Coordinate Grid Research Articles

Анализ изображений графиков зависимостей физических величин

Physical effects used at the conceptual design stage, described in “primary” sources of information, such as patents, often contain images of dependency graphs linking physical input and output quantities. Analysis of this information and its use to expand the description of a physical effect is a relevant task. The development of a method for analyzing graphic images for classifying dependency graphs of input and output physical quantities is described. This requires forming a labeled array of dependency graphs, as well as conducting computational experiments to identify the most effective architectures of neural network models. Algorithms for segmenting images of dependency graphs have been developed, allowing one to get rid of noisy (for the classification task) parts of the figure, such as coordinate axes, their designations, coordinate grids, etc.), the effectiveness of the OpenCV and scikit-image libraries has been tested on solving this problem. The formed labeled array contains more than 26 thousand images of dependency graphs. An algorithm for clustering images of dependency graphs by 9 classes (concave increase, concave decrease, convex increase, convex decrease, linear increase, linear decrease, constancy, jump increase, jump-like decrease) has been developed and implemented in software. Based on the results of the work, it can be concluded that all 3 methods of image clustering (LSTM, CNN and ViT) show almost the same results on the test dataset: Accuracy, Precision, Recall, F1-Score, AUC-ROC – 98%. At the same time, on arbitrary images from the patent array, the accuracy of the analysis decreases: for the LSTM and ViT methods by about 10%, and for CNN by about 2%.

Impact of accretor size on the morphology of supersonic, non-relativistic, axisymmetric Bondi-Hoyle-Lyttleton accretion flows

Fast-moving accretors are a ubiquitous phenomenon in astrophysics. Their interaction with the surrounding gas can leave characteristic imprints on the form of morphological structures like bow shocks, Mach cones, and trails with different densities. We study how various physical processes affect the flow structure around an accretor with a one-way surface, its accretion rate, and accretion anisotropy. These processes correspond to distinct length scales: the Bondi radius, the stand-off distance of the bow shock, and the Hoyle-Lyttleton radius. We conducted adiabatic hydrodynamic simulations using a spherical coordinate grid centred on the accretor's location. By varying the accretor's (numerical) size across various scales — from much smaller than the stand-off distance to much larger than the Bondi radius — we analyse how the processes on these spatial scales affect the physics of the steady-state flow. All simulations achieve a steady state. When the accretor is smaller than the stand-off distance, a bow shock forms ahead of the object, and a nearly spherically symmetric atmosphere develops within this distance. Accretors smaller than the Hoyle-Lyttleton radius produce a Mach cone, while larger accretors exhibit a supersonic-to-subsonic flow transition on larger scales. Fully resolved simulations align with the Hoyle-Lyttleton theory, showing slightly anisotropic accretion with enhanced inflow from behind the object. In contrast, larger accretors approach the geometrical limit, with accretion primarily from the flow direction and a low-density `shadow' forming behind the object. The accretor's size greatly influences the small-scale and large-scale morphologies. Resolving the Hoyle-Lyttleton radius is essential for representing large-scale flow characteristics. Resolving the smaller stand-off distance is required only for studying the bow shock in front of the moving object: since the stand-off distance determines the bow shock's position, its non-resolution does not affect the larger-scale flow morphology.

Blockchain Research and Development Activities Sponsored by the U.S. Department of Energy and Utility Sector

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research.

A Two-Stage Resilience Enhancement Strategy for Distribution Networks Considering Extreme Weather Conditions

Background: In the context of increasing extreme weather events and natural disasters, enhancing the resilience and rapid recovery capabilities of distribution networks has become a critical focus in power system research. Objective: Previous studies primarily focused on the post-disaster power restoration phase of distribution networks affected by extreme weather without fully addressing the critical role of mobile energy storage in pre-disaster prevention. To bridge this gap, this paper proposes a two-stage resilience enhancement strategy for distribution networks, incorporating multi-source coordination and network reconfiguration. Methods: This paper presents a two-stage resilience enhancement strategy for distribution networks under extreme weather conditions. This strategy combines the coordinated optimization of various DERs with network reconfiguration. In the pre-disaster stage, optimization is conducted to manage uncertainties in wind and photovoltaic output. A pre-deployment approach for energy storage is introduced, incorporating the coordination of the power grid and transportation network to maximize expected load power recovery and minimize node voltage deviation. In the post-disaster stage, the strategy integrates distributed resources such as photovoltaic systems, wind power, and mobile energy storage. It uses a multi-source coordinated dynamic scheduling method to optimize spatiotemporal energy distribution. Results: The results show that this approach significantly improves post-disaster recovery efficiency. It reduces load shedding and enhances the system's adaptability and disaster resistance. Conclusion: The proposed approach enhances the overall resilience and recovery capacity of the distribution network. This patented technology will be applied in the future.

Poly[di-aqua-[μ2-1,4-bis-(pyridin-3-ylmeth-yl)piperazine][μ2-4-(2-carboxyl-atoeth-yl)benzoato]cobalt(II)

A layered cobalt coordination polymer containing both 4-(2-carboxyl-atoeth-yl)benzoate (ceb) and 1,4-bis-(3-pyridyl-meth-yl)piperazine (3-bpmp) ligands, [Co(C10H8O4)(C16H20N4)(H2O)2] n or [Co(ceb)(3-bpmp)(H2O)2] n , was isolated and structurally characterized by single-crystal X-ray diffraction. Chain-like [Co(ceb)(H2O)2] n units are oriented parallel to [101]. These are connected into (4,4)-grid coordination polymer layers by tethering 3-bpmp ligands. The layer motifs stack in an AAA pattern mediated by O-H⋯N hydrogen-bonding inter-actions between the aqua ligands in one layer and 3-bpmp piperazinyl N atoms in the abutting layer.

Clustering moves: Spatial network analysis in residential mobility

In the past sixty years, network analysis has become a valuable analytic tool in the study of interpersonal relationships and also in the construction of spatial relationships within urban geographies. Researchers have applied network analysis in a wide array of spatial applications; however, the power of network analysis has yet to be applied to understanding patterns in residential mobility. Residential movement patterns take place over longer time scales and potentially greater distances than daily journeys or commutes, but equally create networks connecting places and people. This research demonstrates how applying network analysis to residential moves may lend insight to open questions in the study of residential mobility, using fine-grained data on residential moves held by the city of Zurich, Switzerland. By creating a weighted directed network linking locations on a 400 meter coordinate grid with residents moving between them, the work demonstrates that network analysis can reveal geographies that overwrite the political boundaries commonly used to categorize residential spaces in the city. The new geographies produced by this method capture a wholistic picture of moving behaviour independent of reported residential choices. By tracking completed moves rather than stated aims, the uncovered mobility geographies sidestep the common categorisation of voluntary versus structurally determined residential choices, aggregating accumulated choices to demonstrate some of the geographic specificities of residential moving behaviour.

Pattern-based Remote Switch for Domestic and Technological Operations

Abstract: This research explores the development of a remote-control system for domestic appliances using user-drawn patterns on a grid, aiming to provide an intuitive and accessible control method. The system is designed around a 3x3 grid of infrared (IR) LEDs and receivers that detect patterns drawn by the user with a finger or stylus. Each grid coordinate is mapped to a specific action, such as turning lights on or off or adjusting the speed of a fan. The detected patterns are transmitted to a NodeMCU ESP8266 microcontroller, which decodes the patterns and activates the corresponding appliance through Wi-Fi commands and relays. The innovative aspects of this system include its simple and intuitive interaction method, allowing users to define custom patterns for various actions, which enhances personalized control. The pattern-based interaction is particularly beneficial for individuals with limited dexterity or mobility, offering an accessible alternative to traditional remote controls. This project demonstrates the potential of combining hardware components, such as IR LEDs and receivers, with embedded C programming to create a user-friendly and efficient home automation solution. The proposed system not only simplifies the user experience but also provides a flexible and customizable interface for controlling domestic appliances.

Геоинформационная система глазного дна

Purpose. To develo fundus operational geoinformation system for accurate topographic identification of the pathological process characteristics. Material and methods. An operational digital fundus geoinformation system was developed using available program «Transporter Online». A digital fundus photograph of the patient with active retinopathy of prematurity (ROP), obtained with a «RetCam» retinal camera, was placed in a cartesian field. Next, taking the center of the optic nerve head (ONH) as the zero point, we built parallels with a step equal to one optic disc radius and meridians with a step of 10 degrees by analogy with the TABO scale (Technische Ausschuss fur Brillen – Optik). Results. As a result, a geoinformation system of the fundus was built in the form of a coordinate grid comparable to a geographic coordinate system. Using the developed system, it is possible to measure many parameters on the retina. As the developed system is used, the number and information content of the parameters will undergo changes depending on the assigned tasks. It is possible to use the fundus geoinformation system for any retinal pathology, in particular for active ROP. To eliminate the influence of the scale factor and the reliability of quantitative indicators of image magnification, digitization is carried out based on the results of measuring angles and linear dimensions in «units» of the optic disc radius, which determines the individuality and originality of the proposed system. Conclusion. The proposed fundus geoinformation system will provide accurate topographic identification of the pathological process characteristics on the retina surface. The obtained coordinates of the fundus structures, which are of research interest, will form an operational navigation system that will significantly facilitate the work of ophthalmologists in transmitting information, analyzing the dynamics and outcomes of the pathological process, and can also be as the basis for a further transition to artificial intelligence use. Key words: retina, fundus, topography, geoinformation system

Design and analysis of a new feature optimisation method for circuit breakers defect identification

AbstractThe vibration signals of a circuit breaker (CB) contain important action timing information. The optimisation of features extraction for vibration signals generated during the operation process of CBs is crucial for rapid defect location and identification for CB. The authors propose a new feature optimisation method, defined as energy trajectory entropy of vibration signals via tracing the action characteristics of the main shaft of CBs. Firstly, an image tracking algorithm is employed to dynamically capture the key frames of the high‐speed image sequence of the main shaft and accurately divide the action sequence. The “cluster” instantaneous energy waveforms of the vibration signal, divided by zones, are then diffused by the twiddle factor in the polar coordinate sub grid area, while the energy trajectory entropy algorithm (ETE) is utilised to investigate the subtle changes in the energy release process. The ETE scale parameters are optimised using a support vector machine identification model. This enables rapid location of defective components in CBs, resulting in a significant reduction in time cost. The experiment has confirmed the strong feature correlation between CBs action and vibration signals, offering new ideas for achieving non‐invasive defect identification of CBs.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Data Mining Virtual Contaminated Sites to Develop an Educational Learning Tool supported by a k-NN Machine Learning Predictive Algorithm for use in Environmental Engineering Education

Recent work by Mumford et al. used high-resolution numerical simulations of contaminated sites to evaluate the state of practice for contaminated site investigation1. These simulations were originally developed in collaboration with academic and industry partners, including the U.S. Department of Defense's Environmental Security Technology Certification Program (ESTCP), as a training tool for environmental monitoring and performance optimization. This project focuses on the development of an algorithm to determine available borehole information in those simulations based on user-input, to help leverage this work to create an educational tool. The algorithm begins by validating user-specified coordinates to ensure they fall within an acceptable range. Leveraging principles from linear algebra and a grid-based mapping system, it identifies the nearest existing borehole on a predefined coordinate grid. To optimize efficiency, the algorithm utilizes a 2-D array to store and retrieve coordinate data. It calculates distances from the input coordinates to all available borehole locations on the grid, to identify the shortest distance and select the closest borehole, enabling students to map contamination within virtual sites. In addition to boreholes, the application incorporates data for Membrane Interface Probes (MIPs), monitoring wells, groundwater samples, and soil samples that are used to investigate contaminated sites, providing a comprehensive tool for training environmental professionals. The k-nearest neighbors (k-NN) algorithm predicts Membrane Interface Probe (MIP) Photoionization Detector (ECD) readings at future locations, which indicate chlorinated contaminant levels. Two models were evaluated: one using spatial data and ECD values, and another incorporating electrical conductivity (EC) and hydraulic conductivity (K), which are also measured by MIP. By making use of additional parameters, variations in soil properties were better represented, leading to more accurate predictions. Through iterative testing and refinement, the tool’s accuracy and user-interface have been enhanced, ensuring robust reliability for classroom applications. These tools are slated for integration into 4th-year subsurface contamination courses at Queen's University, Carleton, and the University of Iowa. By automating borehole selection, instructors can interact more with students and dedicate less time to data processing tasks. The k-NN application will enable the use of machine learning for site assessment to optimize MIP instrument placement to reduce bias. Assistance from Cole Van De Ven (Carleton University) and Jessica Meyer (University of Iowa) in collecting virtual contaminated site data is gratefully acknowledged.

WITHDRAWN: Short-term Load Forecasting Method Based on LSTM Optimized by the Hybrid Dung Bettle Optimization Algorithm

Forecasting electricity load is a crucial responsibility for the power system. Precise short-term power load forecasting (STLF) is a valuable tool for regional production planning, energy conservation, emission reduction, and grid coordination and dispatch. This study suggests a model for STLF that makes use of an optimized Long and Short-Term Memory (LSTM) neural network through the Hybrid Dung Beetle Optimization algorithm (HDBO-LSTM). Firstly, the Hybrid Dung Beetle Optimization (HDBO) algorithm is used to optimize the LSTM network's hyperparameters to address the problem that the LSTM network's random hyperparameter selection will materially influence the accuracy of load forecasting. Secondly, to address the problems that the original Dung Beetle Optimization (DBO) algorithm has poor global exploration ability and is easy to falls into local optimization, multiple improvement strategies are proposed to enhance the DBO algorithm, and the HDBO algorithm, which has a stronger searching ability and faster convergence speed, is proposed. By comparing with several algorithms on 10 benchmark functions, HDBO shows better search performance. Finally, the HDBO-LSTM load forecasting model is developed and evaluated using the Electrotechnical Mathematical Modeling Competition's power load dataset. The outcomes show that HDBO-LSTM outperforms the other comparison models in terms of accuracy and predicting efficacy.

The technique for approximation of three-dimensional surfaces in order to synthesize algorithms for preventing aircraft collisions with obstacles

The work marks the beginning of the practical application of algorithms for making the aircraft recovery maneuver from three-dimensional constraint surfaces, which are a combination of terrain and artificial obstacles. An analysis of events leading to aviation accidents was carried out, and a comparison of on-board systems for preliminary notification of aircraft crews about collisions with natural or artificial obstacles was made. It is shown that such systems are insufficient due to their passive-recommendatory nature of issuing warnings. A question has been raised about the need to implement an active automatic collision avoidance system with spatial obstacles. In order to apply existing algorithms for the aircraft recovery maneuver from spatial constraint surfaces, a technique has been developed for approximating three-dimensional surfaces (obstacles) specified on a digital terrain map in the form of discrete height readings with a certain step on a coordinate grid. A paraboloid of revolution was chosen as a continuous 2nd order surface approximating the obstacle, and its characteristic parameters were determined. To determine the characteristic parameters of the paraboloid, an algorithm for determining the intersection of a three-dimensional surface and a plane, based on the principle of determining the intersection of triangles in space, as well as a method for selecting the inflection point of the terrain, based on determining the value of the terrain height gradient, are proposed for use. The construction of an approximating paraboloid using the example of a natural obstacle in the form of a mountain range is given. When synthesizing algorithms for preventing collisions of aircraft with obstacles, the need to take into account not only the parameters of the constraint surfaces and dynamic characteristics of aircraft, but also the accuracy characteristics of data sources about their position is noted. Promising application areas of the developed methodology are shown.

A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called “grid–coordinate” based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process.

MODELING THE DYNAMICS OF DEFORMABLE OBJECTS BASED ON VOLUMETRIC PATCHES OF FREE FORMS

A method for solving nonlinear implicit numerical integration problems based on volumetric patches of free forms for modeling deformable objects with high-speed dynamics is proposed. This method improves the accuracy, consistency and controllability of deformation modeling and animation. The method is characterized by speed, accuracy, stability and uses optimization with region decomposition. The method is well suited for modeling deformable bodies with a large time step, in a wide range of deformation dynamics. We propose decomposed optimization, an optimization method based on free-form patches with region decomposition to minimize incremental potentials at each time step. The method uses quadratic matrix decomposition to combine non-overlapping subregions. The Hessian is evaluated once at the beginning of the time step. The advantages of the method are as follows. Geometric primitives and their mathematical models are proposed, which allow the reasonable application of these primitives to solve problems of volume-oriented modeling. Such requirements are met by volumetric patches of free forms based on analytical perturbation functions relative to the base triangles. The decomposed Hessian is constructed for each subregion and calculated using a set of vertices taken from a complete non-decomposed grid. The weights add the missing second-order Hessian data to the vertices of the subregions from the neighbors along the decomposition boundaries. Thanks to this, the descent is performed along the grid coordinates. There is no need to add gradients. During the descent, the gradient is determined. The Hessians under the region are calculated and factorized in parallel once per time step. They are used as an initializer at each iteration. Then the results are mixed together. This ensures stable and continuous high-quality modeling. An automated and reliable optimization method adapted for modeling nonlinear materials, high-speed dynamics and large deformations is proposed.

Coordination of SRF-PLL and Grid Forming Inverter Control in Microgrid with Solar PV and Energy Storage

Recently, there has been a huge advancement in renewable energy integration in power systems. Power converters with grid-forming or grid-following topologies are typically employed to link these decentralized power sources to the grid. However, because distributed generation has less inertia than synchronous generators, their use of renewable energy sources threatens the electrical grid’s reliability. Suitable control approaches for ensuring frequency and voltage stability in the grid-connected form of operation are established in this study, which offers dynamic, seamless power switching in the islanded mode of operation. In this research, effective Phase Locked Loop (PLL) techniques for grid-forming (GFM) and grid-following (GFL) converters are designed to achieve a smooth transition from grid-tied to islanded mode of operation. In this work, PLL configurations are implemented while considering the active and reactive power, frequency, voltage, and current parameters of the system, and ensuring voltage and frequency stability. The simulation results in a microgrid network that ensures a smooth transition of power transfer while switching between modes of operation, and supports the voltage and frequency stability of the system.

An Empirical Study on Wind-solar Energy Reserve in Inner Mongolia in the Context of Carbon Neutrality

This study explores the empirical research of wind and solar energy reserves in the Inner Mongolia region within the broader context of carbon neutrality. Through detailed surveys and data analysis of wind and solar energy resources, we comprehensively assess their potential, reserves, and sustainability. Our findings reveal Inner Mongolia’s abundant wind and solar resources, pivotal in advancing carbon neutrality strategies. However, overcoming challenges such as cost, efficiency, sustainability, and grid coordination of energy storage technologies is imperative to fully leverage this potential. Therefore, this paper proposes suggestions for improvement, including technological innovation, planning optimization, and energy coordination measures. These recommendations aim to address current challenges and enhance the utilization of wind and solar resources in Inner Mongolia, providing valuable insights for future energy planning and policy formulation. This study is a guiding framework for promoting sustainable energy development in the region.

Quantum algorithm for the Vlasov simulation of the large-scale structure formation with massive neutrinos

Investigating the cosmological implication of the fact that neutrino has finite mass is of importance for fundamental physics. In particular, massive neutrino affects the formation of the large-scale structure (LSS) of the universe, and, conversely, observations of the LSS can give constraints on the neutrino mass. Numerical simulations of the LSS formation including massive neutrino along with conventional cold dark matter is thus an important task. For this, calculating the neutrino distribution in the phase space by solving the Vlasov equation is a suitable approach, but it requires solving the PDE in the (6+1)-dimensional space and is thus computationally demanding: Configuring ngr grid points in each coordinate and nt time grid points leads to O(ngr6) memory space and O(ntngr6) queries to the coefficients in the discretized PDE. We propose a quantum algorithm for this task. Linearizing the Vlasov equation by neglecting the relatively weak self-gravity of the neutrino, we perform the Hamiltonian simulation to produce quantum states that encode the phase-space distribution of neutrino. We also propose a way to extract the power spectrum of the neutrino density perturbations as classical data from the quantum state by quantum amplitude estimation with accuracy ε and query complexity of order Õ[(ngr+nt)/ε]. Our method also reduces the space complexity to O[polylog(ngr/ε)] in terms of the qubit number, while using quantum random access memories with O(ngr3) entries. As far as we know, this is the first quantum algorithm for the LSS simulation that outputs the quantity of practical interest with guaranteed accuracy. Published by the American Physical Society 2024

A calculative cosmography: Geodesy and absolute space

Visual signifiers of absolute space such as grids, points, and straight lines are often identified with absolute space as a concept, to the extent that critiques of one are taken to apply to the other. This article argues that this is a misidentification that forecloses attention to meaningful dimensions of the coordinate grid while muddling the concept of absolute space. Attending to the geodetic science that gives rise to these visual signifiers shows that the space they are used to construct is entirely relational, and that the mythless rationalism with which they and absolute space are commonly associated can be productively questioned through attention to geodesy’s performative and cosmological implications. This raises the question of how we might better understand absolute space. The suggested answer is a cosmological claim that can be pursued and pictured but never realized. Absolute space is absolute as a sovereign can be: neither empirically nor visually, but as a motivating idea that presents itself as an explanation.

Hybrid Wind-Solar System with UPQC

The hybrid wind-solar system is a combination of renewable energy sources, specifically wind and solar, that is utilized for power generation. While wind power has historically been used for various purposes such as sails, windmills, and wind pumps, it is now predominantly used for electricity generation. This system incorporates solar panels and small wind turbine generators to produce electricity. The advantage of hybrid systems is that they can generate electricity as needed, taking into account the varying peak working hours of solar and wind systems throughout the year. To ensure a stable power system, UPQC (Unified Power Quality Conditioner) is employed. This multipurpose power conditioner serves multiple functions, including preventing harmonic load current from entering the power system, rectifying voltage fluctuations, and compensating for voltage disturbances from different power sources. It is specifically designed to minimize interference with sensitive or critical loads. With its series and shunt compensation capabilities, UPQC effectively manages power flow, reactive power, harmonics, and voltage disturbances. In this paper, the work addresses the issue of electricity quality that arises from the installation of wind turbines in the grid. To get over this issue, the suggested course of action demonstrates how to install a BESS and a unified power quality conditioner (UPQC) at the connection point. Even though wind power fluctuates, the use of battery energy storage technology helps to provide a constant power supply. With the power system block established in MATLAB/SIMULINK, one can simulate the UPQC control method of a grid-connected wind energy generating plant. As a result, the power's quality will increase. When the recommended approach lowers the load's and the induction generator's reactive power requirements, it is successful. Furthermore, the plan for raising the grid's power quality criteria and developing the grid coordination rule are also provided.