Fuzzy logic based on-line fault detection and classification in transmission line

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

transform techniques are computationally complex. The fuzzy logic based fault classification techniques are comparatively simpler as it requires only some linguistic rules. In (Ferrero et al. 1995) identified the nature of fault (whether LG or LLG), but the involved phases in the fault could not been identified and phase fault is not considered. In (Wang and Keerthipala 1998) reported the improved technique based on fuzzy-neural approach and considered both the symmetrical and unsymmetrical fault. But this method required extra effort to obtain training of ANN. In (Dash et al. 2000) showed all the ten types of fault identification by fuzzy-neural approach. In (Das and Reddy 2005;Yadav and Swetapadma 2015b;Saradarzadeh and Sanaye-Pasand 2014) proposed fuzzy logic methodology to identify the ten types of faults.
In this paper fuzzy logic based fault detection and classification on real time has been proposed. Post-fault three phase currents; zero sequence and positive sequence current samples are taken into account for fault classification. The proposed logic detects and classifies the faults at maximum delay of 100 ms or less with higher accuracy and also this speed can be further increased and detection time can be improved. Real time data acquisition ensures control within specified time limit.

Methods
The method adopted for the study is applied on single line diagram shown in Fig. 1. 20 numbers of different faults have been created on test-bed for tuning the fuzzy membership function and fuzzy rules. The Data are acquired through CRIO and post fault data generated for different types of fault are used to evaluate the performance of the proposed fuzzy logic based fault classification system. The power system is developed taking into consideration the acquired data as shown in Fig. 1 in Lab view software. The fuzzy logic based fault classification is first experimented i.e. on offline environment for finding the optimal system. This optimal fuzzy logic based classification system is then applied on the system for any fault on real time. It is observed during the analysis of the data that depending on the type of fault i.e. line to ground faults, line to line faults, line to line to ground faults or three phases fault, the waveform changes accordingly. It is significant to mention that during fault the voltage tends to reduce to zero and current tends to rise.
Different types of faults are characterized in terms of δ 1 , δ 2 , δ 3 and δ 4 , which calculations are shown below (Susilo et al. 2013). Post fault current samples are solved as below. where I a , I b , and I c represent the sample of three phase currents. I 0 and I 1 are zero sequence and positive sequence current. Fuzzy rule based method for fault classification is developed on the basis of δ 1 , δ 2 , δ 3 , δ 4 . Zero sequence current, I 0 has been taken into account to detect the presence of ground fault and δ 4 represents the ground fault detection.
Fuzzy rule base for fault classification: • If δ 1 is high and δ 2 is medium and δ 3 is low and δ 4 is high it is an L a − G fault; • If δ 1 is low and δ 2 is high and δ 3 is medium and δ 4 is high it is an L b − G fault; • If δ 1 is medium and δ 2 is low and δ 3 is high and δ 4 is high it is an L c − G fault; • If δ 1 is medium and δ 2 is high and δ 3 is low and δ 4 is low it is an L a − L b fault; • If δ 1 is low and δ 2 is medium and δ 3 is high and δ 4 is low it is an L a − L c fault; • If δ 1 is high and δ 2 is low and δ 3 is medium and δ 4 is low it is an L b − L c fault; • If δ 1 is medium and δ 2 is high and δ 3 is low and δ 4 is high it is an L a − L b − G fault; • If δ 1 is low and δ 2 is medium and δ 3 is high and δ 4 is high it is an L a − L c − G fault; • If δ 1 is high and δ 2 is low and δ 3 is medium and δ 4 is high it is an L b − L c − G fault; • If δ 1 is medium and δ 2 is medium and δ 3 is medium and δ 4 is low it is an L a − L b − L c fault; The triangular membership function has been used to present different fuzzy variables in the antecedent and consequent parts of the fuzzy rules as shown in Fig. 2. Mendal (1995) describes the triangular membership function as triplets with respect to the points A, B and C. It is observed that points A and C have membership value of 0.0 while point B has membership value of 1.0. Extensive study has been carried out to select proper triplets values of triangular membership function of δ 1 , δ 2 , δ 3 and δ 4 . The selected triplets for triangular membership function of fuzzy variables in antecedents parts and consequent part are shown in Tables 1, 2 and 3 respectively. Figure 3 presents the block diagram of the proposed methodology. The ADC (analog to digital converter) is connected with FPGA (field-programmable gate array) hardware. The FPGA can directly access the ADC acquired values and send it to RT (real Table 1 Fuzzy variables in the antecedent parts of fuzzy rules for δ 1 , δ 2 , δ 3

Input variables Triangular triplets
High 0.54 1 1  For every sample the FPGA acquire value is put into a FIFO (fast in fast out) queue, which can be accessed from the RT processor. The RT processor polls 1000 values for each channel, which means 5 consecutive cycles of 50 Hz signal. Once 5 cycle data is present, the RT processes the data and measures the RMS value of the signal acquired, and also checks for fault conditions. The sampling rate and the number of sample to detect the fault can be varied using the user control; this gives an option to change the parameters of the fault detector leading to improvement of efficiency, accuracy and response time. Three phase current data from test-bed have been acquired through CRIO. The signals acquired are normalized and different faults are characterized in terms of δ 1 , δ 2 , δ 3 and δ 4 . After analysis of data obtained, triplet values are selected in antecedent and consequent parts to represent various fuzzy variables. Rules base are then prepared for classifying the fault type. After successful compilation of simulation fuzzy logic, the generated logic is dumped to the Field Programmable Gate array (FPGA) control LabVIEW hardware. In CRIO three modules have been used for high voltage data acquisition modules, high current data acquisition module and relay switch module for protection. The real time, line voltage and current values from the test bed are used as input to the FPGA control. When fault Fig. 3 Block diagram of the proposed methodology occurs in the system, relay switch module detect fault and the relay will trip after intentional 5 ms delay. The type of fault occurring in the system will be displayed on the host PC.

Real time monitoring and controlling
Laboratory Virtual Instrument Engineering Workbench (LabVIEW) is a powerful and flexible instrumentation and analysis software application tool which was developed in 1986 by the National Instruments. LabVIEW is extremely flexible and commonly used for data acquisition, instrument control, data processing and industrial automation. The CRIO device can interface between computers and Test-bed set up. Figure 4 shows schematic of real time monitoring and controlling.  Figure 5 presents front panel of LabVIEW graphical user interface (GUI) created. Figure 6 shows real time program for fuzzy logic based fault classification.

Hardware implementation
We have used National Instruments Controller with 667 MHz Dual-Core ARM Cortex-A9 processor running in the NI Linux Real-Time, also integrated Chassis has Artix-7  Table 4 shows the compilation result. The proposed logic for fault detection and classification has been tested on an experimental transmission line module on 360 km of π-model. The picture of the hardware set-up is shown in the Fig. 7. Table 5 represent the data of the test-bed.

Results and discussion
The hardware set up is connected properly. Ten different faults are created as shown in Fig. 1 on 360 km double circuit line. For fault detection and classification LabVIEW fuzzy logic tool kit has been used and the system is protected at 5 ms delay triggered using LabVIEW through CRIO controller relay module. It is worth to mention that proposed logic is able to detect fault, trip the line as well as classify type of the fault occurred. Figure 8 shows the voltage waveform before introduction of fault in the system.
The voltage and current waveforms for La − G fault are presented in Figs. 9 and 10. It is observed that when fault occurs in the system, voltage of L a reduces and current increases. Figure 11 presents graphical result of fault classification which follow fuzzy rule base of L a − G fault i.e. δ 1 is high and δ 2 is medium and δ 3 is low and δ 4 is high. Figures 12 and 13 show voltage and current waveforms for L a − L b − L c fault. It can be seen that when fault occurs in the system, voltage of L a , L b and L c reduces and current increases. It is also observed in Fig. 14 that δ1, δ 2 , δ 3 and δ 4 satisfy fuzzy rule base for L a − L b − L c fault. The fuzzy logic based fault identification and classification is easy and simple since it only require computation of some ratios and differences of ratios of post fault current samples. When different fault are introduced in the system the corresponding fuzzy logic output are presented in Table 6. All the faults are checked graphically to   1  14  27  40  53  66  79  92  105  118  131  144  157  170  183  196  209  222  235  248  261  274  287  300  313  326  339 Va Vb Vc Vn give automatic protection in real time. The system operation is fast, reliable, and secure. Proposed logic is simple since it requires only some linguistic rules. The results show that proposed techniques is simple, fast, reliable and secure.  25  37  49  61  73  85  97  109  121  133  145  157  169  181  193  205  217  229  241  253  265  277  289 Ia Ib Ic In