A function of grid-connected VSGs is facilitating the recovery of sagging voltages to nominal levels, achieved by supplying reactive power (Q) to the grid. During normal operation, however, a VSG is required to supply...
A function of grid-connected VSGs is facilitating the recovery of sagging voltages to nominal levels, achieved by supplying reactive power (Q) to the grid. During normal operation, however, a VSG is required to supply active power (P) at unity power factor and, therefore, in order to supply the Q required in the event of a sagging voltage, the VSG must change its control mode from P-supply to PQ- or Q-supply. Such transitions may induce high current flows—caused by the sudden reduction of the voltage during the sag—which are potentially damaging to the power semiconductor devices of the VSG; if these effects are to be avoided, the supplied current must be limited to its rated value, but this may result in a low power transfer from the VSG to the grid. To circumvent these problems, in this paper we propose a LVRT strategy using an active SFCL. We show that the proposed strategy, which combines a PQ decoupling scheme with an active SFCL, ensures maximum power transfer with an improved transient response during LVRT.
In laser powder bed fusion additive manufacturing, using fixed values for the process parameters (beam power, velocity, and other parameters) may not lead to homogeneously distributed heat in all locations in the buil...
In laser powder bed fusion additive manufacturing, using fixed values for the process parameters (beam power, velocity, and other parameters) may not lead to homogeneously distributed heat in all locations in the build, especially around complex design features. This could lead to builds with defects, leading to poor mechanical and micro-structure properties. To guarantee heat homogeneity, the process parameters need to be actively controlled to adapt to different locations in the build. Builds with varying geometrical features would need different control strategies. In this work, we propose to use reinforcement learning (RL) to control, for the first time, simultaneously multiple AM process parameters to achieve consistent melting properties. Our results show that using RL as a multiple-input multiple-output control system achieves a more consistent meltpool geometry.
Flatness of discrete-time systems can be characterized by two simple properties. There exists a map, a submersion, from the flat coordinates and their forward shifts to the state and the input of the discrete-time sys...
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Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accur...
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Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM *** this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.
The application of the digital sliding mode for a linear system with a linear switching surface is considered. The digital sliding mode is understood as a time-discrete control that brings the representing point in th...
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Objective: Enhancing the reliability of myoelectric controllers that decode motor intent is a pressing challenge in the field of bionic prosthetics. State-of-the-art research has mostly focused on Supervised Learning ...
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High penetration of renewable energy (RE) generations in power systems results into a low inertia-weak power grid. To increase inertia of the latter systems, the RE to grid interfacing inverters can be operated to mim...
High penetration of renewable energy (RE) generations in power systems results into a low inertia-weak power grid. To increase inertia of the latter systems, the RE to grid interfacing inverters can be operated to mimic synchronous generators. This technology is known as virtual synchronous generator (VSG). However, as the grid weakens there is severe coupling between active power (P) and reactive power (Q). Hence, a VSG requires a PQ decoupling technique for its successful operation under this case (connection to the weak power grid). Therefore, this paper proposes a virtual power circle with variable center and radius for independent control of both P and Q. The method is implemented using virtual impedance. The efficacy of the proposed scheme to decouple PQ is validated using a synchronverter connected to the weak grid in MATLAB/Simulink environment.
This article develops a new controller design approach to stabilize system states onto the equilibrium at an arbitrarily selected time instant irrespective of the initial system states and parameters. By the stabiliza...
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In the aircraft management system, the universality of aircraft target detection neural network model compression has been verified for many engineering application conditions such as full-range intelligent maneuverin...
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ISBN:
(数字)9798331543037
ISBN:
(纸本)9798331543044
In the aircraft management system, the universality of aircraft target detection neural network model compression has been verified for many engineering application conditions such as full-range intelligent maneuvering target recognition in specific areas, long-distance maneuvering target detection, and deep space recognition of non-cooperative targets. A data set suitable for aerospace has been selected to complete variable-scale recognition without basically reducing the accuracy. The model is compressed to one-tenth, and the inference time is controlled at the millisecond level. Using neural network compression and quantization after pruning, a 90% compression rate SSD fixed-point model is achieved to control the resize process and effectively prevent precision dissipation, target loss of lock, and energy consumption expansion. Multi-acceleration IP core parallel computing is deployed on the mainland Zynq series single chip to achieve a peak performance of 1.2TOPS and an mAP value of 0.95, meeting the strict requirements of aerospace vehicles for single-time target recognition accuracy.
The automation of a bascule bridge, located in the Netherlands, is studied. The modeling of the system is worked out in the framework of Ramadge-Wonham and analyzed per automation component of the bridge. The desired ...
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