Business Analytics and IT in Smart Grid – Part 3: New Application Aspect and the Quantitative Mitigation Analysis of Piecewise Monotonic Data Approximations on the iSHM Class Map Footprints of Overhead Low-Voltage Broadband over Power Lines Topologies Contaminated by Measurement Differences

This paper investigates the efficiency and accuracy of the best L1 piecewise monotonic data approximation (best L1PMA) in order either to approximate the transfer functions of distribution BPL networks or to reveal the aforementioned transfer functions when various faults occur during their determination. The contribution of this paper is quadruple. First, based on the inherent piecewise monotonicity of distribution BPL transfer functions, a piecewise monotonic data approximation is first applied in BPL networks; best L1PMA is outlined and applied during the determination of distribution BPL transfer functions. Second, suitable performance metrics such as the percent error sum (PES) and fault PES are reported and applied so as to assess the efficiency and accuracy of the best L1PMA during the determination of distribution BPL transfer functions. Third, the factors of distribution BPL networks that influence the performance of best L1PMA are identified. Fourth, the accuracy of the best L1PMA is assessed with respect to its inherent properties, namely, the assumed number of monotonic sections and the nature of faults, that is, faults that follow either continuous uniform distribution (CUD) or normal distribution (ND), during the determination of distribution BPL transfer functions. Finally, best L1PMA may operate as the necessary intermediate antifault method for the theoretical and practical transfer function determination of distribution BPL networks.

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Management Information Systems and Data Science in the Smart Grid – Inner Class Area Capacity Distribution of the iSHM Class Maps of Overhead Low-Voltage Broadband over Power Lines Topologies

Trends in Renewable Energy ◽

10.17737/tre.2020.6.3.00122 ◽

2020 ◽

Vol 6 (3) ◽

pp. 245-265

Author(s):

Athanasios Lazaropoulos

Keyword(s):

Information Systems ◽

Smart Grid ◽

Data Science ◽

Low Voltage ◽

Management Information Systems ◽

Power Lines ◽

Management Information

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Main Line Fault Localization Methodology in Smart Grid – Part 2: Extended TM2 Method, Measurement Differences and L1 Piecewise Monotonic Data Approximation for the Overhead Medium-Voltage Broadband over Power Lines Networks Case

Trends in Renewable Energy ◽

10.17737/tre.2017.3.3.0037 ◽

2017 ◽

Vol 3 (3) ◽

Author(s):

Athanasios Lazaropoulos

Keyword(s):

Smart Grid ◽

Fault Localization ◽

Main Line ◽

Power Lines ◽

Data Approximation ◽

Medium Voltage ◽

Piecewise Monotonic

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Towards Modal Integration of Overhead and Underground Low-Voltage and Medium-Voltage Power Line Communication Channels in the Smart Grid Landscape: Model Expansion, Broadband Signal Transmission Characteristics, and Statistical Performance Metrics (Invited Paper)

ISRN Signal Processing ◽

10.5402/2012/121628 ◽

2012 ◽

Vol 2012 ◽

pp. 1-17 ◽

Cited By ~ 11

Author(s):

Athanasios G. Lazaropoulos

Keyword(s):

Smart Grid ◽

Performance Metrics ◽

Low Voltage ◽

Power Grid ◽

Signal Propagation ◽

Power Lines ◽

Delay Spread ◽

Transmission Characteristics ◽

Medium Voltage ◽

End To End

The established statistical analysis, already used to treat overhead transmission power grid networks, is now implemented to examine the factors influencing modal transmission characteristics and modal statistical performance metrics of overhead and underground low-voltage/broadband over power lines (LV/BPL) and medium-voltage/broadband over power lines (MV/BPL) channels associated with power distribution in smart grid (SG) networks. The novelty of this paper is threefold. First, a refined multidimensional chain scattering matrix (TM2) method suitable for overhead and underground LV/BPL and MV/BPL modal channels is evaluated against other relative theoretical and experimental proven models. Second, applying TM2 method, the end-to-end modal channel attenuation of various LV/BPL and MV/BPL multiconductor transmission line (MTL) configurations is determined. The LV/BPL and MV/BPL transmission channels are investigated with regard to their spectral behavior and their end-to-end modal channel attenuation. It is found that the above features depend drastically on the frequency, the type of power grid, the mode considered, the MTL configuration, the physical properties of the cables used, the end-to-end distance, and the number, the electrical length, and the terminations of the branches encountered along the end-to-end BPL signal propagation. Third, the statistical properties of various overhead and underground LV/BPL and MV/BPL modal channels are investigated revealing the correlation between end-to-end modal channel attenuation and modal root-mean-square delay spread (RMS-DS). Already verified in the case of overhead high-voltage (HV) BPL systems, this fundamental property of several wireline systems is also modally validated against relevant sets of field measurements, numerical results, and recently proposed statistical channel models for various overhead and underground LV/BPL and MV/BPL channels. Based on this common inherent attribute of either transmission or distribution BPL networks, new unified regression trend line is proposed giving a further boost towards BPL system intraoperability. A consequence of this paper is that it aids in gaining a better understanding of the range and coverage that BPL solutions can achieve; a preliminary step toward the system symbiosis between BPL systems and other broadband technologies in an SG environment.

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Main Line Fault Localization Methodology (MLFLM) in Smart Grid – The Underground Medium- and Low-Voltage Broadband over Power Lines Networks Case

Trends in Renewable Energy ◽

10.17737/tre.2018.4.1.0045 ◽

2017 ◽

pp. 15-42

Author(s):

Athanasios Lazaropoulos

Keyword(s):

Smart Grid ◽

Low Voltage ◽

Fault Localization ◽

Main Line ◽

Power Lines

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Information Technology, Artificial Intelligence and Machine Learning in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Branch Number Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies

Trends in Renewable Energy ◽

10.17737/tre.2021.7.1.00133 ◽

2021 ◽

Vol 7 (1) ◽

pp. 87-113

Author(s):

Athanasios Lazaropoulos

Keyword(s):

Neural Network ◽

Machine Learning ◽

Smart Grid ◽

Low Voltage ◽

Performance Comparison ◽

Power Lines ◽

Branch Number ◽

Topology Identification ◽

The Neural Network ◽

Network Identification

Broadband over Power Lines (BPL) networks that are deployed across the smart grid can benefit from the usage of machine learning, as smarter grid diagnostics are collected and analyzed. In this paper, the neural network identification methodology of Overhead Low-Voltage (OV LV) BPL networks that aims at identifying the number of branches for a given OV LV BPL topology channel attenuation behavior is proposed, which is simply denoted as NNIM-BNI. In order to identify the branch number of an OV LV BPL topology through its channel attenuation behavior, NNIM-BNI exploits the Deterministic Hybrid Model (DHM), which has been extensively tested in OV LV BPL networks for their channel attenuation determination, and the OV LV BPL topology database of Topology Identification Methodology (TIM). The results of NNIM-BNI towards the branch number identification of OV LV BPL topologies are compared against the ones of a newly proposed TIM-based methodology, denoted as TIM-BNI.

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