service. Power outages and blackouts have become unacceptable disruptions to our daily lives and routine activities [1]. The world experienced at least thirteen major electrical power supply outage in year 1965 to 2012 [2]. Blackouts can be initiated by many causes, such as severe demand and generation imbalance, protection system failures and incorrect or slow actions of system operator. Hence, power system protection plays one of the important roles in maintaining the power system stability. Overcurrent relay is one of the protection devices in power system. Overcurrent relays are usually used for both primary and back-up protective relays, applied in every protective zone in the system. The basic principle is that when the current flowing into the overcurrent relay exceeds a predetermined amount, the relay operates with or without an intended time delay and trips the associated circuit breakers [3]. Several faults may cause overcurrent relay to malfunction. In the area of relay protection, faster response is needed while the malfunction of relay protection is not acceptable. When fault occurs at the overcurrent relay, the person in charge of the maintenance works will only be able to describe the faults that they see with their naked eyes. Only experienced workers will be able to predict the actual faults of the relay. Once a fault is observed, the faulty overcurrent relay will be sent to the manufacturer for the relay to go through a Root Cause Analysis to identify the actual fault. The process is time consuming and repairing works of the faulty overcurrent relay will only be made after the actual fault is identified. Thus, the aim of this paper is to discuss the development of a Graphical User Interface (GUI) that can be used to identify the actual fault of the overcurrent relay using Backpropagation Neural Network (BPNN). The GUI developed will be able to be used by inexperienced person to predict the actual fault of the overcurrent relay without having to wait for the results from the Root Cause Analysis. This will expedite repairing time, improve system availability and reduce operating cost. The paper starts with the introduction in Section I followed by Section II which explains the data of the overcurrent relay faults. Section III describes the training of the overcurrent relay faults using backpropagation neural network and Section IV explains the development of the GUI with neural network to identify the relay faults. Section V concludes this paper. II. OVERCURRENT RELAY FAULT DATA The overcurrent relay fault is collected and separated into two categories which are: i. the general overcurrent relay faults and ii. actual overcurrent relay faults. The general faults data is collected based on visual inspection on the overcurrent relay while actual faults are obtained after the Root Cause Analysis is conducted on the overcurrent relay. Both general and actual overcurrent relay faults are then converted into numerical data manually as shown in Appendix A. For instance the general fault in Appendix A of Display Error Code Software Failure is defined as number one while the corresponding actual fault, DSP Chip Failure, is defined as number tree. Then all general faults of Display Error Code Software Failure is defined as number one while all the actual faults of DSP Chip Failure is defined with number three. 78

2 Faults Categories The 3rd National Graduate Conference (NatGrad2015), Universiti Tenaga Nasional, Putrajaya Campus, 8-9 April In this paper, general overcurrent relay faults are presented as inputs to the neural network while the actual overcurrent relay faults are presented as output. All this data is then presented Fig. 1. Hence, neural network is used to perform the identification of the overcurrent relay faults. Before the neural network can be used to identify the overcurrent relay fault, it needs to be trained. Note that all the data in Appendix A is used to train the neural network. Once the neural network is trained only one value that represents the general fault is be entered to test and whether the neural network is able to identify the correct value which correspond the correct actual fault Overcurrent Relay Faults Data Input Relay Types of Faults Fig 1. Input Data and Output Data Output Relay III. TRANING RELAY FAULTS USING BPNN The basic element of a neural network is the processing node, which performs two functions. First it sums the values of its inputs. This sum is then passed through an arbitrary activation function to produce the node's output value as shown in Fig. 2. ( ) (1) Where NET is the sum of weighted inputs to the processing node The goal of the training is to minimize the overall error between the desired and actual outputs of the network. It has been proven that backpropagation learning with sufficient hidden layers can approximate any nonlinear functions to arbitrary accuracy. This makes backpropagation learning neural network a good candidate for signal prediction and system modeling [4]. MATLAB Neural Network/ Data Manager (nntool) is used to create the network. Firstly, the input which is the general faults and output which is the actual faults are loaded into the network as input and target data, respectively. Then, a network property is defined as the feedforward backpropagation. Neural network parameters such as number of layers, number of neurons and the type of transfer function needs also to be defined. Once defined, the network is now ready to be trained. The training of the network need to be conducted repeatedly since the network parameters are changed through trial and error. The network parameters that gives the minimum training Mean Squared Error (MSE) will be chosen and tested. The performance of the trained neural network can be determined through Mean Square Error (MSE) which is defined as ( ) (2) Where = vector of n prediction and = vector of true value. The training went through several combinations of network parameters and it was found that the network starts to show improvements in the performance when the number of hidden layer is set to two layers and the transfer function for both layers are defined as log-sigmoid (logsig). Table I listed ten of the training processes conducted with the two logsig hidden layers and uses purelin transfer function at the output layer. From Table I it can be seen that gives the minimum MSE of x with the 30 neurons are defined for the first hidden layer and fifteen neurons for the second hidden layer. Note that for the output layer, the number of neuron is one since there is only one output signal. With this trained neural network, the network is then exported to MATLAB workspace and simulink blocks diagram is generated. Both Simulink blocks diagram and the architecture of the network is shown in Figure 3a and Figure 3b. Fig 2. Neuron model For the backpropagation training algorithm, the activation function must be differentiable. The most common form is the sigmoid function which is defined as No. of Hidden Neuron (L1) TABLE I. PERFORMANCE OF NEURAL NETWORK No. of Hidden Neuron (L2) Transfer Function at L1 Transfer Functio n at L2 Transfer Function at Output Mean Square Error (MSE) 30 5 Logsig Logsig Purelin Logsig Logsig Purelin Logsig Logsig Purelin x Logsig Logsig Purelin Logsig Logsig Purelin x Logsig Logsig Purelin Logsig Logsig Purelin x Logsig Logsig Purelin Logsig Logsig Purelin x Logsig Logsig Purelin x

3 (a) the GUI component such as Push Button. In this paper, the Push Button is named as Simulate as shown in Fig.4. By using M-file Editor, code can be added to the call-back to perform the functions needed. When user selects the General Fault Categories in the Input List Box and click Simulate as shown in Fig. 4, a numerical input data representing the General Faults Categories will be generated and sent to the Simulink trained neural network to identify the corresponding numerical value of the actual fault. Once the neural network has simulated the numerical value of the actual fault it is then sent bank to the GUI and the numerical actual fault is transformed into the respective actual fault. Thus, not only the user does not have to know the neural network that runs with the GUI but also even an inexperienced user can identify the actual fault. General fault is choose. (b) Fig. 3. Simulink Block Diagrams of (a) Trained Fault Identification Neural Network (b) The architecture of the Trained Neural Network The trained neural network is then tested and see whether it is able to output the correct numerical value when a single numerical value is inserted. Table II shows some of the numerical inputs tested into the neural network ad ir shows that the neural network is able to give the corresponding numerical value. TABLE II. RESULTS VALIDATI Input Data Target Data Output Data Since the neural network can only performs in numerical value, thus it is more users friendly when it performs in graphical user interface (GUI). The general faults of overcurrent relay can choose directly from the GUI and simulated results also can be performing in statement form instead of numerical form. IV OVERCURRENT RELAY FAULT IDENTIFICATI WITH GUI In this paper, the trained neural network is presented in a graphical user interface (GUI) to make the system user friendly. Graphical user interface is a graphical display in one or more windows where it contains controls. GUIDE is providing the tools for creating GUIs. The GUIDE will automatically generate an M-file that controls the GUI operation after the GUI is saved. This M- file provide code to initialize the GUI. It also contains a framework for the GUI call-backs which is the routines that execute in response to user generated events interacts with Push Button Actual fault is displayed after pressing the simulate button. Fig 4. Example of simulate results V. CCLUSSI The overcurrent relay faults are identified using Backpropagation Neural Network where it consists of two hidden layers and one output layer with trained MSE equal to 0.422x The raw data of overcurrent relay faults is interpreted into numerical data so that it can be trained using neural network. A GUI is design based on the trained neural network where it is able to perform the results in statement forms instead of numerical form. Thus, when general fault of overcurrent relay is chosen in the system, the actual faults can be generated directly through this GUI. This developed GUI can be used even by non-experienced personal to identify the actual fault of the overcurrent relay. REFERENCES [1] S. P. A. K. Abdullah Asuhaimi Mohd Zin, "Protection System Analysis Using Fault Signatures in Malaysia," Electrical Power \& Energy Systems, vol. 45, pp

4 205, February [2] Blackout: World's 13 Biggest Power Outages, [3] A. K. R. Yin Lee Goh and F. H. N. Aidil Azwin Zainul Abidin, "Modelling of Overcurrent Relay Using Digital Signal Processor," in IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010), [4] A. Abraham, Handbook of Measuring System Design, P. H. Sydenham and R. Thorn, Eds., John Wiley & Sons,

5 APPENDIX A General Overcurrent Relay Faults OVERCURRENT RELAY GENERAL AND ACTUAL FAULTS Actual Overcurrent Relay Faults Input Relay (General Overcurrent Relay Faults) Output Relay (Actual Overcurrent Relay Faults) Display Error Code_Software Failure DSP Chip Failure 1 3 Display Error Code_Relay Booted DSP Chip Failure Repeatedly 2 3 No Screen Display_Healthy & O/C LED DSP Chip Failure 4 3 Others_Same Termial Block Sticker Defect in Assembly 21 1 Others_Relay Tread Faulty Defect in Assembly 22 1 Defective Component_Defective RL2 Contact Defective Component 10 2 Relay Can't Power Up_Stop Working DSP Chip Failure After A While 28 4 Key Pad Malfunction_Keypad LEFT Faulty Defect in Assembly 12 1 Others_Hardware Alarm DSP Chip Failure 23 4 Faulty LCD Module Faulty LCD Module 11 5 Key Pad Malfunction_Keypad No DSP Chip Failure Respond 13 4 No Screen Display_Healthy & LED D8 DSP Chip Failure 5 4 Key Pad Malfunction_LCD Display "ALARM" Defective Component 14 2 Relay Booting Error_Healthy On & DSP Chip Failure Booted Repeatedly 26 3 Relay Booting Error_Healthy On & DSP Chip Failure Booted Repeatedly 26 3 No Screen Display_Healthy & LED DSP Chip Failure D8 Blinked 6 4 Hardware Failure Hardware Failure 18 6 Key Pad Malfunction_LED D8 Blinked & DSP Chip Failure Mini Relay Tripping

6 Relay Can't Power Up_Healthy & Warning LED & LCD Display Defective Component 29 2 "ALARM" In Beginning Hardware Failure Hardware Failure 18 6 Key Pad Malfunction_Healthy DSP Chip Failure 16 4 No Screen Display_Healthy & LCD Backlight Faulty LCD Module 7 5 No Screen Display_Healthy & O/C LED DSP Chip Failure 4 3 No Fault Found_Initially No Display No Fault Found 19 7 No Fault Found_L1 & L2 Display Value No Fault Found 20 7 Key Pad Malfunction_Front Port Failed Respond & No. 4 Configurable LED Blinking Key Pad Malfunction_Front Port Failed Respond & No. 4 Configurable LED Blinking Key Pad Malfunction_Front Port Failed Respond & No. 4 Configurable LED Blinking Normal Repair 17 8 Normal Repair 17 8 Normal Repair 17 8 No Screen Display_Healthy & O/C LED Normal Repair 4 8 No Screen Display_Relay No Display Normal Repair 8 8 Display Error Code_Display Booting Error Message Normal Repair 3 8 Relay Unable to Reset Normal Repair 30 8 Others_LED Warning Normal Repair 24 8 No Screen Display_Relay No Display Normal Repair 8 8 Others_No Software Normal Repair