arXiv subjects

Jimmy Chih-Hsien Peng

Publications and source records attributed to Jimmy Chih-Hsien Peng.

10 recordsLinked to original sources

arXiv2026

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability. Existing training methods inherit the Block Training paradigm from LLM pre-training, which introduces destructive gradient noise in instruct models due to attention leakage from unrelated contexts. Using GSNR analysis, we theoretically characterize this issue and propose Finetuning-aligned Sequential Training (FAST), a sequential training paradigm specifically designed for instruct models. FAST aligns SAE training with the data distribution and activation patterns of instruct models, substantially improving both reconstruction fidelity and feature interpretability. Experimental results show that FAST achieves higher GSNR, a significantly lower log-scaled MSE of 0.6468 compared to the baseline's 5.1985, and a near-zero Delta Loss (-0.51\% to 0.37\%). Moreover, on Llama-3.2-3B-it, FAST produces 21.1\% high-quality features, substantially outperforming baseline methods that achieve 7.0\% and 10.2\%. We further find that intervening on special token activations through SAEs can improve generation quality, revealing new opportunities for fine-grained control. Our codes are available as open source at https://github.com/Geaming2002/FAST.

cs.CL↗

arXiv2026

Fast frequency response with heterogeneous communication delay management under the SCION Internet architecture

System operators can increasingly exploit distributed energy resources (DERs) and controllable loads (CLs) to provide frequency response services. In conventional practice, communication between the system operator and flexible devices relies on the Border Gateway Protocol (BGP)-based Internet. However, existing BGP-based architectures face challenges in providing latency-guaranteed control, while direct private and proprietary communication networks lead to additional deployment and maintenance costs. In contrast, the SCION-based Internet architecture supports latency-minimum path selection, which makes it suitable for latency-sensitive frequency contingency services such as fast frequency response (FFR). Hence, this paper proposes a real-time reserve dispatch framework to optimally select a portfolio of flexible devices to deliver FFR services using the SCION-based Internet. First, an analytical expression of the system frequency dynamics with respect to heterogeneous communication latencies is derived. Next, a cyber-physical co-optimization model is formulated to jointly schedule communication paths and physical flexibility resources for real-time FFR provision. To improve the computation efficiency, we propose a heuristic FFR allocation algorithm to approximate the optimal response portfolio, integrating contributions from both DERs and CLs. Numerical case studies demonstrate the benefits of the proposed algorithm and its capability to approximate the optimality of the reserves allocation while significantly reducing the computation time.

eess.SY↗

arXiv2022

Coupled power generators require stability buffers in addition to inertia

Increasing the inertia is widely considered to be the solution to resolving unstable interactions between coupled oscillators. In power grids, Virtual Synchronous Generators (VSGs) are proposed to compensate the reducing inertia as rotating synchronous generators are being phased out. Yet, modeling how VSGs and rotating generators simultaneously contribute energy and inertia, we surprisingly find that instabilities of a small-signal nature could arise despite fairly high system inertia. Importantly, we show there exist both an optimal and a maximum number of such VSGs that can be safely supported, a previously unknown result directly useful for power utilities in long-term planning and prosumer contracting. Meanwhile, to resolve instabilities in the short term, we argue that the new market should include another commodity that we call stability storage, whereby -- analogous to energy storage buffering energy imbalances -- VSGs act as decentralized stability buffers. While demonstrating the effectiveness of this concept for a wide range of energy futures, we provide policymakers and utilities with a roadmap towards achieving a 100% renewable grid.

eess.SY↗

arXiv2021

Identification of Stability Regions in Inverter-Based Microgrids

A new method for the stability assessment of inverter-based microgrids is presented in this paper. Directly determining stability boundaries by searching the multidimensional space of inverters' droop gains is a computationally prohibitive task. Instead, we build a certified stability region by utilizing a generalized Laplacian matrix eigenvalues, which are a measure of proximity to stability boundary. We establish an upper threshold for the eigenvalues that determines the stability boundary of the entire system and demonstrate that this value depends only on the network's R/X ratio but does not depend on the grid topology. We also provide a conservative upper threshold of the eigenvalues that are universal for any systems within a reasonable range of R/X ratios. We then construct approximate certified stability regions representing convex sets in the multidimensional space of droop gains that could be utilized for gains optimization. We show how the certified stability region can be maximized by properly choosing droop gains, and we provide closed-form analytic expressions for the certified stability regions. The computational complexity of our method is almost independent of the number of inverters. The proposed methodology has been tested using IEEE 123 node test system with 10 inverters.

eess.SY↗

arXiv2020

Identification and Stabilization of Critical Clusters in Inverter-Based Microgrids

A new method for stability assessment of inverter-based microgrids is presented in this paper. It leverages the notion of critical clusters -- a localized group of inverters with parameters having the highest impact on the system stability. The spectrum of the weighted network admittance matrix is proposed to decompose a system into clusters and rank them based on their distances from the stability boundary. We show that each distinct eigenvalue of this matrix is associated with one cluster, and its eigenvectors reveal a set of inverters that participate most in the corresponding cluster. The least stable or unstable clusters correspond to higher values of respective eigenvalues of the weighted admittance matrix. We also establish an upper threshold for eigenvalues that determines the stability boundary of the entire system and demonstrate that this value depends only on the grid type (i.e. $R/X$ ratio of the network) and does not depend on the grid topology. Therefore, the proposed method provides the stability certificate based on this upper threshold and identifies the lines or inverter droop settings needed to be adjusted to restore or improve the stability.

eess.SY↗

arXiv2020

Identification of Critical Clusters in Inverter-based Microgrids

In this paper, we investigate the stability properties of inverter-based microgrids by establishing the possible presence of the so-called critical clusters - groups of inverters with their control settings being close to the stability boundary. For this, we consider the spectrum of the weighted admittance matrix of the network and show that its distinct eigenvalues correspond to inverter clusters, whose structure can be revealed by the corresponding eigenvector. We show that the maximum eigenvalue of the weighted admittance matrix corresponds to the cluster, closest to stability boundary. We also establish, that there exists a boundary on the value of this eigenvalue, that corresponds to the stability of the overall system. Thus, we make it possible to certify the stability of the system and find the groups of inverters in which control settings are closest to the stability boundary.

eess.SY↗

arXiv2020

Traffic networks are vulnerable to disinformation attacks

Disinformation continues to attract attention due to its increasing threat to society. Nevertheless, a disinformation-based attack on critical infrastructure has never been studied to date. Here, we consider traffic networks and focus on fake information that manipulates drivers' decisions to create congestion. We study the optimization problem faced by the adversary when choosing which streets to target to maximize disruption. We prove that finding an optimal solution is computationally intractable, implying that the adversary has no choice but to settle for suboptimal heuristics. We analyze one such heuristic, and compare the cases when targets are spread across the city of Chicago vs. concentrated in its business district. Surprisingly, the latter results in more far-reaching disruption, with its impact felt as far as 2 kilometers from the closest target. Our findings demonstrate that vulnerabilities in critical infrastructure may arise not only from hardware and software, but also from behavioral manipulation.

cs.SI↗

arXiv2019

How weaponizing disinformation can bring down a city's power grid

Social technologies have made it possible to propagate disinformation and manipulate the masses at an unprecedented scale. This is particularly alarming from a security perspective, as humans have proven to be the weakest link when protecting critical infrastructure in general, and the power grid in particular. Here, we consider an attack in which an adversary attempts to manipulate the behavior of energy consumers by sending fake discount notifications encouraging them to shift their consumption into the peak-demand period. We conduct surveys to assess the propensity of people to follow-through on such notifications and forward them to their friends. This allows us to model how the disinformation propagates through social networks. Finally, using Greater London as a case study, we show that disinformation can indeed be used to orchestrate an attack wherein unwitting consumers synchronize their energy-usage patterns, resulting in blackouts on a city-scale. These findings demonstrate that in an era when disinformation can be weaponized, system vulnerabilities arise not only from the hardware and software of critical infrastructure, but also from the behavior of the consumers.

cs.SI↗

arXiv2019

Generative Adversarial Networks for Real-time Stability of Inverter-based Systems

In islanded systems with droop-controlled sources, the droop coefficients need to be tuned in real-time using supervisory control to maintain asymptotic stability. In contrast to offline tuning methods, online domain-of-stability estimation yields non-conservative droop gains in real-time, ensuring good power sharing performance as the operating point varies. The challenge in the conventional online domain-of-stability estimation process is its unscalability and high computational complexity. In this paper, an efficient alternative using conditional Generative Adversarial Networks (cGANs) is described. We demonstrate that the notion of power system stability can be learned by such deep neural networks, and that they can offer a scalable alternative to conventional domain-of-stability estimation methods in islanded distribution systems. The implementation of cGANs-based stability assessment is described for an LV distribution test case and its advantages demonstrated.

eess.SY↗

arXiv2019

EV Charging Optimization based on Day-ahead Pricing Incorporating Consumer Behavior

With the increasing penetration of electric vehicles (EVs) into the automotive market, the electricity peak demand would increase significantly due to home-EV-charging. This paper tackles this problem by defining an 'ideal' EV consumption profile, from which a day-ahead pricing model is derived. Based on historical residential EV-use data ranging over a year, we demonstrate that the proposed optimization process results in a pricing profile that achieves a dual objective of minimizing the total electricity cost, as well as the peak aggregate system demand. Importantly, the proposed formulation is simple, and accounts for the tradeoff between consumer convenience in terms of the number of available charging slots during a day and the reduction in the total electricity cost. This technique is demonstrated to be scalable with respect to the size of the community whose EV charging demands are being optimized.

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