Raw Wind Data Preprocessing: A Data-Mining Approach

A B S T R A C TIn power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.

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“…When other raw data sources are used, the outliers should be carefully preprocessed. Two alternative approaches are presented in [25] and [26].…”

Section: Date Sourcementioning

confidence: 99%

A conditional model of wind power forecast errors and its application in scenario generation

2018

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“…Starting from these results, in [18] an empirical methodology aimed at detecting anomalies in aggregate wind speed data has been proposed. Although this technique allows the detection and classification of local outlier factors, showing interesting results in several case studies [19], its application is typically restricted to wind power forecasting since it does not allow to model the operation states of both the electrical grid (i.e., network congestions) and the wind turbine (i.e., derated/fault), which are required in on-line condition monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

De¹,

Vaccaro

Villacci

2018

Electronics

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Abstract:The massive penetration of wind generators in existing electrical grids is causing several critical issues, which are pushing system operators to enhance their operation functions in order to mitigate the effects produced by the intermittent and non-programmable generation profiles. In this context, the integration of wind forecasting and reliability models based on experimental data represents a strategic tool for assessing the impact of generators and grid operation state on the available power profiles. Unfortunately, field data acquired by Supervisory Control and Data Acquisition systems can be characterized by outliers and incoherent data, which need to be properly detected and filtered in order to avoid large modeling errors. To deal with this challenging issue, in this paper a novel methodology fusing Fuzzy clustering techniques, and probabilistic-based anomaly detection algorithms are proposed for wind data filtering and data-driven generator modeling

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“…Consequently, it is crucial for forecast accuracy improvement to make the wind power conversion function adaptive and robust. Raw data preprocessing before training is one possible way to handle data quality related problems in power curve modeling . Those methods could mitigate the effect of abnormal data to some extent, but they do not help capturing the time‐varying and scattered properties of the power curve essentially.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting

Qiao

et al. 2016

Wind Energy

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Wind farm power curve modeling, which characterizes the relationship between meteorological variables and power production, is a crucial procedure for wind power forecasting. In many cases, power curve modeling is more impacted by the limited quality of input data rather than the stochastic nature of the energy conversion process. Such nature may be due the varying wind conditions, aging and state of the turbines, etc. And, an equivalent steady-state power curve, estimated under normal operating conditions with the intention to filter abnormal data, is not sufficient to solve the problem because of the lack of time adaptivity. In this paper, a refined local polynomial regression algorithm is proposed to yield an adaptive robust model of the time-varying scattered power curve for forecasting applications. The time adaptivity of the algorithm is considered with a new data-driven bandwidth selection method, which is a combination of pilot estimation based on blockwise least-squares parabolic fitting and the probability integral transform. The regression model is then extended to a more robust one, in which a new dynamic forgetting factor is defined to make the estimator forget the out-of-date data swiftly and also achieve a better trade-off between robustness against noisy data and time adaptivity. A case study based on a real-world dataset validates the properties of the proposed regression method. Results show that the new method could flexibly respond to abnormal data at different lead times and has better performance than common benchmarks for short-term forecasting.

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Raw Wind Data Preprocessing: A Data-Mining Approach

Cited by 126 publications

References 16 publications

A conditional model of wind power forecast errors and its application in scenario generation

A conditional model of wind power forecast errors and its application in scenario generation

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting

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