Real-time Voltage Stability Monitoring and Control for Load Areas: A Hybrid Approach

This dissertation proposes a hybrid approach for real-time monitoring and controlling voltage stability of a load area fed by N tie lines. This hybrid approach integrates both simulation-based and measurement-based approaches for voltage stability assessment (VSA). First, for measurement-based VSA (MBVSA), a new method is proposed for monitoring and control of load areas, which adopts an N+1 buses equivalent system so as to model and monitor individual tie lines of a load area compared to a traditional MBVSA method adopting a Thevenin equivalent. For each tie line, the new method solves the power transfer limit against voltage instability analytically as a function of all parameters of that equivalent, which is online identified from real-time synchronized measurements on boundary buses of the load area. Thus, this new MBVSA method can directly calculate the real-time power transfer limit on each tie line. Second, in order to assess the voltage stability margins under an n-1 contingency, based on the proposed MBVSA method, two sensitivity analyses have been performed, which are respectively for the parameter sensitivity of the equivalent system and the sensitivity of the tie line flow under an n-1 contingency. Third, the proposed MBVSA method implemented for both the real-time condition and potential n-1 contingencies is integrated with the simulation-based VSA approach to form a hybrid approach. The MBVSA method helps reduce the computation burden by eliminating the unimportant contingencies while the simulation-based method provides accurate information for specific “what if” scenarios such as stability limit and margin indices under n-1 contingency conditions. In addition, simulation using the model of the system can provide recommendations for preventive control if potential voltage instability is identified. This proposed hybrid VSA approach has been validated on the NPCC (Northeast Power Coordinating Council) Large-scale Test Bed (LTB) system developed by the CURENT (Center for Ultra-Wide-Area Resilient Electric Energy Transmission Networks), and also implemented on the CURENT Hardware Test Bed (HTB) system. The effectiveness of the MBVSA in real-time monitoring and closed-loop control against voltage instability has been validated

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate Power-Voltage (P-V) curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximants method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P-V curve with moderate computational burden. The proposed method is illustrated in detail on a 4-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council (NPCC) 48-geneartor, 140-bus power system.Comment: Revised and Submitted to IEEE Transaction on Power System

1-(2-Fluoro­benzyl­ideneamino)pyridinium bis­(1,2-dicyano­ethene-1,2-dithiol­ato)nickelate(II)

In the title complex, (C12H10FN2)2[Ni(C4N2S2)2], the anion lies on an inversion center with the NiII ion coordinated by four S atoms in a slightly distorted square-planar environment. In the unique cation, the dihedral angle between the benzene and pyridine rings is 7.1 (2) Å

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

PreCaCycleGAN: Perceptual Capsule Cyclic Generative Adversarial Network for Industrial Defective Sample Augmentation

Machine vision is essential for intelligent industrial manufacturing driven by Industry 4.0, especially for surface defect detection of industrial products. However, this domain is facing sparse and imbalanced defect data and poor model generalization, affecting industrial efficiency and quality. We propose a perceptual capsule cycle generative adversarial network (PreCaCycleGAN) for industrial defect sample augmentation, generating realistic and diverse defect samples from defect-free real samples. PreCaCycleGAN enhances CycleGAN with a U-Net and DenseNet-based generator to improve defect feature propagation and reuse and adds a perceptual loss function and a capsule network to improve authenticity and semantic information of generated features, enabling richer and more realistic global and detailed features of defect samples. We experiment on ten datasets, splitting each dataset into training and testing sets to evaluate model generalization across datasets. We train three defect detection models (YOLOv5, SSD, and Faster-RCNN) with original data and augmented data from PreCaCycleGAN and other state-of-the-art methods, such as CycleGAN-TSS and Tree-CycleGAN, and validate them on different datasets. Results show that PreCaCycleGAN improves detection accuracy and rate and reduces the false detection rate of detection models compared to other methods on different datasets, demonstrating its robustness and generalization under various defect conditions