Enhancing stability region of time‐delayed smart power grids by non‐integer controllers

In this study, the impacts of non‐integer order controller on the stable parameter space of the microgrid (MG) frequency control system with fixed communication time delay are investigated and discussed with the help of the stability boundary locus (SBL) method. This study proposes a non‐integer order controller for the load frequency control (LFC) of the MG systems. To that end, the load frequency model of the MG is formulated and then the characteristic equations of this model are obtained. Then, with the help of this characteristic equation, the stable parameter space of the non‐integer controller is determined with regard to different time delay (τ ) and fractional integral order values (α ) using the SBL method. In order to show the accuracy of the obtained stable parameter space, time domain and generalized modified Mikhailov (GMM) criterion studies are carried out for different values of (τ ) and (α ). According to the results obtained, the areas of stable parameter space according to different α values and τ = 1.6 are calculated as 444.8860 for α = 0.4 , 342.9728 for α = 0.7 , 259.3578 for α = 1 , 216.2541 for α = 1.3 and 159.6826 for α = 1.6 . In addition, the areas of stable parameter space according to different τ values and α = 1.4 are calculated as 784.5222 for τ = 1 , 106.3219 for τ = 2 , 29.6959 for τ = 3 and 11.5946 for τ = 4 . Despite the extreme variability arising from nature of resources that make up the MG, the designed non integer order controller with the values selected within the stable parameter space stably carries out LFC control of the MG.     

高可靠重载升降装置技术研究

传统大型升降装置体积大，升降速度低，同步性差、启停冲击大，可靠性低，不满足大型贵重负载的升降需求。通过机械、电子、液压一体化设计，对大型贵重负载重载升降装置的设计方案进行了对比论证，并给出了综合最优解,为某重要产品的设计提供了技术支撑。     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

移动式计量装置验收专用负荷箱的研制

本项目着力研发一种移动式计量装置验收专用负荷箱,可依据现场实际情况将其接入到专变低压出线处,提供稳定的测试负荷电流,完成计量装置验收关键步骤。主要的技术内容:通过接入电容器组,提供计量装置验收带负荷测试所需的电流,按不同容量电容器组配置合适分组空气开关,保护电容器组,降低同时投入时的冲击电流。本项目制作的计量装置验收专用负荷箱将使用高强度箱体,并制作不同容量的负荷箱以满足不同的计量装置验收负荷要求,以提高计量装置验收工作质量和率,降低电能计量差错风险。     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Selection of features from power theories to compose NILM datasets

The load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load Monitoring (NILM) approaches. This occurs since knowledge about individualized consumption per appliance allows to create strategies striving for energy savings, improvement of energy efficiency, and creating energy awareness to consumers. Moreover, by using feature extraction to devise energy disaggregation, one can achieve accurate identification of electric appliances. However, even though several literature works propose distinct features to be utilized, no consensus exists in the literature about the most appropriate set of features that ensure high accuracy on load disaggregation. Thus, beyond presenting a critical analysis of some significant features often selected in the literature, this paper proposes identifying the most relevant ones considering collinearity and machine learning algorithms. The results show that high-performance metrics can be achieved with fewer features than usually adopted in the literature. Moreover, it is demonstrated that the Conservative Power Theory can offer the most representative features for appliance identification, leading to efficient power consumption disaggregation.     

一种用于电气设备状态监测的新式FBG传感器北大核心CSCD

为了监测绕组变压器的静态应力场和发生短路等故障时的动态应力变化,设计了一种用于电气设备状态监测的新式FBG传感器。该传感器由聚醚醚酮材料封装的FBG构成,通过内部圆锥形空腔结构实现将轴向应力集中于FBG敏感位置。通过仿真对不同压力强度下传感器结构的应力场部分及形变趋势进行了计算与分析,论证了设计的合理性。实验分别对静态载荷和动态冲击进行测试,结果显示,在静态压载测试中,当100 N相似文献   

Techno-economic assessment of hydrogen refueling station: A case study in Croatia

Hydrogen refueling station (HRS) capacity and location depend on the users, which makes it difficult to select the most favorable option before potential users are actually identified. As in Croatia, at least for now, there are no hydrogen users, this study considers a wide range of HRS capacities and their different configurations. These include hydrogen production and charging station within one existing wind farm in Croatia or both nearby the users, the hydrogen production within the wind farm and the charging station nearby the users, while hydrogen is delivered to the station with a tube trailer, and configuration of hydrogen production within the wind farm with a mobile charging station in case of several users in different locations. Each HRS configuration is evaluated by the obtained levelized cost of hydrogen depending on the capital, and operation and maintenance costs within the HRS techno-economic analysis provided.     

大跨径网架屋顶设计与施工实践

某物资仓库拟采用框架结构和40 m跨网架屋顶。首先根据规范确定了屋顶荷载，并根据该建筑物位于台风区的实际，着重研究了风荷载的取值情况。该网架采用螺栓球节点，形式为正放四角锥形，施工采用分段整体吊装和高空散装的安装方式。实际使用情况表明，该物资库采用网架结构可靠耐用，且缩短了施工工期，取得了较好的经济效益。