Exact Modeling of Non-Gaussian Measurement Uncertainty in Distribution System State Estimation

Vanin, Marta; Acker, Tom Van; D’hulst, Reinhilde; Hertem, Dirk Van

doi:10.1109/tim.2023.3287253

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…This could mean that the simplifications and assumptions made in current State Estimation methods do not provide an accurate representation of the true system state, leading to erroneous estimates (Ahmad et al, 2018). Furthermore, the relationships between loads on low-voltage distribution network buses are not linear or Gaussian, meaning that conventional least-squares state estimation results in a sub-optimal model (Vanin et al, 2023). Lastly, there is the issue of imbalance to consider.…”

Section: Introductionmentioning

confidence: 99%

Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation

Lu,

Stephen,

Brown

2024

DCE

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Traditionally, electricity distribution networks were designed for unidirectional power flow without the need to accommodate generation installed at the point of use. However, with the increase in Distributed Energy Resources and other Low Carbon Technologies, the role of distribution networks is changing. This shift brings challenges, including the need for intensive metering and more frequent reconfiguration to identify threats from voltage and thermal violations. Mitigating action through reconfiguration is informed by State Estimation, which is especially challenging for low voltage distribution networks where the constraints of low observability, non-linear load relationships, and highly unbalanced systems all contribute to the difficulty of producing accurate state estimates. To counter low observability, this paper proposes the application of a novel transfer learning methodology, based upon the concept of conditional online Bayesian transfer, to make forward predictions of bus pseudo-measurements. Day ahead load forecasts at a fully observed point on the network are adjusted using the intraday residuals at other points in the network to provide them with load forecasts without the need for a complete set of forecast models at all substations. These form pseudo-measurements that then inform the state estimates at future time points. This methodology is demonstrated on both a representative IEEE Test network and on an actual GB 11 kV feeder network.

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Section: Introductionmentioning

confidence: 99%

Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation

Lu,

Stephen,

Brown

2024

DCE

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“…Due to the rapid growth of renewable energy sources and the widespread adoption of distributed energy resources, the grid structure of distribution networks has become more complex. Moreover, the operational status of the distribution network will undergo frequent changes due to the uncontrollability and volatility associated with the output of new energy sources [1]. Hence, there is a focus on researching state inference methods for distribution networks, a pursuit of considerable significance in enhancing the operational reliability of such networks.…”

Section: Introductionmentioning

confidence: 99%

State inference for low-observable distribution system based on graph convolutional network

Xuan,

Sun,

et al. 2024

J. Phys.: Conf. Ser.

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Distribution network state inference refers to the process of calculating the state variables of each node by using measurement data and network models in the operation of the distribution system. However, the uneven measurement layout and insufficient measurement accuracy in the distribution network have brought great challenges to the state inference of the distribution network. This paper proposes a low-observable distribution network state inference method based on a graph convolution network (GCN), which uses sparse measurement data to infer missing measurement information. Firstly, the observability of the distribution network is analyzed by the numerical probability analysis method. Secondly, the GCN is employed to extract feature information from measurement data and integrate these features. The state inference model of the distribution network based on the GCN is established. Subsequently, power flow constraints of the distribution network are incorporated into the GCN training process to enhance the precision of the generated data. Ultimately, the efficacy of the proposed method is validated using the IEEE 33-node distribution system.

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“…In the examination of three‐phase state estimation under conditions of unbalanced loading or non‐transposed lines, a significant portion of the proposed methods focuses on the distribution network, with particular emphasis on radial networks [23–25]. Furthermore, a limited number of papers have investigated the influence of imbalance on the state estimation problem in the transmission network, revealing that the impact of unbalanced loading outweighs that of non‐transposed lines [26].…”

Section: Introductionmentioning

confidence: 99%

Three‐phase state estimation in transmission networks with unbalanced loading utilising SCADA measurements

Mofidnakhaei,

Dobakhshari

2024

IET Generation Trans & Dist

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State estimation stands as one of the most crucial applications in energy management systems of control centres, ensuring the secure operation of the power system. The real‐time availability of accurate system variables, including voltages, currents, and power flows, presents an excellent opportunity to consider the actual network characteristics, including imbalances. This paper presents a linear and non‐iterative method to solve the three‐phase state estimation problem in transmission systems with unbalanced loading. The method utilises supervisory control and data acquisition measurements obtained from remote terminal units. The formulation is based on complex variables derived from traditional supervisory control and data acquisition measurements, with assumptions made regarding the availability of certain three‐phase bus voltages, as well as a number of three‐phase currents and active and reactive power flows. The proposed algorithm employs the classical weighted least squares technique for solving the three‐phase state estimation problem. Due to its straightforward and linear nature, the method ensures no convergence issues. To validate its effectiveness, the proposed method is validated on a three‐bus network and the IEEE 39‐bus network simulated using the three‐phase model within the DIgSILENT Power Factory environment.

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Exact Modeling of Non-Gaussian Measurement Uncertainty in Distribution System State Estimation

Cited by 5 publications

References 54 publications

Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation

Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation

State inference for low-observable distribution system based on graph convolutional network

Three‐phase state estimation in transmission networks with unbalanced loading utilising SCADA measurements

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