ICT Based HIL Validation of Voltage Control Coordination in Smart Grids Scenarios

This paper aims to validate the capability of renewable generation (ReGen) plants to provide online voltage control coordination ancillary service to the system operators in smart grids. Simulation studies about online coordination concepts from ReGen plants have already been identified in previous publications. However, here, the results are validated through a real-time Hardware-In-the-Loop framework using an exemplary benchmark grid area in Denmark as a base case that includes flexible renewable power plants providing voltage control functionality. The provision of voltage control support from ReGen plants is verified on a large-scale power system against the baseline scenario, considering the hierarchical industrial controller platforms used nowadays in power plants. Moreover, the verification of online voltage control support is carried out by taking into account a communication network as well as the associated data traffic patterns obtained from a real network. Based on the sets of recordings, guidelines and recommendations for practical implementation of the developed control algorithms for targeted ancillary service are made. This provides a deep insight for stakeholders, i.e., wind turbine and photo-voltaic system manufacturers and system operators, regarding the existing boundaries for current technologies and requirements for accommodating the new ancillary services in industrial application

Focus on the Challenges: Analysis of a User-friendly Data Search Approach with CLIP in the Automotive Domain

Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches. Thus, engineers need to fully understand their large image data sets for the development and test of machine learning algorithms. However, current approaches lack automatability, are not generic and are limited in their expressiveness. Hence, this paper aims to analyze a state-of-the-art text and image embedding neural network and guides through the application in the automotive domain. This approach enables the search for similar images and the search based on a human understandable text-based description. Our experiments show the automatability and generalizability of our proposed method for handling large data sets in the automotive domain

Validating Performance Models for Hybrid Power Plant Control Assessment

The need for simple, but accurate performance models of wind turbine generators (WTGs), photovoltaic (PV) plants, and battery energy storage systems (BESS) for various hybrid power plant (HPP) studies motivates the present work. Particularly, the development and verification stage of HPP controls requires reduced-order models to minimize the complexity and computation effort of simulation platforms. In this paper, such models are proposed, and the most essential parts of the models are validated through field measurements. The models target power system integration studies involving active and reactive power, as well as frequency and voltage regulation where detailed models, as proposed in the standards, can be cumbersome. Field measurements of two Vestas WTGs, one 1-MW PV plant, and one 1-MW/1-MWh BESS are used for model validation. The results show that the WTG and PV performance models correctly estimate the power generation variability according to fluctuations in wind speed and solar irradiance. The BESS performance model provides satisfactory results related to grid-forming control performance and estimation of state-of-charge. The presented validation work enables using the proposed performance models for power system studies and HPP control design in all model-based design stages, that is, preliminary analysis, design, verification, and validation with a high level of confidence