APPLICATION OF SUPPORT VECTOR MACHINE MODEL IN WIND POWER PREDICTION BASED ON PARTICLE SWARM OPTIMIZATION

Ning Lu; YING Liu

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APPLICATION OF SUPPORT VECTOR MACHINE MODEL IN WIND POWER PREDICTION BASED ON PARTICLE SWARM OPTIMIZATION

机译：基于粒子群优化的支持向量机模型在风电预测中的应用

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Wind energy is a kind of renewable and clean energy, and wind power is a non-hydropower renewable energy which has the best technical and economic conditions for large-scale development. It is characterized by fluctuation, intermittency, low energy density, etc., so wind power is also fluctuating. When a large-scale wind farm is connected to a power grid, great fluctuation in wind power will cause adverse effect to the power balance and frequency adjustment of the power grid. If the generation power of the wind farm can be prediction, the electricity dispatch department can arrange dispatch plans in advance according to the change in wind power and better protect the power balance and operation safety of the power grid. In this article, a SVM model is used to predict wind power and modified PSO is used to optimize SVM parameters, realizing the optimized selection of the SVM model parameters, which makes such prediction more close to actual law. Actual calculation examples shows that the prediction method used in the article has good convergence, high prediction precision and actual application value.

机译：风能是一种可再生和清洁能源，风能是一种非水电可再生能源，具有大规模发展的最佳技术和经济条件。它的特点是波动，间歇性，能量密度低等，因此风能也在波动。当大型风电场接入电网时，风电的较大波动会对电网的功率平衡和频率调整产生不利影响。如果可以预测风电场的发电量，电力调度部门可以根据风力的变化提前安排调度计划，更好地保护电网的电力平衡和运行安全。本文采用支持向量机模型对风电功率进行预测，采用改进的粒子群优化算法对支持向量机的参数进行优化，实现了对支持向量机模型参数的优化选择，使这种预测更加接近实际规律。实际算例表明，本文所采用的预测方法收敛性好，预测精度高，具有实际应用价值。

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来源
《Discrete and continuous dynamical systems▼hSeries S》 |2015年第6期|1267-1276|共10页
作者
Ning Lu; YING Liu;
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作者单位

School of Automation, Wuhan University of Technology Wuhan, China;

Wuhan Electric Power Dispatching and Communication Center Wuhan, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Wind power generation; support vector machine (SVM); particle swarm; parameter optimization; prediction;

机译：风力发电;支持向量机（SVM）;粒子群参数优化;预测;

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2. Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine [J] . Runjie Shen, Danqiong Hua, Yiying Wang, E3S Web of Conferences . 2020,第10期

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3. Earth pressure prediction in sealed chamber of shield machine based on parallel least squares support vector machine optimized by cooperative particle swarm optimization [J] . Liu Xuanyu, Zhang Kaiju Measurement and Control: Journal of the Institute of Measurement and Control . 2019,第7a8期

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4. Application of Support Vector Machine Based on Particle Swarm Optimization in Low Voltage Line Loss Prediction [C] . Tan Min, Wang Xinghua, Li Qing, Joint International Mechanical, Electronic and Information Technology Conference . 2015

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5. Support vector machines for regression and its application for prediction of machine degradation based on vibration signals. [D] . Lin, Hui. 2006

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6. Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization [O] . Haifeng Xing, Bo Hou, Zhihui Lin, 2017

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7. Application of Support Vector Machine Based on Particle Swarm Optimization in Low Voltage Line Loss Prediction [O] . Tan Min, Wang Xinghua, Li Qing, 2015

机译：基于粒子群优化在低压线路损耗预测中的支持向量机应用

APPLICATION OF SUPPORT VECTOR MACHINE MODEL IN WIND POWER PREDICTION BASED ON PARTICLE SWARM OPTIMIZATION

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