In‐building power lines as high‐speed communication channels: channel characterization and a test channel ensemble

Since the main distribution line faults can be securely identified as outlined in the first and second paper, this third paper presents the methodology of localizing the main distribution line fault when broadband over power lines (BPL) networks have already been deployed across the distribution power grids. The main issue of this paper is the detailed presentation of the main line localization methodology (MLFLM) as well as well as its performance assessment when measurement differences occur. The contribution of this paper, which is focused on the application of MLFLM, is double. First, the procedure, which is followed in order to create the database of faults and is used by MLFLM, is here analytically presented. This database is based on the application of the main distribution line fault identification percentage metric (MDLFI) to coupling reflection coefficients of all possible fault OV MV BPL topologies (modified OV MV BPL topologies). Second, the performance assessment of MLFLM is investigated with respect to the nature of the measurement differences and the location of main distribution line faults across the distribution power grid. Keywords: Smart Grid; Intelligent Energy Systems; Broadband over Power Lines (BPL) Networks; Power Line Communications (PLC); Faults; Fault Analysis; Fault Localization; Distribution Power Grids IntroductionA major advantage of the BPL networks is the fact that their development is simple and economically advantageous since their deployment is based on the already operating power grid infrastructure. As concerns the potential of the smart grid operation, BPL technology can act as either an autonomous communications system or a cooperative communications network that interoperates with other wired and wireless communications solutions in an integrated intelligent IP-based network environment. Anyway, the synergy of telecommunications solution under the aegis of the smart grid can support a myriad of smart grid applications [1]- [4].In the first paper [5], the determination of the channel attenuation and reflection coefficient of overhead medium-voltage (OV MV) BPL networks have been achieved by extending the original TM2 method, which is the core part of the top-down approach of the well-established hybrid method [3], [6]-[22] during the normal operation of OV MV

“…As it is usually done [9], [27], [28], [29], [18], [30], the selection of representative coupling schemes is a typical procedure for the sake of reducing manuscript size and simplicity.…”

Section: Ov MV Bpl Topology Database With Mdlfi and Mlflmmentioning

confidence: 99%

Main Line Fault Localization Methodology in Smart Grid – Part 3: Main Line Fault Localization Methodology (MLFLM)

Lazaropoulos

2017

“…Conversely, as frequency increases, noise PSD of background noise reduces and tends to spectrally flat AWGN. Indeed, the noise PSDs of the noise models tends to coincide at higher frequencies [22], [34]- [36], [39], [41], [42], [55]- [58].…”

Section: Noise Characteristicsmentioning

confidence: 99%

“…Regardless of the power grid type -either SISO or MIMO, either OV or underground (UN), either HV or medium-voltage (MV) or low-voltage (LV)-, BPL noise may be considered as the superposition of five noise types, namely [1]- [11], [22], [30]- [36], [39], [41]- [43], [52], [55]- [58]: (i) the colored background noise; (ii) the narrowband noise; (iii) the periodic impulsive noise asynchronous to the mains frequency; (iv) the periodic impulsive noise synchronous to the mains frequency; and (v) the asynchronous impulsive noise. As it has already been reported in [1] and analyzed in [4], [6], [12], [13], [23], [29], [44], only the non resolvable limitations should be considered during the BPL capacity computations.…”

Section: Noise Characteristicsmentioning

confidence: 99%

“…the FL noise model that is the spectrally flat AWGN model where 1 n and 2 n are equal to zero (proposed by [1], [4], [6], [12], [23], [29], [44]); 2. the OPERA noise model that is suitable for outdoor urban and suburban noise environments (presented in [30]); 3. the MEN noise model (presented in [42], [43]); 4. the PH1 noise model that is appropriate for urban noise conditions (presented in [41], [56], [57]); 5. the PH2 noise model that is focused on industrial noise environments (presented in [41], [56], [57]); 6. the ESM1 noise model (presented in [58]); and 7. the ESM2 noise model that is appropriate for urban noise conditions (presented in [58]). In Fig.…”

Section: Noise Characteristicsmentioning

confidence: 99%

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Capacity Performance of Overhead Transmission Multiple-Input Multiple-Output Broadband over Power Lines Networks: The Insidious Effect of Noise and the Role of Noise Models

Lazaropoulos

2016

Extending the analysis already presented in [1], this paper considers broadband potential of overhead (OV) transmission multiple-input multiple-output (MIMO) broadband over power lines (BPL) networks when different noise conditions occur and different well-proven noise models are adopted. The contribution of this paper is two-fold. First, the broadband potential of a great number of indicative OV high-voltage (HV) BPL topologies and of MIMO transmission schemes is studied in terms of appropriate capacity metrics. The relevant numerical results reveal the significant dependence of ΜΙΜΟ capacity metrics on noise conditions. Second, various well-known BPL noise models from the literature are compared on the basis of their achieved OV HV MIMO BPL capacity. Through the careful study of the capacity results of noise models, it is demonstrated that spectrally flat additive white Gaussian noise (AWGN) may be comfortably assumed as an efficient noise model in transmission MIMO BPL networks. Also in MIMO BPL networks, the comparative capacity analysis of noise models shows small differences among them in the 3-88MHz frequency range.

“…• Arbitrarily, the WtG 3 coupling scheme is applied during the following simulations [6], [18], [55]- [58].…”

Section: Mdlfi Mlflm Database and Localization Proceduresmentioning

confidence: 99%

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

Lazaropoulos¹

2017

This paper assesses the performance of the main line fault localization methodology (MLFLM) when its application is extended to underground medium-and low-voltage broadband over power lines (UN MV and UN LV BPL) networks, say UN distribution BPL networks. This paper focuses on the localization of main distribution line faults across UV MV and UN LV BPL networks. By extending the MLFLM procedure, which has successfully been applied to overhead medium-voltage (OV MV) BPL networks, the performance assessment of MLFLM is investigated with respect to the nature of the main distribution line faults, the intensity of the measurement differences and the fault location across the main distribution lines of the underground distribution power grid (either MV or LV grid).