Design of Diversified Intelligent Control System for Energy-saving Optimization of Solar Greenhouse in North China

Intelligent greenhouse can promote the development of modern agriculture, realize the high quality and high yield of crops, and also bring greater economic benefits. In accordance with the climate conditions in northwest China, a set of intelligent control system for diversified environment of solar greenhouse was designed. The system divides the annual greenhouse control into six stages according to the optimal energy saving. It uses modern detection technology to collect the greenhouse environmental temperature, environmental humidity, soil humidity, CO_(2) concentration and illumination parameters under different working modes. It uses programmable logic control technology to realize the data processing of various parameters and the action control of rolling film, wet curtain fan and other actuators. It uses KingView monitoring software to realize the monitoring and manual control of greenhouse environment parameters. The operation results indicate that the control system runs stably and basically meets the control requirements.

Application Strategy of PLC Technology in Energy-Saving Control of Tunnel Lighting

In this study,we investigated on the application of planar lightwave circuit(PLC)technology in energy-saving control of tunnel lighting.The application status of PLC in the field of energy saving followed by the necessity of energy saving in tunnel lighting was analyzed.Finally,the application of PLC in tunnel lighting energy-saving control around the three dimensions of system overall architecture design,control scheme,and program control process was investigated.The results showed that the system meets the requirements of control effect,robustness,and visual effect after trial operation,and is suitable for practical applications.

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.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

Energy-Saving and Control of General Indoor Lighting Elements Fed from a Micro-Controlled Hybrid Power Generation System

In this study, an off grid wind-solar hybrid power generation system was established at Afyon Kocatepe University to meet the energy need of lighting system of three different laboratories. It is planned to efficiently use the energy obtained from the designed hybrid power generation system. For this purpose, PIC 16F877 was used in controlling of lighting load of laboratories. The off-grid wind-solar hybrid power generation system consists of 570 W 24 V mono crystal solar panels, 600 W wind power generation system and accumulator groups. The load control circuit made with PIC 16F877 is designed in a manner that will control the lighting armature groups individually activate and deactivate the armature groups according to intensity of illumination in environment. Besides, separately from generation and storing units constituting the hybrid power generation system, data in kWh are recorded by means of software in 10 seconds intervals. With the obtained power generation and storing data, analyzing of power consumption data when the load control system in active or passive position is made. According to analysis results, with controlling of lighting load and using of energy obtained from off grid wind-solar hybrid power generation system, 20.6% energy saving has been ensured.

基于SMART原则的医院感染预防与控制效果评价

目的:医院感染在增加患者痛苦和病死率的同时,也造成了巨大的经济损失。采取有效的措施改善医院感染情况,做好医务人员手部清洁、消毒刻不容缓。SMART原则即在目标设置时遵循明确性、可衡量性、可接受性、相关性、时限性原则。本研究在医院感染预防与控制工作中结合SMART原则,围绕手卫生等医院感染问题采取一系列措施,并对实施效果进行评价。方法:本研究为回顾性研究。2020年1至6月,在中南大学湘雅医院国际医疗部运用SMART原则开展了感染预防与控制干预工作,实施感染预防与控制质量改进计划。以指标数据作为目标衡量标准,建立以医院感染预防与控制知识知晓度、手卫生依从率为过程监测,手术部位感染率、导尿管相关尿路感染率为结果监测的评价指标。将干预后(2020年7月至2021年6月)各项评价指标与干预前(2019年1至12月)进行比较。结果:纳入在国际医疗部工作的51名医务人员,其中医师12名,护士39名;男性6名,女性45名;年龄21~57(30.2±7.1)岁。干预后医务人员医院感染预防与控制知识知晓度、手卫生依从率较干预前均明显提升(均P<0.05),但手术部位感染率、导尿管相关尿路感染率干预前后差异均无统计学意义(均P>0.05)。干预后内、外科病房医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),外科病房医务人员医院感染预防与控制知识知晓度较低但改进明显,内科病房医务人员手卫生依从率较高且改进明显;干预后男性和女性医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),均为男性较女性提升明显;干预后年龄<30岁、30~40岁的医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),年龄30~40岁的医务人员医院感染预防与控制知识知晓度较低但2项指标改进均最明显;干预后医生、护士的医院感染预防与控制知识知晓度均较干预前有所提高(均P<0.05),且医生知识知晓度改进较为明显,护士手卫生依从率较干预前有所提高(P<0.05);干预后初级、中级职称医务人员医院感染预防与控制知识知晓度、手卫生依从率均较干预前有所提高(均P<0.05),均为中级职称医务人员改进较为明显。结论:基于SMART原则,结合多种国内外感染预防与控制相关评价标准,实施质量改进计划,可有效提升医务人员感染预防和控制知识知晓度及手卫生依从率,强化医院感染的预防与控制,保障患者的安全。

Smart homing guidance strategy with control saturation against a cooperative target-defender team

A smart homing guidance strategy with control saturation against a target-defender team is derived. It is noteworthy that a cooperative strategy of the target-defender team is applied,which has been proved more challenging for the homing guidance.The defender missile is launched by the target and guided by a cooperative augmented proportional navigation(APN). At the same time, the target performs a one-switch maneuver to cooperate and minimize the defender's acceleration requirement. The problem is analyzed for arbitrary-order linear dynamics of the agents in the linearized form but validated by the mathematical simulations by using nonlinear kinematics. The perfect information of three agents' states is assumed. Then, a method to deal with the target-defender team is proposed. It contains a combined performance index penalizing the miss distance relative to the target and energy consumption in the whole duration. Besides, the specific miss distance related to the defender is regarded as an inequality constraint. An analytical solution for the smart guidance strategy against the APN guided defender is derived. Meanwhile, the control saturations are introduced to get more realistic and reasonable insights to this practical target-missile-defender problem. A simple but effective iterative searching technique is proposed to determine the saturation time points. The solution provides an optimal homing strategy to evade the defender with a specific miss distance and intercept the target with the minimum miss distance in the minimum energy manner. Nonlinear two-dimensional simulation results are used to validate the theoretical analysis. By comparison with the optimal differential game guidance(ODGG) and the combined minimum effort guidance(CMEG), the superiority of this smart guidance strategy is concluded.

Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems:An Uncertainty Handling Perspective

Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy.Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties.The burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning level.The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),respectively.Finally,an outlook on future research and applications is presented.

Smart Transformer-based Medium Voltage Grid Support by Means of Active Power Control

In the last decades the voltage regulation has been challenged by the increase of power variability in the electric grid,due to the spread of non-dispatchable generation sources.This paper introduces a Smart Transformer(ST)-based Medium Voltage(MV)grid support by means of active power control in the ST-fed Low Voltage(LV)grid.The aim of the proposed strategy is to improve the voltage profile in MV grids before the operation of On-Load Tap Changer in the primary substation transformer,which needs tens of seconds.This is realized through reactive power injection by the AC/DC MV converter and simultaneous decrease of the active power consumption of voltage-dependent loads in ST-fed LV grid,controlling the ST output voltage.The last feature has two main effects:the first is to reduce the active power withdrawn from MV grid,and consequently the MV voltage drop caused by the active current component.At the same time,higher reactive power injection capability in the MV converter is unlocked,due to the lower active power demand.As result,the ST increases the voltage support in MV grid.The analysis and simulation results carried out in this paper show improvements compared to similar solutions,i.e.the only reactive power compensation.The impact of the proposed solution has been finally evaluated under different voltage-dependence of the loads in the LV grid.

Smart Grid, Smart Controllers and Home Energy Automation—Creating the Infrastructure for Future

Integration of unpredictable renewable power sources into the Grid is leading to the development of wide area control algorithms and smart grid. Smart meters are the first step in the building a smart consumer interface. Much more, however, would be required in building a smart grid than just smart meters. This paper explores the conceptual architecture of smart grid. It highlights the need for additional infrastructure to realize full potential of smart grid. The information presented in this paper is an attempt to uncover what the future in smart grid could be and what infrastructure would be required to tap its potential. As smart grid evolves, more functionality would be built in the constituents. The paper also proposes mathematical basis for some of the controller algorithms.

基于S7200 SMART PLC和组态王的雾炮机控制系统设计

为了实现对多台雾炮机的集中管理、合理雾化除尘,提出了基于S7200 SMART PLC与组态王的雾炮机控制系统设计方案:将S7200 SMART PLC作为控制系统的现场控制装置,通过继电器输出控制雾炮机中的被控电机;利用组态王软件对控制系统进行设计,实现工作模式的选择、工作指令的下达及对设备状态的监视;通过TCP/IP方式实现PLC与上位机之间的实时通信。现场运行结果表明,该控制系统运行良好,操作灵活简单,提升了雾炮机的工作效率。

Stimuli-responsive Membranes： Smart Tools for Controllable Mass-transfer and Separation Processes

As emerging artificial biomimetic membranes, smart or intelligent membranes that are able to respond to environmental stimuli are attracting ever-increasing interests from various fields. Their permeation properties including hydraulic permeability and diffusional permeability can be dramatically controlled or adjusted self-regulatively in response to small chemical and/or physical stimuli in their environments. Such environmental stimuli-responsive smart membranes could find myriad applications in numerous fields ranging from controlled release to separations. Here the trans-membrane mass-transfer and membrane separation is introduced as the beginning to initiate the requirement of smart membranes, and then bio-inspired design of environmental stimuli-responsive smart membranes and four essential elements for smart membranes are introduced and discussed. Next, smart membrane types and their applications as smart tools for controllable mass-transfer in controlled release and separations are reviewed. The research topics in the near future are also suggested.

Control Effects and Material Parameters of Piezoelectric Smart Moment Controllers

The control using piezoelectric smart moment (PSM) controllers for seismically excited structures was studied.The radical principle of PSM controller was introduced firstly and then the different formulae of control shear force for different structures were derived with the stiffness ratio of columns taken into consideration.With the active control algorithm based on the theory of modern optimal control,this study proposes a simulative computation on the frame structure and mill structure respectively,and the results indicate that the installation of this smart controller with proper parameters can significantly reduce seismic responses of different structures. The optimal parameters of the damper can be identified through a parameter study.

Finite element modeling and feedback control of piezoelectric smart structures

Presents the general formula derived with a smart beam structure bonded with piezoelectric material using the piezoelectricity theory, elastic mechanism and Hamilton principle for electromechanically coupled piezoelectric finite element and dynamic equations, the second order dynamic model built, and the expression of state space, and the analysis of conventional speed and position feedback and the design of optimum feedback controller for output, the finite element models built for a piezoelectric cantilever beam, and the feedback controller designed eventually, and concludes with simulation results that the vibration suppression obtained is very satisfactory and the algorithms proposed are very useful.

Distributed demand side management via smart appliances contributing to frequency control

Nowadays renewable energy has become a trend for energy production but its variable nature has made balancing of demand and supply of the power grid difficult. Dynamic demand management using smart appliances is proposed to serve as a way that part of the regulation burden of balancing demand and supply is shifted to the demand side. However, if all appliances respond to the same frequency deviation, they may start to synchronize, causing large power overshoots and instability of the power grid. Therefore, the idea of implementing randomness into the frequency control of the appliances is proposed and this is what we call a stochastic approach. Simulators are built from scratch to model both scenarios. The effect of synchronization is analyzed and the parameters that can affect the synchronization are investigated. It has been found that the larger the contribution from the smart appliances to the power grid, the easier and faster the synchronization takes place. The stochastic approach solves the problem of synchronization and averages out the large power overshoot. However, the overall performance of stochastic operations is unacceptable due to the randomness in the operation though the mean and variance are as expected. More advanced feedback policies and schemes may be designed to achieve a better performance.

Robust distributed controller design of rotational single-link smart materials beam

A dynamic modelling and controller design were presented for a single-link smart materials beam, a flexible beam bonded with piezoelectric actuators and sensors for better control performance. Taking into account bounded disturbances, a robust distributed controller was constructed based on the system model, which was described by a set of partial differential equations (PDEs) and boundary conditions (BCs) . Subsequently, a finite dimensional controller was further developed, and it was proven that this controller can stabilize the finite dimensional model with arbitrary number of flexible modes. Keywords Dynamic modelling - Robust distributed controller - Flexible beam - Smart material

Nonlinear active control of damaged piezoelectric smart laminated plates and damage detection

Considering mass and stiffness of piezoelectric layers and damage effects of composite layers, nonlinear dynamic equations of damaged piezoelectric smart laminated plates are derived. The derivation is based on the Hamilton＇s principle, the higher- order shear deformation plate theory, von Karman type geometrically nonlinear straindisplacement relations, and the strain energy equivalence theory. A negative velocity feedback control algorithm coupling the direct and converse piezoelectric effects is used to realize the active control and damage detection with a closed control loop. Simply supported rectangular laminated plates with immovable edges are used in numerical computation. Influence of the piezoelectric layers＇ location on the vibration control is in- vestigated. In addition, effects of the degree and location of damage on the sensor output voltage are discussed. A method for damage detection is introduced.

Performance of joint dual links dynamic power control and smart antenna for TDMA/TDD cellular mobile communications

Interference cancellation is made available by using smart antenna at cellular base stations. Well distributed cumulative probability of signal to interference plus noise power ratio appears to be vital for cellular mobile multimedia communications. A scenario of dual links dynamic power control combined to a solution of smart antenna is proposed to adjust the instant transmission power in terms of the disparity from the favorite range. Simulation results show that this method is quite effective to improve the cumulative distribution probability performance. Meanwhile, accompanying low power consumption is also obtained at both base stations and mobile stations.