A Scalable Data-Driven Monitoring Approach for Distribution Systems

Ferdowsi, Mohsen; Benigni, Andrea; Löwen, Artur; Zargar, Behzad; Monti, Antonello; Ponci, Ferdinanda

doi:10.1109/tim.2015.2398991

Cited by 35 publications

(20 citation statements)

References 19 publications

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“…While many states are implausible (e.g., all switches open), the amount of valid states nevertheless grows significantly with the number of switches. Instead of retraining up to 2 s networks individually if, e.g., a line is modernized or a DG added, which is required in the approach of [17], only the single ANN has to be retrained in our approach.…”

Section: Ann Architecturementioning

confidence: 99%

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Menke

Bornhorst

Braun

2019

International Journal of Electrical Power & Energy Systems

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The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of distribution grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor distribution grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution system monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different power grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photovoltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors.

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Section: Ann Architecturementioning

confidence: 99%

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Menke

Bornhorst

Braun

2019

International Journal of Electrical Power & Energy Systems

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“…To address this issue, the neural network-based NARX model was developed for state estimation and monitoring [48]. ANNbased scalable state estimator incorporating local state estimation was developed [71]. The data based on forecasting of load and generation was utilised to train neural network with supervised learning to handle uncertainties [72].…”

Section: Data-driven State Estimatormentioning

confidence: 99%

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

Bhattar

Pindoriya

Sharma

2021

IET cyber-phys. syst.

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The penetration of renewable energy sources (RES) in distribution systems has entailed heavy deployment of monitoring and control infrastructure in the distribution system. This deployment of sensors and communication infrastructure has led to the emergence of a modern grid with a complex cyber-physical network. On one hand, the cyber-physical system can play a pivotal role in coordination and control of distribution systems through distribution system state estimation (DSSE). On the other hand, the risk caused by cyber-attack has emerged as a major challenge for grid operation. The modelling aspects, namely DC loads, plug-in-vehicle and distributed generators (DGs) in DSSE and security aspects in cyber-physical distribution systems, are highlighted herein. This paper is divided into different sections emphasising on the problems and challenges associated with DSSE, cyber-attack focussing primarily on impact of false data injection (FDI) attack and co-simulation platform for investigation of vulnerability of cyber-physical systems. A comparative study among various DSSE and challenges namely, pseudo-measurement, time synchronisation and communication issues are discussed. The potential investigation of FDI and need for a co-simulation platform in distribution systems for risk analysis are also addressed. The opportunities and future research in the field of cyber-physical distribution systems are discussed in depth. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In contrast this article starts from the reverse case of a completely unknown network, then adding some amount of prior knowledge. Our approach is thus complementary to the existing works such that it can contribute to the development of advanced grid monitoring algorithms [18,19].…”

Section: Introductionmentioning

confidence: 96%

Inferring power-grid topology in the face of uncertainties

et al. 2018

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We develop methods to efficiently reconstruct the topology and line parameters of a power grid from the measurement of nodal variables. We propose two compressed sensing algorithms that minimize the amount of necessary measurement resources by exploiting network sparsity, symmetry of connections, and potential prior knowledge about the connectivity. The algorithms are reciprocal to established state estimation methods, where nodal variables are estimated from few measurements given the network structure. Hence, they enable an advanced grid monitoring where both state and structure of a grid are subject to uncertainties or missing information.

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A Scalable Data-Driven Monitoring Approach for Distribution Systems

Cited by 35 publications

References 19 publications

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

Distribution system monitoring for smart power grids with distributed generation using artificial neural networks

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

Inferring power-grid topology in the face of uncertainties

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