The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

In modern autonomous systems, measurement repeatability and precision are crucial for robust decision-making algorithms. Stereovision, which is widely used in safety applications, provides information about an object’s shape, orientation, and 3D localisation. The camera’s lens distortion is a common source of systematic measurement errors, which can be estimated and then eliminated or at least reduced using a suitable correction/calibration method. In this study, a set of cameras equipped with Basler lenses (C125-0618-5M F1.8 f6mm) and Sony IMX477R matrices are calibrated using a state-of-the-art Zhang–Duda–Frese method. The resulting distortion coefficients are used to correct the images. The calibrations are evaluated with the aid of two novel methods for lens distortion measurement. The first one is based on linear regression with images of a vertical and horizontal line pattern. Based on the evaluation tests, outlying cameras are eliminated from the test set by applying the 2$\sigma$ criterion. For the remaining cameras, the MSE was reduced up to 75.4 times, to 1.8 px−6.9 px. The second method is designed to evaluate the impact of lens distortion on stereovision applied to bird tracking around wind farms. A bird’s flight trajectory is synthetically generated to estimate changes in disparity and distance before and after calibration. The method shows that at the margins of the image, lens distortion might introduce errors into the object’s distance measurement of +17%−+20% for cameras with the same distortion and from −41% up to $+\infty$ for camera pairs with different lens distortions. These results highlight the importance of having well-calibrated cameras in systems that require precision, such as stereovision bird tracking in bird–turbine collision risk assessment systems.

With the expansion of green energy, more and more data show that wind turbines can pose a significant threat to some endangered bird species. The birds of prey are more frequently exposed to collision risk with the wind turbine blades due to their unique flight path patterns. This paper shows how data from a stereovision system can be used for an efficient classification of detected objects. A method for distinguishing endangered birds from common birds and other flying objects has been developed and tested. The research focused on the selection of a suitable feature extraction methodology. Both motion and visual features are extracted from the Bioseco BPS system and retested using a correlation-based and a wrapper-type approach with genetic algorithms (GAs). With optimal features and fine-tuned classifiers, birds can be distinguished from aeroplanes with a 98.6% recall and 97% accuracy, whereas endangered birds are delimited from common ones with 93.5% recall and 77.2% accuracy.

Permutation grammars are an extension of context-free grammars with rules having the same symbols on both sides but possibly in a different order. An example of a permutation rule of length 3 is ABC → CBA. If these non-context-free rules are of length at most n, then we say that permutation grammar is of order n and all such grammars generate a family of permutation languages Perm_n. In 2010 Nagy showed that there exists a language such that it cannot be generated by a grammar of order 2, but rules of length 3 are enough. In other words, a strict inclusion $Perm_2 \subsetneq Perm_3$ was obtained. We extend this result proving that $Perm_{4n \minus 2} \subsetneq Perm_{4n \minus 1}$ for n ≥ 1.

A permutation rule is a non-context-free rule whose both sides contain the same multiset of symbols with at least one non-terminal. This rule does not add or substitute any symbols in the sentential form, but can be used to change the order of neighbouring symbols. In this paper, we consider regular and linear grammars extended with permutation rules. It is established that the generative power of these grammars relies not only on the length of the permutation rules, but also whether we allow or forbid the usage of erasing rules. This is quite surprising, since there is only one non-terminal in sentential forms of derivations for regular or linear grammars. Some decidability problems and closure properties of the generated families of languages are investigated. We also show a link to a similar model which mixes the symbols: grammars with jumping derivation mode.