Battery energy storage systems (BESS) are crucial for microgrids, enhancing dynamic performance and mitigating uncertainties inherent to isolated environments characterized by intermittent power generation. When micro...
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ISBN:
(数字)9798350381832
ISBN:
(纸本)9798350381849
Battery energy storage systems (BESS) are crucial for microgrids, enhancing dynamic performance and mitigating uncertainties inherent to isolated environments characterized by intermittent power generation. When microgrid is supplied by wind power in connection with BESS, the bidirectional DC-DC converter associated with BESS assumes a critical responsibility—ensuring the control of a wind power supplied DC-link. This permits the Voltage Source Inverter (VSI) to synthesize consistent voltages for the connected AC load. However, challenges emerge, particularly in arduous stress on switches of the bidirectional converter attributed to elevated current ripple. This stress does not only prejudice the overall system’s functioning but can also potentially leading it to shutdown in case of switch failure. Traditionally, the conventional buck-boost converter topology has been the go-to choice for this application, nevertheless, exploring novel converter topologies, such as interleaved bidirectional DC-DC converter (IBDC) has garnered attention, promising enhanced performance. This paper’s goal is to conduct a comparative analysis of the operation of these converters in an islanded microgrid supplied by a single Permanent Magnet Synchronous Generators (PMSG) small-scale wind turbine with a BESS system. Load variation with Maximum Power Point Tracking (MPPT) operation of the PMSG is performed during MATLAB/Simulink environment simulations. The results suggest significant advantages for the interleaved three-phase converter, such as reduced battery charging current ripple, switch disturbance, and voltage ripple in the DC-link.
FPGAs are a compelling substrate for supporting machine learning inference. Tools such as High-Level Synthesis and hls4ml can shorten the development cycle for deploying ML algorithms on FPGAs, but can struggle to han...
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ISBN:
(纸本)9798400713965
FPGAs are a compelling substrate for supporting machine learning inference. Tools such as High-Level Synthesis and hls4ml can shorten the development cycle for deploying ML algorithms on FPGAs, but can struggle to handle the large on-chip storage needed for many of these models. In particular the high BRAM usage found in many of these flows can cause Place & Route failures during synthesis. In this paper we propose using a Simulated-Annealing based flow to perform BRAM-aware quantization. This approach trades off inference accuracy with BRAM usage, to provide a high-quality inference engine that still meets on-chip resource constraints. We demonstrate this flow for Transformer-based machine learning algorithms, which include Flash Attention in a Stream-based Dataflow architecture. Our system imposes minimal accuracy drops, yet can reduce BRAM usage by 20%-50%, and improve power efficiency by 264%-812% compared to existing Transformer-based accelerators on FPGAs
The c-axis permittivity of 1T-TaS2 - a quasi-2D charge-density-wave material - changes upon illumination due to light-induced reorganization of CDW stacking. Here we probe the mechanism of this reorganization and find...
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We report a flexible temperature sensor based on Ti02 photonics that shows double the sensitivity compared to silicon photonics. This high sensitivity and biocompatibility pave the way towards point-of-care temperatur...
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Accurate weekly electricity load prediction is of utmost importance for electricity providers to ensure uninterrupted power supply to customers. This study applies an Artificial Neural Network (ANN) to achieve precise...
Accurate weekly electricity load prediction is of utmost importance for electricity providers to ensure uninterrupted power supply to customers. This study applies an Artificial Neural Network (ANN) to achieve precise weekly electricity load prediction. The dataset used for the ANN model consists of three months’ worth of data, including daily workload profiles, holiday work profiles, temperature, and humidity. For model training, 90% of the data is utilized with the Levenberg-Marquardt algorithm, while the remaining 10% is used for testing. The Mean Average Percentage Error (MAPE) is employed as the error metric. Based on the test results, the weekly load prediction error rate using ANN is determined to be 1.78% based on the MAPE value.
We report a flexible temperature sensor based on TiO2 photonics that shows double the sensitivity compared to silicon photonics. This high sensitivity and biocompatibility pave the way towards point-of-care temperatur...
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We demonstrate a dynamically tunable plasmonic metasurface enabled by light-tunable optical constants of a quantum material - 1T-TaS2. We observe a relative reflectance change of 10% under low-intensity incoherent ill...
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The carbon brushes and slip rings of a hydrogen-erator are the main components guiding the excitation current from the bridge to the rotor windings. The brushes' temperature are crucial to infer their operational ...
The carbon brushes and slip rings of a hydrogen-erator are the main components guiding the excitation current from the bridge to the rotor windings. The brushes' temperature are crucial to infer their operational condition and, the generator status. This work presents the temperature measurement of six Fiber Bragg Gratings (FBG) sensors installed in a 370 MVA electric generator brushes. The results show the sensors' capacity to monitor the brushes' temperature in accordance with the current flowing through them. Together with the sensors, an Artificial Intelligence (AI) technique was applied to the measured temperature to detect anomalous events regarding the current supplied to the rotor windings. The optical sensors combined with the AI could detect five events of abnormal current behaviour. One is presented in detail in this paper. This sensing system can be further applied to online fault detection using the temperature measured by the FBGs as a brush condition indicator and a generator operation and maintenance tool.
This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and ...
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This paper proposes an analytical target modifi-cation for linear robust model predictive control strategies in order to deal with time-varying references defined by dynamic signal targets. The new approach can be dir...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
This paper proposes an analytical target modifi-cation for linear robust model predictive control strategies in order to deal with time-varying references defined by dynamic signal targets. The new approach can be directly integrated to linear robust model predictive control algorithms that achieve piecewise constant reference tracking if recursive feasibility is ensured for any set-point. The main contribution is to present a direct analytical approach that provides a potentially improved steady-state tracking error performance with the same computation complexity of the original MPC for tracking piecewise constant reference. A simulation case study based on the trajectory tracking control of a quadrotor is used to illustrate the usefulness of the new analytical target modification layer.
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