Optimization-based Settingless Algorithm Combining Protection and Fault Identification

The demand for faster protection algorithms is growing due to the increasingly faster dynamics in the system. The majority of existing algorithms require empirically selected set-points, which may reduce sensitivity to internal faults and cause security problems. This paper addresses these challenges by proposing a settingless time-domain unit protection algorithm for medium-voltage lines. The main idea of the algorithm is to identify which model of a protected line, i.e. healthy or with an internal fault, is more consistent with the input measurements. This is done by solving a number of small-scale convex optimization problems, which at the same time determine the characteristics of an internal fault that best fit the measurements. Thus, the proposed algorithm merges protection, fault location and fault type identification functionalities. The algorithm's performance is extensively tested on a grid model in MATLAB Simulink for different types of generation and grid operating conditions. The results demonstrate that the algorithm can operate quickly and reliably, and accurately estimate fault characteristics even in the presence of noisy measurements and uncertain line parameters

Optimization of Power System Operation: Approximations, Relaxations, and Decomposition

With higher penetration of renewable generation and market liberalization, operating points of electric power systems become increasingly variable and less predictable. To ensure economically efficient and secure operation of such systems, fast and robust optimization algorithms are required. Despite considerable research efforts, the development of these algorithms remains a challenge due to the nonlinearity and high dimensionality of system models. This dissertation focuses on the optimal power flow (OPF) problem, which is at the heart of techniques used in power system operation and planning. As this problem is non-convex and highly nonlinear, modern solvers cannot always find its locally optimal or even feasible point. To address this issue, an approximation of the OPF problem is proposed that helps reduce its complexity without compromising the solution quality. Moreover, the obtained solution is guaranteed to be physically meaningful. Next, this work presents several computationally efficient techniques for strengthening convex relaxations of the OPF problem. A tighter relaxation helps provide a better estimate of a globally optimal solution of the original problem and recover a physically meaningful operating point. Lastly, this work presents several approaches to incorporating risk-based security indices in the OPF problem. To reduce the computational burden of solving the resulting problems, decomposition algorithms are employed. The proposed techniques were tested on grids of various sizes. The results demonstrate that these techniques can potentially help improve optimization tools used in power system operation

Optimization-based Settingless Algorithm Combining Protection and Fault Identification

The current geometric designs of fish guidance structures with vertical bars for run-of-river hydropower plants result in high head losses and asymmetric turbine admission flow. To address these issues, we develop innovative curved bar designs and experimentally investigate different rack configurations with curved bars in a laboratory flume. The present paper (Part II) focuses on the hydraulic performance of the novel curved bar designs with regard to flow fields, while the companion paper (Part I) reports the results on the head losses. The detailed flow fields obtained by 3D velocity measurements show that the curved bars promote flow conditions favourable for both fish guidance and turbine operation. The flow straightening effect of the curved bars leads to quasi-symmetrical turbine admission flow and reduced head losses. The findings are discussed with regard to fish protection and guidance, and optimal engineering application.ISSN:0885-8977ISSN:1937-420

Comparison of Optimal Power Flow Formulations in Active Distribution Grids

Active Distribution Networks (DNs) are expected to host an increasing number of Distributed Generators (DGs) and other Distributed Energy Resources (DERs), offering new flexible sources and enabling the provision of ancillary services to system operators. Centralized DER controls that use Optimal Power Flow (OPF) methods necessitate tractable and scalable computational tools that can handle large DNs with satisfactory performance. In this paper, we compare an iterative OPF method against the standard exact AC OPF calculations in terms of the computational effort and solution quality. Furthermore, we highlight the suitability of the selected formulation to offer voltage support (VS) as an ancillary service to the transmission network. The results are demonstrated using a joint medium and low voltage grid, and show that tractable OPF formulations can unlock financial business cases for DNs that can actively participate in VS schemes

Expansion planning of active distribution networks achieving their dispatchability via energy storage systems

This paper presents a combined framework for power distribution network expansion planning (DNEP) and energy storage systems (ESSs) allocation in active distribution networks (ADNs) hosting large amount of photovoltaic (PV) generations and loads. The proposed DNEP ensures the reliable operation of the targeted ADN with the objective of achieving its dispatchability while minimizing grid losses by determining the optimal grid expansion to connect new nodes, the reinforcement of existing lines, and the ESS allocation. The allocated ESSs compensate for the stochastic power flows caused by the stochastic loads and generation, allowing ADNs to follow a pre-defined power schedule at the grid connection point. The grid constraints are modeled by using a modified augmented relaxed optimal power flow (AR-OPF) model that convexifies the classical AC-OPF providing the global optimal and the exact solution of the OPF problem for radial networks. The DNEP problem’s complexity is handled by employing a sequential algorithm where new nodes are added one by one, following the priorities determined by the user. In each stage of the sequential planning, the Benders decomposition algorithm determines the optimal solution for investments and ADN operation iteratively. Moreover, the siting and sizing problems associated with the ESSs and line investment are solved separately to enhance the convergence speed. Simulations are conducted on a real 55-node Swiss ADN hosting sizeable stochastic photovoltaic generation

Improving Dynamic Performance of Low-Inertia Systems through Eigensensitivity Optimization

An increasing penetration of renewable generation has led to reduced levels of rotational inertia and damping in the power network. The consequences are higher vulnerability to disturbances and deterioration of the dynamic response of the system. To overcome these challenges, novel converter control schemes that provide virtual inertia and damping have been introduced, which raises the question of optimal distribution of such devices throughout the network. This paper presents a comprehensive framework for performance-based allocation of virtual inertia and damping to the converter-interfaced generators in a detailed low-inertia system. This is achieved through an iterative, eigensensitivity-based optimization algorithm that determines the optimal controller gains while simultaneously preserving small-signal stability and ensuring that the damping ratio and frequency response after disturbances are kept within acceptable limits. Two conceptually different problem formulations are presented and validated on a modified version of the well known Kundur's two-area system as well as a larger 59-bus South-East Australian network

Genetic and Phylogenetic Characterization of Tataguine and Witwatersrand Viruses and Other Orthobunyaviruses of the Anopheles A, Capim, Guamá, Koongol, Mapputta, Tete, and Turlock Serogroups

The family Bunyaviridae has more than 530 members that are distributed among five genera or remain to be classified. The genus Orthobunyavirus is the most diverse bunyaviral genus with more than 220 viruses that have been assigned to more than 18 serogroups based on serological cross-reactions and limited molecular-biological characterization. Sequence information for all three orthobunyaviral genome segments is only available for viruses belonging to the Bunyamwera, Bwamba/Pongola, California encephalitis, Gamboa, Group C, Mapputta, Nyando, and Simbu serogroups. Here we present coding-complete sequences for all three genome segments of 15 orthobunyaviruses belonging to the Anopheles A, Capim, Guamá, Kongool, Tete, and Turlock serogroups, and of two unclassified bunyaviruses previously not known to be orthobunyaviruses (Tataguine and Witwatersrand viruses). Using those sequence data, we established the most comprehensive phylogeny of the Orthobunyavirus genus to date, now covering 15 serogroups. Our results emphasize the high genetic diversity of orthobunyaviruses and reveal that the presence of the small nonstructural protein (NSs)-encoding open reading frame is not as common in orthobunyavirus genomes as previously thought

Physical Vapor Deposition Features of Ultrathin Nanocrystals of Bi2(TexSe1- x)3

Structural and electronic properties of ultrathin nanocrystals of chalcogenide Bi2(TexSe1-x)3were studied. The nanocrystals were formed from the parent compound Bi2Te2Se on as-grown and thermally oxidized Si(100) substrates using Ar-assisted physical vapor deposition, resulting in well-faceted single crystals several quintuple layers thick and a few hundreds nanometers large. The chemical composition and structure of the nanocrystals were analyzed by energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, electron backscattering, and X-ray diffraction. The electron transport through nanocrystals connected to superconducting Nb electrodes demonstrated Josephson behavior, with the predominance of the topological channels [ Stolyarov et al. Commun. Mater., 2020, 1, 38 ]. The present paper focuses on the effect of the growth conditions on the morphology, structural, and electronic properties of nanocrystals

New insights into synthesis of nanocrystalline hexagonal BN

Uncovering the mechanism behind nanocrystalline hexagonal boron nitride (h-BN) formation at relatively low temperatures is of great scientific and practical interest. Herein, the sequence of phase transformations occurring during the interaction of boric acid with ammonia in a temperature range of 25-1000 °C has been studied in detail by means of thermo-gravimetric analysis, X-ray diffraction, infrared spectroscopy, X-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. The results indicate that at room temperature boric acid reacts with ammonia to form an ammonium borate hydrate (NH4)2B4O7x4H2O. Its interaction with ammonia upon further heating at 550 °C for 1 h leads to the formation of turbostratic BN. Nanocrystalline h-BN is obtained either during heating in ammonia at 550 °C for 24 h or at 1000 °C for 1 h. This result is important for the development of novel cost-effective and scalable syntheses of h-BN nanostructures, such as nanosheets, nanoparticles, nanofibers, and nanofilms, as well as for sintering h-BN ceramic materials