Calendar

Jun
29
Tue
Dissertation Defense: Yan Jiang
Jun 29 @ 1:00 pm
Dissertation Defense: Yan Jiang

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Leveraging Inverter-Interfaced Energy Storage for Frequency Control in Low-Inertia Power Systems

Abstract: The shift from conventional synchronous generation to renewable inverter-interfaced sources has led to a noticeable degradation of frequency dynamics in power systems, mainly due to a loss of inertia. Fortunately, the recent technology advancement and cost reduction in energy storage facilitate the potential for higher renewable energy penetration via inverter-interfaced energy storage. With proper control laws imposed on inverters, the rapid power-frequency response from energy storage contributes to mitigating the degradation. A straightforward choice is to emulate the droop response and/or inertial response of synchronous generators through droop control (DC) or virtual inertia (VI), yet they do not necessarily fully exploit the benefits of inverter-interfaced energy storage. This thesis thus seeks to challenge this naive choice of mimicking synchronous generator characteristics by advocating for a principled control design perspective.

To achieve this goal, we build an analysis framework for quantifying the performance of power systems using signal and system norms, within which we perform a systematic study to evaluate the effect of different control laws on both frequency response metrics and storage economic metrics. More precisely, under a mild yet insightful proportionality assumption, we are able to perform a modal decomposition which allows us to get closed-form expressions or conditions for synchronous frequency, Nadir, rate of change of frequency (RoCoF), synchronization cost, frequency variance, and steady-state effort share. All of them pave the way for a better understanding of the sensitivities of various performance metrics to different control laws.

Our analysis unveils several limitations of traditional control laws, such as the inability of DC to improve the dynamic performance without sacrificing the steady-state performance and  the unbounded frequency variance introduced by VI in  the presence of frequency measurement noise. Therefore, rather than clinging to the idea of imitating synchronous generator behavior via inverter-interfaced energy storage, we prefer searching for better solutions.

We first propose dynam-i-c Droop control (iDroop)—inspired by the classical lead/lag compensator—which is proved to enjoy many good properties. First of all, the added degrees of freedom in iDroop allow to decouple the dynamic performance improvement from the steady-state performance. In addition, the lead/lag property of iDroop makes it less sensitive to stochastic power fluctuations and frequency measurement noise. Last but not least, iDroop can also be tuned either to achieve the zero synchronization cost or to achieve the Nadir elimination, by which we mean to remove the overshoot in the transient system frequency. Particularly, the Nadir elimination tuning of iDroop exhibits the potential for a balance among various performance metrics in reality. However, iDroop has no control over the RoCoF, which is undesirable in low-inertia power systems for the risk of falsely triggering protection.

We then propose frequency shaping control (FS)—an extension of iDroop—whose most outstanding feature is its ability to shape the system frequency dynamics following a sudden power imbalance into a first-order one with the specified synchronous frequency and RoCoF by adjusting two independent control parameters respectively.

We finally validate theoretical results through extensive numerical experiments performed on a more realistic power system test case that violates the proportionality assumption, which clearly confirms that our proposed control laws outperform the traditional ones in an overall sense.

Committee Members

  • Enrique Mallada, Department of Electrical and Computer Engineering
  • Pablo A. Iglesias, Department of Electrical and Computer Engineering
  • Dennice F. Gayme, Department of Mechanical Engineering
  • Petr Vorobev, Center for Energy Science and Technology, Skolkovo Institute of Science and Technology
Jun
30
Wed
Dissertation Defense: Ashwin Bellur
Jun 30 @ 10:00 am
Dissertation Defense: Ashwin Bellur

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Bio-Mimetic Sensory Mapping with Attention for Auditory Scene Analysis

Abstract: The human auditory system performs complex auditory tasks such as having a conversation in a busy cafe or picking the melodic line of a particular instrument in an ensemble orchestra, with remarkable ease. The human auditory system also exhibits the ability to effortlessly adapt to constantly changing conditions and novel stimulus. The human auditory system achieves these through complex neuronal processes. First the low dimensional signal representing the acoustic stimulus is mapped to a higher dimensional space through a series of feed-forward neuronal transformations; wherein the different auditory objects in the scene are discernible. These feed-forward processes are then further complemented by the top-down processes like attention, driven by the cognitive regions to modulate the feed-forward processes in a manner that shines the spotlight on the object of interest; the interlocutor in the example of a busy cafe or the instrument of interest in the ensemble orchestra.

In this work, we explore leveraging these mechanisms observed in the mammalian brain, within computational frameworks, for addressing various auditory scene analysis tasks such as speech activity detection, environmental sound classification and source separation. We develop bio-mimetic computational strategies to model the feed-forward sensory mapping processes as well as the corresponding complementary top-down mechanisms capable of modulating the feed-forward processes during attention.

In the first part of this work, we show using Gabor filters as an approximation for the feed-forward processes, that retuning the feed-forward processes under top-down attentional feedback are extremely potent in enabling robust behavior in detecting speech activity. We introduce the notion of memory to represent prior knowledge of the acoustic objects and show that memories of objects can be used to deploy the necessary top-down feedback. Next, we expand the feed-forward processes using data-driven distributed deep belief system consisting of multiple streams to capture the stimulus from different spectrotemporal resolutions, a feature observed in the human auditory system. We show that such a distributed system with inherent redundancies, further complemented by top-down attentional mechanisms using distributed object memories allow for robust classification of environmental sounds in mismatched conditions. Finally, we show that incorporating the ideas of distributed processing and attentional mechanisms using deep neural networks leads to state-of-the-art performance for even complex tasks such as source separation. Further, we show that in such a distributed system, the sum of the parts are better than the individual parts and that this aspect can be used to generate real-time top-down feedback; which in turn can be used to adapt the network to novel conditions during inference.

Overall, the results of the work show that leveraging theses biologically inspired mechanisms within computational frameworks lead to enhanced robustness and adaptability to novel conditions, traits of the human auditory system that we sought to emulate.

Committee Members

Mounya Elhilali, Department of Electrical and Computer Engineering

Najim Dehak, Department of Electrical and Computer Engineering

Rama Chellappa, Department of Electrical and Computer Engineering

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