XAI for transparent wind turbine power curve models

Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticised for being opaque 'black boxes', which hinders their application in practice. We apply Shapley values, a popular explainable artificial intelligence (XAI) method, and the latest findings from XAI for regression models, to uncover the strategies ML models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. Therefore, we call for a more prominent role of XAI methods in model selection. Moreover, we propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring. This can help to reduce downtime and increase the utilization of turbines in the field.Comment: Accepted as a workshop paper at 'Tackling Climate Change with Machine Learning', ICLR 202

Towards transparent and robust data-driven wind turbine power curve models

Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent years, data-driven machine learning methods have outperformed parametric, physics-informed approaches. However, they are often criticised for being opaque "black boxes" which raises concerns regarding their robustness in non-stationary environments, such as faced by wind turbines. We, therefore, introduce an explainable artificial intelligence (XAI) framework to investigate and validate strategies learned by data-driven power curve models from operational SCADA data. It combines domain-specific considerations with Shapley Values and the latest findings from XAI for regression. Our results suggest, that learned strategies can be better indicators for model robustness than validation or test set errors. Moreover, we observe that highly complex, state-of-the-art ML models are prone to learn physically implausible strategies. Consequently, we compare several measures to ensure physically reasonable model behaviour. Lastly, we propose the utilization of XAI in the context of wind turbine performance monitoring, by disentangling environmental and technical effects that cause deviations from an expected turbine output. We hope, our work can guide domain experts towards training and selecting more transparent and robust data-driven wind turbine power curve models.Comment: 10 pages, 8 figures, under review in IEEE Transactions on Sustainable Energ

Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. Its predominant application is to monitor turbine condition without the need for additional sensing equipment. Most approaches apply semi-supervised anomaly detection methods, also called normal behaviour models, that require clean training data sets to establish healthy component baseline models. In practice, however, the presence of change points induced by malfunctions or maintenance actions poses a major challenge. Even though this problem is well described in literature, this contribution is the first to systematically evaluate and address the issue. A total of 600 signals from 33 turbines are analysed over an operational period of more than 2 years. During this time one-third of the signals were affected by change points, which highlights the necessity of an automated detection method. Kernel-based change-point detection methods have shown promising results in similar settings. We, therefore, introduce an appropriate SCADA data preprocessing procedure to ensure their feasibility and conduct comprehensive comparisons across several hyperparameter choices. The results show that the combination of Laplace kernels with a newly introduced bandwidth and regularisation-penalty selection heuristic robustly outperforms existing methods. More than 90 % of the signals were classified correctly regarding the presence or absence of change points, resulting in an F1 score of 0.86. For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0.96 and therefore without any significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data preprocessing, which is key for data-driven methods to reach their full potential. The algorithm is open source and its implementation in Python is publicly available.TU Berlin, Open-Access-Mittel – 202

Die Oekonomie internationalen Umweltschutzes

Die größten Umweltprobleme der Gegenwart besitzen ausnahmslos eine internationale Dimension. Die Ausdünnung der Ozonschicht, die globale Erwärmung, die Zerstörung der Tropenwälder oder die Meeresverschmutzung machen nicht vor nationalen Grenzen halt. Da auf internationaler Ebene keine zentrale Instanz existiert, die die Nutzung grenzüberschreitender Umweltgüter wirkungsvoll regulieren könnte, ist zum Schutz freiwillige zwischenstaatliche Zusammenarbeit erforderlich. Zentrale Aufgabe der Arbeit ist es daher, die ökonomischen Bedingungen für internationale Kooperation in Umweltschutzfragen herauszuarbeiten und auf konkrete Fragestellungen anzuwenden. Die Untersuchung beschränkt sich dabei nicht auf die wohlfahrtsökonomische Perspektive, sondern schließt auch die Sichtweise der Neuen Politischen Ökonomie ein

Die Oekonomie internationalen Umweltschutzes

Die größten Umweltprobleme der Gegenwart besitzen ausnahmslos eine internationale Dimension. Die Ausdünnung der Ozonschicht, die globale Erwärmung, die Zerstörung der Tropenwälder oder die Meeresverschmutzung machen nicht vor nationalen Grenzen halt. Da auf internationaler Ebene keine zentrale Instanz existiert, die die Nutzung grenzüberschreitender Umweltgüter wirkungsvoll regulieren könnte, ist zum Schutz freiwillige zwischenstaatliche Zusammenarbeit erforderlich. Zentrale Aufgabe der Arbeit ist es daher, die ökonomischen Bedingungen für internationale Kooperation in Umweltschutzfragen herauszuarbeiten und auf konkrete Fragestellungen anzuwenden. Die Untersuchung beschränkt sich dabei nicht auf die wohlfahrtsökonomische Perspektive, sondern schließt auch die Sichtweise der Neuen Politischen Ökonomie ein

Urban Digital Twins for Smart Cities and Citizens:The Case Study of Herrenberg, Germany

Cities are complex systems connected to economic, ecological, and demographic conditions and change. They are also characterized by diverging perceptions and interests of citizens and stakeholders. Thus, in the arena of urban planning, we are in need of approaches that are able to cope not only with urban complexity but also allow for participatory and collaborative processes to empower citizens. This to create democratic cities. Connected to the field of smart cities and citizens, we present in this paper, the prototype of an urban digital twin for the 30,000-people town of Herrenberg in Germany. Urban digital twins are sophisticated data models allowing for collaborative processes. The herein presented prototype comprises (1) a 3D model of the built environment, (2) a street network model using the theory and method of space syntax, (3) an urban mobility simulation, (4) a wind flow simulation, and (5) a number of empirical quantitative and qualitative data using volunteered geographic information (VGI). In addition, the urban digital twin was implemented in a visualization platform for virtual reality and was presented to the general public during diverse public participatory processes, as well as in the framework of the "Morgenstadt Werkstatt" (Tomorrow's Cities Workshop). The results of a survey indicated that this method and technology could significantly aid in participatory and collaborative processes. Further understanding of how urban digital twins support urban planners, urban designers, and the general public as a collaboration and communication tool and for decision support allows us to be more intentional when creating smart cities and sustainable cities with the help of digital twins. We conclude the paper with a discussion of the presented results and further research directions

Microscale CFD Simulations of a Wind Energy Test Site in the Swabian Alps with Mesoscale Based Inflow Data

The current study describes analyses of the WINSENT wind energy test site located in complex terrain in Southern Germany by highly resolved numerical simulations. The resolved atmospheric turbulence is simulated with Delayed Detached Eddy Simulations by the flow solver FLOWer without consideration of the research wind turbines. The mean inflow and wind direction of the analysed time period is provided by precursor simulations of project partners. The simulation model chain consists of three codes with different time scales and resolutions. The model chain provides a data transfer from mesoscale WRF simulations to OpenFOAM. As a next step OpenFOAM provides inflow data in the valley of the terrain site for the present FLOWer simulations, the code with the highest resolution in space and time. The mean velocity field provided by OpenFOAM is superimposed with fluctuations that are based on measurements to obtain the small turbulent scales within the FLOWer simulations, which the previous tools of the model chain can not resolve. Comparisons with the two already installed met masts clarify that the current FLOWer simulations provide an adequate agreement with measured data. The results are verified with the application of a second simulation, in which a homogeneous velocity profile is superimposed with turbulence. Thus, comparisons with measured data showed that the benefit of using the inflow data of this model chain is especially evident near the ground

A GIS-Based Planning Approach for Urban Power and Natural Gas Distribution Grids with Different Heat Pump Scenarios

Next to building insulation, heat pumps driven by electrical compressors (eHPs) or by gas engines (geHPs) can be used to reduce primary energy demand for heating. They come with different investment requirements, operating costs and emissions caused. In addition, they affect both the power and gas grids, which necessitates the assessment of both infrastructures regarding grid expansion planning. To calculate costs and CO2 emissions, 2000 electrical load profiles and 180 different heat demand profiles for single-family homes were simulated and heat pump models were applied. In a case study for a neighborhood energy model, the load profiles were assigned to buildings in an example town using public data on locations, building age and energetic refurbishment variants. In addition, the town’s gas distribution network and low voltage grid were modeled. Power and gas flows were simulated and costs for required grid extensions were calculated for 11% and 16% heat pump penetration. It was found that eHPs have the highest energy costs but will also have the lowest CO2 emissions by 2030 and 2050. For the investigated case, power grid investments of 11,800 euros/year are relatively low compared to gas grid connection costs of 70,400 euros/year. If eHPs and geHPs are combined, a slight reduction of overall costs is possible, but emissions would rise strongly compared to the all-electric case.BMWi, 03ET4020C, Verbundvorhaben: Analyse von Strukturoptionen zur Integration erneuerbarer Energien in Deutschland und Europa unter Berücksichtigung der Versorgungssicherheit, Teilvorhaben: Analyse der Verteilnetzebene (INTEEVER-AVN)BMWi, 03ET4069C, Verbundvorhaben INTEEVER-II: Analyse der Integration erneuerbarer Energien in Deutschland und Europa unter Berücksichtigung der Versorgungssicherheit und dezentraler Flexibilitäte

Dynamic-stall measurements using time-resolved pressure-sensitive paint on double-swept rotor blades

The study presents an optimized pressure-sensitive paint (PSP) measurement system that was applied to investigate unsteady surface pressures on recently developed double-swept rotor blades in the rotor test facility at the German Aerospace Center (DLR) in Göttingen. The measurement system featured an improved version of a double-shutter camera that was designed to reduce image blur in PSP measurements on fast rotating blades. It also comprised DLR's PSP sensor, developed to capture transient flow phenomena (iPSP). Unsteady surface pressures were acquired across the outer 65% of the rotor blade with iPSP and at several radial blade sections by fast-response pressure transducers at blade-tip Mach and Reynolds numbers of M_{tip} = 0.282 - 0.285 and Re_{tip}= 5.84 - 5.95 * 10^5. The unique experimental setup allowed for scanning surface pressures across the entire pitch cycle at a phase resolution of 0.225 deg azimuth for different collective and cyclic-pitch settings. Experimental results of both investigated cyclic-pitch settings are compared in detail to a delayed detached eddy simulation using the flow solver FLOWer and to flow visualizations from unsteady Reynolds-averaged Navier Stokes (URANS) computations with DLR's TAU code. The findings reveal a detailed and yet unseen insight into the pressure footprint of double-swept rotor blades undergoing dynamic stall and allow for deducing "stall maps", where confined areas of stalled flow on the blade are identifiable as a function of the pitch phase