Transmission line voltage classes identification based on particle swarm optimization algorithm and PCNN

Bai, Wenhao; Zhang, Wenbin; Zhou, Nianrong; Tang, Lijun; Ma, Chuxia; Shi, Xinghua

doi:10.1080/00150193.2017.1390963

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Unscientific and unreasonable power grid construction will cause great damage to society, national economy, and power grid operation. Scientific planning of power grid can avoid unreasonable investment and construction to a certain extent [3]. Reasonable power grid planning can save investment to the greatest extent, promote the healthy development of itself and other industries, and improve economic and social benefits.…”

Section: Introductionmentioning

confidence: 99%

Low-Voltage Diagnosis of Energy Distribution Network Based on Improved Particle Swarm Optimization Algorithm

Wang

Dai

et al. 2022

Wireless Communications and Mobile Computing

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In order to improve the research on low-voltage fault diagnosis of energy distribution network, a low-voltage multiobjective diagnosis of AC/DC energy distribution network based on ICHPSO is proposed in this paper. Firstly, the types of objective functions are listed, the objective function is transformed into a single-objective function by using the linear weighting method, and the hybrid particle swarm optimization (HPSO) diagnosis algorithm is improved, which can guide the particles to escape from the local optimal solution space, prevent the particles from falling into the local optimal too early, and improve the search accuracy and global search ability of the algorithm. At the same time, the individuals with high fitness are retained, and the convergence performance of the algorithm is improved. Then, the algorithm is coded, and an example of AC/DC energy distribution network with improved ieee33 is adopted. DC side data comes from the first 13 nodes of AC side data, and the voltage level is 10 kV. The diagnostic effects of different schemes were compared. The results show that the expected mean value of node voltage deviation after power compensation diagnosis is 2.26%, which is 61.18% lower than that before diagnosis; the expected average voltage loss is 0.77 kv, which is 52.1% lower than that before diagnosis; the expectation of network loss is 196.45 kw, which is 49.56% lower than that before diagnosis; the minimum voltage of the AC side node (node 18) is expected to be 0.9514 p.u., 2.75% higher than that before diagnosis; and the minimum voltage of DC side node (node 8) is expected to be 0.9918 p.u., 1.11% higher than that before diagnosis. The comprehensive experimental results show that the proposed method can greatly reduce the node voltage deviation, voltage loss, and network loss of AC/DC energy distribution network, improve the node voltage quality, and maintain the stable operation of the whole AC/DC energy distribution network.

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Section: Introductionmentioning

confidence: 99%

Low-Voltage Diagnosis of Energy Distribution Network Based on Improved Particle Swarm Optimization Algorithm

Wang

Dai

et al. 2022

Wireless Communications and Mobile Computing

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show abstract

“…The GWO convergence curve is presented in Figure 4b, where the vertical coordinates represent the best fitness values selected by calculation of the tested function in the population region search. To demonstrate the GWO convergence advantages, we also applied two current methods, which are the GACA [17] and PSO algorithms [16], for comparison. As shown in Figure 4b, the number of iterations is set as 500 times, in order to conveniently and more clearly compare the convergence results.…”

Section: The Proposed Bemd-gwo-pcnn Algorithmmentioning

confidence: 99%

“…Zhu et al [15] researched a memristive PCNN (M-PCNN) for medical image denoising processes, which makes the network have a biological function. Bai et al [16] applied particle swarm optimization (PSO) to the implementation of PCNN parameter optimization. Shen et al [17,18] proposed an innovative genetic ant colony algorithm (GACA)-based combination with the PCNN approach to accomplish good quality image denoising.…”

Section: Introductionmentioning

confidence: 99%

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

Min

Wang

et al. 2018

Applied Sciences

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Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.

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High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

Luo

2020

Multimed Tools Appl

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Transmission line voltage classes identification based on particle swarm optimization algorithm and PCNN

Cited by 6 publications

References 9 publications

Low-Voltage Diagnosis of Energy Distribution Network Based on Improved Particle Swarm Optimization Algorithm

Low-Voltage Diagnosis of Energy Distribution Network Based on Improved Particle Swarm Optimization Algorithm

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

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