Application of Smart Meters in High Impedance Fault Detection on Distribution Systems

Chakraborty, Soham; Das, Sarasij

doi:10.1109/tsg.2018.2828414

Cited by 91 publications

(48 citation statements)

References 35 publications

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“…2) Smart meter measurements: 3% of true values for active and reactive powers. The smart meters, which become available for distribution grids owing to their cheap costs, are assumed to be installed at each load bus [36].…”

Section: Simulation Testmentioning

confidence: 99%

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Zhang

Wang

Liu

2020

IEEE Trans. Smart Grid

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Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Further, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method.Index Terms-State estimation, phasor measurement units, multiple GPS spoofing attacks, unbalanced distribution systems, attack identification and correction.

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Section: Simulation Testmentioning

confidence: 99%

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Zhang

Wang

Liu

2020

IEEE Trans. Smart Grid

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“…To improve the reliability and accuracy of this postprocessing step, we introduce the disaggregation result as a regularisation term. Then the optimisation problem becomes: 4) where M N optimisation variables, S m j , take values from a discrete set (0 or 1), and λ m ≥ 0 is the weight of the regularisation term for Appliance m. (Here, again, S m * j , j = 1, · · · , N , is the estimate obtained by an initial NILM method used.) In this optimisation set-up, the fidelity term shows how far the result is from the observation, while the regularisation term weights our confidence in the original NILM output.…”

Section: B Post-processing Nilm: Proposed Problem Formulationmentioning

confidence: 99%

“…The key motivator for ongoing large-scale smart metering deployments worldwide [1], [2] is to maximise benefits of the smart grid. Smart meter data has been shown to improve grid operation and maintenance of distribution networks [3], fault detection [4], non-technical loss detection [5], outage prediction [6], load forecasting [7], demand response [8] and improving customer satisfaction, including accurate billing and meaningful energy feedback via Non-Intrusive Load Monitoring (NILM), that is, disaggregating the total household consumption down to the load level [9], [10], [11]. Hence, smart meter data analytics are critical to the success of the smart grid [12].…”

Section: Introductionmentioning

confidence: 99%

A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring

Jakovetić

Zhao

et al. 2019

IEEE Trans. Smart Grid

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The large-scale deployment of smart metering worldwide has ignited renewed interest in electrical load disaggregation, or non-intrusive load monitoring (NILM). Most NILM algorithms disaggregate one appliance at a time, remove the estimated appliance contribution from the total load, and then move on to disaggregate the next appliance. On one hand, this is efficient since multi-class classification is avoided and analytical models for each appliance can be developed independently of other appliances with the benefit of being transferred to unseen houses that have different sets of appliances. On the other hand, however, these methods can significantly under-or over-estimate the total consumption since they do not minimise the difference between the measured aggregate load and the sum of estimated individual loads. Motivated by minimising the latter difference without losing the benefits of existing NILM algorithms, we propose novel post-processing approaches for improving the accuracy of existing NILM. This is posed as an optimisation problem to refine the final NILM result using regularisation, based on the level of confidence in the original NILM output. First, we propose a heuristic method to solve this (combinatorial) boolean quadratic problem through relaxing zero-one constraint sets to compact zero-one intervals. Convex-based solutions, including norm-1, norm-2 and semi-definite programming-based relaxation, are proposed trading off accuracy and complexity. We demonstrate good performance of the proposed post-processing methods, applicable to any event-based NILM, compared with 4 state-of-the-art benchmarks, using public REFIT and REDD electrical load datasets.

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“…Thus, development of an effective protection system is crucial paving the way for satisfying these requirements [2]. High‐impedance fault (HIF) detection poses a key challenge to the distribution network protection engineers [3–5]. There are two types of HIF faults: unbroken and broken.…”

Section: Introductionmentioning

confidence: 99%

HIF detection in distribution networks based on Kullback–Leibler divergence

Nezamzadeh-Ejieh

Sadeghkhani

2019

IET Generation, Transmission & Distribution

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Application of Smart Meters in High Impedance Fault Detection on Distribution Systems

Cited by 91 publications

References 35 publications

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring

HIF detection in distribution networks based on Kullback–Leibler divergence

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