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intelligent Combined Prediction of Wind Power Based on Numerical Weather Prediction and Fuzzy Clustering

IFAC-PapersOnLine 2015年第28期48卷 538-543页

作者： Jiaran, Yang Xingcheng, Wang Xiaofen, Luo Cheng, Jiang Information Science and Technology College Dalian Maritime University Dalian116026 China Huaneng Weihai Power Generation CO. LTD Weihai264205 China Beijing Electric Power Company Beijing China

Wind power prediction accuracy has important implications for the scheduling and stable operation of the power system. An intelligent Combined Prediction algorithm of Wind Power Based on Numerical Weather Prediction and Fuzzy Clustering is proposed in the paper. Based on numerical weather prediction (NWP) data and using the method of fuzzy clustering subtraction, the original NWP data is divided into several typical weather patterns; T-S fuzzy model, time-series model, multiple linear regression model and gray model are established respectively according to different whether types; the combination of multi-model is optimized using intelligent optimization algorithms and the optimal combination prediction model is obtained. Prediction results of a domestic wind farm indicate that the proposed combination prediction method is valid and effective in short-term wind power prediction, with better prediction accuracy. © 2015

关键词： Weather forecasting Fuzzy clustering Linear regression Numerical methods Wind power Combination predictions intelligent optimization intelligent optimization algorithm Multiple linear regression models Numerical weather prediction Prediction algorithms Short Term wind power predictions Wind power predictions

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optimization Modeling for Vibration of Composite Conical Shell Suitable for A Wide Range of Complex Boundaries: Used for Discontinuous Winding Structure

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第SUPPL 1期12卷 531-556页

作者： Wang, Ting Fan, Ye Zhao, Yang Li, Xinghong Wang, Hui Lu, Sheng Chongqing Univ Posts & Telecommun Inst Adv Sci Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Adv Mfg Engn Chongqing 400065 Peoples R China Xi An Jiao Tong Univ State Key Lab Strength & Vibrat Mech Struct Xian 710049 Peoples R China

BackgroundThe study of shell vibration also has clear engineering significance and scientific *** practical engineering, the spring stiffness configuration of the classical boundary is difficult to simulate the actual boundary situation, which in turn requires the use of elastic boundaries for *** this, this paper is a pioneering proposal to optimize the spring stiffness setting by introducing an intelligent optimization algorithm. Firstly, the vibration model of an isotropic truncated conical shell is established by applying a modified Fourier series method. Then the new characteristic equation is derived by setting the artificial spring stiffness as the parameter to be optimized. The objective function is then obtained from the deviations of the modal parameters to build the optimization model. Subsequently, the optimized model is optimized using an improved particle swarm algorithm and an immune algorithm. Finally, the optimized semi-analytical solution is compared with the finite element analysis results for *** results show that the method in this paper can find a better spring stiffness configuration to simulate complex boundary *** optimization algorithm can be applied to the cone, column, spherical shell, and other shells and combination shells. The stator end winding is used as an example. Similarly, and characteristic equation under the theory of composite materials are innovatively established;the objective function is extended to the optimization model of characteristic equation for stator-winding. To solve the vibration optimization model of complex structural constraints, the pathological characteristics of data introduced by the numerical difference between the small vibration and the order of magnitude of constraint stiffness are solved. The spring stiffness of small-diameter end of the boundary spring for the stator-winding model has been optimized.

关键词： Energy method Conical shell Complex boundary intelligent optimization algorithm Artificial spring Stator winding

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A Compensation Method for Random Error of Gyroscopes Based on Support Vector Machine and Beetle Antennae Search algorithm

A Compensation Method for Random Error of Gyroscopes Based o...

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International Conference on Computer, Control and Robotics (ICCCR)

作者： Wang, Pengfei Li, Guangchun Gao, Yanbin Harbin Engn Univ Dept Intelligent Sci Syst & Engn Harbin Peoples R China

ISBN: (纸本)9781728190358

Gyroscope is a common inertial sensor for inertial navigation system(INS). Its accuracy is affected by various error sources and noises. Among them, the deterministic error can be eliminated by calibration. However, random error is difficult to be evaluated due to its uncertainty, so random error has a great negative impact on the accuracy of gyroscope. Therefore, in order to improve the precision of gyroscope, it is necessary to compensate the random error. This paper proposes a compensation method for random error of gyroscopes based on support vector machine (SVM) and beetle antennae search (BAS) algorithm. Firstly, the original time series data of random error is processed in groups by a slide window, and then the SVM model is trained by using the original data of random error. The two key parameters in SVM are optimized by BAS algorithm. Finally, The trained SVM model is used to predict the gyroscopes random error data, and the prediction results are used to complete the compensation of gyroscopes data. Experimental results show the effectiveness of the proposed scheme, and the compensation accuracy is better than the traditional autoregressive integrated moving average (ARIMA) method.

关键词： gyroscope random error compensation intelligent optimization algorithm beetle antennae search support vector machine

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Multi-Classification Decision Fusion Based on Stacked Sparse Shrink AutoEncoder and GS-Tabnet for Network Intrusion Detection 10

Multi-Classification Decision Fusion Based on Stacked Sparse...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Wang, Ziqi Guan, Ziyue Wu, Xiangxi Bi, Jing Yuan, Haitao Zhou, MengChu Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9798350373981;9798350373974

With the rapid development of the Internet, various network invasive behaviors are increasing rapidly. This seriously threatens the economic development of individuals, enterprises, and society. Network intrusion detection is important in network security systems, which can be regarded as a classification problem. It aims to distinguish between the specific categories of various network behaviors and determine whether the behavior belongs to network intrusion. However, network intrusions present a diverse and fast-changing trend, making categorizing difficult. Due to feature redundancy, uneven distribution of sample numbers, and inefficient parameter optimization, traditional rule-based approaches fail to achieve satisfying classification accuracy. This work proposes a multi-classification intrusion detection model based on Stacked Sparse Shrink AutoEncoder (SSSAE), Genetic Simulated annealing-based particle swarm optimization optimized Tabnet classifier (GS-Tabnet), and Decision Fusion (DF), called for SGTD short. Among them, SSSAE extracts multiple feature sets from the input data. Then GS-Tabnet trains a classifier for each feature set. Finally, the decision fusion fuses the results from these classifiers to obtain the final classification result. SGTD is compared with eight multi-classification benchmark models, and its intrusion detection accuracy is superior to its peers.

关键词： Feature learning autoencoder network intrusion detection intelligent optimization algorithm

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Parameter estimation for chaotic systems based on improved boundary chicken swarm optimization

Parameter estimation for chaotic systems based on improved b...

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International Symposium on Infrared Technology and Application / International Symposium on Robot Sensing and Advanced Control

作者： Chen, Shaolong Yan, Renhuan Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China

ISBN: (纸本)9781510607729;9781510607736

Estimating unknown parameters for chaotic system is a key problem in the field of chaos control and synchronization. Through constructing an appropriate fitness function, parameter estimation of chaotic system could be converted to a multidimensional parameter optimization problem. In this paper, a new method base on improved boundary chicken swarm optimization (IBCSO) algorithm is proposed for solving the problem of parameter estimation in chaotic system. However, to the best of our knowledge, there is no published research work on chicken swarm optimization for parameters estimation of chaotic system. Computer simulation based on Lorenz system and comparisons with chicken swarm optimization, particle swarm optimization and genetic algorithm shows the effectiveness and feasibility of the proposed method.

关键词： Chicken swarm optimization Chaotic system Parameter estimation Lorenz system Cross-border processing intelligent optimization algorithm Particle group optimization Genetic algorithm Convergence rate Convergence precision

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An Improved Jaya optimization algorithm with Hybrid Logistic-Sine-Cosine Chaotic Map 14

An Improved Jaya Optimization Algorithm with Hybrid Logistic...

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14th International Conference on Advanced Computational Intelligence

作者： Lei, Weidong Zhang, Zhanbo Zhu, Jiawei Lin, Yishuai Hou, Jing Sun, Ying Xian Univ Sci & Technol Sch Management Xian 710054 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xian Univ Sci & Technol Coll Comp Sci & Technol Xian 710054 Peoples R China

ISBN: (数字)9781665470452

ISBN: (纸本)9781665470469;9781665470452

Jaya optimization algorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but there still has much room to improve its performance, especially for solving high-dimensional and non-convex problems. Hence, this paper proposes an improved Jaya optimization algorithm with a novel hybrid logistic-sine-cosine chaotic map, which is named IJaya for short. The hybrid logistic-sine-cosine chaotic map is applied to balance the exploration and the exploitation processes of Jaya optimization algorithm. Seven benchmark testing functions with different scale settings are used to evaluate the performance of our improved algorithm. Computational results indicate that our improved Jaya optimization algorithm outperforms greatly its original version on most testing functions with high-dimensions.

关键词： improved Jaya optimization algorithm hybrid chaotic maps swarm intelligence continuous optimization intelligent optimization algorithm

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Short-term Wind Power Prediction Based on Variational Mode Decomposition and Hybrid Neural Networks

Short-term Wind Power Prediction Based on Variational Mode D...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Wang, Heng Yu, Xiaodong Yu, Xuanzhou Jiang, Zhao Song, Shangqiang Xu, Rui Zang, Hongzhi Qilu Univ Technol Sch Informat & Automat Shandong Acad Sci Jinan Peoples R China Huaneng JinanHuangtai Power Generat Co Ltd Jinan Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan Peoples R China

ISBN: (纸本)9798350339345

Due to the widespread use of wind energy worldwide, driven by its cleanliness and renewability. And the instability of wind power can have a major impact on the grid. In this study, the combination prediction model based on improved LSTM and improved BPNN was proposed. Firstly, the raw data were decomposed into components using variational modal decomposition, which can This effectively reduces complexity and facilitates feature extraction. Then, in order to reduce the error accumulation when predicting each component separately, the Sample Entropy (SE) and Fuzzy Entropy (FE) were respectively used to calculate the complexity of the components and reconstruct them. Finally, based on the complexity of the new sequence, both improved LSTM and improved BP were used for prediction. The results showed that the reconstructed mean square error (MSE) and mean absolute error (MAE) were reduced by 0.3989 and 0.1981 compared to a single prediction, and the computation time was reduced by two-thirds. To verify the effectiveness of the hybrid prediction model, its prediction results were compared with six different prediction results. The results showed that the model had the best predictive performance and obtained the smallest performance evaluation metrics: MSE=0.1159, RMSE=0.3404, MAE=0.2447, and MAPE=0.0629. In addition, the model had the highest Spearman correlation coefficient of 99.79% between the predicted and true values.

关键词： Wind power prediction Variational modal decomposition Fuzzy entropy LSTM neural networks intelligent optimization algorithm

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Performance assessment of fault classifier of chemical plant based on support vector machine 12

Performance assessment of fault classifier of chemical plant...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Zhang, Xin Hu, Jinqiu Zhang, Laibin China Univ Petr Coll Mech & Transportat Engn Beijing Peoples R China

ISBN: (纸本)9781509040933

Support vector machine (SVM) plays an important part in fault diagnosis of chemical plant, and intelligent optimization algorithms are used to optimize the SVM parameters, including the penalty parameter C and parameter g of different kernel function, to improve performance of its faults classification. To assess SVM faults classification capability based on diverse optimization algorithms and various kernel functions, an evaluation index system that is based upon accuracy, recall and precision was proposed, which comprehensively considers overall accuracy, false alarm probability, missing detection probability and robustness of SVM fault classifiers. Tennessee Eastman (TE) process benchmark was used as simulation platform to evaluate SVM classifying faults ability. The results showed that SVM with radical basic function (RBF) is the most sensitive to the optimization algorithm and that SVM with polynomial kernel optimized by Grid Search Method (GSM-Polynomial-SVM) provides the highest robustness. The suggested evaluation index system is conducive to selecting optimum faults classifier and could be used as a framework for future comparison.

关键词： SVM faults classifier performance intelligent optimization algorithm kernel function evaluation index system

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Coarse and Fine-grained Crossover Free Search based Handover Decision Scheme with ABC Supported

Coarse and Fine-grained Crossover Free Search based Handover...

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12th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Wang Tong Wang Xing-wei Huang Min Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Provin Peoples R China

ISBN: (纸本)9780769550602

To find an optimal handover solution of terminal users under heterogeneous wireless networks, Pareto optimum under Nash equilibrium of users' utility and network provider utility is achieved or approached for the found solution. QoS (quality of service) requirements, user preferences to access network coding schemes, user preferences to access network providers, user handoff history record, costs user willing to pay and the profit of suppliers are considered comprehensively in this paper. With introducing the knowledge of fuzzy mathematics and microeconomics, access networks and terminals are described. With the help of gaming analysis, an intelligent handover decision scheme with ABC (always best connected) supported is proposed by using coarse-grained and fine-grained crossover free search based intelligent optimization algorithm. After carrying out simulation and performance evaluation, the results show that the mechanism proposed in this paper is feasible and effective.

关键词： NGI (next generation Internet) QoS (quality of service) ABC (always best connected) handover decision intelligent optimization algorithm search based on coarse-grained and fine-grained crossover

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Modeling and Challenges of Deep Learning-Based Shared Charging Piles for Timeshare Rentals 24

Modeling and Challenges of Deep Learning-Based Shared Chargi...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Yan, Song Zhang, Yixiang Yu, Jian Hu, Yehong Li, Yongchang Bao, Yun Zhang, Chuang Chen, Jun State Grid Tongling Power Supply Co Tongling Anhui Peoples R China

ISBN: (纸本)9798400716638

Aiming at the problem of an orderly power exchange strategy for electric bus clusters under actual traffic conditions, a low-carbon economic scheduling model for electric bus clusters with power exchange operation is first established. According to the characteristics of the model, a hybrid particle swarm algorithm is proposed to solve the model, and the speed and position update operators of the hybrid particle swarm algorithm are designed. The deep learning algorithm is used to obtain the running time of each bus trip, which solves the problem of obtaining the running time of bus trips and improves the accuracy of the model. The model is verified with real cases, and the model is utilized to optimize the genetic algorithm by using the Multi-target optimization legacy algorithm to solve the problem and verify the effectiveness of peak and valley time-sharing tariffs on grid load optimization.

关键词： battery charging and battery swapping strategies operation dispatching strategies intelligent optimization algorithm

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