Bayesian Inference Approach for Information Fusion in Distribution System State Estimation

Massignan, Julio A. D.; London, João Bosco Augusto; Bessani, Michel; Maciel, Carlos Dias; Fannucchi, Rodrigo Z.; Miranda, Vladimiro

doi:10.1109/tsg.2021.3128053

Cited by 36 publications

(26 citation statements)

References 53 publications

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“…In [58], smart meter and pseudo measurements are processed along with PMU and SCADA measurements as part of a Bayesian estimation solution. The principle behind this work is similar to that of [57].…”

Section: Integrated Estimationmentioning

confidence: 99%

Research Trends and Applications of PMUs

2022

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This work is a survey of current trends in applications of PMUs. PMUs have the potential to solve major problems in the areas of power system estimation, protection, and stability. A variety of methods are being used for these purposes, including statistical techniques, mathematical transformations, probability, and AI. The results produced by the techniques reviewed in this work are promising, but there is work to be performed in the context of implementation and standardization. As the smart grid initiative continues to advance, the number of intelligent devices monitoring the power grid continues to increase. PMUs are at the center of this initiative, and as a result, each year more PMUs are deployed across the grid. Since their introduction, myriad solutions based on PMU-technology have been suggested. The high sampling rates and synchronized measurements provided by PMUs are expected to drive significant advancements across multiple fields, such as the protection, estimation, and control of the power grid. This work offers a review of contemporary research trends and applications of PMU technology. Most solutions presented in this work were published in the last five years, and techniques showing potential for significant impact are highlighted in greater detail. Being a relatively new technology, there are several issues that must be addressed before PMU-based solutions can be successfully implemented. This survey found that key areas where improvements are needed include the establishment of PMU-observability, data processing algorithms, the handling of heterogeneous sampling rates, and the minimization of the investment in infrastructure for PMU communication. Solutions based on Bayesian estimation, as well as those having a distributed architectures, show great promise. The material presented in this document is tailored to both new researchers entering this field and experienced researchers wishing to become acquainted with emerging trends.

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Section: Integrated Estimationmentioning

confidence: 99%

Research Trends and Applications of PMUs

2022

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“…It should be noted that the degrading of decision-making is due to the uncertainties of prediction. The uncertainties can come from two aspects: i) energy prediction [14]- [17]: the uncertain prediction of renewable generation and load demand due to their intrinsic stochasticity and volatility; ii) system estimation [26], [27]: the uncertain estimation of system parameters (e.g., line impedance) due to bad data and measuring noise. This work focuses on improving the decision quality under the energy prediction uncertainty by assuming the state estimation is accurate, which is consistent with other state-ofthe-art works [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

Safety-Aware Semi-End-to-End Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Sang

Long

et al. 2023

IEEE Trans. Smart Grid

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Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to bridge the gap from the downstream voltage regulation to the upstream multiple prediction models in a coordinated differential way. The semi-end-to-end model maps the input features to the optimal var decisions via prediction, decision-making, and decisionevaluating layers. It leverages the neural network and the secondorder cone program (SOCP) to formulate the stochastic PV/load predictions and the var decision-making/evaluating separately. Then the var decision quality is evaluated via the weighted sum of the power loss for economy and the voltage violation penalty for safety, denoted by regulation loss. Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality. Case studies verify the effectiveness of the proposed model with lower power loss for economy and lower voltage violation rate for safety awareness.

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“…Consequently, the real-time operating condition of PDSs has been usually determined through the statistical characterization of their loads performed by a process called load aggregation [ 10 , 11 , 12 ]. Nevertheless, in recent years, the development of new equipment for real-time measurements, such as micro-PMUs (Phasor Measurement Units) and Smart Meters (SMs), has motivated the development of Distributions System State Estimators (DSSEs) [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of Embedded State Estimator Modules for Decentralized Monitoring of Power Distribution Systems Using IoT Components

Gonzaga

Márquez-Sánchez

Santos

et al. 2022

Sensors

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Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy.

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Bayesian Inference Approach for Information Fusion in Distribution System State Estimation

Cited by 36 publications

References 53 publications

Research Trends and Applications of PMUs

Research Trends and Applications of PMUs

Safety-Aware Semi-End-to-End Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Validation of Embedded State Estimator Modules for Decentralized Monitoring of Power Distribution Systems Using IoT Components

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