Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review

Ibrahim, Ibrahim Anwar; Hossain, M. J.

doi:10.1109/access.2021.3120803

Cited by 14 publications

(5 citation statements)

References 275 publications

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“…Besides, it can be challenging to choose the best learning rate, and they run the risk of finding local minima [44]. As seen in [45], it is also important to make sure that at least first-degree differentiation continuity is maintained when using techniques like the conjugate gradient, the IP, and the Newton-based approach.…”

Section: Related Workmentioning

confidence: 99%

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

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Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Besides, it uses physics principles to build the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, RPINN’s robustness against noisy input measurements makes it particularly valuable in environments with fluctuating information. Additionally, RPINN’s ANN-based foundation offers significant flexibility and scalability.

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Section: Related Workmentioning

confidence: 99%

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

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“…The more practical equation to calculate discharge time obtained from manufacturers is given as in (17):…”

Section: Battery Modellingmentioning

confidence: 99%

Implementation of a P&E management system for a dual-source EV powered by different batteries

Thakre,

C. Tapre,

Somnath Kadlag

et al. 2023

Bulletin EEI

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There is an era started in the field of power energy (PE) management in the electric vehicles (EVs) application cloud, which impacts the smart grid in significant ways and necessitates the collaboration of multiple branches of engineering. Most of the problems with EVs stem from their limited range, which can be improved by incorporating additional forms of energy storage. This paper makes an effort to bring a fresh viewpoint to the description of the power and energy management problem facing EV, taking into account all the needs of such a vehicle. Using a novel power and energy management system, the proposed methodology enables a systematic approach to this multidisciplinary problem. Implementing a power and energy management system for a dual-source EV using lead-acid batteries and ultra-capacitors (UCs) exemplifies the novel framework's capabilities. The electronic power development architecture is outlined in detail, along with the implementing modular blocks that make up the entire system architecture.

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“…Accurate network topology models are essential for effective analysis and operation of power grids. The node and branch information of distribution networks is fundamental for power system load flow calculation, through which the operating status of various lines and components in the system can be predicted, as well as the voltage stability of key nodes [4]. At the same time, topology information is a prerequisite for power grid fault analysis.…”

Section: Introductionmentioning

confidence: 99%

Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

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Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

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Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review

Cited by 14 publications

References 275 publications

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Implementation of a P&E management system for a dual-source EV powered by different batteries

Improved topology identification for distribution network with relatively balanced power supplies

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