Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms

Munshi, Amr A.

doi:10.1109/access.2020.3001437

Cited by 11 publications

(8 citation statements)

References 48 publications

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“…The elbow method is applied on the feature-related information embedded into the low-dimensional space [28] to observe the elbow values of classes with different numbers of clusters, as shown in Fig. 3.…”

Section: ) Generating the Optimal Scenariomentioning

confidence: 99%

Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-Head Self-Attention

Liu,

Hu,

Chen

et al. 2024

Prot. Control Mod. Power Syst.

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The source and load uncertainties arising from increased applications of renewable energy sources such as wind and photovoltaic energy in the power system have had adverse effects on optimal planning and dispatching. Models for generating typical renewable energy and load scenarios are constructed to reduce such effects and improve the applicability of a planning and optimal dispatching model of power systems with a high proportion of renewable energy. The traditional clustering-based model for representing such scenarios cannot handle high-dimensional time-series data and consequently the feature-related information obtained cannot fully reflect the characteristics of the data. Thus, a deep convolutional embedded clustering model based on multi-head self-attention is proposed. First, a variational mode decomposition model is optimized to reduce the influence of noise-related signals on the feature extraction. The deep features are then extracted from the data using an improved convolutional autoencoder, and the appropriate number of clusters is determined using the elbow method. Following this, the network parameters are optimized based on the sum of losses during reconstruction and clustering. Subsequently, typical scenarios are then generated based on the optimized network model. Finally, the proposed method is evaluated based on data visualization and evaluation metrics. It is shown that the quality of features and the accuracy of clustering can be effectively improved by the proposed scenario generation method. Index Terms-Deep embedding for clustering, extracting features of time-series data, multi-head self-attention mechanism, scenario generation, uncertainty.  Ⅰ. INTRODUCTION ith continuous advance in power systems with a large integration of renewable energy, the volatility and periodicity of wind and photovoltaic (PV) _____________________________________

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Section: ) Generating the Optimal Scenariomentioning

confidence: 99%

Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-Head Self-Attention

Liu,

Hu,

Chen

et al. 2024

Prot. Control Mod. Power Syst.

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show abstract

“…The second is based on wind speed time series with a fixed duration. Reference [49] proposes that the wind speed time series data are separated into different segments with each representing a period of 24-hours. Similarly, reference [50] selects wind speed series based on a 10min sliding window from historical data as wind processes.…”

Section: Definition and Division Of Wind Processmentioning

confidence: 99%

“…In many papers, the purpose of wind process division and pattern recognition is to apply in wind speed and power prediction. Establishing different prediction models according to different pattern recognition results to improve the prediction accuracy, such as studies in [49][50][51]. In addition, reference [48] uses the divided wind processes to identify wind speed ramp events.…”

Section: Definition and Division Of Wind Processmentioning

confidence: 99%

An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling

Zhen

Qiu

Mei

et al. 2022

International Journal of Electrical Power & Energy Systems

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“…To classify the missing, isolated and fault data, an unsupervised clustering method is proposed [98] to analyze and train the data obtained from a 1.5 MW wind farm in advanced before clustering is carried out. Another advanced clustering applied for wind power pattern simulation [99] is based on the evaluations on three clustering methods with eight indicators. The result shows the best clustering algorithm was modified by integrating the Silhouette index as an objective function for k-means and a wind generation model was tested for the potency of the new algorithm.…”

Section: Applications Of Data Clustering In Electrical Power Systemsmentioning

confidence: 99%

Review on Supervised and Unsupervised Learning Techniques for Electrical Power Systems: Algorithms and Applications

Chen¹

2021

IEEJ Transactions Elec Engng

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Machine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro-grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications.

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Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms

Abstract: xxxx xx xxxxxxx xxxx xx, xxxx, xxx xx xxxx xxxx xxx xxxx xxxx, xxxx.

Cited by 11 publications

References 48 publications

Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-Head Self-Attention

Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-Head Self-Attention

An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling

Review on Supervised and Unsupervised Learning Techniques for Electrical Power Systems: Algorithms and Applications

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