Complexity Analysis of Wind Energy, Wind Speed and Wind Direction in the light of nonlinear technique

TL;DR

This study applies a nonlinear multifractal analysis to ten years of wind data, revealing complex behaviors and strong correlations between wind direction, speed, and energy, which can inform better wind power optimization.

Contribution

It introduces the use of Multifractal Detrended Cross-correlation Analysis (MFDXA) to study the complexity and correlations in wind data, highlighting the importance of wind direction in energy generation.

Findings

Wind direction has the highest multifractality among studied variables.

Strong cross-correlation exists between wind energy and wind direction.

Wind data exhibit significant multifractal behavior, indicating complex dynamics.

Abstract

Wind energy has an inherent intermittent character due to certain inevitable factors of nature, such as availability of wind at different weather conditions, wind direction etc. To study the intermittent character of wind energy, its daily data along with the two other important quantities, wind speed and wind direction measured in a "showcase" wind farm for a span of ten years are analyzed applying a nonlinear robust tool Multifractal Detrended Cross-correlation Analysis (MFDXA). MFDXA is a meticulous application for computation of cross-correlation between simultaneously measured nonstationary time series. Significant difference in degree of multifractality is observed for wind energy, wind speed and wind direction. Wind direction is found to possess the highest degree of multifractality implying that the degree of complexity of wind direction is higher than wind speed or energy. Further strong cross-correlation between wind energy and wind direction is an indication that the direction of wind is one of the crucial factors in generation of wind energy. Thus, the cross-correlation analysis between wind energy - wind speed, and between wind energy - wind direction gives significant information about the scaling behavior, which may have necessary inputs towards optimization of wind power generation.