Improving Margin and Sensitivity Method for 330kv Transmission Network Security Analysis Using Wavelet Based Extreme Learning Technique
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Abstract
The persistent power failure in the transmission network has jeopardized business and industrial activities as a result of low power margin and low sensitivity in the network. The power system operates stably at a certain loading level referred to as the “base case loading”. This work provides the process of surmounting this ugly development by the introduction of ‘improved margin and sensitivity method for 330kv transmission network security analysis using wavelet-based extreme learning technique’. The load flow of the characterized network was carried out to establish the buses that caused low margin and low sensitivity in the network, and a wavelet rule-based method was developed that will improve the margin and sensitivity of the network. An Artificial Neural Network (ANN) was trained in the developed wavelet rule base to improve the margin and sensitivity of network security analysis. A SIMULINK model for wavelet-based extreme learning technique was developed, and the algorithm that will implement the process was also developed. The implementation process worked smoothly with the designed SIMULINK model. The conventional per-unit voltage of weak buses does not attain the threshold voltage of 0.95 through 1.05 for stability, which was achieved by the new technique. The results show that the conventional per unit volts of weak bus 1 is 0.86, but was improved to 0.95pu volts. The power margin from 58.85MW to 64.73MW, which is about 9.99%. improvement. The sensitivity is 0.000983, but was improved to 0.001086, which is about 9.96% improvement. The conventional sensitivity in bus 6 is 0.000987, which increased to 0.001086. And the conventional power margin was 42.31MW, increased to 46.54 MW, which is about 10% improvement. The incorporation of artificial intelligence (AI) in the system resulted to consistent power supply in the transmission system. The process was also validated and justified by calculating the percentage improvement in the margin and sensitivity of the network with and without the wavelet-based extreme learning technique, and it was observed that the system witnessed over ten percent (10%) improvement.
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