Application Research of BP Neural Network Algorithm in Power Line Loss Calculation

Su, Dai; Ma, Yunlong; Wang, Kai; Liu, Bowen; Liu, Changming

doi:10.1088/1742-6596/2146/1/012008

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…They have been successfully employed in automatic control systems, composition optimization tasks, pattern analysis, graphics processing, robot control, and various medical applications. The versatility of artificial neural networks makes them a powerful and adaptable tool for addressing complex challenges in a range of domains (Su Dai et al, 2022).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Advancing Electrical Losses Assessment Methods in Power Systems

Rexhepi,

Rexhepi

2024

Ecol. Eng. Environ. Technol.

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Research on Financial Early Warning Based on Combination Forecasting Model

2022

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Since entering the 21st century, “economic globalization” has become a hot topic. Under the impact of “economic globalization”, the competition of the Chinese domestic market continues to intensify, and Chinese enterprises are facing enormous pressure for survival and development. Among them, there are many cases of poor business operation caused by financial crisis which have directly put these companies in trouble, even causing them to go bankrupt. Therefore, it is very practical to establish a scientific data model to analyze and predict the financial data of enterprises. It can not only monitor the financial status of the enterprise in real time, but also play an effective financial early warning role. This research focuses on using the combined forecasting method to establish a more comprehensive financial early warning model to solve the related financial crisis forecasting problem. Specifically, two different forecasting methods are first adopted in this study to conduct financial early warning research. The first is time series forecasting. It is a dynamic data processing statistical method, which is often used in forecasting research in the business field. The second is the BP neural network algorithm (referred to as BP), which is an error back-propagation learning algorithm, which is often used in the field of artificial intelligence. Then, the prediction error values of the two methods are compared and they are applied to the combined prediction method. Finally, a new error prediction formula is obtained. The result shows that the BP method provides the best performance over others, while the combinational forecasting method offers better performance than any single method.

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Application Research of BP Neural Network Algorithm in Power Line Loss Calculation

Cited by 2 publications

References 10 publications

Advancing Electrical Losses Assessment Methods in Power Systems

Advancing Electrical Losses Assessment Methods in Power Systems

Research on Financial Early Warning Based on Combination Forecasting Model

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