Application of BP Neural Network for Line Losses Calculation Based on Quantum Genetic Algorithm

Liu, Kewen; Zhou, Huaiying; Zhanyong, Yang; Qu, Fumin

doi:10.1109/iscid.2011.9

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…In the field of deep learning, Liu et al proposed a novel method based on the quantum genetic algorithm (QGA) and BP neural network to accurately predict line loss (Liu et al, 2011). Artificial neural networks (ANNs) can also be used for forecasting (Alamin et al, 2020;Ti et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

et al. 2022

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Line loss prediction of ultrahigh voltage transmission lines is the key for ensuring the safe, reliable, and economical operation of the power system. However, the strong volatility of line loss brings challenges to the prediction of transmission line loss. For more accurate prediction, this article uses ensemble empirical mode decomposition (EEMD) to decompose the line loss and proposes the EEMD–LSTM–SVR prediction model. First of all, this article performs feature engineering on power flow, electric energy, and meteorological data and extracts the exponentially weighted moving average (EWMA) feature from the line loss. After the integration of the time dimension, this article mines the curve characteristics from the time series and constructs a multidimensional input dataset. Then, through ensemble empirical mode decomposition, the line loss is decomposed into high-frequency, low-frequency, and random IMFs. These IMFs and the standardized multidimensional dataset together constitute the final input dataset. In this article, each IMF fusion dataset is sent to LSTM and support vector regression models for training. In the training process, the incremental cross-validation method is used for model evaluation, and the grid search method is used for hyperparameter optimization. After evaluation, the LSTM algorithm predicts high-frequency IMF1 and 2 and random IMF4 and 5; the SVR algorithm predicts low-frequency IMF6 and 7 and random IMF3. Finally, the output value of each model is superimposed to obtain the final line loss prediction value. Also, the comparative predictions were performed using EEMD–LSTM, EEMD–SVR, LSTM, and SVR. Compared with the independent prediction models EEMD–LSTM and EEMD–SVR, the combined EEMD–LSTM–SVR algorithm has a decrease in mean absolute performance error% by 2.2 and 25.37, respectively, which fully demonstrates that the combined model has better prediction effect than the individual models. Compared with that of SVR, the MAPE% of EEMD–SVR decreases by 11.16. Compared with that of LSTM, the MAPE% of EEMD–LSTM is reduced by 32.72. The results show that EEMD decomposition of line loss series can effectively improve the prediction accuracy and reduce the strong volatility of line loss. Compared with that of the other four algorithms, EEMD–LSTM–SVR has the highest R-square of 0.9878. Therefore, the algorithm proposed in this article has the best effectiveness, accuracy, and robustness.

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Section: Literature Reviewmentioning

confidence: 99%

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

et al. 2022

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“…By graphical processing of the inspection images, the performance parameter evaluation of the inspection images is finally derived. ,en, according to the given weight calculation method, the weighting operation is performed for each parameter [19], and the current score of each device is calculated according to the existing state integral algorithm. Finally, the corresponding monitoring report is generated based on the evaluation results of each parameter.…”

Section: Inspection Processmentioning

confidence: 99%

Inspection Technology of Power Communication Network Based on Machine Vision Graphic Recognition

Zhong

Ling

Shi

2022

Scientific Programming

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With the continuous improvement of maintenance management level and the continuous progress of fault diagnosis technology, equipment condition maintenance has also gradually entered the stage of market use. Especially, in the electric power industry, with the development of the research and production practice of condition maintenance theory, its application areas are becoming more and more extensive. In recent years, with the increasing popularity of computer network technology, the development of communication network monitoring technology has also made great progress. However, the monitoring of communication equipment in China is still in the primary stage, and the complexity of the equipment and the diversity of the equipment make the research on its condition detection a very challenging task. The study introduces the application of computer vision-based graphic recognition technology in power communication networks, which includes two modules: FasterR-CNN and RPN. The model provides real-time monitoring of various performance indicators of power communication network equipment and feedback on its working status, repairs the equipment according to the monitoring results, timely detects potential safety hazards, and makes a maintenance cycle reasonable planning, ensuring the normal operation of the communication network.

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“…Neural networks have attracted increasing attention from researchers in many fields, including information processing, computer science, economics, medicine and mathematics, and have been used to solve a wide range of problems such as data mining, function approximation, pattern recognition, expert system and data prediction, etc [3][4][5]. The neural networks is also one of the best tools to solve this problem, because it has the ability to learn the approximate any nonlinear continuous function and comprehensive ability about disorderly information, meanwhile this ability are needed for the analysis of stock investment [6].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Stock Price Based on Back Propagation Neural Network

Zhang

Chen³

et al. 2014

AMR

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The stock market is a nonlinear dynamics system with enormous information, which is difficult to predict effectively by traditional methods. The model of stock price forecast based on BP Neutral-Network is put forward in this article. The paper try to find the way how to predictive the stock price. Exhaustive method is used for the hidden layer neurons and training method determination. Finally the experiment results show that the algorithm get better performance in stock price prediction.

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Application of BP Neural Network for Line Losses Calculation Based on Quantum Genetic Algorithm

Cited by 7 publications

References 6 publications

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

Inspection Technology of Power Communication Network Based on Machine Vision Graphic Recognition

Forecasting Stock Price Based on Back Propagation Neural Network

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