Performance Assessment Of Wind Turbines Research Articles

A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines

In the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance.

Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements

Quantifying a wind turbine’s holistic, system-level power production efficiency in its commercial operating condition is one of the keys to reducing the levelized cost for energy of wind energy and thus contributing significantly to the Sustainable Development Goal 7.2: “By 2030, increase substantially the share of renewable energy in the global energy mix.” It is so important because designers and operators need an effective baseline quantification in order to be able to identify best practices or make operation and maintenance decisions that produce actual improvements. However, this task is highly challenging due to the stochastic nature of the wind and the complexity of wind turbine systems. It is imperative to carry out accurate, trust-worthy performance assessment and uncertainty quantification of wind turbine generators. This article provides a concise overview of the existing schools of thought in terms of wind turbine performance assessment and highlights a few important technical considerations for future research pursuit.

SCADA data analysis for long-term wind turbine performance assessment: A case study

The long-term analysis of wind turbine performance is a complex task, because the extracted power has a multivariate dependence on ambient conditions and working parameters, which in general changes during the machine lifetime due to aging effects and-or technology optimization. For this reason, the aim of this study is developing appropriate techniques based on SCADA data analysis. A real-world test case is discussed: a decade (2011–2020) of data has been analyzed for seven 2-MW wind turbines owned by ENGIE Italia, which underwent a control optimization in 2018. Four wind turbines in the long run display even a positive drift, which is due to the control upgrade; two have a slight performance decline with age which is compatible with literature estimates; one wind turbine has a noticeable worsening in time, which is exacerbated rather than compensated by the control optimization. It is therefore argued that the long-term performance trend depends on the history of each wind turbine and the resulting scenario is complex. The control optimization provides the expected improvement on six wind turbines out of seven, but leads to a worsening on the worst wind turbine: therefore, the health status of each machine should be monitored before altering the operation.

Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring

Several studies have used the power curve as a critical indicator to assess the performance of wind turbines. However, the wind turbine internal operation is affected by various parameters, particularly by blade pitch angle. Continuous monitoring of blade pitch angle can be useful for power performance assessment of wind turbines. The blade pitch curve describes the nonlinear relationship between pitch angle and hub height wind speed which to date has been little explored for wind turbine condition monitoring. Gaussian Process models are nonlinear and nonparametric technique, based on Bayesian probability theory. Such models have the potential give results quickly and efficiently. In this paper, we propose a Gaussian Process model to predict blade pitch curve of a wind turbine for condition monitoring purposes. The obtained Gaussian Process based blade pitch curve is then compared with a conventional approach based on a binned blade pitch curve for identifying operational anomalies purposes. Finally, the weaknesses and strengths of these methods are summarised. SCADA data from healthy wind turbines are used to train and evaluate the performance of these techniques.

Performance Assessment of Wind Turbines: Data-Derived Quantitative Metrics

Deteriorating performance of wind turbines results in power loses. A two-phase approach for performance evaluation of wind turbines is presented at past and future time intervals. Historical wind turbine data is utilized to determine the past performance, while performance at future time horizons calls for power prediction. In phase I of the proposed approach, wind power is predicted by an ensemble of extreme learning machines using parameters such as wind speed, air temperature, and the rotor speed. In phase II, the predicted power is used to construct Copula models. It has been demonstrated that the parameters of the Copula models serve as usable metrics for expressing performance of wind turbines. The Frank Copula model performs best among the five parametric models tested.

Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves

Prognostics and Health Management (PHM) can offer substantial improvements in reliability and availability of the wind turbine asset. Driven by reducing the Operation and Maintenance (O&M) cost of wind turbines, many research efforts have been conducted to realize reliable wind turbine performance degradation assessment. Despite these efforts, it is still challenging to assess the actual degradation trend of wind turbine which will be suitable for prediction analysis. In this study, a novel similarity metric for machine performance curves is proposed and a framework of wind turbine performance assessment methodology is presented. The proposed algorithm evaluates the health condition of wind turbine by performing principal component analysis on the quasi-linear region of the power curve. The proposed methodology has been validated on a dataset collected from a large scale onshore wind turbine for a period of two years. The result exhibits a gradual degradation trend of wind turbine and indicates the ability of proposed approach to trend and assess the turbine degradation before downtime happens. The result from the proposed method also reveals its robustness to wind resolution in the power curve, which still exhibits a very similar degradation trend when the wind resolution of power curve has been down sampled.

Performance assessment of tall building-integrated wind turbines for power generation

In response to the gradual degradation of the natural environment, there is a growing interest to advance the development of sustainable buildings, in which renewable energy technologies are often incorporated to strategically minimize the carbon footprint of buildings. Pearl River Tower, a 71-storey tall building in Guangzhou, China, was designed to be the most energy efficient tall building in the world. In addition to a series of energy-efficient measures, the most eye-catching innovation in the design of Pearl River Tower is the deployment of four wind turbines installed in four openings in the tall building for power generation. This study aims to provide a performance assessment of these tall building-integrated wind turbines. At the first step, a 1:150 scaled wind tunnel testing is carried out to experimentally assess the wind speed amplification effects inside the four openings. Comparative analysis and discussions corresponding to four different cases are presented. At the second step, the results from the wind tunnel testing, in conjunction with the statistical analysis results of long-term meteorological wind data recorded in Guangzhou, are applied for the performance assessment of these wind turbines for power generation. The outcomes of this study are expected to provide useful information for architects and engineers involved in the design of sustainable tall buildings.

A computational framework for life-cycle management of wind turbines incorporating structural health monitoring

The integration of structural health monitoring into life-cycle management strategies can help facilitating a reliable operation of wind turbines and reducing the life-cycle costs significantly. This article presents a life-cycle management framework for online monitoring and performance assessment of wind turbines, enabling optimum maintenance and inspection planning at minimum associated life-cycle costs. Incorporating continuously updated monitoring data (i.e. structural, environmental, and operational data), the framework allows capturing and understanding the actual wind turbine condition and, hence, reduces uncertainty in structural responses as well as load effects acting on the structure. As will be shown in this article, the framework integrates a variety of heterogeneous hardware and software components, including sensors and data acquisition units, server systems, Internet-enabled user interfaces as well as finite element models for system identification, and a multiagent system for self-detecting sensor malfunctions. To validate its capabilities and to demonstrate its practicability, the framework is deployed for continuous monitoring and life-cycle management of a 500-kW wind turbine. Remote life-cycle analyses of the monitored wind turbine are conducted, and case studies are presented investigating both the structural performance and the operational efficiency of the wind turbine.

Wind turbine performance assessment using multi-regime modeling approach

Prognostics & health management system is an integral component of any wind energy program to ensure high turbine availability and reliability. Traditional vibration-based condition monitoring practices have been proposed to be utilized with wind turbines as they have demonstrated varying degrees of success with other rotary machinery. However, high-frequency data such as vibration and acoustic emission signals, generally, are not collected and recorded due to limitations with data storage capacities. In addition, the highly dynamic operating conditions of a wind turbine pose a challenge to conventional frequency domain analysis tools. Thus, a systematic framework that utilizes multi-regime modeling approach is proposed to consider the dynamic working conditions of a wind turbine. Three methods were developed, and they were evaluated using SCADA (supervisory control and data acquisition) data only that have been collected from a large-scale on-shore wind turbine for 27 months. Empirical observations from the results of the three methods indicate the ability of the approach to trend and assess turbine degradation prior to known downtime occurrences.