检索结果-内蒙古大学图书馆

Heuristic Evaluation of supervisory control and data acquisition (SCADA) Displays

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 1767 - 1774页

作者： Thao Pham Alexander P. Boone Mary K. Ngo Pacific Science & Engineering Group San Diego CA USA

supervisory control and data acquisition (SCADA) is a computer software architecture that collects, analyzes, and delivers real-time production data to an accompanying human-machine interface (HMI) display. SCADA displays can help ensure efficiency in industrial settings by allowing users to monitor and control production processes, see system issues, and directly interact with equipment or devices (e.g., sensors, motors). However, SCADA displays tend to be highly complex and cluttered with data and interactive components. In this research, we conducted an evaluation of various SCADA displays available online using Neilson’s ten usability heuristics. The findings revealed that those displays scored poorly in the evaluation, indicating the need for redesign concepts that prioritize user needs. We then provide redesigned displays, conduct computational modeling to ensure the effectiveness of the redesigns, and propose a set of SCADA display design guidelines aimed at improving those displays to support user needs and, ultimately, improve user performance.

关键词： SCADA Heuristic evaluation Display supervisory control and data acquisition Heuristic principles

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker

引用

IEEE ACCESS 2025年 13卷 2474-2485页

作者： Pan, Zesheng Fang, Ruiming Wei, Tingyu Shang, Rongyan Peng, Changqing Huaqiao Univ Sch Informat Sci & Engn Xiamen 361021 Peoples R China

With the rapid increase in the installed capacity of wind power in China, early warning and identification of defects in wind turbine units has become crucial for the healthy development of the wind power industry. This paper proposes a comprehensive fault warning method for the entire wind turbine unit that is distinct from most current methods that focus solely on partial components. It addresses the complex coupling between various monitoring data from the supervisory control and data acquisition system and the internal defects of the unit. Utilizing a sample covariance matrix and the dynamic network marker theory, the method integrates diverse data inputs to predict potential failures. Case validation reveals that this approach maintains robustness across different operating conditions, issues warnings approximately 6 hours and 40 minutes before faults occur, and effectively identifies fault types. This capability is beneficial for initiative maintenance and rational planning of maintenance schedules in wind turbines.

关键词： Covariance matrices Eigenvalues and eigenfunctions Training Signal processing algorithms Real-time systems Predictive models Power system stability Monitoring Long short term memory Feature extraction Fault detection fault diagnosis network theory predictive maintenance renewable energy systems supervisory control and data acquisition wind power

来源：评论

学校读者我要写书评

暂无评论

Enhanced IoT-Based Optimization for a Hybrid Power System in Cartwright, Labrador

引用

ENERGIES 2025年第7期18卷

作者： Orie, Raymond Otabil, Lynna Agorua, Jonathan Iqbal, Mohammad Tariq Mem Univ Newfoundland Fac Engn & Appl Sci Sch Grad Studies Dept Engn St John NF A1C 5S7 Canada

The existing electricity infrastructure in Cartwright depends on diesel generators and needs renewable energy integration and remote monitoring. This project aims to enhance the proposed hybrid system with IoT-based optimization by leveraging a low-cost open-source SCADA system and accomplished monitoring and control capabilities. Electrical data were collected and analyzed from the energy system via sensors using the Arduino UNO R4 Wi-Fi as an RTU. The designed SCADA system would optimize Cartwright's energy system, allowing for real-time remote tracking and control via the Arduino IoT cloud platform. The voltage and current values obtained with the setup were accurate and close to the actual multimeter values over the measurement range. The project outcome included efficient real-time data acquisition and visualization on remote dashboards, enabling cloud monitoring of key electrical parameters. An alert mechanism was incorporated as a buzzer alarm in the event of under-voltage readings to trigger intervention from operators to take swift action to ensure system reliability and safety. One observation made was that, while the buzzer is not directly tied to current readings, it can be programmed to signal more issues like over-current.

关键词： Arduino UNO R4 cartwright hybrid systems internet of things remote monitoring renewable energy integration supervisory control and data acquisition

来源：评论

学校读者我要写书评

暂无评论

Advanced power curve modeling for wind turbines: A multivariable approach with SGBRT and grey wolf optimization

引用

ENERGY CONVERSION AND MANAGEMENT 2025年 332卷

作者： Yin, Wenliang Jia, Mengqian Liu, Lin Li, Ming Guo, Youguang Lei, Gang Zhu, Jian Guo Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia

Accurate power curve modeling is crucial for improving the operational efficiency and performance of gridconnected wind turbines (WTs). To enhance the modeling quality and eliminate input variable interactions, this paper proposes a novel multivariable power curve prediction approach that integrates advanced machine learning techniques, namely stochastic gradient boosting regression tree (SGBRT) and grey wolf optimization (GWO), with innovative data preprocessing and feature selection methods. The specific works and novelties are as follows. 1) The raw data is cleaned in a two-dimensional Copula space, using wind wheel speed as an auxiliary criterion and a probabilistic description, to handle data uncertainties and nonlinear dependencies. 2) A partial mutual information (PMI) method is presented for data characteristics analysis, based on which eight significant parameters are selected as modeling input variables, reducing computational complexity while enhancing prediction accuracy. 3) A power curve prediction model considering multiple input variables is established using SGBRT, and its hyperparameters are optimized through a GWO algorithm, guided by a fitness function combining the indicators of root mean square error (RMSE), mean absolute error (MAE) and R squared (R2). 4) Validated with real SCADA data from WTs in service, the proposed model achieves superior performance, with the smallest standardized residuals (6.56 %), RMSE (around 27 kW), MAE (19.27 kW), and superior average R2 (98.61 %) for all speed regions. Comparative studies indicate that the proposed approach outperforms existing methods, offering significant improvements in accuracy, efficiency, robustness and adaptability for WT power curve modeling.

关键词： Wind power generation power curve modeling machine learning supervisory control and data acquisition (SCADA) stochastic gradient boosting regression tree (SGBRT) grey wolf optimization (GWO)

来源：评论

学校读者我要写书评

暂无评论

data-driven wind power prediction model based on improved generative adversarial network

引用

INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2025年

作者： Peng, Zhang Fan, Huibo Ge, Chen Lin, Chi Gong, Zhimin Ma, Zhiyuan Cao, Mengnan Mengdong Concord New Energy Co Ltd Tongliao 028000 Inner Mongolia Peoples R China Shanghai Power Equipment Reserch Inst CO LTD Shanghai 200233 Peoples R China SPIC NEI MONGOL Corp Hohhot 028000 Peoples R China

Accurate and timely wind power forecast is difficult because of the volatility and intermittency of wind energy, which are strongly influenced by meteorological conditions. In order to solve this problem, this research suggests a data-driven wind power prediction technique based on Elman neural networks and an enhanced generative adversarial network (GAN). First, outliers in the supervisory control and data acquisition (SCADA) power and wind speed data are found by combining the methods of density clustering and regression threshold truncation. To guarantee the temporal continuity of wind power data with the original characteristics, minority class samples are then generated using an enhanced GAN and added to the dataset. In order to obtain an accurate wind power prediction, the balanced data is then fed into a prediction model that combines the ABC algorithm and Elman neural networks. The fitness value is the mean squared error (MSE) of the training data, and the optimization objective is the connection weights of the Elman neural network. Results from experiments show that the suggested model outperforms earlier state-of-the-art techniques and significantly increases wind power prediction accuracy. It also has the advantages of high stability and quick convergence speed, and it can capture the long-term dependencies of wind power data.

关键词： wind power prediction generative adversarial network density clustering supervisory control and data acquisition artificial bee colony Elman neural network

来源：评论

学校读者我要写书评

暂无评论

Back-propagation-based multivariate state estimation technique: A lightweight adaptive condition monitoring approach for wind turbine

引用

NEUROCOMPUTING 2025年 611卷

作者： Yang, Dongsheng Han, Huanying Karimi, Hamid Reza Zhu, Yesheng Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Politecn Milan Dept Mech Engn Milan Italy

Aimed at the challenge of wind turbine condition monitoring with insufficient available data caused by the harsh conditions, this paper presents a lightweight adaptive condition monitoring approach based on the back-propagation algorithm and the multivariate state estimation technique. In order to maximize the utility of available data, the multivariate state estimation technique is used to extract the features from multiple historical data. Moreover, buffer space is adopted to update dynamically while avoiding the effect of outliers. Then, the operator is modified so that the model can be trained and updated by the back-propagation algorithm, thereby achieving lightweight. The model performs well in condition monitoring with small samples, even discontinuous samples, that are verified through experiments based on field wind turbine datasets. The experimental results and comparative analyses demonstrate the effectiveness, generality, and advantage over others.

关键词： Back-propagation Condition monitoring Multivariate state estimation technique supervisory control and data acquisition Wind turbine

来源：评论

学校读者我要写书评

暂无评论

Advancing food systems with industry 5.0: A systematic review of smart technologies, sustainability, and resource optimization

Sustainable Futures

引用

Sustainable Futures 2025年 9卷

作者： Kumari Amita Singh Falguni Patra Tabli Ghosh Nikhil Kumar Mahnot Himjyoti Dutta Raj Kumar Duary Department of Dairy Science & Food Technology Institute of Agricultural Sciences Banaras Hindu University (BHU) Varanasi Uttar Pradesh 221005 India Mansinhbhai Institute of Dairy & Food Technology Dudhsagar Dairy Campus Mehsana Gujarat 384002 India Department of Food Engineering and Technology Tezpur University Tezpur Assam 784028 India Department of Food Technology Rajiv Gandhi University Rono Hills Doimukh Arunachal Pradesh 791112 India Department of Food Technology Mizoram University Mizoram India

The advent of Industry 5.0 represents a transformative shift towards integrating advanced computational technologies—such as AI, IoT, robotics, and big data—with human-centric collaboration to achieve sustainable food production systems. This systematic review examines the link between Industry 5.0 and sustainability, focusing on computational innovations, resource management, and eco-friendly practices within food industries. The study addresses the pressing challenge of balancing industrial growth with environmental stewardship and identifies the untapped potential of Industry 5.0 to enhance food safety, reduce waste, and enable personalized food solutions. Using a qualitative methodology, the review synthesizes findings from scholarly articles, industry reports, and case studies to analyze applications of smart sensors, blockchain, and robotics in optimizing supply chains, precision farming, and vertical agriculture. Results indicate that Industry 5.0 facilitates real-time monitoring, predictive analytics, and adaptive production, improving resource efficiency and minimizing environmental impacts. Smart packaging, digital twins, and AR are highlighted as emerging tools for enhancing traceability, quality control, and consumer engagement. However, high implementation costs, data management challenges, and the need for policy frameworks emerge as critical barriers. The discussion emphasizes how these challenges can be mitigated through multidisciplinary research, cross-sector collaboration, and government incentives. Overall, Industry 5.0 offers a pathway to sustainable, efficient, and personalized food systems by leveraging cutting-edge technologies while fostering human-machine collaboration. Future research should focus on scalable applications, cost-effective solutions, and regulatory alignment to accelerate Industry 5.0 adoption in food sectors. This study underscores the urgency of advancing computational technologies to shape resilient and sustainable food p

关键词： Industry 5.0 Internet of Things Blockchain Digital twin Cobots Food industries IoT Internet of Things 1IR first industrial revolution 2IR second industrial revolution IT information technology 3IR third industrial revolution 4IR fourth industrial revolution AI artificial intelligence 3D three dimensional ML machine learning IoE internet of everything EC edge computing COBOTS collaborative robots 4G fourth generation 5G fifth generation 6G sixth generation XR extended reality AR augmented reality RAS robotics and autonomous system THz terahertz UAVs unmanned aerial vehicles HACCP hazard analysis and critical control point CNNs convolutional neural networks ANN artificial neural networks CPSs cyber physical, systems CPPS cyber physical production system GMP good manufacturing practices GHP good hygienic practices MAP modified atmospheric packaging NFC near field communication NLP Natural language processing ANN artificial neural network TDS total dissolved solids EC electrical conductivity PLC programmable logic controller SCADA supervisory control and data acquisition

来源：评论

学校读者我要写书评

暂无评论

Performance analysis of the Diass solar power plant cleaning system

Next Research

引用

Next Research 2025年第1期2卷

作者： Mohamed Cherif Aidara Mbaye Jacques Diop Amy Mbaye Fatim Amadou Ndiaye Mamadou Lamine Ndiaye Laboratoire Eau-Énergie-Environnement – Procédés Industriels (LE3PI)/Ecole Supérieure Polytechnique Dakar Sénégal

This paper explores the challenges of cleaning solar photovoltaic systems to optimize their energy performance. The cleaning system used at the Diass solar power plant is the object of study. To evaluate the efficiency of this system, variations of the corrected temperature Performance Ratio (PR corr ) are examined. This parameter allows us to see only the effect of dust on the performance of PV solar panels. A falling PR corr corresponds to the presence of dust on the panel surface while the opposite corresponds to the start of cleaning. An evaluation of the panels cleaning costs at the DIASS power plant is made, factoring in the costs associated with consumables. Results show an average lost rate of 7.24 % for a duration of six (6) months during 2022, and an optimal cleaning period of 15 days to maximize energy gain, with an average of 9.91 % energy gain per cleaning cycle. However, the economic values associated with these cycles vary, highlighting a variable profitability of the cleaning system. To make this system profitable, the absolute energy gain after a cleaning cycle must be at least 8 %.

关键词： Cleaning Solar installation Performance ratio Efficiency Variation PR corr corrected temperature Performance Ratio PR Performance Ratio MWp Mega Watt peak TS transformer stations AC Alternative Current SCADA supervisory control and data acquisition STC Standard Test Conditions EN AC Electricity production measured in alternating current (kW) P STC Nominal power of modules installed in standard conditions (kW) G POA radiation under standard test conditions (STC) (1,000 W/m 2 ) G STC radiation under standard test conditions (STC) (1000 W/m 2 ) T m Module temperature T n nominal temperature of the module Tamb ambient temperature W G energy gain due to the cleaning action W Clean energy after cleaning W NoClean energy before cleaning C G cost of energy gain due to the cleaning operation T inj Kwh injection cost (45 F CFA) C N total cleaning cost C C consumable costs (water, energy, and preventive maintenance) C m workforce cost DCC Duration between two Cleaning Cycles

来源：评论

学校读者我要写书评

暂无评论

Machine learning for power system stability and control

Results in Engineering

引用

Results in Engineering 2025年 26卷

作者： Rakibul Islam Mir Araf Hossain Rivin Sharmin Sultana MD Amaddus Bepary Asif Mahathir Mohammad Mustafizur Rahaman International American University Los Angeles CA United States Dept. of Mathematics Louisiana Tech University Ruston USA Dept. of Business Administration and Management International American University Los Angeles USA Dept. of Business Administration and Management Southern California State University Los Angeles USA Dept. of College of Technology and Engineering Westcliff University Irvine USA

Applying machine Learning (ML) techniques to power system control and stability has become a game-changing strategy for dealing with the increasing complexity of contemporary electrical grids. This review paper demonstrates how machine learning approaches can stabilize and manage three different power system types —voltage, small signal, and transient —for the integration of renewable energy sources. data-driven methods and artificial neural networks can utilize sensors and actuator activities in conjunction with machine learning technologies that enable vector *** ensures consistent power input and output in power systems, maximizing system restoration and safeguarding the entire system. Additionally, when the voltage source is autonomously controlled, ML technology simultaneously diagnoses and detects defects. Although obstacles are identified due to the lack of sophisticated monitoring, operation, and control, researchers are developing additional usage features, such as federated learning and physics-informed neural networks. Not all the data is available for testing, but researchers are currently working to obtain 99 %.

关键词： Machine learning Power system control system data-driven Artificial neural networks Power system security ML Machine Learning RES Renewable Energy and Storage systems AGI Artificial General Intelligence FL Fuzzy Logic ANFISs Adaptive-network-based Fuzzy Inference Systems ANNs Artificial Neural Networks AGC Automated Generation control VSC Voltage Source Converters FACTS Flexible AC Transmission Systems PMUs Phasor Measurement Units SVMs Support Vector Machines IoT Internet of Things RL Reinforcement Learning FC Flight controller GAs Genetic Algorithms LMIs Linear Matrix Inequalities ISFS Improved Stochastic Fractal Search HBF Hybrid Bacteria Foraging STATCOM Static Synchronous Compensator SCADA supervisory control and data acquisition CNNs Convolutional Neural Networks RNNs Recurrent Neural Networks

来源：评论

学校读者我要写书评

暂无评论

Numerical and experimental validation of vortex generator effect on power performance improvement in MW-class wind turbine blade

引用

RENEWABLE ENERGY 2023年 212卷 443-454页

作者： Moon, Hyeongi Jeong, Junhee Park, Sunho Ha, Kwangtae Jeong, Jae-Ho Gachon Univ Seongnam 13120 South Korea Unison Wind Power R&D Ctr Daejeon 34113 South Korea Korea Elect Power Corp Naju 58322 South Korea Univ Ulsan Ulsan 44610 South Korea

As blades become larger, performance loss occurs due to separation at the root. In particular, it will become more evident in offshore wind turbines equipped with large blades, and there is a possibility of solving through VG. This test, verified near the shore, can be further utilized at sea. The cost of attaching the VG pairs to the wind turbine blade is high, so it is important to predict aerodynamic performance by CFD analysis. Vortex generators were attached to the blades of a 2.3 MW wind turbine to validate the effect of the VGs on the wind turbine performance. Aerodynamic performance data for two months were measured using supervisory control and data acquisition and compared with data generated by the blade without VGs during the same period in 2018. As a result, it was confirmed that when VG was attached, the power generation performance was improved at high wind speed, while the performance decreased at low wind speed. The power generation performance of a 2.3 MW wind turbine with VGs was improved by 4.83% at a wind speed of 10 m/s, and the total annual energy production increased by 1.87% for operation in the wind speed range of 4-11 m/s. The VGs were precisely installed with the aid of laser tracking technology to correspond with the locations indicated by the CAE model, which resulted in a maximum error of 0.037%. The attachment coordinates were set by referring to the separation line determined by computational fluid dynamics analysis. The CFD analysis of the 2.3 MW wind turbine blade with VGs was performed using the RANS equation and the CFX package of ANSYS. The CFD results indicated that the torque was increased by 2.80% for the rated wind speed of 10 m/s. In addition, the effect of VGs was investigated by analyzing the vortex behavior and velocity profile of the fluid passing through the VG. The effect of the VGs on the AEP was also calculated using GH-bladed, a tool based on blade element momentum theory for designing wind turbine blades. These re

关键词： Wind turbine Blade Vortex generator Computational fluid dynamics supervisory control and data acquisition Verification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：