Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material compositio...
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The quest for improving human lifestyle has driven the development of novel concepts and technologies aimed at enhancing the quality of life. One of these concepts includes a Reinforcement Learning (RL) method, fallin...
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This paper introduces a grid forming(GFM) inverter equipped with a hybrid islanding detection method which combined the positive feedback(PF) of the point of common coupling(PCC) voltage based active method used and t...
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In the energy sector, the application of renewable energy sources especially solar photovoltaics (PV), is expanding exponentially. Inverters find application in converting DC power from solar PV generating arrays to A...
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For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from mu...
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For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.
Faults on distribution networks due to abnormal weather events can lead to disruption and can cause high socio-economic losses. In line with the rising frequency of such events, the paper proposes an algorithm for the...
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We consider a setting in which N agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the serv...
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Given the widely acknowledged depletion of fossil fuel reserves within the next 50-60 years, it is imperative that we expedite the transition towards renewable energy sources for power generation and consumption. Depe...
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A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many ***, many practical domains involve data that are non-identically distributed acros...
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A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many ***, many practical domains involve data that are non-identically distributed across sources, and possibly statistically dependent within its source, violating vital assumptions in existing theoretical studies of representation *** addressing these issues, we establish statistical guarantees for learning general nonlinear representations from multiple data sources that admit different input distributions and possibly dependent ***, we study the sample-complexity of learning T + 1 functions f*(t) ◦ g* from a function class F × G, where f*(t) are task specific linear functions and g* is a shared nonlinear *** approximate representation ĝ is estimated using N samples from each of T source tasks, and a fine-tuning function fˆ(0) is fit using N′ samples from a target task passed through ĝ.Our results show that the excess risk of the estimate fˆ(0) ◦ ĝ on the target task decays as Õ (Equation presented), where C(G) denotes the complexity of ***, our rates match that of the iid setting, while requiring fewer samples per task than prior analysis and admitting no dependence on the mixing *** support our analysis with numerical experiments performing imitation learning over non-linear dynamical systems. Copyright 2024 by the author(s)
The grid-following inverter's dq admittance model manifests a negative resistance in the low-frequency range due to the phase-locked loop, potentially leading to low-frequency instabilities and limiting the maximu...
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