With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting m...With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.展开更多
Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning,making the power system operation scenario highly diversified and variable.In conventional power system ...Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning,making the power system operation scenario highly diversified and variable.In conventional power system planning,some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation.While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power.In this paper,we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning.Specifically,based on an annual time-series data of wind power and load,a combined weighted clustering method is used to pick the typical scenarios of power grid operation,and the edge operation points far from the clustering center are extracted as the extreme scenarios.The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process,to further improve the efficiency and rationality of the extreme-scenario extraction.A set of case studies was used to verify the performance of the method,providing an intuitive understanding of the extreme scenario variety under wind power integration.展开更多
Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia ar...Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.展开更多
The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind powe...The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.展开更多
为实现风电出力时间序列的高性能模拟,文中提出了一种基于SAGA-KM(simulated annealing and genetic algorithms-K-means)算法实现典型风电场景分类和基于Copula函数进行风电日过程马尔可夫过程建模的风电模拟方法。SAGA-KM算法将传统K...为实现风电出力时间序列的高性能模拟,文中提出了一种基于SAGA-KM(simulated annealing and genetic algorithms-K-means)算法实现典型风电场景分类和基于Copula函数进行风电日过程马尔可夫过程建模的风电模拟方法。SAGA-KM算法将传统KM算法与遗传算法和退火算法相结合,能显著提高风电场景分类效果;基于Copula函数建立的马尔可夫链精细概率模型,以去粗粒化方式实现马尔可夫过程蒙特卡洛模拟,克服了粗粒化引起的概率分布偏差。针对甘肃省某风电场数据进行实际模拟,结果表明文中方法生成模拟序列的统计分布特性、自相关函数特性和日均功率的分布特性与实测数据都非常接近,该方法能很好地保留风电序列的概率分布特性和随时间变化的波动特性,具有重要的工程实用价值。展开更多
基金funded by Liaoning Provincial Department of Science and Technology(2023JH2/101600058)。
文摘With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.
基金supported by Innovation Fund Program of China Electric Power Research Institute(NY83-19-003)
文摘Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning,making the power system operation scenario highly diversified and variable.In conventional power system planning,some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation.While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power.In this paper,we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning.Specifically,based on an annual time-series data of wind power and load,a combined weighted clustering method is used to pick the typical scenarios of power grid operation,and the edge operation points far from the clustering center are extracted as the extreme scenarios.The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process,to further improve the efficiency and rationality of the extreme-scenario extraction.A set of case studies was used to verify the performance of the method,providing an intuitive understanding of the extreme scenario variety under wind power integration.
基金supported by the Natural Science Foundation of China under contact(61233007)
文摘Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.
基金supported by the China Datang Corporation project“Study on the performance improvement scheme of in-service wind farms”,the Fundamental Research Funds for the Central Universities(2020MS021)the Foundation of State Key Laboratory“Real-time prediction of offshore wind power and load reduction control method”(LAPS2020-07).
文摘The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.
文摘为实现风电出力时间序列的高性能模拟,文中提出了一种基于SAGA-KM(simulated annealing and genetic algorithms-K-means)算法实现典型风电场景分类和基于Copula函数进行风电日过程马尔可夫过程建模的风电模拟方法。SAGA-KM算法将传统KM算法与遗传算法和退火算法相结合,能显著提高风电场景分类效果;基于Copula函数建立的马尔可夫链精细概率模型,以去粗粒化方式实现马尔可夫过程蒙特卡洛模拟,克服了粗粒化引起的概率分布偏差。针对甘肃省某风电场数据进行实际模拟,结果表明文中方法生成模拟序列的统计分布特性、自相关函数特性和日均功率的分布特性与实测数据都非常接近,该方法能很好地保留风电序列的概率分布特性和随时间变化的波动特性,具有重要的工程实用价值。
