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Short-Term Wind Speed and Wind Power Prediction Based on Meteorological Model Modification

Short-Term Wind Speed and Wind Power Prediction Based on Met...

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data driven control and learning systems (ddcls)

作者： Tianze Liu Yan Zhao Gang Sun Yanjuan Ma School of Electric Power Shenyang Institute of Engineering Shenyang China School of Renewable Energy Shenyang Institute of Engineering Shenyang China State Grid Jinzhou Electric Power Supply Company State Grid Liaoning Electric Power Supply Co. Ltd Jinzhou China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Accurate prediction of wind speed and wind power is of great significance to the operation, planning, dispatching and control of power system. In order to make full use of the effective information provided by SCADA system and NWP to further improve the prediction accuracy of wind speed and wind power. A short-term wind speed and wind power prediction method based on meteorological correction model is proposed in this paper. Firstly, the meteorological model based on matrix completion algorithm is established to modify the meteorological data. Secondly, the network is trained with the data of meteorological model modification as input and the actual power of fan as output, and the prediction model based on LSTM network is established. Finally, the short-term prediction of wind speed and wind power is completed. the measured data from a wind farm is used for verification. the research results show that the information in multiple data sources can be well used in the proposed method to complete the prediction of wind speed and wind power. And in the future, the waste of wind resources can be effectively reduced, so as to realize the economic and stable operation of the power grid.

关键词： Wind power generation data models Predictive models Wind speed Logic gates Mathematical model Time series analysis

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Multi-source Heterogeneous data Fusion Method for Pipe Gallery Condition Monitoring

Multi-source Heterogeneous Data Fusion Method for Pipe Galle...

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data driven control and learning systems (ddcls)

作者： Gang Wang Jingwen Liu Guopeng Li Zhilei Li Zhidan Gong Wenlin Huang Helan Wang Guoyuan Cai State Grid Hebei Electric Power co. LTD Shijiazhuang State Grid Hebei Electric Power co. LTD Xiong’an New District Power Supply Company Xiong’an Xiamen Great Power Geo Information Technology co. LTD Xiamen Shanghai Guyuan Electric Technology co. LTD Shanghai

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In view of the exponential growth of the pipeline inspection data volume, the lack of utilization and analysis of the data, this paper proposes a method named subspace principal component analysis (SPCA) for pipe gallery condition monitoring that integrates multi-source heterogeneous data, aiming to improve the intelligent operation and maintenance level of pipe gallerys. First, interconnected distributed heterogeneous data sources are fused into a unified data set based on the JSON-based middleware method. Second, in order to reduce the complexity of condition monitoring and improve the accuracy, data with similar characteristics are assigned to the same subspace. then, in each subspace, the principal component analysis (PCA) method is used to mine information and extract features. Furthermore, the features of each subspace are fused, and the local outlier factor (LOF) method that does not require data distribution is used to construct the condition monitoring model and analyze the running state. Finally, the effectiveness and superiority of the proposed method are illustrated by testing it on the operation and maintenance data of the pipe gallery and comparing it with the classical methods.

关键词： Feature extraction Distributed databases Condition monitoring Principal component analysis data integration Mathematical model data mining

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Approach of driving system for lower limb rehabilitation training robot based on strong tracking filtering

Approach of driving system for lower limb rehabilitation tra...

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data driven control and learning systems (ddcls)

作者： Dawei Jiang Guoquan Shi Bing Zhang Yuan Tian School of Mechatronical Engineering Changchun University of Science and Technology Changchun China School of Mechatronical Engineering Changchun University of Technology Changchun China Jilin University Changchun China Changchun Vocational Institute of Technology Changchun China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

To solve the problem of lowering the control accuracy of the robotic servo system of lower limb rehabilitation training due to inaccurate models and load disturbances, this paper takes the one degree of freedom(DOF) lower limb rehabilitation training robot servo system as the research object, and establish a mathematical model of the servo system of the lower limb rehabilitation training robot including several subsystems such as the execution system, feed system and drive system. A control algorithm for driving system of lower limb rehabilitation training robot based on strong tracking filter(STF) is adopted. through simulation analysis, the stability of STF algorithm is compared, which shows that the proposed algorithm has the advantages of fast tracking speed, noise suppression and high accuracy. the experimental results further show that the established servo system model of the lower limb rehabilitation training robot matches the actual nonlinear system model. the STF algorithm has better tracking ability, high control accuracy and smoother operation. the research results provide a reference for the research on the servo system of lower limb rehabilitation equipment.

关键词： Training Servomotors Robots Mathematical model control systems Synchronous motors Turning

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Visual Tracking Algorithm with Multi-Gaussian Correlation Filters Based on Confidence Feedback Adjustment

Visual Tracking Algorithm with Multi-Gaussian Correlation Fi...

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data driven control and learning systems (ddcls)

作者： Li Wang Cong Wang Beijing Key Laboratory of Urban Intelligent Control Technology North China University of Technology Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In order to improve the speed and robustness of hierarchical convolutional features for visual tracking algorithm, the multi-Gaussian correlation filters for visual tracking based on confidence feedback adjustment is proposed. Our method extracts features for tracking from two appropriate convolutional layers, and proposes the average feature energy ratio to reduce the feature dimensions, along with the sparser model update scheme avoiding over-fitting to improve the tracking speed. then different Gaussian labeled functions are input to construct the multi-Gaussian correlation filters and adaptively fuse all the predicted results of multiple filters, expecting for higher accuracy. Finally, it introduces the average peak-to-correlation energy and the maximum response ratio to evaluate the tracking results, and adjust the learning rate and search area to further improve the robustness of the tracker. this algorithm is tested on the OTB100 benchmark datasets. the results show that it has an average distance precision of 88.6%, an improvement of 4.9 percentage points over the original hierarchical convolutional features for visual tracking approach, as well an average speed of 29.2 frames per second, 3 times that of the original method. the proposed multi-Gaussian correlation filters can effectively improve the speed and accuracy and has good robustness whatever challenges like occlusion, deformation and similar background interferences subjecting to.

关键词： Target tracking Correlation Feature extraction Filtering algorithms Robustness Convolution Classification algorithms

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Wasserstein Distance guided Adversarial Imitation learning with Reward Shape Exploration

Wasserstein Distance guided Adversarial Imitation Learning w...

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data driven control and learning systems (ddcls)

作者： Ming Zhang Yawei Wang Xiaoteng Ma Li Xia Jun Yang Zhiheng Li Xiu Li The Shenzhen International Graduate School Tsinghua University Shenzhen P. R. China Tsinghua University Beijing P. R. China The Business School Sun Yat-Sen University Guangzhou P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

the generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks. However, almost all GAIL and its extensions only design a kind of reward function of logarithmic form in the adversarial training strategy with the Jensen-Shannon (JS) divergence for all complex environments. the fixed logarithmic type of reward function may be difficult to solve all complex tasks, and the vanishing gradients problem caused by the JS divergence will harm the adversarial learning process. In this paper, we propose a new algorithm named Wasserstein Distance guided Adversarial Imitation learning (WDAIL) for promoting the performance of imitation learning (IL). there are three improvements in our method: (a) introducing the Wasserstein distance to obtain more appropriate measure in adversarial training process, (b) using proximal policy optimization (PPO) in the reinforcement learning stage which is much simpler to implement and makes the algorithm more efficient, and (c) exploring different reward function shapes to suit different tasks for improving the performance. the experiment results show that the learning procedure remains remarkably stable, and achieves significant performance in the complex continuous control tasks of MuJoCo 1 .

关键词： Shape Task analysis Optimization Generative adversarial networks Training Q measurement Reinforcement learning

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Sensor Correlation Network Based Anomaly Detection for thermal systems on Ships

Sensor Correlation Network Based Anomaly Detection for Therm...

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data driven control and learning systems (ddcls)

作者： Wei Zheng Hongkuan Zhou Zhiqiang Qiu Zhiwu Ke Mo Tao Zhaoxu Chen Science and Technology on Thermal Energy and Power Laboratory Wuhan China Wuhan Second Ship Design and Research Institute China State Shipbuilding Corporation Limited Wuhan China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, we propose an approach to handle the anomaly detection for the thermal system on ships by the sensor associated network method. A large number of sensors are placed in different positions of the thermal system. these sensors form a topological network which can represent the operation state of the thermal system. there are both linear correlation and nonlinear correlation between the operating parameters reflected by these sensors. the MAS index from MINE is utilized to represent the correlation information between sensors when the thermal system is in dynamic operation condition. Based on the MAS correlation coefficient, the sensor correlation network is constructed to represent the dynamic operation process of thermal system. Using DBSCAN clustering algorithm, the large topological network is divided into different subnetworks. When the system is running dynamically, the Manhattan distance between the sub networks can reflect the running state of the system. Based on the distance of sub networks, the similarity of sensor networks with continuous changes is calculated and the similarity correlation sequence is formed. through the matching between the similarity correlation sequence and the historical experience sequence, we can judge whether the system dynamic condition is abnormal. By the simulation experiment data, we verify the effectiveness of the proposed method.

关键词： Correlation thermal sensors Marine vehicles Heuristic algorithms Anomaly detection Power system dynamics Nonlinear dynamical systems

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Novel L2-Discriminant Locality Preserving Projection Integrated with Adaboost and Its Application to Fault Diagnosis

Novel L2-Discriminant Locality Preserving Projection Integra...

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data driven control and learning systems (ddcls)

作者： Xiao Hu Yang Zhao Yuan Xu Yan-Lin He Qun-Xiong Zhu College of Information Science & Technology Beijing University of Chemical Technology Beijing China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Nowadays, the safety of industrial production processes has been paid more and more attention. Fault diagnosis methods based on data-driven techniques have been widely discussed in recent years. For handling the high dimensional data generated by large and complex systems, reducing the data dimension to extract fault information has been widely studied. However, the performance of traditional dimension reduction methods is limited due to the high complexity and integration of process data. As a result, the accuracy of fault diagnosis is unacceptable. In order to handle this limitation, a novel fault diagnosis model integrating L2-Discriminant Locality Preserving Projection with AdaBoost is proposed in this paper. Discriminant Locality Preserving Projection (DLPP) as a kind of manifold learning methods is good at extracting information from high-dimensional data but its performance is subject to the singular matrix decomposition problem. So, the L2 regularization term is incorporated into the objective function of DLPP to solve the singular matrix decomposition problem. After extracting fault information using the proposed L2-DLPP method, AdaBoost is adopted to classify faults. In order to verify the performance of the proposed fault diagnosis methodology, a case study using the Tennessee Eastman process (TEP) is carried out. the effectiveness of the proposed fault diagnosis methodology is confirmed by simulation results.

关键词： Fault diagnosis Matrix decomposition Training data mining Linear programming Mathematical model Biological system modeling

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A data-driven Operating Performance Assessment Method based on Weighted Multi-Sphere Support Vector data Description

A Data-Driven Operating Performance Assessment Method based ...

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data driven control and learning systems (ddcls)

作者： Chuanfang Zhang Kaixiang Peng Jie Dong Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing P. R. China National Engineering Research Center for Advanced Rolling Technology University of Science and Technology Beijing Beijing P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In modern hot rolling process, operating performance assessment is of great practical significance for guiding the production adjustment for operators. From the perspective of classification, operating performance assessment is a multi-class classification problem. Since support vector data description (SVDD) is a one-class classifier, conventional methods usually construct an independent SVDD model for each class, which ignores the correlation among different classes. the hyperspheres of different classes may not be isolated but overlapped. If a test sample exists in the overlapping region, how to determine which class it belongs to is a knotty problem. Moreover, conventional methods treat all samples equally, but in practice, the sample number of different classes can be imbalanced, which will affect the classification performance of SVDD. In this study, an operating performance assessment method based on weighted multi-sphere SVDD (WMSVDD) is proposed for solving the aforementioned issues. WMSVDD considers the interactions among different classes in a unified way, optimizes the hyperspheres of different classes globally, and introduces a weight coefficient to the model for eliminating the affects of uneven class sizes. Simulation results on a real hot rolling process illustrate the effectiveness of the proposed method comparing to the traditional multi-class SVDD.

关键词： Support vector machines Optimized production technology Training Training data Production Optimization Kernel

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Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Cluster-based Relevance Analysis Between Construction Mode a...

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data driven control and learning systems (ddcls)

作者： Jiran Zhu Hua Leng Haiguo Tang Min Fan Yaling Liu Qing Yang College of Electronic and Information Engineering Tongji University Jiading Shanghai China State Grid Corporation of Hunan Electric Power corporation research institute Changsha Hunan China College of Electrical and Information Engineering Hunan University Changsha Hunan China School of Automation Chongqing University Chongqing Key Laboratory of Complex System Safety and Control (Ministry of Education) Chongqing University Chongqing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. this study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. this method clusters the DASs of different cities, and analyzes the clustering results. then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. the case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.

关键词： Urban areas Principal component analysis Investment Optimization Sensitivity analysis Power distribution Automation

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A Spatial-information-based Semi-supervised Soft Sensor for f-CaO Content Prediction in Cement Industry

A Spatial-information-based Semi-supervised Soft Sensor for ...

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data driven control and learning systems (ddcls)

作者： Xiaoyu Jiang Le Yao Gaopan Huang Jinchuan Qian Bingbing Shen Lu Xu Zhiqiang Ge State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou P. R. China Alibaba Group Ali Center Nanjing P. R. China Shandong Donghua Cement Co. Ltd Longquan Town Zibo P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

f-CaO is a key factor affecting the quality of cement in production. In this paper, the cement clink production process is introduced and discussed in detail. the time delay between the variables leads to an inaccurate matching relationship with each other and defects the performance of traditional soft sensors. To this end, a semi-supervised spatial-information-based soft sensor for f-CaO content is proposed. First, we analyzed the relationship between process variables and quality variable and then reconstruct the input of samples into data matrix by stitching unlabeled process data together. the semi-supervised structure helps retain process information in the data. then, an end-to-end soft sensor based on CNN is established: convolution and pooling operations are used to extract the features of two-dimensional data containing spatial information; a multi-layer perceptron models the extracted features regressively. Further, in order to solve the defect of insufficient generalization ability of the CNN-based model, a framework for spatial feature extracting and transferring is proposed. Compared with the multilayer perceptron, strong regression models with spatial features get better prediction accuracy. An actual cement production case is used to verify the effectiveness of the proposed method.

关键词： Feature extraction Production Raw materials Calcination Kilns data models Predictive models

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