电力系统自动化发展方向分析

计算机技术、控制技术及信息技术的发展,电力系统自动化面临着空前的变革,多媒体技术、智能控制技术将迅速进入电力系统自动化领域。     

基于DSP的智能电源控制单元设计与实现

本文利用TI公司TMS320F28335芯片高效的浮点运算能力,结合片上丰富的外设,设计并实现了一种具有高可靠性的智能电源控制单元。该控制单元周期性地对各片上外设进行自检维护,完成多路负载通道控制、电压、电流的实时监控,并对故障进行指示、处理和上报,同时提供人机交互界面更新状态信息。经过验证,该控制单元工作稳定,具备良好的工程应用价值。     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

Hydrogen and ethanol: Production,storage, and transportation

The present paper provides insights into the feasibility of using hydrogen and bioethanol blends as energy carriers in the foreseeable future upon discussions on the advantages and the disadvantages. The comprehensive overviews on the production, storage, and transportation of hydrogen and bioethanol have been made; and the current problems and potential solutions for the three stages have been summarized. Finally, the prospections on hydrogen and bioethanol could be expect optimistically.     

智能网联汽车变车距队列控制与仿真

智能网联汽车的发展是将来智能交通系统中的一个重要方向,受基础设施条件以及试验成本的限制,目前的测试验证仍以计算机仿真为主。针对现有仿真软件难以满足智能网联汽车测试需求的问题,以开源软件PLEXE为基础,通过对其道路交通模拟器中的车辆动力学模型、通信拓扑结构以及车辆队列控制器等关键模块进行二次开发,设计了面向智能网联汽车队列控制研究的可视化仿真平台,对变车距队列控制算法进行仿真验证。仿真结果表明:上述平台可完成队列控制的可视化仿真,相比其它软件更加直观形象。     

车寨煤矿综采工作面智能化技术

为响应晋煤集团公司实现智慧矿山的号召，全面落实集团公司转型发展的重要措施，真正实现“一矿一策一模式”的建矿理念，车寨矿井全面实施井下智能化综采工作面，充分结合车寨矿井实际情况，坚持整体规划、统一标准、分步实施的原则，在现有技术水平的基础上，因地制宜地开展智能化综采工作面相关技术工作。通过综采工作面智能化技术，对降低成本、减少人员劳动强度、提高设备的使用效率、促进实现本质安全、提高企业管理水平起到非常重要的保障性作用，经过细致规划，精细实施，车寨矿井成为业务信息化、调度可视化、决策智能化的先进智慧化矿山。     

Strategic planning optimization for natural gas to liquid transportation fuel (GTL) systems

A strategic planning optimization model is proposed for a network of natural gas to liquids (GTL) systems, and it is solved using a rolling horizon strategy. The model formulation determines the strategic and tactical decisions of the GTL supply chain over a long time horizon. The decisions to build new GTL refineries may be made over the span of 30 years and their operations cover the span of 60 years. Multiple capacities of GTL refineries (i.e., 1, 5, 10, 50, and 200 thousand barrels per day) that produce gasoline, diesel, and kerosene commensurate to the United States demand ratio may exist in the network. The parameter inputs include the locations, availabilities, and prices of natural gas in the United States discretized by county, the delivery locations of fuel products, and the transportation costs of every input and output of the refinery, defined for each time period. Formulated as a large-scale mixed-integer linear optimization (MILP) model, the problem is solved using a rolling horizon strategy for tractability. Case studies on the state of Pennsylvania are presented for different planning schemes and their impact on the economic performance of the GTL network is discussed.     

Negotiation based adaptive learning sequences: Combining adaptivity and adaptability

This study proposes a negotiation-based approach to combine the notion of adaptivity (system-controlled adaptation) and adaptability (user-controlled adaptation) for an adaptive learning system. The system suggests adaptations and the student also submits his/her adaptation preference. When the student preference opposes the system suggestion, the student then negotiates with the system to reach an agreement of adaptation. A negotiation-based adaptive learning system (NALS) is implemented to support the generation of personalized adaptive learning sequences by system negotiations with students regarding assessments of learning performance (i.e. negotiated open student model) of the current content and choices of the next learning content (i.e. negotiation of adaptation). Students require two metacognitions in deciding adaptive learning sequences: self-assessment for evaluating their understanding of the current content and regulation for choosing appropriate learning content. Negotiated open student model are used for assist student self-assessment and negotiation of adaptation are used for assist student regulation of content choices. An experiment was conducted to compare a system-controlled adaptive learning system (SALS, adaptivity), a user-controlled adaptive learning system (UALS, adaptability), and a NALS. The results revealed that NALS promoted better metacognitions in student calibration (i.e. self-assessment) accuracy and learning content choices (i.e. regulation). Preliminary evidences also showed that NALS promoted better student performance in a delay test. The results further suggested that students with poor calibration accuracy and inappropriate content choices were not suitable to use UALS and were suitable to use SALS. The NALS can also be used for training students to make appropriate adaptation for learning.     

Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Large sized transformers are an important part of global power systems and industrial infrastructures. An unexpected failure of a power transformer can cause severe production damage and significant loss throughput the power grid. In order to prevent power facilities from malfunctions and breakdowns, the development of real-time monitoring and fault prediction tools are of great interests to both researches and practitioners. This research develops an intelligent engineering asset management system for power transformer maintenance. The system performs real-time monitoring of key parameters and uses data mining and fault prediction models to detect transformers’ potential failure under various operating conditions. Principal component analysis (PCA) and a back-propagation artificial neural network (BP-ANN) are the algorithms adopted for the prediction model. Historical industrial power transformer data from Taiwan and Australia are used to train and test the failure prediction models and to verify the proposed general methodology as comparative case studies. The PCA algorithm reduces the number of the primary dissolved gasses as the key factor values for BP-ANN prediction modeling inputs. The system yields effective predictions when verified using various operating condition data from Australia and Taiwan power companies. The accuracy rates are much higher when compared to the fault prediction results without using PCA. The intelligent system combining PCA and BP-ANN algorithms, developed in this research, can be adopted by asset managers in different regions to develop suitable maintenance and repair strategies for transformer failure preventions.     

Utilizing sensor data to model students’ creativity in a digital environment

While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.