Cable Health Monitoring in Distribution Networks using Power Line Communications

Huo, Yinjia; Prasad, Gautham; Lampe, Lutz; Leung, Victor C. M.

doi:10.1109/smartgridcomm.2018.8587458

Cited by 15 publications

(15 citation statements)

References 21 publications

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“…If the sequence analysis considers only its stationary behavior, it is better to obtain the marginal spectrum applying Equation (8). The result is shown in Figure 5 using different graphic scales and with different frequency resolution.…”

Section: Marginal Wavelet Transform Of Electricity Demandmentioning

confidence: 99%

“…The straightforward availability of greater datasets poses a challenge on the data analytic side [4] making possible new and more efficient approaches to several applications such as fault detection [5], predictive maintenance [6], transient stability analysis [7], electric device state estimation [8], power quality monitoring [9], topology identification [10], renewable energy forecasting [11], and non-technical loss detection [12].…”

Section: Introductionmentioning

confidence: 99%

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Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

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Section: Marginal Wavelet Transform Of Electricity Demandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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show abstract

“…The authors of ( [3], [19]) use channel frequency response and SNR estimates from PLC modem and augumented machine learning to identify thermal degradation and short-circuit faults respectively in powerline cables through simulation studies. Although, these works prove by fault detection over simulations, we have built one of our testbeds with commercial off-the-shelf (COTS) PLC modem and evaluated the soft fault identification accuracy with random load conditions.…”

Section: Introductionmentioning

confidence: 99%

Measurement-based Condition Monitoring of Railway Signaling Cables

Vijay¹,

Prasad²,

Huo³

et al. 2021

2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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We propose a composite diagnostics solution for railway infrastructure monitoring. In particular, we address the issue of soft-fault detection in underground railway cables. We first demonstrate the feasibility of an orthogonal multitone time domain reflectometry based fault detection and location method for railway cabling infrastructure by implementing it using software defined radios. Our practical implementation, comprehensive measurement campaign, and our measurement results guide the design of our overall composite solution. With several diagnostics solutions available in the literature, our conglomerated method presents a technique to consolidate results from multiple diagnostics methods to provide an accurate assessment of underground cable health. We present a Bayesian framework based cable health index computation technique that indicates the extent of degradation that a cable is subject to at any stage during its lifespan. We present the performance results of our proposed solution using real-world measurements to demonstrate its effectiveness.

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“…noisy data impacted by electrical disturbance and are unable to discern precise information about cable defects, e.g., age of degradation or accurate location of a fault [11], [14]. PLCbased monitoring techniques, on the other hand, reuse the high-frequency broadband communication signals as probing waves to provide effective cable diagnostics [5], [15].…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, data-driven methods that were designed to use machinelearning (ML) techniques to intelligently detect and assess cable health are resilient against such challenges [5], [6], [19]. These methods harness ML classification and regression techniques to detect, locate, and assess various smart grid network anomalies, such as cable degradation and faults and network intrusions [5], [6], [15], [20]. However, these methods are not universally applicable since the machines used here are typically trained under a specific operating network topology to detect a few known types of characterized anomalies.…”

Section: Introductionmentioning

confidence: 99%

Power Line Communication and Sensing Using Time Series Forecasting

Huo¹,

Prasad²,

Lampe³

et al. 2021

Preprint

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Monitoring grid assets continuously is critical in ensuring the reliable operation of the electricity grid system and improving its resilience in case of a defect. In light of several asset monitoring techniques in use, power line communication (PLC) enables a low-cost cable diagnostics solution by re-using smart grid data communication modems to also infer the cable health using the inherently estimated communication channel state information. Traditional PLC-based cable diagnostics solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In contrast, we develop an asset monitoring technique in this paper that can detect various types of anomalies in the grid without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the assumption that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to determine the significance level of the resultant Mahalanobis distance to build our anomaly detector. We demonstrate the effectiveness and universality of our solution via evaluations conducted using both synthetic and real-world data extracted from low-and medium-voltage distribution networks.

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Cable Health Monitoring in Distribution Networks using Power Line Communications

Cited by 15 publications

References 21 publications

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Measurement-based Condition Monitoring of Railway Signaling Cables

Power Line Communication and Sensing Using Time Series Forecasting

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