Analysis of Data Aggregation Schemes for Smart Grid Communications
Journal Title: IOSR Journal of Mobile Computing & Application (IOSR-JMCA) - Year 2019, Vol 6, Issue 1
Abstract
lot has been said about different techniques of aggregating data in a smart grid communications. What is yet to be extensively explored is the concept of smart grid application in Nigeria's power sector. The smart grid can exchange data about current electricity status, pricing data and control commands in real-time. Due to these specific characteristics, the process of electricity’s generation, transmission and distribution in the smart grid environment can be managed efficiently and reliably. However, smart grid technology may not be right for all power networks due to requirement for substantial resources. This work has studied the data aggregation scheme in smart grid communication, which is characterized by the review of previous studies that have proposed data aggregation structures for smart grid communications, and was evaluated using the benefit of proposed data aggregation scheme and its impact on smart grid structure as well as its potential limitations. The usage of neural networks was employed for the detection, classification and location of faults on transmission lines. The method employed made usage of the phase voltages and phase currents (scaled with respect to their pre-fault values) as inputs to the neural networks. To simulate the various faults model and to obtain the training data set, MATLAB R2015a was used along with the SimPowerSystems toolbox in Simulink. The performance of the model was analyzed using Mean Square Error (MSE). It was observed that the configuration for the chosen ANN was 6 – 10 – 5 – 3 – 1 and the number of iterations required for the training process were 37. It can be seen that the mean square error in fault detection achieved by the end of the training process was 9.43e-5 and that the number of validation check fails were zero by the end of the training process. More so, the configuration for the chosen ANN for fault location was 6-7-1 with 5 iterations required for the training process.
Authors and Affiliations
Usman Mohammed, G. M. Wajiga, M. K. Ahmed
Keywords
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