Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems

This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power systems.Despite success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large extent.This long-standing problem is addressed here by developing a novel MPC-based framework that i)computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements,and ii)employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs,thereby accelerating the overall control computation by multiple times.Additionally,a realistic control coordination scheme among static var compensators(SVC),load-shedding(LS),and load tap-changers(LTC)is presented that incorporates the practical delayed actions of the LTCs.The performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems,with±20%variations in nominal loading conditions together with contingencies.We show that our proposed methodology speeds up the online computation by 20-fold,bringing it down to a practically feasible value(fraction of a second),making the MPC real-time and feasible for power system control for the first time.

A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs

A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicity-frequent regions.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

CNC机床丝杠热误差实时补偿设计及自动补偿试验

为了降低数控机床丝杠传动系统因热误差引起的定位误差,完成机床工作台各运行阶段的实时补偿。用非接触模式为丝杠非电机连接端安装位移检测器,以电涡流传感器时间监测丝杠端面位置数据,获得丝杠产生的总热误差,通过热补偿得到每段丝杠的热误差程度,确定每段坐标系原点发生的偏移,完成自主补偿机床丝杠热误差的效果。研究结果表明:采用分段补偿方法可以获得比其他热误差补偿模型更优的丝杠全段补偿性能。分别检测丝杠各段发生的热误差再对其实施补偿,可以根据丝杠各部位热误差程度实施补偿。

Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.

A machine learning approach to quality-control Argo temperature data

本文提出了一种基于机器学习的Argo浮标温度异常值检测方法.该方法采用机器学习无监督算法高斯混合模型对Argo浮标数据进行聚类分析,并构建包围所有数据点的最小多边形的凸包.基于射线投影算法实现点在多边形内分析,通过自动识别数据点位于凸包内外来判断该数据点数据质量的好坏.本文采用南海区域Argo浮标数据对该方法进行测试,结果表明该方法可以识别70%以上的包含异常值的温度剖面,同时自动标记出各异常值点.

Design and Experimentation of Multi-Rod Grain Sampling Machine

In order to enhance grain sampling efficiency, in this work a truss type multi-rod grain sampling machine is designed and tested. The sampling machine primarily consists of truss support mechanism, main carriage mechanism, auxiliary carriage mechanism, sampling rods, and a PLC controller. The movement of the main carriage on the truss, the auxiliary carriage on the main carriage, and the vertical movement of the sampling rods on the auxiliary carriage are controlled through PLC programming. The sampling machine accurately controls the position of the sampling rods, enabling random sampling with six rods to ensure comprehensive and random sampling. Additionally, sampling experiments were conducted, and the results showed that the multi-rod grain sampling machine simultaneously samples with six rods, achieving a sampling frequency of 38 times per hour. The round trip time for the sampling rods is 33 seconds per cycle, and the sampling length direction reaches 18 m. This study provides valuable insights for the design of multi-rod grain sampling machines.

A Machine Learning-Based Web Application for Heart Disease Prediction

This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.

High Accurate Interpolation of NURBS Tool Path for CNC Machine Tools

Feedrate fluctuation caused by approximation errors of interpolation methods has great effects on machining quality in NURBS interpolation, but few methods can efficiently eliminate or reduce it to a satisfying level without sacrificing the computing efficiency at present. In order to solve this problem, a high accurate interpolation method for NURBS tool path is proposed. The proposed method can efficiently reduce the feedrate fluctuation by forming a quartic equation with respect to the curve parameter increment, which can be efficiently solved by analytic methods in real-time. Theoretically, the proposed method can totally eliminate the feedrate fluctuation for any 2nd degree NURBS curves and can interpolate 3rd degree NURBS curves with minimal feedrate fluctuation. Moreover, a smooth feedrate planning algorithm is also proposed to generate smooth tool motion with considering multiple constraints and scheduling errors by an efficient planning strategy. Experiments are conducted to verify the feasibility and applicability of the proposed method. This research presents a novel NURBS interpolation method with not only high accuracy but also satisfying computing efficiency.

Modeling and Control of Hybrid Machine Systems—a Five-bar Mechanism Case

A hybrid machine （HM） as a typical mechatronic device, is a useful tool to generate smooth motion, and combines the motions of a large constant speed motor with a small servo motor by means of a mechnical linkage mechanism, in order to provide a powerful programmable drive system. To achieve design objectives, a control system is required. To design a better control system and analyze the performance of an HM, a dynamic model is necessary. This paper first develops a dynamic model of an HM with a five-bar mechanism using a Lagrangian formulation. Then, several important properties which are very useful in system analysis, and control system design, are presented. Based on the developed dynamic model, two control approaches, computed torque, and combined computed torque and slide mode control, are adopted to control the HM system. Simulation results demonstrate the control performance and limitations of each control approach.

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC （computer numerical control） machine tools. Currently, there are many review literatures intro- ducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature moni- toring technology and thermal deformation monitoring technology. A new optical measurement technology called the ＂fiber Bragg grating （FBG） distributed sensing tech- nology＂ for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articlesto guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

Design and Accuracy Analysis of a Metamorphic CNC Flame Cutting Machine for Ship Manufacturing

The current research of processing large size fabrication holes on complex spatial curved surface mainly focuses on the CNC flame cutting machines design for ship hull of ship manufacturing. However, the existing machines cannot meet the continuous cutting requirements with variable pass conditions through their fixed configuration, and cannot realize high-precision processing as the accuracy theory is not studied adequately. This paper deals with structure design and accuracy prediction technology of novel machine tools for solving the problem of continuous and high-precision cutting. The needed variable trajectory and variable pose kinematic characteristics of non-contact cutting tool are figured out and a metamorphic CNC flame cutting machine designed through metamorphic principle is presented. To analyze kinematic accuracy of the machine, models of joint clearances, manufacturing tolerances and errors in the input variables and error models considering the combined effects are derived based on screw theory after establishing ideal kinematic models. Numerical simulations, processing experiment and trajectory tracking experiment are conducted relative to an eccentric hole with bevels on cylindrical surface respectively. The results of cutting pass contour and kinematic error interval which the position error is from -0.975 mm to ＋0.628 mm and orientation error is from -0.01 rad to ＋0.01 rad indicate that the developed machine can complete cutting process continuously and effectively, and the established kinematic error models are effective although the interval is within a ＇large＇ range. It also shows the matching property between metamorphic principle and variable working tasks, and the mapping correlation between original designing parameters and kinematic errors of machines. This research develops a metamorphic CNC flame cutting machine and establishes kinematic error models for accuracy analysis of machine tools.

Temperature prediction control based on least squares support vector machines

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.

Support vector machine-based multi-model predictive control

In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.

Thermal Error Modeling Method with the Jamming of Temperature-Sensitive Points'Volatility on CNC Machine Tools

Aiming at the deficiency of the robustness of thermal error compensation models of CNC machine tools, the mechanism of improving the models＇ robustness is studied by regarding the Leaderway-V450 machining center as the object. Through the analysis of actual spindle air cutting experimental data on Leaderway-V450 machine, it is found that the temperature-sensitive points used for modeling is volatility, and this volatility directly leads to large changes on the collinear degree among modeling independent variables. Thus, the forecasting accuracy of multivariate regression model is severely affected, and the forecasting robustness becomes poor too. To overcome this effect, a modeling method of establishing thermal error models by using single temperature variable under the jamming of temperature-sensitive points＇ volatility is put forward. According to the actual data of thermal error measured in different seasons, it is proved that the single temperature variable model can reduce the loss of fore- casting accuracy resulted from the volatility of tempera- ture-sensitive points, especially for the prediction of cross quarter data, the improvement of forecasting accuracy is about 5 μm or more. The purpose that improving the robustness of the thermal error models is realized, which can provide a reference for selecting the modelingindependent variable in the application of thermal error compensation of CNC machine tools.

HYBRID CONTROL OF HYDRAULIC PRESS MACHINE BASED ON ROBUST CONTROL

A robust control algorithm is proposed to focus on the non-linearity and variables of the hydraulic press machine with the proportional vatve. The proposed robust controller does not need to design stable compensator in advance, which is simple in design and has large scope of uncertainty applications. The feedback gains of the proposed robust controller are small, so it is easily implemented in engineering applications. The theoretical and experimental research on the position and speed control of the hydraulic press machine is carried out. The control requirements of the hydraulic press machine during the working process are met in the position and speed at the same time. Experimental results show that the proposed controller has better robustness subject to load variables and adaptability of parameter variations of the hydraulic press machine with the proportional valve.

Reliability Analysis of Electrical System of CNC Machine Tool Based on Dynamic Fault Tree Analysis Method

The electrical system of CNC machine tool is very complex which involves many uncertain factors and dynamic stochastic characteristics when failure occurs.Therefore,the traditional system reliability analysis method,fault tree analysis(FTA)method,based on static logic and static failure mechanism is no longer applicable for dynamic systems reliability analysis.Dynamic fault tree(DFT)analysis method can solve this problem effectively.In this method,DFT first should be pretreated to get a simplified fault tree(FT);then the FT was modularized to get the independent static subtrees and dynamic subtrees.Binary decision diagram(BDD)analysis method was used to analyze static subtrees,while an approximation algorithm was used to deal with dynamic subtrees.When the scale of each subtree is smaller than the system scale,the analysis efficiency can be improved significantly.At last,the usefulness of this DFT analysis method was proved by applying it to analyzing the reliability of electrical system.

A Novel Fuzzy Direct Torque Control System for Three-level Inverter-fed Induction Machine

Diode clamped multi-level inverter （DCMLI） has a wide application prospect in high-voltage and adjustable speed drive systems due to its low stress on switching devices, low harmonic output, and simple structure. However, the problem of complexity of selecting vectors and capacitor voltage unbalance needs to be solved when the algorithm of direct torque control （DTC） is implemented on DCMLI. In this paper, a fuzzy DTC system of an induction machine fed by a three-level neutral-point-clamped （NPC） inverter is proposed. After introducing fuzzy logic, optimal selecting switching state is realized by applying various strategies which can distinguish the grade of the errors of stator flux linkage, torque, the neutral-point potential, and the position of stator flux linkage. Consequently, the neutral-point potential unbalance, the dr/dr of output voltage and the switching loss are restrained effectively, and desirable dynamic and steady-state performances of induction machines can be obtained for the DTC scheme. A design method of the fuzzy controller is introduced in detail, and the relevant simulation and experimental results have verified the feasibility of the proposed control algorithm.

The Innovation of CNC Machine Interface Based on DNC System

This paper describes the innovation schemes of the interface of a CNC machine which cannot communicate with a computer by a Direct Numerical Control(DNC)interface and the functions of a DNC interface system.One architecture of hardware and software of a practi- cal single-chip computer based on DNC interface system developed by the authors is given. Without any change of the original hardware and software,this DNC interface system has been used to innovate the CNC machine's interface to implement the direct communication between a computer and a CNC machine and to achieve no tape transmission of a part program and ma- chine parameters.It has been demonstrated that this DNC interface system has certain practical values in improving the reliability,efficiency and production management of CNC/NC machine.