Power Systems Stability through Piecewise Monotonic Data Approximations – Part 1: Comparative Benchmarking of L1PMA, L2WPMA and L2CXCV in Overhead Medium-Voltage Broadband over Power Lines Networks

Athanasios G. Lazaropoulos

Abstract


This first paper assesses the performance of three well-known piecewise monotonic data approximations (i.e., L1PMA, L2WPMA, and L2CXCV) during the mitigation of measurement differences in the overhead medium-voltage broadband over power lines (OV MV BPL) transfer functions.

The contribution of this paper is triple. First, based on the inherent piecewise monotonicity of OV MV BPL transfer functions, L2WPMA and L2CXCV are outlined and applied during the determination of theoretical and measured OV MVBPL transfer functions. Second, L1PMA, L2WPMA, and L2CXCV are comparatively benchmarked by using the performance metrics of the percent error sum (PES) and fault PES. PES and fault PES assess the efficiency and accuracy of the three piecewise monotonic data approximations during the determination of transmission BPL transfer functions. Third, the performance of L1PMA, L2WPMA, and L2CXCV is assessed with respect to the nature of faults —i.e. faults that follow either continuous uniform distribution (CUD) or normal distribution (ND) of different magnitudes—.

The goal of this set of two papers is the establishment of a more effective identification and restoration of the measurement differences during the OV MV BPL coupling transfer function determination that may significantly help towards a more stable and self-healing power system.

Citation: Lazaropoulos, A. G. (2017). Power Systems Stability through Piecewise Monotonic Data Approximations – Part 1: Comparative Benchmarking of L1PMA, L2WPMA and L2CXCV in Overhead Medium-Voltage Broadband over Power Lines Networks. Trends in Renewable Energy, 3(1), 2-32. DOI: 10.17737/tre.2017.3.1.0029


Keywords


Smart Grid; Intelligent Energy Systems; Broadband over Power Lines (BPL) networks; Power Line Communications (PLC); Faults; Fault Analysis; Fault Identification and Prediction; Power System Stability; Distribution Power Grids

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DOI: http://dx.doi.org/10.17737/tre.2017.3.1.0029

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