Research on Operation Reliability Evaluation of Power-generating Unit

Abstract Objective and reasonable evaluation is the basis of power-generating unit optimal operation, but evaluation method based on single weight can’t adapt to the high accuracy requirement of comprehensive evaluation. In order to achieve the accurate evaluation of power-generating unit operating reliability, this paper firstly calculates subjective weight and objective weight of target attribute by analytic hierarchy process method and entropy weight method. It calculates the associated weight of target attribute by grey relational degree method, and it obtains the linear combined weight by relaxation factor. Then, the power-generating unit operating reliability is evaluated by improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and the evaluation result is obtained. Finally, the relaxation factor is continuously evaluated from 0 to 1 to perform sensitivity analysis. This paper evaluates the operation reliability of nine wind turbines in a wind farm, and the evaluation result is closer to actual operation state of wind turbines. This method has high reliability and practical value, and it provides a new technical means for making a reasonable maintenance plan of wind turbines.

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FAILURE ANALYSIS OF WIND TURBINES IN ZAFARANA WIND FARM, SUEZ GULF, EGYPT

The International Conference on Applied Mechanics and Mechanical Engineering ◽

10.21608/amme.2012.37035 ◽

2012 ◽

Vol 15 (15) ◽

pp. 1-20

Author(s):

H. Saleh ◽

A. Abou El-Azm ◽

S. Abd El-Hady

Keyword(s):

Failure Analysis ◽

Wind Turbines ◽

Wind Farm ◽

Suez Gulf ◽

Zafarana Wind Farm

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Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

Volume 4: Ocean Engineering; Ocean Renewable Energy; Ocean Space Utilization, Parts A and B ◽

10.1115/omae2009-79159 ◽

2009 ◽

Author(s):

Jose´ G. Rangel-Rami´rez ◽

John D. So̸rensen

Keyword(s):

Condition Monitoring ◽

Wind Turbines ◽

Wind Farm ◽

Offshore Structures ◽

Offshore Wind ◽

Offshore Wind Turbine ◽

Maintenance Planning ◽

Offshore Wind Turbines ◽

Inspection And Maintenance ◽

Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

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Wealth of Wind and Visitors: Tourist Industry Attitudes towards Wind Energy Development in Iceland

Land ◽

10.3390/land10070693 ◽

2021 ◽

Vol 10 (7) ◽

pp. 693

Author(s):

Anna Dóra Sæþórsdóttir ◽

Margrét Wendt ◽

Edita Tverijonaite

Keyword(s):

Land Use ◽

Wind Energy ◽

Wind Turbines ◽

Wind Farm ◽

Service Providers ◽

Wind Farms ◽

Energy Development ◽

Tourism Industry ◽

Land Use Conflicts ◽

Wind Energy Development

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.

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Analysis of long-term temperature monitoring of multiple wind turbines

Measurement and Control ◽

10.1177/00202940211013061 ◽

2021 ◽

pp. 002029402110130

Author(s):

Xian Wang ◽

Qian-cheng Zhao ◽

Xue-bing Yang ◽

Bing Zeng

Keyword(s):

Wind Turbines ◽

Temperature Rise ◽

Performance Optimization ◽

Wind Farm ◽

Engineering Practice ◽

Multiple Perspectives ◽

Temperature State ◽

Condition Monitoring Systems ◽

Historical Temperature

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.

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Operation and approximation based on the history of failure modes recorded by SCADA system of Amougdoul Moroccan wind farm using FMECA maintenance model

Wind Engineering ◽

10.1177/0309524x21992456 ◽

2021 ◽

pp. 0309524X2199245

Author(s):

Kawtar Lamhour ◽

Abdeslam Tizliouine

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Failure Modes ◽

Wind Farms ◽

Scada System ◽

History Of ◽

Criticality Analysis ◽

Maintenance Model ◽

Reliability And Availability ◽

Critical Subsystems

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.

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Frequency Scan–Based Mitigation Approach of Subsynchronous Control Interaction in Type-3 Wind Turbines

Energies ◽

10.3390/en14154626 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4626

Author(s):

Faris Alatar ◽

Ali Mehrizi-Sani

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Operating Conditions ◽

Steady State Operation ◽

Significant Damage ◽

Frequency Scan ◽

Type 3 ◽

Phase Angles ◽

Wind Energy Resources ◽

Control Interaction

Integration of wind energy resources into the grid creates several challenges for power system dynamics. More specifically, Type-3 wind turbines are susceptible to subsynchronous control interactions (SSCIs) when they become radially connected to a series-compensated transmission line. SSCIs can cause disruptions in power generation and can result in significant damage to wind farm (WF) components and equipment. This paper proposes an approach to mitigate SSCIs using an online frequency scan, with optimized phase angles of voltage harmonic injection to maintain steady-state operation, to modify the controllers or the operating conditions of the wind turbine. The proposed strategy is simulated in PSCAD/EMTDC software on the IEEE second benchmark model for subsynchronous resonance. Simulation results demonstrate the effectiveness of this strategy by ensuring oscillations do not grow.

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Optimal Temperature-Based Condition Monitoring System for Wind Turbines

Infrastructures ◽

10.3390/infrastructures6040050 ◽

2021 ◽

Vol 6 (4) ◽

pp. 50

Author(s):

Payam Teimourzadeh Baboli ◽

Davood Babazadeh ◽

Amin Raeiszadeh ◽

Susanne Horodyvskyy ◽

Isabel Koprek

Keyword(s):

Monitoring System ◽

Wind Turbines ◽

Wind Farm ◽

Measurement Data ◽

Real Data ◽

Offshore Wind ◽

Risk Indicator ◽

Real World Data ◽

The Real ◽

Data Driven Modeling

With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.

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Integrated AHP-BPNN Model for Wind Farm Investment Evaluation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.541-542.966 ◽

2014 ◽

Vol 541-542 ◽

pp. 966-971

Author(s):

Xiang Feng Zhang ◽

Tian Yu Liu ◽

Bin Jiao

Keyword(s):

Wind Farm ◽

Evaluation Method ◽

Back Propagation ◽

Wind Farms ◽

Back Propagation Neural Network ◽

Analytic Hierarchy ◽

Farm Investment ◽

Hierarchy Process ◽

Power Project ◽

Bpnn Model

The construction of wind farms grows quickly in China. It is necessary for stakeholders to estimate investment costs and to make good decisions about a wind power project by making a budget for the investment. This paper proposed an evaluation method by integrating the analytic hierarchy process (AHP) with back-propagation neural network (BPNN) to evaluate wind farm investment. In the AHP-BPNN model, the AHP method is used to determine the factors of wind farm investment. The factors with high importance are reserved while those with low importance are eliminated, which can decrease the number of inputs of the BPNN. The experiment results show that the integrated model is feasible and effective.

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A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

SIMULATION ◽

10.1177/00375497211028605 ◽

2021 ◽

pp. 003754972110286

Author(s):

Eduardo Pérez

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Computational Study ◽

Scheduling Algorithm ◽

Discrete Event ◽

Wind Farms ◽

Online Scheduling ◽

Maintenance Scheduling ◽

Lead Times ◽

System Specification

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

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