D. Liu
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32 records found
1
The growing integration of converter-interfaced Renewable Energy Sources (RES) has diminished synchronous inertia, complicating short-term frequency stability. Fast Active Power Response (FAPR) from Modular Multilevel Converters (MMCs) in offshore Wind Power Plants (WPPs) is crucial for mitigation. Yet, Transmission System Operators (TSOs) often lack proprietary control details for real-time assessment. This paper proposes an Artificial Neural Network (ANN)-based method to estimate FAPR in mixed systems of MMCs and Synchronous Generators (SGs) using only grid-observable measurements: frequency, Rate of Change of Frequency (RoCoF), and initial loading. A synthetic dataset was generated via RSCAD simulations of a multi-terminal HVDC network, with variations in inertia, loading, and wind speed. The ANN maps frequency/RoCoF polynomials to SG/MMC power response trajectories. Results demonstrate mean absolute errors of approximately 130-160 MW for the reconstructed curves, enabling TSOs to infer FAPR without proprietary control knowledge and enhancing frequency security in converter-dominated grids.
Distribution system operators (DSOs) often lack high-quality data on low-voltage distribution networks (LVDNs), including the topology and the phase connection of residential customers. The phase connection is essential for phase balancing assessment and distributed energy resources (DERs) integration. The existing load profiles-based approaches rely on stepwise subtraction of the identified customers in a step-by-step identification procedure, while the accuracy of each step is not guaranteed. This paper introduces a siamese neural network model to identify single-phase connections without requiring stepwise subtraction. It comprises self-taught learning (STT) and a phase-label identification strategy. The introduced self-taught learning enables DSOs to train a recurrent neural network-based Siamese network (RSN) only relying on an unlabelled dataset. Besides, the siamese network (SN) is robust to noise and fluctuations in the data to a certain extent, making the proposed method robust to measurement errors. A Kendall correlation-based phase modification strategy is introduced to modified phase labels with lower confidence, aiming to mitigate the accuracy loss induced by the limited generalization of SN. The proposed approach is tested on the IEEE European low voltage test feeder and a residential network in the Netherlands Simulation results illustrate the feasibility and robustness of the proposed approach on incomplete datasets. The accuracy exceeded 83% and 90%, respectively, when using datasets of less than 20 days with and without measurement errors.
High-Temperature superconducting (HTS) generators are being considered as a competitive candidate in large direct-drive (DD) wind turbines because of their features of being lightweight and compact. Normally a large air gap is inevitable in partially HTS generators, sacrificing the torque producing capability. In this paper, multi-phase armature windings for HTS generators are investigated to reduce the air gap length in HTS generators while not compromising generators' performance. Therefore, the torque density of HTS generators can be improved without any added costs. Five different multi-phase armature winding schemes are studied in the paper. Their performance regarding torque production and rotor losses in a 10 MW DD HTS generator are examined. The findings show that employing multi-phase armature windings can reduce the mechanical air gap without generating extra eddy current losses in the rotor, and the torque production can be improved by up to 9.1%. In addition, the alternating magnetic field reaching the HTS field winding are also reduced by using multi-phase armature windings, resulting in lower AC losses and cooling costs.
A method for comparing the levelized cost of energy (LCoE) of different superconducting drive trains is introduced. The properties of a 10-MW MgB2 superconducting direct-drive generator and the cost break down of the nacelle components are presented and scaled up to a turbine with a rotor diameter of up to 280 m. The partial load efficiency of the generator is evaluated for a constant cooling power of 0, 50, and 100 kW, and the annual energy production is used to determine the impact on the LCoE.
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Topologies of superconducting direct drive wind turbine generators are based on a combination of superconducting wires wound into field coils, copper armature windings, steel laminates to shape the magnetic flux density and finally structural materials as support. But what is the most optimal topology for superconducting wind turbine generators? This question is investigated by assuming some unit cost of the different materials and then minimizing the cost of the active materials of a 10 MW and 9.65 rpm direct drive wind turbine generator intended to be mounted in front of the INNWIND.EU King-Pin concept nacelle. A series of topologies are investigate by adding more iron components to the generator, such as rotor back iron, field winding pole, magnetic teeth and armature back iron. This method is used to investigate 6 topologies and to determine the optimal cost of the different topologies by using the current cost of 4 ∈/m for the MgB2 wire from Columbus Superconductors and also a possible future cost of 1 ∈/m if a superconducting offshore wind power capacity of 10 GW has been introduced by 2030 as suggested in a roadmap. The obtained topologies are compared to what is expected from a permanent magnet direct drive generators and the further development directions are discussed. Finally an experimental INNWIND.EU demonstration showing that the current commercial MgB2 wires can be wound into functional field coils for wind turbine generators is discussed.