Neural Network-based Power Flow Model

Pham, Thuan; Li, Xingpeng

doi:10.1109/greentech52845.2022.9772026

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…The input data for the proposed NN model include the initial voltage magnitude, the sum of active power injection, and reactive power injection for each bus. The output data for training will be steadystate voltage magnitude for each bus and active power flow for each branch (Pham and Li, 2022).…”

Section: Methodsmentioning

confidence: 99%

Advancing Electrical Losses Assessment Methods in Power Systems

Rexhepi,

Rexhepi

2024

Ecol. Eng. Environ. Technol.

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The operation of modern power systems requires a sophisticated technological infrastructure to effectively manage and evaluate their parameters and performance. This infrastructure includes the generation, transmission and distribution power system components. This paper provides an overview of the loss evaluation to a part of Kosovo's power system, substation with wind and photovoltaic (PV) energy sources integrated (SS Mramori, SS Kitka, and SS Kamenica) and the analysis of the loss assessment methods. One the assessment method in the research encompass simulated loss scenarios and their corresponding values in network components, employing the simulation based on the respective software tools. In current trends, power systems are visualized through the Supervisory Control and Data Acquisition (SCADA) platform. However, in Kosovo, although losses are integral to the SCADA system, they are represented as a overall value in the online mode, not encompassed depict losses per-components in real-time. This limitation hinders effective online power system optimization regarding the losses. As consequence, the purpose of this study is proposal a logical method developed through neural networks. The methodology incorporates various parameters, including as inputs variables; voltages, currents, active and reactive powers, and their computed values for extracting losses (X(x 1 , x 2 , ..., x n )). These parameters undergo systematic processing through hidden layers (Y(x 1 , x 2 , ..., x n )), leading to the classification of components within the power system. Finally, at the output stage (A(x 1 , x 2 , ..., x n )), an assessment is conducted based on the level of losses observed in the components of the power system. This implementation method promises significant benefits for transmission systems, impacting not only reducing losses, power quality but also yielding economic advantages.

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Section: Methodsmentioning

confidence: 99%

Advancing Electrical Losses Assessment Methods in Power Systems

Rexhepi,

Rexhepi

2024

Ecol. Eng. Environ. Technol.

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“…Compared to collective pitch control and gain scheduling proportional-integral individual pitch control, the proposed method achieved power output closer to the rated level. In response to the obstacles posed by offshore wind farms situated at a considerable distance from the primary grid and the necessity for precise power flow examination within the primary grid, Pham and Li devised an MLP model [77]. This model aimed to forecast power flow outcomes by leveraging historical power system data.…”

Section: Lian Et Al Developed An Mlp-based Regression Model To Relate...mentioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

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