scholarly journals Research on Digital Twin and Collaborative Cloud and Edge Computing Applied in Operations and Maintenance in Wind Turbines of Wind Power Farm

2021 ◽  
Author(s):  
Fuxing Li ◽  
Luxi Li ◽  
You Peng
Keyword(s):  
Cloud Computing ◽  
Wind Turbine ◽  
Wind Farm ◽  
Edge Computing ◽  
Fault Prediction ◽  
Cloud Data ◽  
Digital Twin ◽  
Operation And Maintenance ◽  
Data Source ◽  
Computing Node

For the increasingly prominent problems of wind turbine maintenance, using edge cloud collaboration technology to construct wind farm equipment operation and maintenance framework is proposed, digital twin is used for fault prediction and diagnosis. Framework consists of data source layer, edge computing node layer, public or private cloud. Data source layer solves acquisition and transmission of wind turbine operation and maintenance data, edge computing node layer is responsible for on-site data cloud computing, storage and data transmission to cloud computing layer, receiving cloud computing results, device driving and control. The cloud computing layer completes the big data calculation and storage from wind farm, except that, based on real-time data records, continuous simulation and optimization, correct failure prediction mode, expert database and its prediction software, and edge node interaction and shared intelligence. The research explains that wind turbine uses digital twin to do fault prediction and diagnosis model, condition assessment, feature analysis and diagnosis, life prediction, combining with the probabilistic digital twin model to make the maintenance plan and decision-making method.

Research on early Fault Prediction of Wind Turbine Gearbox

2012 ◽  
Vol 608-609 ◽  
pp. 522-528
Author(s):  
Hong Shan Zhao ◽  
Yan Sheng Liu ◽  
Xiao Tian Zhang ◽  
Wei Guo
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Linear Regression Analysis ◽  
Fault Prediction ◽  
Temperature Prediction ◽  
Wind Turbine Gearbox ◽  
Maintenance Costs ◽  
Time Value ◽  
Normal Behavior ◽  
Simulation Results

Fault of gearbox is one of the significant causes which lead to high cost of wind farm, so early fault prediction of gearbox is meaningful for ensuring reliable running and reducing maintenance costs. With condition monitoring data, the relation between gearbox temperature and potential faults was researched and a new method for online fault prediction of wind turbine gearbox was presented. First, the temperature prediction model for normal behavior of gearbox was built up by non-linear regression analysis. Then, a detecting function which can indicate the deviation between actual running state and prediction state of gearbox was introduced. The condition of gearbox could be monitored by comparing the real-time value of detecting function with the chosen threshold. Theoretical analysis and simulation results demonstrated that this method could predict the abnormality of gearbox in time, and it can be applied to monitor the running condition of gearbox.

scholarly journals Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3132 ◽  
Author(s):  
Jorge Maldonado-Correa ◽  
Sergio Martín-Martínez ◽  
Estefanía Artigao ◽  
Emilio Gómez-Lázaro
Keyword(s):  
Early Detection ◽  
Literature Review ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Systematic Literature Review ◽  
Supervisory Control ◽  
Wind Farm ◽  
Effective Solution ◽  
Operation And Maintenance ◽  
Research Questions

Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

scholarly journals Assessing offshore wind turbine reliability and availability

2017 ◽  
Vol 233 (1) ◽  
pp. 267-282 ◽  
Author(s):  
Iraklis Lazakis ◽  
Maria A Kougioumtzoglou
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Bayesian Belief Networks ◽  
Hazard Identification ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Operation And Maintenance ◽  
Integrated Risk Analysis ◽  
Main Components ◽  
Criticality Analysis

The renewables sector and particularly offshore wind energy is a fast developing industry over the last few years. Especially, activities related to the installation, and operation and maintenance of offshore wind turbines become a challenging task with inherent risks. This article assesses the risks related to the above stages of a wind farm lifecycle using the failure mode, effects and criticality analysis and hazard identification methods. All works, from installation to operation and maintenance, are considered together with the wind turbine main components. An integrated risk analysis methodology is presented addressing personnel Safety (S), Environmental impact (E), Asset integrity (A), and Operation (O). The above is supplemented by a cost analysis with the aid of Bayesian belief networks method to assist the decision-making process related to installation, and operation and maintenance tasks. All major risks and critical wind turbine components are identified as well as measures are suggested to prevent or mitigate them. Moreover, inspection and maintenance plans are elaborated in general for the mentioned activities.

An opportunistic maintenance strategy for offshore wind turbine based on accessibility evaluation

2019 ◽  
Vol 44 (5) ◽  
pp. 455-468
Author(s):  
Xie Lubing ◽  
Rui Xiaoming ◽  
Li Shuai ◽  
Hu Xin
Keyword(s):  
Wind Turbine ◽  
Preventive Maintenance ◽  
Wind Farm ◽  
Weather Conditions ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Strategy ◽  
Offshore Wind Farm ◽  
Opportunistic Maintenance ◽  
Operation And Maintenance

The maintenance costs of offshore wind turbines operated under the irregular, non-stationary conditions limit the development of offshore wind power industry. Unlike onshore wind farms, the weather conditions (wind and waves) have greater impacts on the operation and maintenance of offshore wind farm. Accessibility is a key factor related to the operation and maintenance of offshore wind turbine. Considering the impact of weather conditions on the maintenance activities, the Markov method and dynamic time window are applied to represent the weather conditions, and an index used to evaluate the maintenance accessibility is then proposed. As the wind turbine is a multi-component complex system, this article uses the opportunistic maintenance strategy to optimize the preventive maintenance age and opportunistic maintenance age for the main components of the wind turbine. Taking the minimum expectation cost as objective function, this strategy integrates the maintenance work of the key components. Finally, an offshore wind farm is taken for simulation case study of this strategy; the results showed that the maintenance cost of opportunistic maintenance strategy is 10% lower than that of the preventive maintenance strategy, verifying the effectiveness of the opportunistic maintenance.

scholarly journals EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3616 ◽  
Author(s):  
Kai Fan ◽  
Jie Yin ◽  
Kuan Zhang ◽  
Hui Li ◽  
Yintang Yang
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Low Cost ◽  
Bloom Filter ◽  
Edge Computing ◽  
Security And Privacy ◽  
Network Bandwidth ◽  
Computing Model ◽  
Retrieval Scheme ◽  
Data Source

Edge computing is an extension of cloud computing that enables messages to be acquired and processed at low cost. Many terminal devices are being deployed in the edge network to sense and deal with the massive data. By migrating part of the computing tasks from the original cloud computing model to the edge device, the message is running on computing resources close to the data source. The edge computing model can effectively reduce the pressure on the cloud computing center and lower the network bandwidth consumption. However, the security and privacy issues in edge computing are worth noting. In this paper, we propose an efficient auto-correction retrieval scheme for data management in edge computing, named EARS-DM. With automatic error correction for the query keywords instead of similar words extension, EARS-DM can tolerate spelling mistakes and reduce the complexity of index storage space. By the combination of TF-IDF value of keywords and the syntactic weight of query keywords, keywords who are more important will obtain higher relevance scores. We construct an R-tree index building with the encrypted keywords and the children nodes of which are the encrypted identifier FID and Bloom filter BF of files who contain this keyword. The secure index will be uploaded to the edge computing and the search phrase will be performed by the edge computing which is close to the data source. Then EDs sort the matching encrypted file identifier FID by relevance scores and upload them to the cloud server (CS). Performance analysis with actual data indicated that our scheme is efficient and accurate.

Modeling of Cyber-Physical Systems and Digital Twin Based on Edge Computing, Fog Computing and Cloud Computing Towards Smart Manufacturing

2018 ◽  
Author(s):  
Qinglin Qi ◽  
Dongming Zhao ◽  
T. Warren Liao ◽  
Fei Tao
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Cyber Physical Systems ◽  
Edge Computing ◽  
System Level ◽  
Smart Manufacturing ◽  
Level System ◽  
Physical Systems ◽  
Unit Level ◽  
Digital Twin

Nowadays, smart manufacturing has attracted more and more interesting and attentions of researchers. As an important prerequisite for smart manufacturing, the cyber-physical integration of manufacturing is becoming more and more important. Cyber-physical systems (CPS) and digital twin (DT) are the preferred means to achieve the interoperability and integration between the physical and cyber worlds. From the perspective of hierarchy, CPS and DT can be divided into unit level, system level, and SoS (system of system) level. To meet the different requirements of each level, the following three complementary technologies, i.e., edge computing, fog computing and cloud computing, are instrumental to accelerate the development of various CPS and DT. In this article, the perspectives of unit-level, system-level, and SoS-level of CPS and DT supported by edge computing, fog computing and cloud computing are discussed.

scholarly journals Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1506 ◽  
Author(s):  
Jonghwa Choi ◽  
Sanghyun Ahn
Keyword(s):  
Cloud Computing ◽  
Response Times ◽  
Edge Computing ◽  
Time Interval ◽  
Service Provisioning ◽  
Cloud Data ◽  
Service Demand ◽  
Optimal Service ◽  
Cloud Data Centers ◽  
Iot Devices

In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.

Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

2020 ◽  
Vol 140 (9) ◽  
pp. 1030-1039
Author(s):  
W.A. Shanaka P. Abeysiriwardhana ◽  
Janaka L. Wijekoon ◽  
Hiroaki Nishi
Keyword(s):  
Stream Processing ◽  
Community Services ◽  
Edge Computing ◽  
Computing Node ◽  
Smart Community

A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Service ◽  
Third Party ◽  
Group Collaboration ◽  
Cloud Data ◽  
Cloud Data Storage ◽  
Important Trend ◽  
Data Update ◽  
Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

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