Network loss management and allocating the transmission losses to loads and generation units according to their transactions

Khosravi, Masoud; Monsef, Hassan; Aliabadi, Mahmoud H.

doi:10.1049/iet-gtd.2019.0503

Cited by 6 publications

(1 citation statement)

References 31 publications

(39 reference statements)

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“…In Equation (10), the sample variance is σ , the sample LLR data is x i , the sample LLR mean is x, and the number of class samples is n. The study makes a judgement on line loss data outliers based on Lajda's criterion, i.e., where the distance from the overall mean is greater than three times the sample variance, a kernel of data points is required and data culling is considered. distribution planning, which helps to improve the economy of distribution network operation [20]. Traditional LLC methods have low accuracy, incomplete access to input data, and high manual dependency, while deep NNM will be used as a new generation of artificial intelligence technology to calculate line losses [21].By analyzing the fundamental principles and optimization algorithms of neural networks, this study utilizes a backpropagation (BP) neural network as the underlying model of LLC.…”

Section: A Design Of Line Loss Characteristics Recognition Methods Fo...mentioning

confidence: 99%

Design of Line Loss Rate Calculation Method for Low-Voltage Desk Area Based on GA-LMBP Neural Network Model

Jiang,

Li,

Cai

et al. 2023

IEEE Access

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The distribution network line loss computation method needs to be enhanced in light of the ongoing growth of the national power grid. The study classifies and segments the station data using a decision tree model and a multi-feature volume weighted station clustering algorithm. It then uses a back propagation neural network as a substrate, along with the Levenberg-Marquard algorithm and genetic algorithm for optimization. Collect and organize relevant data on line loss rates in low-voltage substation areas, including information on energy meters, meter boxes, and lines. Next, construct a genetic algorithm neural network model and use the backpropagation algorithm for training. Evaluate the accuracy and stability of various models by comparing the error between predicted and actual line loss rates through experiments. Finally, optimize the neural network parameters and network structure to improve the model's prediction accuracy and robustness. The experimental data showed that compared to the density-based spatial clustering algorithm for noisy applications, the contour coefficient metrics of the proposed multi-feature volume weighted station clustering algorithm improved by 0.05 and the average consumption time of the algorithm was reduced by 75%. Compared to the back-propagation neural network model optimized by the Levenberg-Marquard algorithm, the root-mean-square error of the neural network model optimized by the addition of the genetic algorithm for the calculation of the line loss rate of the four station samples was reduced by 72%, 55%, 53% and 37%, and the values of R2 were improved by 8.72%, 13.59%, 7.91% and 11.69%, respectively. The testing results demonstrated that the neural network model has good generalization capabilities and a high degree of curve fitting. Also, the relative errors of the calculation of the station area's line loss rate are mainly within the range of 0% and 10%. For the growth of energy conservation in the country, this innovative technology offers a new way to determine and manage line loss of the station area.

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Section: A Design Of Line Loss Characteristics Recognition Methods Fo...mentioning

confidence: 99%

Design of Line Loss Rate Calculation Method for Low-Voltage Desk Area Based on GA-LMBP Neural Network Model

Jiang,

Li,

Cai

et al. 2023

IEEE Access

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show abstract

Reactive power planning problem considering multiple type of FACTS in power systems

Gupta¹,

Babu

2022

Int J Syst Assur Eng Manag

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Power Flow Tracing in Power Systems with Active and Reactive Circulating Power Flows

Gutiérrez-Martínez

Fuerte‐Esquivel

Zamora-Cárdenas

2023

Electric Power Components and Systems

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Network loss management and allocating the transmission losses to loads and generation units according to their transactions

Cited by 6 publications

References 31 publications

Design of Line Loss Rate Calculation Method for Low-Voltage Desk Area Based on GA-LMBP Neural Network Model

Design of Line Loss Rate Calculation Method for Low-Voltage Desk Area Based on GA-LMBP Neural Network Model

Reactive power planning problem considering multiple type of FACTS in power systems

Power Flow Tracing in Power Systems with Active and Reactive Circulating Power Flows

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