Business Analytics and IT in Smart Grid – Part 2: The Qualitative Mitigation Impact of Piecewise Monotonic Data Approximations on the iSHM Class Map Footprints of Overhead Low-Voltage Broadband over Power Lines Topologies Contaminated by Measurement Differences

Business analytics and IT infrastructure preserve the integrity of the smart grid (SG) operation against the flood of big data that may be susceptible to faults, such as measurement differences. In [1], the impact of measurement differences that follow continuous uniform distributions (CUDs) of different magnitudes has been investigated via initial Statistical Hybrid Model (iSHM) footprints during the operation of overhead low-voltage broadband over power lines (OV LV BPL) networks. In this companion paper, the mitigation efficiency of piecewise monotonic data approximations, such as L1PMA and L2WPMA, is qualitatively assessed in terms of iSHM footprints when the aforementioned measurement difference CUD of different intensities are applied.Citation: Lazaropoulos, A. G. (2020). Business Analytics and IT in Smart Grid – Part 2: The Qualitative Mitigation Impact of Piecewise Monotonic Data Approximations on the iSHM Class Map Footprints of Overhead Low-Voltage Broadband over Power Lines Topologies Contaminated by Measurement Differences. Trends in Renewable Energy, 6, 177-203. DOI: 10.17737/tre.2020.6.2.0011