Spatial-Temporal Graph Learning (ST-GL) is a prominent research area due to its unique capability to effectively learn real-world graphs. Applications of ST-GL pose stringent and various demands on not only real-time inference with low energy cost and high accuracy but also fast training. Unfortunately, as Moore's Law approaches its limits and ST-GL model complexity drastically grows, the gap between digital hardware's computational power and STGL application demands is widening. In response, this paper introduces Nature-GL, a nature-powered graph learning paradigm that exploits the principle of entropy increase to advance graph learning. In particular, Nature-GL transforms both the training and inference of real-valued ST-GL into electron-speed natural annealing processes of a parameterized dynamical system that represents the target graphs. Experimental results across four real-world applications with six datasets demonstrate that Nature-GL achieves orders-of-magnitude speedups in both training and inference, delivering higher accuracy compared to Graph Neural Networks.

Time-Independent Differential Equations (TIDEs) are central to modeling equilibrium behavior across a wide range of scientific and engineering domains.Conventional numerical solvers offer reliable solutions but incur significant computational costs due to fine-grained discretization and iterative procedures.Machine learning-based approaches address this by replacing iterative solving with one-time inference; however, they inevitably sacrifice accuracy and incur even higher costs once the training of sophisticated models is taken into account.Consequently, designing a TIDE solver that achieves high accuracy, broad applicability, and exceptional computational efficiency remains a fundamental challenge.In this paper, we propose DS-TIDE, a novel hardware solver that is inspired by, and subsequently leverages, the intrinsic connection between Dynamical Systems (DS) and Differential Equations (DE) to efficiently and accurately solve TIDEs.DS-TIDE employs a CMOS-compatible DS-based processor, whose physical states evolve under carefully designed DE-driven dynamics and naturally converge to equilibria -the solutions of the target TIDE within ∼ 1𝜇𝑠.To enhance expressivity, DS-TIDE incorporates Heterogeneous Dynamics with Temporal Layering (HDTL), which solves TIDEs through a three-stage DS evolution -conditioning, solving, and decoding -each governed by specialized dynamics.The entire evolution process is analogous to an infinitely deep neural network temporally unrolled, offering the system the capability of representing complex equations.Furthermore, DS-TIDE is equipped

Gradual transformation of the synchronous generator (SG)-dominated power systems to inverter-based resource (IBR)-dominated power systems is bringing new challenges regarding power system protection. Traditional protection algorithms were originally developed for SG-dominated power systems and become less reliable in an IBR-dominated system. New protection schemes, compatible with IBR-dominated power systems, have been an ongoing topic of research. However, such research frequently omits fault-related controls or requirements that influence IBR's fault behavior significantly. This paper provides a study of the fault behavior of grid-following (GFL) and grid-forming (GFM) inverters considering fault-related controls and using electromagnetic transient (EMT) program. For both inverter models, EMT-based generic inverter models are used in this paper. Ride-through controls from IEEE-1547-2018 standards are included as necessary. In addition, the major functions of the generic models that effect fault behavior are explained. Then, the fault current contributions of the GFL and GFM inverters were simulated considering different controls and grid parameters using both a single-inverter-to-infinite-bus system and a modified IEEE 34-node system. The simulation results indicate that the fault current contribution of an inverter or an IBR-dominated system depends on the inverter's operating mode, whether the grid support function and momentary cessation is enabled or not, and fault location.

With the challenges of increased grid dynamics and more variability of power generation from renewable energy sources, rapidly increasing complexity in the grid model, and abundant data from measurements and simulations, the requirements for computational analysis have also increased dramatically. This paper proposes a novel big data analysis approach for power system cascading analysis, prevention, and remediation. The developed techniques will be capable of cascading analysis, better assessment of the systems vulnerability level, as well as proposing potential remediation. Case studies using IEEE 118- bus system and a 563-bus system, with comparisons against a commercial tool, validate the advantages of the developed big data approach: accurate prediction, and more importantly, faster and effective correction actions. The developed techniques could be further used for other power system applications.

In Energy Management Systems, contingency analysis is commonly performed for identifying and mitigating potentially harmful power grid component failures. The exponentially increasing combinatorial number of failure modes imposes a significant computational burden for massive contingency analysis. It is critical to select a limited set of high-impact contingency cases within the constraint of computing power and time requirements to make it possible for real-time power system vulnerability assessment. In this paper, we present a novel application of parallel betweenness centrality to power grid contingency selection. We cross-validate the proposed method using the model and data of the western US power grid, and implement it on a Cray XMT system - a massively multithreaded architecture - leveraging its advantages for parallel execution of irregular algorithms, such as graph analysis. We achieve a speedup of 55 times (on 64 processors) compared against the single-processor version of the same code running on the Cray XMT. We also compare an OpenMP-based version of the same code running on an HP Superdome shared-memory machine. The performance of the Cray XMT code shows better scalability and resource utilization, and shorter execution time for large-scale power grids. This proposed approach has been evaluated in PNNL's Electricity Infrastructure Operations Center (EIOC). It is expected to provide a quick and efficient solution to massive contingency selection problems to help power grid operators to identify and mitigate potential widespread cascading power grid failures in real time.

Operating the electrical power grid to prevent power black-outs is a complex task. An important aspect of this is contingency analysis, which involves understanding and mitigating potential failures in power grid elements such as transmission lines. When taking into account the potential for multiple simultaneous failures (known as the N-x contingency problem), contingency analysis becomes a massively computational task. In this paper we describe a novel hybrid computational approach to contingency analysis. This approach exploits the unique graph processing performance of the Cray XMT in conjunction with a conventional massively parallel compute cluster to identify likely simultaneous failures that could cause widespread cascading power failures that have massive economic and social impact on society. The approach has the potential to provide the first practical and scalable solution to the N-x contingency problem. When deployed in power grid operations, it will increase the grid operator’s ability to deal effectively with outages and failures with power grid components while preserving stable and safe operation of the grid. The paper describes the architecture of our solution and presents preliminary performance results that validate the efficacy of our approach.