Visual Extraction of Refined Operation Mode of New Power System Based on IPSO-Kmeans

Guo, Xiaoli; Shan, Q.; Zhang, Zhenming; Qu, Zhaoyang

doi:10.3390/electronics12102326

Cited by 3 publications

References 27 publications

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An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

Pi,

Yuan,

Zhang

et al. 2024

Concurrency and Computation

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Aiming at the problem that the existing ultra‐short‐term wind power prediction methods lack consideration of the spatial correlation characteristics of wind farms, resulting in insufficient prediction accuracy, an ultra‐short‐term wind power prediction method based on spatiotemporal characteristics fusion is proposed in this article. First, the fluctuation difference of the time window of wind power is input into the K‐means clustering algorithm to cluster wind farms into several clusters based on power fluctuation similarity. Then, the principal component analysis algorithm is used to reduce the dimensionality of numerical weather prediction data combinations in different regions to reduce the impact of redundant information on modeling accuracy. Finally, a convolutional long‐short‐term memory neural network is designed to extract spatiotemporal features of wind power data and output prediction results. The experimental verification on 18 wind farms in a province in China shows that the proposed wind power prediction method has an average root mean square error of only 0.1257 and has certain applicability.

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An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

Pi,

Yuan,

Zhang

et al. 2024

Concurrency and Computation

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Reinforcement learning guided Spearman dynamic opposite Gradient-based optimizer for numerical optimization and anchor clustering

Sun,

Huo,

Jia

et al. 2023

Journal of Computational Design and Engineering

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As science and technology advance, the need for novel optimization techniques has led to an increase. The recently proposed metaheuristic algorithm, Gradient-based optimizer (GBO), is rooted in the gradient-based Newton's method. GBO has a more concrete theoretical foundation. However, GSR and LEO operators in GBO still have some shortcomings. The insufficient updating method and the simple selection process limit the search performance of the algorithm. In this paper, an improved version is proposed to compensate for the above shortcomings, called RL-SDOGBO. First, during the GSR phase, the Spearman rank correlation coefficient is used to determine weak solutions on which to perform dynamic opposite learning. This operation assists the algorithm to escape from local optima and enhance exploration capability. Second, to optimize the exploitation capability, reinforcement learning is used to guide the selection of solution update modes in the LEO operator. RL-SDOGBO is tested on 12 classical benchmark functions and 12 CEC2022 benchmark functions with seven representative metaheuristics, respectively. The impact of the improvements, the scalability and running time of the algorithm, and the balance of exploration and exploitation are analyzed and discussed. Combining the experimental results and some statistical results, RL-SDOGBO exhibits excellent numerical optimization performance and provides high-quality solutions in most cases. In addition, RL-SDOGBO is also used to solve the anchor clustering problem for small target detection, making it a more potential and competitive option.

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A weighted k-mean clustering algorithm based on singular values with offset clustering centers

deng,

lin,

Yuan

et al. 2024

Preprint

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The K-means algorithm is widely used for dataset clustering, but it does not consider the importance of each attribute dimension when dealing with feature attributes and clustering center selection, but rather treats all attributes as having equal importance. In order to solve this problem, this paper proposes a weighted k-mean clustering algorithm (SVW-KMeans) based on singular values with offset clustering centers. The algorithm calculates the weight information of the data points through singular value decomposition to focus on the most significant and most different features, joining the weight calculation to optimize the objective function, and at the same time, the weighted arithmetic mean of the individuals is used as the clustering center, and the clustering center is shifted towards the high importance so as to take into full consideration of the importance of the different features in the clustering process. The experimental results show that the SVW-KMeans algorithm outperforms other algorithms in clustering on synthetic and real datasets, which verifies that the SVW-KMeans algorithm outperforms other mainstream clustering algorithms in terms of clustering quality and stability.

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Visual Extraction of Refined Operation Mode of New Power System Based on IPSO-Kmeans

Cited by 3 publications

References 27 publications

An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

Reinforcement learning guided Spearman dynamic opposite Gradient-based optimizer for numerical optimization and anchor clustering

A weighted k-mean clustering algorithm based on singular values with offset clustering centers

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