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    Item type:Publication, Access status: without files ,
    Reliability-sensitive optimization for provision of ancillary services by tempo-spatial correlated distributed energy resources
    (Springer, 2023)
    Teimourzadeh Baboli, Payam
    ;
    Raeiszadeh, Amin
    ;
    Brand, Michael
    ;
    Lehnhoff, Sebastian
    In this paper, a reliability-sensitive optimization approach for the provision of ancillary services from distributed energy resources is proposed. The main focus here is on small-scale renewable energy sources such as wind turbines and PV arrays, which are highly distributed, and the generated power is weather dependent and intermittent. Therefore, providing a reliable ancillary service through these unreliable resources is a complicated problem addressed here. In this paper, the tempo-spatial correlation between these renewable resources is mathematically modeled by pair-copula functions and included in the reliability evaluation procedure using the non-sequential Monte Carlo simulation method. An optimization problem is formulated using proposed joint reliability model to find the minimum number of renewable resources to provide ancillary services within a desired reliability level. The proposed optimization model is applied to real wind farms in Lower Saxony in Germany to provide the spinning reserve service. The results prove that including the correlation concept in the reliability evaluation procedure leads to the more realistic scenarios that play an essential role in maintaining the service’s reliability level.
    Publicationtype: Conference Paper
      22
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    Item type:Publication, Access status: with files ,
    Optimal temperature-based condition monitoring system for wind turbines
    (Multidisciplinary Digital Publishing Institute (MDPI), 2021-03-26)
    Teimourzadeh Baboli, Payam
    ;
    Babazadeh, Davood
    ;
    Raeiszadeh, Amin
    ;
    Horodyvskyy, Susanne
    ;
    Koprek, Isabel
    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.
    Publicationtype: Journal Article
    Scopus© Citations 11  208  168
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    Item type:Publication, Access status: without files ,
    Two-Stage Condition-based Maintenance Model of Wind Turbine: From Diagnosis to Prognosis
    (2020-09-28)
    Baboli, Payam Teimourzadeh
    ;
    Raeiszadeh, Amin
    ;
    Babazadeh, Davood
    ;
    Meiners, Jens
    Due to the growing share of wind turbines, the challenges in the maintenance planning of already installed offshore and onshore wind farms are increasing and motivate the author to explore the risk analysis of key components of wind turbines. In this paper, a two-step condition-based maintenance model for wind turbines is proposed. In the first stage, the so-called diagnostic stage, the normal behavior of these components was estimated through a tailor-made artificial neural network. In the second stage, the prognosis stage, the deviation of the real-time measurement data from the estimated values was calculated. If the deviation increases beyond a confidence band, an alarm is triggered and a proposed risk indicator is updated. By increasing the proposed risk indicator, the corresponding anomaly is detected and condition-based maintenance programs can be planned. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By implementing the proposed model using this real-world data, it is shown that the proposed risk indicator is fully consistent with the upcoming wind turbine failures.
    Publicationtype: Conference Paper
      51