Performance Enhancement of GPS based Line Fault Location Using Radial Basis Function Neural Network


The following description summarizes the details the implementation by describing the parameters that the user can control for his particular application, and specifics on what important system parameters are made available to the user for observation and use by the end application. In order to the testing and debugging of the proposed method, most of the code was reported to work on-line on a personal computer using the Microsoft Visual Basic 7 Development Studio Software. To evaluate proposed method performance, Root Mean Square (RMS) was used. It averages the values of prediction errors avoiding the canceling of positive and negative terms. The equation for the error is given by [12]:

.......................................(7)

Where T is the number of records in the data set. In preparing the training data, all input and output variables are normalized in the range [0,1] to reduce the training time. Observation at time t is applied to RBF NN input and the network must predict the value of instant t +1. Fig. 4 and 5 show SSP -UTOD (Sub Seconds Portion of UTOD) predictions for 200 test data by using proposed RBF NN, before and after SA, respectively.


Fig.3: SSP -UTOD 200 predictions using RBF NN with (4,4,1) structure (SA = on)



Fig.4: SSP -UTOD 100 predictions using RBF NN with (4,4,1) structure (SA = off )


To evaluate the performance of the presented training algorithms, they were tested by collected data sets. Table 2 show prediction errors (the difference between the predicted and real values) significance characteristics for 500 test data using proposed RBF.

Table 2: 500 prediction errors significance characteristics by using proposed RBF
Parameters Error Value [nsec] (SA off)Error Value [nsec] (SA on)
Max 103.7 238.5
Min -120.6-381.6
RMS 39.2 169.8
Average 0.985 0.279
Variance 0.0000000031 0.0000000578
Standard Deviation 1.75 7.60
Table 2: 500 prediction errors significance characteristics by using proposed RBF


4. Conclusions
A key element in traveling wave fault locator is a source of reliable precise time. The fault location is determined by accurately time tagging the arrival of the traveling wave at each end of the line and comparing the time difference to the total propagation time of the line. The time signal is obtained via satellite from the GPS. GPS is the only system available. It also has the reliability and availability necessary for power systems. In this paper, a new approach of using RBF NN for GPS receiver timing errors modeling and prediction has been proposed. The purpose of this paper is to present a model with high predictive ability good fitting accuracy. The proposed RBF NN was implemented on collected real data. An experimental setup was designed and implemented for this purpose. The experimental results emphasize that GPS timing error RMS can reduce from 340nsec and 200nsec to less then 170nsec and 40nsec by using proposed RBF NN prediction, before and after SA, respectively.

Acknowledgements
This research is supported by Iran University of Science and Technology grants.

References

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