State Estimation of Asymmetrical Distribution Networks by μ-PMU Allocation: A Novel Stochastic Two-stage Programming

Abdolahi, Arya; Kalantari, Navid Taghizadegan

doi:10.1016/j.epsr.2022.108738

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The DSSE methods are divided into traditional model-driven methods and data-driven methods. In terms of model-driven methods, A two-stage programming model was proposed to configuration phasor measurement unit (PMU) and realization DSSE [1]. In [2], the AC optimal power flow method based on the polar coordinate power-voltage formulation and the rectangular current-voltage formulation are used for state estimation, respectively.…”

Section: A Related Workmentioning

confidence: 99%

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State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

Ren,

Chu,

2023

IEEE Access

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Section: A Related Workmentioning

confidence: 99%

“…The node importance in the DSSE framework of this paper is calculated through two types of importance. 1 1…”

Section: B Measurement Device Configurationmentioning

confidence: 99%

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

Ren,

Chu,

2023

IEEE Access

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“…As the amount of measured data increases, the more accurate the estimation result can be. Abdolahi and Kalantari [6] proposed a method to minimize the measurement device numbers in large scale asymmetric distribution networks with an innovative twostage stochastic programming model.…”

Section: Introductionmentioning

confidence: 99%

Role of Voltage Control Devices in Low Voltage State Estimation Process

Táczi,

Vokony,

Barancsuk

et al. 2023

International Transactions on Electrical Energy Systems

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Megatrends, such as the proliferation of distributed generation, electrification, and the appearance of aggregator companies, put the low voltage power grids under intense pressure. Since the network infrastructure developments cannot keep up with the trends, distribution system operators turned to alternative solutions. Smart grid assets, such as on-load tap-changing distribution transformers or serial low voltage regulators, are promising solutions. However, the energy transition cannot be handled with the network expansive on the distribution level. Control centers are predicted to expand to this voltage level in the near future, and distribution system state estimation could be an enabler of all functionalities. On the low voltage level, data scarcity is a great challenge in observability; therefore, research must focus on the creation of pseudomeasurements and integration of available data sources. This paper examines the inclusion of smart assets from the conceptual point to the application on two sites, based on data from operational environments, both with a pseudomeasurement and an integrated metering point approach. The results showed that integrating smart assets could considerably mitigate voltage fluctuations, and reduce estimation errors by two magnitudes on the low voltage network.

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Perspective Chapter: Optimizing μ-PMU Placement for Estimating Asymmetrical Distribution Network States – Introducing a Novel Stochastic Two-Stage Approach

Abdolahi,

Cheshmeh Khavar,

Nazari-Heris

et al. 2024

Applications and Optimizations of Kalman Filter and Their Variants

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Distribution system state estimation plays a crucial role in supplying essential data for system monitoring and control. However, the presence of uncertain parameters such as the variable output of distributed generation (DG), random meter errors, and inaccurate network parameters poses a significant challenge to achieving situational awareness (SA) in distribution systems. To address these challenges, this study introduces an innovative two-stage stochastic programming model. In the first stage, the model focuses on the optimal placement of μ-PMUs with the objective of minimizing installation costs, maximizing measurement redundancy, and enhancing system observability. This is particularly important in the context of partially zero injection nodes (PZIN). In the second stage, the model performs state estimation for three-phase asymmetric DG-integrated distribution systems, aiming to improve SA. The application of the proposed model resulted in the identification of optimal μ-PMU locations in the presence of PZINs and various contingencies. The distribution system state estimation achieved high accuracy and a low error percentage. The feasibility and effectiveness of this methodology were validated using the modified IEEE 85-bus distribution system. In addition, we incorporated extended Kalman filter (EKF) state estimation to compare with the weighted least square method.

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State Estimation of Asymmetrical Distribution Networks by μ-PMU Allocation: A Novel Stochastic Two-stage Programming

Cited by 3 publications

References 35 publications

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

Role of Voltage Control Devices in Low Voltage State Estimation Process

Perspective Chapter: Optimizing μ-PMU Placement for Estimating Asymmetrical Distribution Network States – Introducing a Novel Stochastic Two-Stage Approach

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