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Register a new userEvaluating Load Models and Their Impacts on Power Transfer Limits
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Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic constraints, such as transient stability limits, voltage stability limits and small-signal stability limits. Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits. Currently, multiple load models have been proposed and adopted in the industry and academia, including the ZIP model, ZIP plus induction motor composite model (ZIP + IM) and WECC composite load model (WECC CLM). Each of them has its unique advantages, but their impacts on the power transfer limits are not yet adequately addressed. One existing challenge is fitting the high-order nonlinear models such as WECC CLM. In this study, we innovatively adopt double deep Q-learning Network (DDQN) agent as a general load modeling tool in the dynamic assessment procedure and fit the same transient field measurements into different load models. A comprehensive evaluation is then conducted to quantify the load models impacts on the power transfer limits. The simulation environment is the IEEE-39 bus system constructed in Transient Security Assessment Tool (TSAT).
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In this work, we investigate differential chaos shift keying (DCSK), a communication-based waveform, in the context of wireless power transfer (WPT). Particularly, we present a DCSK-based WPT architecture, that employs an analog correlator at the receiver in order to boost the energy harvesting (EH) performance. By taking into account the nonlinearities of the EH process, we derive closed-form analytical expressions for the peak-to-average-power-ratio of the received signal as well as the harvested power. Nontrivial design insights are provided, where it is shown how the parameters of the transmitted waveform affects the EH performance. Furthermore, it is demonstrated that the employment of a correlator at the receiver achieves significant EH gains in DCSK-based WPT systems.
Atmospheric correction of Earth Observation data is one of the most critical steps in the data processing chain of a satellite mission for successful remote sensing applications. Atmospheric Radiative Transfer Models (RTM) inversion methods are typically preferred due to their high accuracy. However, the execution of RTMs on a pixel-per-pixel basis is impractical due to their high computation time, thus large multi-dimensional look-up tables (LUTs) are precomputed for their later interpolation. To further reduce the RTM computation burden and the error in LUT interpolation, we have developed a method to automatically select the minimum and optimal set of nodes to be included in a LUT. We present the gradient-based automatic LUT generator algorithm (GALGA) which relies on the notion of an acquisition function that incorporates (a) the Jacobian evaluation of an RTM, and (b) information about the multivariate distribution of the current nodes. We illustrate the capabilities of GALGA in the automatic construction and optimization of MODerate resolution atmospheric TRANsmission (MODTRAN) LUTs for several input dimensions. Our results indicate that, when compared to a pseudo-random homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by $sim$75% and (2) the maximum interpolation relative errors by 0.5% It is concluded that automatic LUT design might benefit from the methodology proposed in GALGA to reduce computation time and interpolation errors.
Very small electromechanical coupling coefficient in micro-electromechanical systems (MEMS) or acoustic resonators is quite of a concern for oscillator performance, specially at mmWave frequencies. This small coefficient is the manifestation of the small ratio of motional capacitance to static capacitance in the resonators. This work provides a general solution to overcome the problem of relatively high static capacitance at mmWave frequencies and presents analysis and design techniques for achieving extremely low phase noise and a very high figure-of-merit (FoM) in an on-chip MEMS resonator based mmWave oscillator. The proposed analysis and techniques are validated with design and simulation of a 30 GHz oscillator with MEMS resonator having quality factor of 10,000 in 14 nm GF technology. Post layout simulation results show that it achieves a phase noise of -132 dBc/Hz and FoM of 217 dBc/Hz at offset of 1 MHz.
The prediction of electrical power in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power output can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power output as a function of these environmental conditions in order to maximize the profit. The research community has solved this problem by applying machine learning techniques and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms in which data is arriving continuously and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and performance to be applied on this streaming scenario.
Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the smooth approximation and alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority.
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