Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is *** paper proposes a data preprocessing method which combine...
详细信息
Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is *** paper proposes a data preprocessing method which combines POT with DBSCAN(POT-DBSCAN)to improve the prediction efficiency of wind power prediction ***,according to the data of WT in the normal operation condition,the power prediction model ofWT is established based on the Particle Swarm Optimization(PSO)Arithmetic which is combined with the BP Neural Network(PSO-BP).Secondly,the wind-power data obtained from the supervisory control and data acquisition(SCADA)system is preprocessed by the POT-DBSCAN ***,the power prediction of the preprocessed data is carried out by PSO-BP ***,the necessity of preprocessing is verified by the *** case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile ***,the accuracy of data and prediction model can be improved by using this method.
Differential evolution (DE) algorithm is a classical natural-inspired optimiza-tion algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solutio...
详细信息
Differential evolution (DE) algorithm is a classical natural-inspired optimiza-tion algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insuf-ficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called "DE/current-to-p1 best&p2 best". This strat-egy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising can-didate solution region, and indirectly increases the population diversity of the algo-rithm. We also proposed an adaptive parameter control method, which can effec-tively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are
In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, ...
详细信息
In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given.
High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment;therefore, it is particularly...
详细信息
High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment;therefore, it is particularly important to conduct modelling of wind farm frequency regulation (WFFR) response characteristics. During the modelling process, it is generally necessary to establish a model for each working condition separately, which will bring huge workload. In addition, the accuracy of the model decreases when the frequency response is non-linear. Therefore, this paper investigates the modelling of WFFR response characteristics in different working conditions. A data preprocessing method based on WFFR strategy and modelling methods is introduced. Then, data-based transfer function models of WFFR response characteristics for different working conditions are constructed. After that, the gaps between different models are measured using a gap metric technique to analyse dynamic similarity between models. Finally, in order to make up for the defect of transfer function models, a non-linear autoregressive with exogenous input neural networks (NARXNN) model of WFFR response characteristics is constructed utilising lumped data of all working conditions;then, the trained model is tested by the data of each working condition to verify the accuracy and universality.
As the penetration rate of solar energy in the grid continues to enhance, solar power photovoltaic generation forecasts have become an indispensable aspect of mechanism mobilization and maintenance of the stability of...
详细信息
As the penetration rate of solar energy in the grid continues to enhance, solar power photovoltaic generation forecasts have become an indispensable aspect of mechanism mobilization and maintenance of the stability of the power system. In this regard, many researchers have done a lot of study, and put forward some predictive models. However, many individual prediction systems only consider the prediction accuracy rate without further considering the prediction utility and stability. To fill this gap, a comprehensive system is designed in this paper, which is on the basis of automatic optimization of variational mode decomposition mechanism, and the weight of system is determined by multi objective intelligent optimization algorithm. In particular, it can be proved theoretically that the developed predictive system can achieve the pareto optimal solution. And the designed system is shown to be very effective in forecasting the 2021 photovoltaic power data obtained from Belgium. The empirical study reports that the combination of variational mode decomposition strategy based on genetic algorithm and multi objective grasshopper optimization algorithm is found to be the satisfactory strategy to optimize the predictive system compared with other common mechanism. And the results of several numerical studies show that the designed predictive system achieves the superior performance as compared to the control systems, and in multi-step forecasting, the designed system has better stability than the comparison systems.
暂无评论