Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Bastos, Alvaro Furlani; Santoso, Surya; Krishnan, Venkat; Zhang, Yingchen

doi:10.1109/pesgm41954.2020.9281989

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…The former is highly useful considering that planning and state estimation problems can be represented in terms of optimization; however, considering the future research directions in this area that will need to work with large-scale, realistic systems, detailed models of AC power flow equations, as well as voltage control devices, and that will need to consider both short-term and long-term uncertainties and exploit the spatial and temporal synergies across multiple types of measurements (e.g., sensing for weather, grid, building, asset health), scalable data-driven approaches that can fuse the disparate data from myriad measurements are definitely needed. Machine learning and statistical methods, such as clustering and decision trees [7,220], as well as signal processing techniques [127] have already been used in the past to identify the most representative or influential network nodes to be monitored and their related measurements. Such methods are attractive especially for distribution grid applications [221], given the lack of network models and metering infrastructure, including on the secondary side of service transformers.…”

Section: Future Directionsmentioning

confidence: 99%

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Netto,

Krishnan,

Zhang

et al. 2021

Preprint

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Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimization model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics. Therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernization of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state of the art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions.• H Conjugate transpose of the vector or matrix • | • | Magnitude of a complex-valued variable • card( • ) Cardinality, i.e., the number of elements, of a set • det( • ) Determinant of a matrix • Im( • ) Imaginary part of the complex-valued variable • Re( • ) Real part of the complex-valued variable • * https:// gmlc.doe.gov/ * * https:// globalpst.org/ Measurement placement

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Section: Future Directionsmentioning

confidence: 99%

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Netto,

Krishnan,

Zhang

et al. 2021

Preprint

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“…To avoid such linear models, a deep learning model is proposed in [14], where it is shown that a high accuracy can be achieved by monitoring only a few strategic buses. In [15], an ensemble approach (combining different regression models) is introduced for the deterministic voltage prediction. This work has been extended in [16] and [17] in a probabilistic framework.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Toubeau¹,

Teng²,

Wang³

et al. 2022

Preprint

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“…However, addressing the technical (e.g., time synchronization and slow sampling rates of SM readings [11]) and seemingly intractable non-technical issues (e.g., end-user privacy concerns) associated with SMs requires further research efforts and policy formulations. Extensive work has also been done on optimizing the placement of additional metering instruments by selecting strategic measuring points to increase network observability [12], [13]. Although the expansion of metering and communications infrastructure is a desirable solution to overcome existing observability constraints, it is unfortunately economically and time-wise impractical due to the immense number of distribution system nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Recent efforts have been made to apply machine learning (ML) for non-conventional voltage estimation in distribution systems [13]- [15]. For instance, [14] proposed Artificial Neural Networks algorithm as a low-voltage (LV) state estimator, which estimates voltage magnitudes without the knowledge on underlying physical model of the system.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, [14] proposed Artificial Neural Networks algorithm as a low-voltage (LV) state estimator, which estimates voltage magnitudes without the knowledge on underlying physical model of the system. More recent work [13] explored multiple ML regression algorithmic solutions, including ensemble of regressors, to predict distribution grid voltages, also without complete knowledge of the network topology. While in [14] only available measurements of already deployed SMs were assumed, in [13], the voltage prediction emerged from the premise of full availability of active and reactive powers obtained from SMs at load nodes.…”

Section: Introductionmentioning

confidence: 99%

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Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Marković,

Sajadi,

Florita

et al. 2020

Preprint

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The present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The CATV sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the state of a distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored buses using random forest. The effectiveness of the proposed approach was demonstrated using a 1572-bus feeder from the SMART-DS data set for two case studies -passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

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Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Cited by 17 publications

References 14 publications

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

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