A Review on Distribution System State Estimation Techniques

Majdoub, Meriem; Boukherouaa, Jamal; Cheddadi, Bouchra; Belfqih, Abdelaziz; Sabri, Osama; Haidi, Touria

doi:10.1109/irsec.2018.8702952

Cited by 21 publications

(12 citation statements)

References 59 publications

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“…Furthermore, the large size and the unbalance of DNs can give rise to computational challenges [8]. Even though numerous distribution system state estimation (DSSE) studies are available in the literature, there is no general consensus on the most suitable design criteria [9]. This is partly due to the significant feature variations of DNs, such as 1) voltage level, 2) location, e.g., urban, rural, 3) customer type, e.g., house, small industry, 4) percentage of customers with PV panels, electric vehicles, smart meters, etc.…”

Section: A Background and Motivationmentioning

confidence: 99%

A Framework for Constrained Static State Estimation in Unbalanced Distribution Networks

Vanin

Acker

D’hulst

et al. 2022

IEEE Trans. Power Syst.

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State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor these networks and, successively, to perform control actions. However, designing state estimators for distribution systems carries a significant amount of challenges. This is due to the physical complexity of the networks, e.g., phase unbalance, and limited measurements. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a "one-size-fits-all" state estimator. The present paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of distribution feeders. The framework is also implemented and made available open-source.

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Section: A Background and Motivationmentioning

confidence: 99%

A Framework for Constrained Static State Estimation in Unbalanced Distribution Networks

Vanin

Acker

D’hulst

et al. 2022

IEEE Trans. Power Syst.

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“…With the large-scale access of distributed power sources, the structure and operation mode of the distribution network system are complex and changeable (Majdoub et al, 2018), and the volatility and randomness of the high proportion of distributed generation also bring great challenges to the operation and control of the distribution network (Huang et al, 2022). Therefore, the "observable" and "controllable" capability of the distribution network is of vital importance to ensure the economic and safe operation of the system (Liu et al, 2020), and high accuracy state estimation is an important prerequisite for analyzing the operation state of the distribution network.…”

Section: Introductionmentioning

confidence: 99%

Research on μPMU Configuration Optimization Considering Multiple Operation Modes of Distribution Network

Liang

Wang

et al. 2022

Front. Energy Res.

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A micro-phasor measurement unit (μPMU) configuration optimization approach is proposed in this article, considering the numerous operation modes of distribution network reconfiguration. The PSO algorithm with dynamic adaptability is used to optimize the setup of μPMU and improve the accuracy of state estimation for each distribution network operation mode. The configuration nodes of various operation modes are grouped and assessed by K-means according to the shortest distance, and the weights of the evaluation indexes are calculated by the AHP-CRITIC subjective and objective combination weighting method. The node with the highest comprehensive evaluation index is selected as the configuration node. The probability of multiple operation modes is then introduced. Finally, using the IEEE 118-bus distribution system as an example, the simulation demonstrates the proposed method’s effectiveness in improving distribution network state estimate.

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“…A practical solution of the unobservable grid is to use pseudo measurements [8], which are often forecasted from historical data or calculated by interpolating observed measurement data. In [9], an algebraic-based method is used to determine the observability of the electric grid.…”

Section: Introductionmentioning

confidence: 99%

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

et al. 2021

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The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

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A Review on Distribution System State Estimation Techniques

Cited by 21 publications

References 59 publications

A Framework for Constrained Static State Estimation in Unbalanced Distribution Networks

A Framework for Constrained Static State Estimation in Unbalanced Distribution Networks

Research on μPMU Configuration Optimization Considering Multiple Operation Modes of Distribution Network

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

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