Grid Construction Research Articles

Ontology-Guided Generation of Mechanized Construction Plan for Power Grid Construction Project

Mechanized construction is being fully implemented in the electric power infrastructure domain to ensure construction safety, enhance project quality, and improve efficiency. Traditional methods of designing mechanized construction plans are often inefficient due to their labor-intensive processes and heavy reliance on human expertise. This study introduces and evaluates an ontology-guided system designed to automate mechanized construction planning for power grid projects. The developed ontology effectively models domain-specific knowledge, enabling the semantic integration of data from various sources. By leveraging SPARQL queries, the ontology-guided system incorporates knowledge reasoning capabilities that facilitate the automated selection of construction equipment and the generation of comprehensive construction plans. A prototype system incorporating an ontology-guided mechanism has been developed, showcasing marked enhancements in efficiency and accuracy over traditional manual methods, as evidenced by case studies and expert evaluations. The research results emphasize the potential of ontology-guided systems in innovating architectural planning, providing an extensible and standardized approach. Expert evaluation indicates that the system achieves 71.38% effectiveness in generating mechanized construction plans.

Research on the application of distributed smart grid protection technology scheme

Abstract Distributed smart grid is an organic carrier for achieving renewable energy substitution and promoting new energy consumption. It is an important component of the construction of new power systems. To meet the current operational needs of distributed smart grid construction, a distributed smart grid protection method based on AC/DC hybrid distribution network is proposed. The distributed smart grid protection and application method proposed in this article, has good promotion and application value.

Mobileception-ResNet for transient stability prediction of novel power systems

Power system transient stability prediction (TSP) is particularly important as power systems change and evolve, including the rapid growth of renewable energy, the proliferation of electric vehicles, and the construction of smart grids. Traditional time-domain simulation methods are time-consuming and cannot achieve online prediction. Direct methods are poorly adapted and cannot be applied to complex power systems. Existing machine learning algorithms only classify the transient stability without providing the degree of transient stability of the system. Therefore, a fast and accurate power system TSP method is needed to assist operators in implementing timely measures to improve the stability of the power system running. This study proposes a Mobileception-ResNet network, Mobileception-ResNet is formed by Inception-ResNet-v2, MobileNet-v2, and a fully connected layer. In this study, Mobileception-ResNet and nine comparison models are experimented on two node systems, i.e., the IEEE 10–39 and 69–300 systems. In the IEEE 10–39 system, the root mean square error, mean absolute error, and mean absolute percentage error of Mobileception-ResNet are 44.13 %, 36.74 %, and 39.96 % lower, and the coefficient of determination is 0.04 % higher, respectively, when compared to the comparative model with the best evaluation indicator; in the IEEE 69–300 system, the corresponding values are 2.6 %, 12.83 %, 12.55 %, and 0.01 %, respectively.

A Novel Areal Maintenance Strategy for Large-Scale Distributed Photovoltaic Maintenance

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency.

Construction of a photovoltaic low-voltage grid connected power regulation system based on partition control

Abstract Photovoltaic power generation is a green and renewable power generation mode and has been widely used. However, the power fluctuation of photovoltaic power generation has a significant impact on the power grid, which is very important. Based on the concept of regional control, this paper studies the photovoltaic low-voltage grid-connected power regulation system. The photovoltaic power generation system is divided into multiple regions, and each region is provided with an independent control unit and regulation strategy under the control of multiple control areas. The regional factors are combined, such as light intensity and power voltage. In the case of the city, the impact of the area on the whole is dynamically adjusted to achieve the overall balance of power and the stability of the system. The experimental results show that the photovoltaic low-voltage grid-connected power regulation system constructed by regional control performs well and has a high degree of stability. According to the change in light intensity and load demand, the system can achieve accurate control and distribution of photovoltaic power generation.

A Non-Uniform Grid Graph Convolutional Network for Sea Surface Temperature Prediction

Sea surface temperature (SST) is an important factor in the marine environment and has significant impacts on climate, ecology, and maritime activities. Most existing SST prediction methods consider the ocean as a uniform field and use a uniform grid to predict SST. However, the marine environment is a complex system, and factors such as solar radiation, differences in land and sea thermal properties, and ocean circulation lead to uneven spatial distributions of SSTs. We propose a non-uniform grid construction method based on an SST spatial gradient to encode SST data, as well as a Non-uniform Grid Graph Convolutional Network (NGGCN) model. The NGGCN consists of two spatiotemporal modules, each of which extracts spatial features from the GCN module, captures temporal correlations through the GRU module, and performs feature restoration and output results through the fully connected module. We selected data from the Yellow Sea and Bohai Sea to validate the effectiveness of the NGGCN in predicting SST at different time scales and prediction steps. The results indicate that our model shows a significant improvement in prediction performance compared to other models.

Tetrahedral grids in Monte Carlo radiative transfer

Context. To understand the structures of complex astrophysical objects, 3D numerical simulations of radiative transfer processes are invaluable. For Monte Carlo radiative transfer, the most common radiative transfer method in 3D, the design of a spatial grid is important and non-trivial. Common choices include hierarchical octree and unstructured Voronoi grids, each of which has advantages and limitations. Tetrahedral grids, commonly used in ray-tracing computer graphics, can be an interesting alternative option. Aims. We aim to investigate the possibilities, advantages, and limitations of tetrahedral grids in the context of Monte Carlo radiative transfer. In particular, we want to compare the performance of tetrahedral grids to other commonly used grid structures. Methods. We implemented a tetrahedral grid structure, based on the open-source library TetGen, in the generic Monte Carlo radiative transfer code SKIRT. Tetrahedral grids can be imported from external applications or they can be constructed and adaptively refined within SKIRT. We implemented an efficient grid traversal method based on Plücker coordinates and Plücker products. Results. The correct implementation of the tetrahedral grid construction and the grid traversal algorithm in SKIRT were validated using 2D radiative transfer benchmark problems. Using a simple 3D model, we compared the performance of tetrahedral, octree, and Voronoi grids. With a constant cell count, the octree grid outperforms the tetrahedral and Voronoi grids in terms of traversal speed, whereas the tetrahedral grid is poorer than the other grids in terms of grid quality. All told, we find that the performance of tetrahedral grids is relatively poor compared to octree and Voronoi grids. Conclusions. Although the adaptively constructed tetrahedral grids might not be favourable in most media representative of astrophysical simulation models, they still form an interesting unstructured alternative to Voronoi grids for specific applications. In particular, they might prove useful for radiative transfer post-processing of hydrodynamical simulations run on tetrahedral or unstructured grids.

English Translation of New Vocabulary for Text Analysis of New Energy Industry and International Evaluation of Power Quality

With the advancement of global energy transformation, the rapid rise of the new energy industry has become a strategic highland that countries are eager to seize. The inherent instability and unpredictability of renewable energy production pose significant obstacles to the seamless operation of the power system. To address these challenges, this article employs text analysis, data-driven methods, and artificial intelligence technology in neologisms to conduct an in-depth international evaluation and comparative research on the power quality of the new energy industry. Using text mining techniques to conduct an in-depth analysis of new energy policy texts in various countries, revealing their policy characteristics and trends. At the same time, the combination of data-driven and artificial intelligence technology enables more accurate prediction and decision analysis. Based on the findings of this study, the United States has achieved a significant 35% enhancement in the quality of new energy electricity over the past decade, attributed to its consistent monitoring and improvement initiatives. Meanwhile, Germany, owing to its robust monitoring system, has witnessed a 25% improvement in electricity quality in the last five years. However, in developing nations like India and Brazil, approximately 60% of new energy projects encounter challenges related to grid connectivity and scheduling, resulting in a deterioration of electricity quality. In contrast, only 20% of developed countries have issues with grid access and scheduling. This phenomenon reflects that developing countries still need to increase investment and improve the layout of new energy industries and the construction of power grid infrastructure. Developing countries should actively learn from the successful experiences of developed countries in the new energy industry and power grid construction, formulate appropriate policy measures based on their national conditions, improve the quality of new energy electricity, and promote energy transformation and sustainable development.

Enhancing grid hosting capacity with coordinated non-firm connections in industrial energy communities

A significant increase in grid connection requests from industrial customers has lead to long connection queues. Combined with long lead times on grid construction, the result is significant socioeconomic losses due to lack of grid capacity. Distribution system operators (DSO) have therefore introduced non-firm connections as an alternative, where the new grid customer may connect on the condition that the DSO retains the right to disconnect the grid customer if necessary. An option to potential disconnection is for customers to leverage flexibility to stay below an agreed capacity level. Unfortunately, many existing grid customers possess flexibility potential but lack incentives to utilise it. To address this, we propose a “coordinated non-firm connection”, where the new grid customer forms an energy community with existing grid customers to coordinate flexibility and capacity utilisation. To demonstrate technical feasibility and incentive compatibility, we formulate a complementarity model. Applying this model to a case study involving three industrial grid customers, we showcase both technical potential and incentive compatibility. Results illustrate how energy community members engage in capacity trading within a local market, ensuring adherence to grid limitations. The game theory-based model confirms sufficient economic benefit for all members to participate.

FVDB : A Deep-Learning Framework for Sparse, Large Scale, and High Performance Spatial Intelligence

We present f VDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. f VDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, attention, ray-tracing, meshing, etc. f VDB simultaneously provides a much larger feature set (primitives and operators) than established frameworks with no loss in efficiency: our operators match or exceed the performance of other frameworks with narrower scope. Furthermore, f VDB can process datasets with much larger footprint and spatial resolution than prior works, while providing a competitive memory footprint on small inputs. To achieve this combination of versatility and performance, f VDB relies on a single novel VDB index grid acceleration structure paired with several key innovations including GPU accelerated sparse grid construction, convolution using tensorcores, fast ray tracing kernels using a Hierarchical Digital Differential Analyzer algorithm (HDDA), and jagged tensors. Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines, and we demonstrate its effectiveness on a number of representative tasks such as large-scale point-cloud segmentation, high resolution 3D generative modeling, unbounded scale Neural Radiance Fields, and large-scale point cloud reconstruction.

Research on Security Detection of Embedded Terminals in Power Grids Under Artificial Intelligence

Artificial intelligence (AI) has an important significance in the construction of smart grids. There is a problem of low accuracy of behavior detection due to a large number of attacks and long data fragments in the power grid data. In this paper, a behavior detection method based on a generative adversarial network (GAN) is proposed. The method focuses on optimizing the learning strategies of the generator and discriminator based on a GAN. In this regard, the loss function is converged by maximizing the root mean square error of the attack in the discriminator, and the linear activation function is used as the modulation function before the output in the generator. Therefore, the improved GAN can utilize the data enhancement features in the original mode and ensure the stability of attack behavior recognition, and then, effectively detect the attack information in the grid. The experimental results show that the method in this paper can accurately and effectively monitor abnormal attack behavior in the embedded terminals of the smart grid, and its effective detection rate is 97.6%.

Intelligent Video Monitoring and Analysis System for Power Grid Construction Site Safety Using Wireless Power Transfer

Power grid construction significantly enhances power grid management and risk control. Inconsistent operations at construction sites can jeopardize grid stability and crew safety. Traditional power lines are less favored due to mobility limitations, while batteries add burden and impracticality. To address this, a wireless power transfer and video monitoring system is developed using RF technology and Yolo V3 model. This enables continuous monitoring and employee safety analysis. The system's detection performance is optimized using HPSO, surpassing existing methods in accuracy and speed. It ensures real-time monitoring and improves the detection of potential risk sources, crucial for construction site safety.

A highly stable zinc anode protected by a corrosion inhibitor for seawater-based zinc-ion batteries

The island-based energy storage is of urgent need for the grid construction combined with renewable energy for offshore operation. The direct use of seawater as a substitute of deionized water shows its great promise for aqueous zinc-ion batteries in such a specific situation. However, the metal corrosion, dendrite growth, and hydrogen evolution stand out in the harsh seawater environment. To address these challenges, we proposed a corrosion inhibitor that was effective in the field of metal anti-corrosion, 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTCA), to inhibit anode corrosion caused by Cl– and active H2O molecules by forming a stable solid electrolyte interphase (SEI) film in the seawater-based electrolyte. Besides, PBTCA can chelate with other cations present in seawater, such as Ca2+ and Mg2+, thereby preventing the aggregation and precipitation of sparingly soluble species. Under a current density of 5 mA cm−2, the seawater-based zinc-ion battery exhibited an exceptional cycle life exceeding 2000 h and maintained a Coulombic efficiency of over 99.6% after 2000 cycles. Additionally, the performance of the Zn||ZVO full battery was significantly enhanced with the addition of PBTCA. This study provides a simple, low-cost, and efficient approach for making the seawater-based zinc-ion batteries useable.

Tracking the provincial energy transition in China: A comprehensive index

Achieving an efficient, modern, and sustainable energy transition towards carbon neutrality has attracted worldwide attentions. Composite statistical indices are increasingly recognized as valuable tools for policymakers, offering enhanced understanding of the present condition and anticipated direction of energy transition. In this research, we constructed a comprehensive index to track provincial energy transition in China. With this index, we monitored the energy transition progress of 30 provinces in China. The Provincial energy transition index (PETI) scores exhibited considerable heterogeneity across provinces and throughout different time periods, with eastern provinces having the highest scores, and some northwestern and large coal provinces, like Shanxi, having the lowest scores. These findings indicated that provincial energy transition policies should be tailored to the local energy status and economic structures. The paper delineated conclusions and policy implications derived from the study's extensive evaluation. Specifically, effective ways to further improve a region's sustainable energy transition path include advancing the use of clean energy, vigorously promoting power grid construction, and investing in new energy sectors. The implementation of these strategies is expected to reduce energy consumption and carbon intensity, thereby decreasing carbon emissions, then ultimately accelerating the realization of the dual carbon targets.

The influence of guide vane opening on internal cavitation of Francis turbine

Abstract In response to the theme of national multi energy complementary power grid construction, hydropower stations need to frequently change the opening of active guide vanes. Operation deviates from the rated working condition. And output is adjusted to meet the flexible power demand of users. Cavitation phenomenon as the biggest problem restricting the eccentric operation of Francis turbine. Easily induce cavitation damage to the flow passage components of the unit and shorten the service life of the unit. Therefore, there is a good engineering research background for the cavitation characteristics inside Francis turbines in the process of exploring the variation of guide vane opening. This paper takes a high head Francis turbine as the research object, based on numerical simulation and model test. The results show that the maximum amplitude of the draft tube vortex gradually increases during the process of decreasing the opening. The degree of cavitation is gradually serious, and the streamline distribution of the flow field inside the impeller is uneven. As the opening of the guide vane gradually increases, the vortexof the draft tube gradually decreases. The cavitation degree is weakened, and the streamline distribution inside the impeller is significantly improved. With the increase of the guide vane opening, the low-speed area on the outlet surface of the impeller gradually decreases, resulting in a gradual reduction of the low-speed zone in the straight cone section of the draft tube, a smaller vortex, a weaker cavitation degree. The internal flow distribution of the impeller is significantly improved.

Optimization technology of hydroelectric power plant unit speed control based on the constriction coefficient-based particle swarm gravitational search algorithm fusion model

In recent years, the integration of wind power into hydropower networks has become a trend. But after wind power is connected to the grid, its unstable power will have an impact on the stability of the power grid. In order to improve the impact of wind power grid connection on the stability of the power supply network, a water motor speed control fusion model considering wind power interference is proposed. Firstly, analyze the impact of wind power grid connection and construct a speed regulation model for grid connected units. Secondly, the gravity search algorithm is introduced to optimize the unit speed control parameters. Considering that the algorithm is prone to local convergence, the convergence factor particle swarm optimization algorithm is adopted to optimize the gravity search algorithm. In addition, considering the impact of wind power interference on unit regulation, the Hopf bifurcation theory is introduced to analyze the dynamic regulation parameter range of hydropower units, in order to meet the requirements of stable operation of the power grid. The research contribution is mainly reflected in suppressing wind power grid connection interference through the speed regulation of hydroelectric units. At the same time, considering the impact of multiple interferences on grid connection, an intelligent model is introduced to optimize the speed control parameters of the unit, thereby improving the stable impact of wind power grid connection on the water conservancy grid. In the simulation analysis of hydroelectric unit speed regulation, after adding 5 % disturbance signal, the proposed model under rigid water hammer disturbance could stabilize the unit's stable control within 15 s, which is superior to other models. In the simulation analysis of speed regulation under wind power disturbance, the proposed model could achieve the fastest and most stable speed regulation under rigid water hammer step wind power disturbance, with a duration of 60 s. The proposed model could respond quickly to small overshoot and overshoot in elastic water hammer slope type wind power disturbances. Therefore, the proposed speed regulation technology for hydropower units has excellent application effects in practical scenarios, which will provide effective technical references for wind power grid connection and hydropower unit regulation.

Liquid-solid interface diffusion behaviors of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper

Furfural as an ageing product of cellulose paper can be detected by the furfural content in the oil. With the construction of green power grid, more and more power equipment adopts environmentally friendly insulating fluids. The investigation of the diffusion behaviors of furfural at different oil-paper interfaces can provide a basis for fault detection in power transformers. The liquid-solid interface diffusion behaviors of furfural in natural ester-cellulose paper and mineral oil-cellulose paper insulating systems are investigated by molecular dynamics simulation in this paper. Results show that the range of center-of-mass movement distances of furfural in mineral oil-cellulose paper is about 5 times that of natural ester-cellulose paper. The main reason for this is that the long-chain macromolecules composing the natural ester undergo curling in the extended flow. As polar molecules, natural ester can attract furfural, which is also a polar molecule. And the electrostatic potential mapping interval of furfural is about 4.4 times that of mineral oil, but 1.1 times that of natural ester, with which it overlaps more. These hinder the diffusion of furfural molecules in the natural ester-cellulose paper liquid-solid interface. This research provides theoretical support for the diffusion of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper. It also provides useful guidance for fault detection in transformers using natural ester insulating fluids.

The influence of ripple on DC energy measurement and the improvement of wavelet threshold function filtering method

DC energy has been applied in the construction of DC power grids, photovoltaic power generation, energy storage, and rapid charging of electric vehicles. The application of DC energy is of great significance for energy conservation, emission reduction, carbon reduction, and efficiency increase. In this paper, the reasons for the decrease in DC energy measurement accuracy caused by ripples in voltage and current are analyzed. The minimum requirements of ripples for DC energy meters are analyzed, and the traditional filtering method based on wavelet threshold is studied. An optimized and fast iterative wavelet threshold function was designed, the current signal of electric vehicle charging was selected, and the effectiveness of the algorithm proposed in this paper was verified through simulation.

Electric Grid of Japan before the Great Depression: A Comparison with Germany

Japan is one of the most successfully electrified countries in the world, but it does not have a unified national electric power grid: the country is divided into two zones with different standards for alternating current (50 and 60 Hz). To explore the root of Japan's split grid, this essay adopts technology historian Thomas Hughes’ socio-technical system frameworks and compares the history of national grid formation in Germany and Japan in the 1920s. In both countries, foresighted engineers and entrepreneurs noticed the necessity of a unified national grid—something we see from the case study of Matsunaga Yasuzaemon and Arthur Koepchen. However, government policy in Japan regarding frequency unification was inconsistent, whereas the German provincial governments set up clear visions for grid construction. Moreover, Japanese ideology of the day preferred market competition, whereas in Germany electric utilities coordinated with each other. For Germany, the 1920s heralded the beginning of a European grid, whereas Japan's bifurcated frequency standard hindered the formation of a unified national grid. And Japan's split grid can even result in the electric power system's fragility in the face of natural disasters such as the Great East Japan Earthquake of March 2011.

A Versatile Approach for Adaptive Grid Mapping and Grid Flex-Graph Exploration with a Field-Programmable Gate Array-Based Robot Using Hardware Schemes.

Robotic exploration in dynamic and complex environments requires advanced adaptive mapping strategies to ensure accurate representation of the environments. This paper introduces an innovative grid flex-graph exploration (GFGE) algorithm designed for single-robot mapping. This hardware-scheme-based algorithm leverages a combination of quad-grid and graph structures to enhance the efficiency of both local and global mapping implemented on a field-programmable gate array (FPGA). This novel research work involved using sensor fusion to analyze a robot's behavior and flexibility in the presence of static and dynamic objects. A behavior-based grid construction algorithm was proposed for the construction of a quad-grid that represents the occupancy of frontier cells. The selection of the next exploration target in a graph-like structure was proposed using partial reconfiguration-based frontier-graph exploration approaches. The complete exploration method handles the data when updating the local map to optimize the redundant exploration of previously explored nodes. Together, the exploration handles the quadtree-like structure efficiently under dynamic and uncertain conditions with a parallel processing architecture. Integrating several algorithms into indoor robotics was a complex process, and a Xilinx-based partial reconfiguration approach was used to prevent computing difficulties when running many algorithms simultaneously. These algorithms were developed, simulated, and synthesized using the Verilog hardware description language on Zynq SoC. Experiments were carried out utilizing a robot based on a field-programmable gate array (FPGA), and the resource utilization and power consumption of the device were analyzed.