arXiv subjects

Vladimir Frolov

Publications and source records attributed to Vladimir Frolov.

6 recordsLinked to original sources

arXiv2024

CrossRT: A cross platform programming technology for hardware-accelerated ray tracing in CG and CV applications

We propose a programming technology that bridges cross-platform compatibility and hardware acceleration in ray tracing applications. Our methodology enables developers to define algorithms while our translator manages implementation specifics for different hardware or APIs. Features include: generating hardware-accelerated code from hardware-agnostic, object-oriented C++ algorithm descriptions; enabling users to define software fallbacks for non-hardware-accelerated CPUs and GPUs; producing GPU programming API-based algorithm implementations resembling manually ported C++ versions. The generated code is editable and readable, allowing for additional hardware acceleration. Our translator supports single megakernel and multiple kernel path tracing implementations without altering the programming model or input source code. Wavefront mode is crucial for NeRF and SDF, ensuring efficient evaluation with multiple kernels. Validation on tasks such as BVH tree build/traversal, ray-surface intersection for SDF, ray-volume intersection for 3D Gaussian Splatting, and complex Path Tracing models showed comparable performance levels to expert-written implementations for GPUs. Our technology outperformed existing Path Tracing implementations.

cs.GR↗

arXiv2023

Single-view 3D reconstruction via inverse procedural modeling

We propose an approach to 3D reconstruction via inverse procedural modeling and investigate two variants of this approach. The first option consists in the fitting set of input parameters using a genetic algorithm. We demonstrate the results of our work on tree models, complex objects, with the reconstruction of which most existing methods cannot handle. The second option allows us to significantly improve the precision by using gradients within memetic algorithm, differentiable rendering and also differentiable procedural generators. In our work we see 2 main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us an opportunity to reconstruct 3D model more accurately than existing approaches when a small number of input images are available (even for single image). Second, we join both differentiable and non-differentiable procedural generators in a single framework which allow us to apply inverse procedural modeling to fairly complex generators: when gradient is available, reconstructions is precise, when gradient is not available, reconstruction is approximate, but always high quality without visual artifacts.

cs.GR↗

arXiv2019

Operations- and Uncertainty-Aware Installation of FACTS Devices in a Large Transmission System

Decentralized electricity markets and more integration of renewables demand expansion of the existing transmission infrastructure to accommodate inflected variabilities in power flows. However, such expansion is severely limited in many countries because of political and environmental issues. Furthermore, high renewables integration requires additional reactive power support, which forces the transmission system operators to utilize the existing grid creatively, e.g., take advantage of new technologies, such as flexible alternating current transmission system (FACTS) devices. We formulate, analyze and solve the challenging investment planning problem of installation in an existing large-scale transmission grid multiple FACTS devices of two types (series capacitors and static VAR compensators.) We account for details of AC character of the power flows, probabilistic modeling of multiple-load scenarios, FACTS devices flexibility in terms of their adjustments within the capacity constraints, and long term practical tradeoffs between capital vs operational expenditures (CAPEX vs OPEX). It is demonstrated that proper installation of the devices allows to do both - extend or improve feasibility domain for the system and also decrease long term power generation cost (make cheaper generation available). Nonlinear, nonconvex, and multiple-scenario-aware optimization is resolved through an efficient heuristic algorithm consisting of a sequence of quadratic programmings solved by CPLEX combined with exact AC PF resolution for each scenario for maintaining feasible operational states during iterations. Efficiency and scalability of the approach is illustrated on the IEEE 30-bus model and the 2736-bus Polish model from Matpower.

math.OC↗

arXiv2019

Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization

Many practical planning and operational applications in power systems require simultaneous consideration of a large number of operating conditions or Multi-Scenario AC-Optimal Power Flow (MS-AC-OPF) solution. However, when the number of exogenously prescribed conditions is large, solving the problem as a collection of single AC-OPFs may be time-consuming or simply intractable computationally. In this paper, we suggest a model reduction approach, coined Cloud-AC-OPF, which replaces a collection of samples by their compact representation in terms of mean and standard deviation. Instead of determining an optimal generation dispatch for each sample individually, we parametrize the generation dispatch as an affine function. The Cloud-AC-OPF is mathematically similar to a generalized Chance-Constrained AC-OPF (CC-AC-OPF) of the type recently discussed in the literature, but conceptually different as it discusses applications to long-term planning. We further propose a tractable formulation and implementation, and illustrate our construction on the example of 30-bus IEEE model.

math.OC↗

arXiv2017

Methodology for Multi-stage, Operations- and Uncertainty-Aware Placement and Sizing of FACTS Devices in a Large Power Transmission System

We develop new optimization methodology for planning installation of Flexible Alternating Current Transmission System (FACTS) devices of the parallel and shunt types into large power transmission systems, which allows to delay or avoid installations of generally much more expensive power lines. Methodology takes as an input projected economic development, expressed through a paced growth of the system loads, as well as uncertainties, expressed through multiple scenarios of the growth. We price new devices according to their capacities. Installation cost contributes to the optimization objective in combination with the cost of operations integrated over time and averaged over the scenarios. The multi-stage (-time-frame) optimization aims to achieve a gradual distribution of new resources in space and time. Constraints on the investment budget, or equivalently constraint on building capacity, is introduced at each time frame. Our approach adjusts operationally not only newly installed FACTS devices but also other already existing flexible degrees of freedom. This complex optimization problem is stated using the most general AC Power Flows. Non-linear, non-convex, multiple-scenario and multi-time-frame optimization is resolved via efficient heuristics, consisting of a sequence of alternating Linear Programmings or Quadratic Programmings (depending on the generation cost) and AC-PF solution steps designed to maintain operational feasibility for all scenarios. Computational scalability and application of the newly developed approach is illustrated on the example of the 2736-nodes large Polish system. One most important advantage of the framework is that the optimal capacity of FACTS is build up gradually at each time frame in a limited number of locations, thus allowing to prepare the system better for possible congestion due to future economic and other uncertainties.

cs.CE↗

arXiv2013

Reinforcing Power Grid Transmission with FACTS Devices

We explore optimization methods for planning the placement, sizing and operations of Flexible Alternating Current Transmission System (FACTS) devices installed into the grid to relieve congestion created by load growth or fluctuations of intermittent renewable generation. We limit our selection of FACTS devices to those that can be represented by modification of the inductance of the transmission lines. Our master optimization problem minimizes the $l_1$ norm of the FACTS-associated inductance correction subject to constraints enforcing that no line of the system exceeds its thermal limit. We develop off-line heuristics that reduce this non-convex optimization to a succession of Linear Programs (LP) where at each step the constraints are linearized analytically around the current operating point. The algorithm is accelerated further with a version of the cutting plane method greatly reducing the number of active constraints during the optimization, while checking feasibility of the non-active constraints post-factum. This hybrid algorithm solves a typical single-contingency problem over the MathPower Polish Grid model (3299 lines and 2746 nodes) in 40 seconds per iteration on a standard laptop---a speed up that allows the sizing and placement of a family of FACTS devices to correct a large set of anticipated contingencies. From testing of multiple examples, we observe that our algorithm finds feasible solutions that are always sparse, i.e., FACTS devices are placed on only a few lines. The optimal FACTS are not always placed on the originally congested lines, however typically the correction(s) is made at line(s) positioned in a relative proximity of the overload line(s).

math.OC↗