Complex Grid Research Articles

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

Review of Low Voltage Ride-Through Capabilities in Wind Energy Conversion System

The significance of low voltage ride-through (LVRT) capability in wind energy conversion systems (WECSs) is paramount for ensuring grid stability and reliability during voltage dips. This systematic review delves into the advancements, challenges, and methodologies associated with LVRT capabilities in WECSs. By synthesizing recent research findings, this review highlights technological innovations, control strategies, and regulatory requirements that influence LVRT performance. Key insights include the efficacy of various LVRT techniques, the role of grid codes in shaping LVRT standards, and the integration of advanced control algorithms to improve system resilience. The study offers a comprehensive understanding of the current landscape of LVRT in WECSs and pinpoints future research directions to optimize their performance in increasingly complex grid environments. During the LVRT process, the stator of a double-fed induction generator (DFIG) is directly linked to the power grid. When the external power grid experiences a failure, the stator flux produces a significant transient component, resulting in substantial overvoltage and overcurrent on the rotor side of the DFIG. Failure to implement preventative measures may result in damage to the converter, therefore compromising the safety and stability of how the power system functions.

Cartesian mesh adaptation: Immersed boundary method based on high-order discontinuous Galerkin method

Immersed boundary method (IBM) can easily distinguish fluid and solid regions in the computational region, thereby the workload of complex grid generation can be reduced. To accurately characterize the solid geometry, a large number of cells are required near the solid surface. The h-adaptive algorithm is adopted to reduce the requirement for the number of cells. In addition, considering the inherent adaptability to the h-adaptive Cartesian grids of the discontinuous Galerkin method, a high-order discontinuous Galerkin solver with an IBM is developed. To validate the h-adaptive algorithm and the solver, three cases are tested, including the steady flow past the National Advisory Committee for Aeronautics 0012 airfoil, the steady flow past a cylinder, and the unsteady flow past a cylinder. Compared with the non-adaptive cases, the h-adaptive cases need smaller total number of cells, and the numerical accuracy is significantly improved with an increasing degree of mesh refinement.

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

GIS-Based Assessment of Best Route in Complex Traffic Environment: A Case of Kolkata Municipal Corporation

In an emergency like an urban fire, road accident, or critical condition of patients, the shortest possible route to reach the destination is one of the most sought-after issues in transportation studies. However, the physical distance of the shortest route becomes longer distance than the travel time distance due to traffic congestion in metropolitan cities like that of Kolkata. So, the selection of an alternative route is essential to organize the journey logically. A well-structured road network provides multiple options to road users for selection of the best route from the shortest and alternative route according to their demand. So, the present study aims to identify the structural characteristics of road networks by graph theory and the selection of the best route using the GIS technique. It has been found that the road network of Kolkata is characterized by complex structure and grid configuration of the network. Selection of the best route also ranges from the shortest route to alternative route due to spatial variation of traffic speed on different roads in peak hours. The current study identifies the optimal route in terms of travel time to reach the required destinations during peak hour duration in the city of Kolkata.

Research on standardization of power transformer monitoring and early warning based on multi-source data

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.

Power system loading margin enhancement based on a multi-objective TCSC placement to mitigate the risk of blackouts

In the evolving power systems, loading margin (LM) enhancement has a significant effect on the mitigation of the risk of cascading line outages and thereby the occurrence of large blackouts. On the other hand, flexible alternating current transmission system (FACTS) devices, especially thyristor-controlled series compensated (TCSC) devices, are more effective than other mitigation strategies such as constructing new lines because of environmental and economic reasons. In this paper, a multi-objective framework is presented to maximize the LM with a minimum installation cost of TCSCs. The optimization problem is solved by using the ɛ-constraint method. The benefit of each Pareto solution is calculated by the probabilistic risk assessment, and the most preferred one is selected by a benefit/cost analysis. The proposed method is developed based on a modified alternating current (AC) version of the ORNL–Pserc–Alaska (OPA) model inspired by the self-organized criticality (SOC) theory in complex power grids. Using the benefit/cost criterion, it is revealed that by the proposed approach, a larger value for the most preferred Pareto solution is obtained in comparison with the defined transmission expansion planning (TEP) scenarios. It confirms the suitability of the proposed method to suppress the cascading outages instead of expensive and time-consuming policies such as building new lines.

Molecular dynamics simulation of arc ablation for CuCr contact materials and improvement method of ablation resistance

Renewable energy generation is a development trend worldwide due to sustainability and environmental protection. High-voltage direct current (HVDC) transmission is indispensable in the renewable energy system due to its efficient power transmission capability and adaptability to complex grid structures. The DC vacuum circuit breaker (VCB) is essential for system control and fault protection. However, with the development of DC systems towards high voltage and large current, the ablation resistance of CuCr contact materials in VCBs has become the leading technical bottleneck. In order to enhance the ablation resistance of CuCr contact materials, this paper investigates a new molecular dynamics (MD) simulation model for arc ablation of CuCr contacts; therefore, it proposes a design method for contact materials measured by characteristic parameters of ablation resistance. Results are as follows: Firstly, the Cu particles cause a severe ablation on CuCr contacts with the effect of arcs. Subsequently, the model’s accuracy is verified by the influence of Cr content on ablation resistance. Furthermore, Mo and W metal elements were used to dope and strengthen the CuCr10 contact material, and CuCr8Mo2 and CuCr7Mo3 have an apparent improvement in resistance to arc ablation, which are alternatives for reinforced CuCr10 contact materials.

Numerical study on the size effect on the mixing in 2×1, 3×3 and 5×5 rod bundle subchannels

Mixing Vane Grid (MVG) is considered as one of the most important components in the fuel assembly which not only plays the role of supporting the rod bundles but also improves the Critical Heat Flux (CHF) in the reactor core. Modeling and measuring the flow behavior accurately in the rod bundle is the key to understanding and learning complex grid performance in the fuel assembly and will develop high performance MVG. Usually, the fuel assembly in the reactor core consists of 17 × 17 or 16 × 16 rod bundles, it is hardly to use the original MVGs to perform study. The representative smaller prototypical grids are applied. Different bundle sizes are used including 1 × 1, 2 × 1, 3 × 3 and 5 × 5 et al. It is an absolute question of how the smaller size rod bundles are prototypical that could fully reflect the true flow and heat transfer behavior in a reactor core. In this paper, the effect of bundle size on flow and heat transfer is investigated under sizes of 2 × 1, 3 × 3 and 5 × 5. Firstly, the boundary settings in 2 × 1 are studied and the surface averaged secondary flow and local flow at the gap with 5 × 5 results are compared. Then the 3 × 3 and 5 × 5 bundle sizes are compared under subcooled flow. The center subchannels temperature and the void fraction distributions are analyzed. The effect of non-prototypical cold walls on heat transfer is discussed. The study shows that, different bundle sizes will produce different flow phenomena in the rod bundle, the flow pattern may not be the same with the reactor core fuel assembly, the typical bundle size selection should be based on the research purpose.

Intelligent planning of fire evacuation routes in buildings based on improved adaptive ant colony algorithm

Intelligent planning of fire evacuation routes is an important guarantee for rapid emergency response. Ant colony optimization, as an intelligent bionic algorithm, has notable advantages in route planning. However, traditional ant colony optimization corresponds to a low convergence rate, is easily caught in local optimal solution, and regards route length as the only constraint. To resolve these problems, an improved adaptive ant colony optimization (IAACO) algorithm was proposed in this study. Risk, energy consumption, and route length were taken as key factors to improve the heuristic function, optimize the pheromone update function, and establish multi-objective constraints, The standards for fire evacuation better align with practical requirements. Meanwhile, the adaptive pheromone volatile coefficient was introduced to balance convergence and global searching ability. In addition, the hazard range on the grid map was visualized. The results indicate that under various complex obstacle grid maps, the path inferiority of IAACO is reduced by 61.7% and 58.4%, 43.6% and 36.7%, and 41.6% and 67.7% compared to ACO and IACO, respectively; under the condition of multiple exits, the inferiority is reduced by 63.8% and 54.6%; under the condition of multiple fire sources, the inferiority is reduced by 40.1% and 34.6%; compared with other algorithms, IAACO shows the lowest path inferiority index, 26.6. IAACO is applicable to both the dynamic planning of fire evacuation routes and the evacuation simulation software, Pathfinder, and it performs better than the built-in algorithms of Pathfinder. Facts have proved that the IAACO algorithm significantly improves the safety level of evacuation compared to traditional evacuation methods and other optimization algorithms.

Harmonic Resonance Mechanisms and Influencing Factors of Distributed Energy Grid-Connected Systems

With the rapid development of global energy transformation and new power system, ensuring the stability of distributed energy grid connections is the key to maintaining the reliable operation of the whole power system. This paper constructs detailed impedance models of grid-following (GFL) and grid-forming (GFM) inverters using a harmonic linearization method and thoroughly investigates the mechanisms of resonance when inverters are connected to the grid, as well as the impact of model parameters on the stability of the grid system. This paper also briefly analyzes the scenario where distributed energy and electric vehicles are integrated into the grid simultaneously, demonstrating that grid system stability can be ensured in complex grid situations through reasonable parameter design. Lastly, the accuracy of the proposed impedance models and analysis is verified through MATLAB/Simulink simulations.

Simultaneous voltage control and frequency oscillation damping in Hydro-Québec’s network by using an updated 735-kV shunt reactors automatic switching system

The Hydro-Québec transmission system is among the most extensive and complex power grid in North America due to its very long lines (over 1000 km) joining the generation facilities in the north of the Québec province to the main centers of consumption in the south. Since the 1990s, Hydro-Québec has adopted a defense plan, consisting of various types of protection solutions including different remedial action schemes (RAS) and automatic action strategies, to increase the stability and reliability of its transmission system against extreme contingencies. One of these protection strategies is the automatic switching of shunt reactors (MAIS) 1MAIS is the acronym of the French name of the project as Manoeuvre Automatique d’Inductance Shunt (MAIS). which has been in operation since 1997. The goal of the MAIS system is to keep the voltage profile of the whole 735-kV grid in an acceptable range to increase the transmission capacity. The MAIS system at each substation is entirely independent from neighbour substations and will close or trip shunt reactors just based on local measurements. As a periodical upgrade in all existing protection solutions in the Hydro-Québec power system, new functionalities have been added into the existing MAIS system to develop its new version to improve both the voltage profile and the frequency response. This paper presents this new version including the algorithm and the concept behind the new additional functionalities. The simulation results show the efficiency and performance of the proposed method. On that basis, a deployment on Hydro-Québec’s grid to replace almost all the existing MAIS system with this new version will be realized by 2030.

An improved IPT-PLL technology for single-phase grid-connected inverters in complex power grid conditions

Aiming at the common problems of frequency variations and harmonics in complex power grids, an improved inverse Park transform phase locked loop (IPT-PLL) technology for single-phase converters suitable for micro grid systems is proposed. Firstly, in the phase detection of PLL, the α component of Park transformation is selected as the reference voltage, and its orthogonal component is constructed using a 1/4 fundamental period delay method. Secondly, Lagrange interpolation polynomials are introduced to approximate fractional delay to solve the problem of delay calculation errors caused by frequency changes. Thirdly, in order to compensate for the poor ability of traditional proportional integral (PI) regulators, multi resonant controllers are superimposed to suppress low order harmonic disturbances. Finally, the design method and system performance of the PI regulator and each resonant controller are analyzed theoretically. The experimental results show that the proposed improved IPT-PLL method has strong adaptability to complex power grids. It can significantly improve the tracking performance of power grid frequency, suppress the interference of low order harmonics and DC bias. And it has good dynamic and static performance.

A multifactorial model of intrinsic / environmental motivators, personal traits and their combined influences on math performance in elementary school

Numerous studies have explored the important role of achievement goals, as well as factors such as interest and self-efficacy, for academic performance of students of various ages. Such studies usually focus on the influence of one or two of these factors that are known to be associated with performance. At the same time, achievement goals themselves are influenced by environmental factors such as the influence of “significant others” (parents, teachers) or the overall socio-cultural context. In the present study, we expand the framework of achievement goal theory by building a holistic multifactorial path analysis model of direct and indirect influences, where achievement goals and personality traits such as self-efficacy and interest exert a combined influence on performance, but also receive influence from environmental factors.To achieve this goal, we collected data from 762 5th and 6th grade students, who attended 22 public primary schools in Cyprus. Data was collected with reliable and valid self-report scales such as the Achievement Goal Questionnaire (AGQ-R) and the Patterns of Adaptive Learning Scales (PALS), as well as a battery for Mathematical performance created by the researchers.Our results indicate a robust model that effectively captures the complex grid of associations between these factors of interest. Among other findings, self-efficacy and interest were found to mediate the relation between students’ mastery goals and performance. In sum, this research underscores the profound significance of mastery goals, self-efficacy and interest in Mathematical performance.

Optimization of Frequency Modulation Energy Storage Configuration in Power Grid Based on Equivalent Full Cycle Model

This paper aims to meet the challenges of large-scale access to renewable energy and increasingly complex power grid structure, and deeply discusses the application value of energy storage configuration optimization scheme in power grid frequency modulation. Based on the equivalent full cycle model and a large number of actual operation data, various energy storage technologies are technically analyzed, and the economic and environmental performance of different energy storage configuration schemes are comprehensively evaluated. On this basis, this paper puts forward a set of efficient and economical energy storage configuration optimization strategies to meet the demand of power grid frequency modulation and promote the wide application of energy storage technology. After an in-depth analysis, it is found that the optimized energy storage configuration scheme is excellent in technology, economy, and environmental protection. Specifically, in terms of technical performance, the optimization scheme has significantly improved key indicators such as energy storage efficiency, capacity and power, and response speed, which can better meet the requirements of power grid frequency modulation. Through the verification of actual operation data, it is found that the overall efficiency of the optimized energy storage configuration scheme is above 55%, which is helpful to the stability and efficiency of power grid frequency modulation. In terms of economic performance, although the initial investment cost of the optimization scheme may be high, it is found that it has good economy through the evaluation of long-term operation benefits. Considering that the energy storage system can reduce the operating cost of the power grid, improve the energy utilization rate, and achieve the optimization of cost-effectiveness in the long run, this scheme is economically feasible and attractive. In terms of environmental performance, the optimization scheme effectively reduces the negative impact on the environment by improving energy storage efficiency, reducing emissions, and optimizing resource utilization. This is not only conducive to the sustainable development of the power grid but also in line with the current global trend of promoting green and low-carbon transformation. To sum up, this paper not only provides an efficient and economical energy storage allocation optimization strategy for power grid frequency modulation but also provides a scientific basis for relevant decision-making departments. By promoting the practical application and development of energy storage technology, this paper is helpful to improve the frequency modulation ability of power grid, optimize energy structure, and reduce environmental pollution, and thus achieve the goal of sustainable energy development. The data results and in-depth analysis of this paper provide strong support for the practical application of energy storage configuration optimization scheme and also provide important reference for the further innovation and development of energy storage technology.

A DC fault current fast-computing method of MMC-HVDC grid with short circuit protection equipment

The multi-terminal modular multi-level converter-based high voltage direct current (MMC-HVDC) grid with short circuit protection equipment (SCPE) is so complex that it is difficult to estimate its fault current and analyze the performance of SCPE by conventional time-domain numerical calculation method, it meets three big obstacles. This paper has made significant progress in overcoming these obstacles. 1). By applying the modern electrical circuit theory, a systematic formulation of the differential equation set for fault current calculation is developed to avoid a lot of complex and cumbersome matrix manual calculations. 2). A novel Y-Delta transformation in the s-domain is proposed to develop an eliminating virtual node approach for a complex MMC-HVDC grid, including the ring, radial, and hybrid topologies. 3). It is difficult to solve the equivalent circuit of MMC-HVDC grid with SCPE since SCPE is a time-variable-nonlinear circuit. A canonical voltage source model of SCPE is established to transform the time-variable-nonlinear circuit into a piecewise linear circuit. Based on the three significant progresses, a DC fault current fast-computing method of MMC-HVDC grid with SCPE is put forward to deal with all kinds of MMC-HVDC grids with several kinds of SCPEs. Then, the performance of several kinds of SCPE is analyzed and compared by this method. Consequently, the proposed DC fault current fast-computing method is a new powerful tool to estimate the fault current of MMC-HVDC grid and analyze the performance of SCPE.

Complexity enhancement and grid basin of attraction in a locally active memristor-based multi-cavity map

The complexity of memristive chaotic systems determines whether it is suitable for applications in different subjects. To enhance complexity both in performance and diversity, this article first proposes a discrete model of locally active memristor (LAM) and conceives a three-dimensional memristive multi-cavity map by coupling LAM with the existing sine and cosine modulation (SCM) map. This map is named LAM-SCM map and has numerous independent fixed points with different stabilities. Numerical simulations reveal the memristive parameters-relied lossless displacement and self-shift of multi-cavity attractors, indicating the dynamical effects of LAM on the existing SCM map. The initial-relied dynamics distributions are disclosed by grid basins of attraction, showing the emergence of initial-boosting coexistence. Besides, the performance comparisons verify the superiority of LAM-SCM map over the existing SCM map. Finally, a hardware platform has been developed on FPGA to implement the LAM-SCM map and the captured attractors validate the numerical results. On this basis, we devise a 32-bit pseudorandom number generator (PRNG) based on chaos and implement it on the FPGA-based hardware platform to obtain high-speed and reconfigurable pseudorandom numbers. The results show that the proposed map has enhanced chaos complexity and rich dynamics diversity, which ensures the availability of hardware PRNG.

A mathematical‐boundary‐recognition domain‐decomposition Lattice Boltzmann method combined with large eddy simulation applied to airfoil aeroacoustics simulation

AbstractBeing a direct computational aeroacoustics method, Lattice Boltzmann method (LBM) has great potential and broad application perspective in the field of numerical simulation of aerodynamic noise due to its low dispersion and low dissipation. A series of numerical algorithms and the related improvements based on the standard LBM method are proposed and developed in this paper to adapt to the airfoil noise calculation with complex grid at middle‐high Reynolds number. First, a new mathematical‐boundary‐recognition algorithm based on Green's formula is proposed to deal with complex curved geometric models, which is validated by three‐element airfoil 30P30N benchmark. Then, in order to reduce grid redundancy and improve computing efficiency, the grid refinement technique of domain decomposition model (DDM) is adopted and also improved, which is verified by calculating the flow and sound fields around 2D and 3D cylinders at Reynolds number equal to 90,000. Finally, three different LES turbulence models are combined with the standard MRT‐LBM method, where different finite difference schemes are used to solve Reynolds stress tensor which is different from the traditional one. Through the direct acoustic numerical simulation of NACA0012 airfoil at Reynolds number equal to 200,000, the effects of Smagorinsky models and Wall‐adapting local eddy‐viscosity (WALE) model on aerodynamic noise prediction are compared and analyzed. Overall, the proposed methodology is shown to be appropriate for predicting the aerodynamic noise at low Mach number and can successfully simulate the generation and propagation of far field acoustics.

Research on data-driven model for power grid fault diagnosis fusing topological quantification information

In increasingly complex power grid operation scenarios and fault modes, rapid and accurate fault identification is highly important for improving the reliability of power systems. Faced with the massive amount of available power grid data, the rapid development of artificial intelligence technology provides a powerful tool for power grid fault diagnosis. However, existing data-driven diagnosis methods lack quantitative power grid topology change representations, cannot integrate power grid fault topology with alarm information, and have limited effectiveness in terms of diagnosing complex faults. To address these issues, a data-driven power grid fault diagnosis model that integrates topological quantitative features is proposed. By studying the changes in the topological connection relationships of equipment before and after a power grid fault and the topological connectivity in a power failure zone, based on the basic topological network indicators in graph theory, a representation of the quantitative features of the power grid fault topology is achieved, and these quantitative features and the alarm information are integrated to construct a data-driven power grid fault diagnosis model. The model uses the light gradient boosting machine algorithm to determine the types of complex faults and accurately identify faulty equipment, addressing the lack of model diagnosis effects considered in previous studies. Finally, the accuracy and effectiveness of the model are verified by using simulated fault cases.

Three dimensional interface normal prediction for Volume-of-Fluid method using artificial neural network

In the numerical simulations of multi-phase flow using Volume-of-Fluid (VOF) method, the calculation of the interface normal is a crucial point. In this paper, a machine learning method is used to develop an artificial neural network (ANN) model to make more accurate prediction of the local normal vector from neighboring volume fractions. Spherical surfaces with different radii are intersected with a structural background grid to generate 84328 groups of data: 3×3×3 neighboring volume fractions are used as input, and normal vector as output. Using 90% data as training dataset, the ANN model is well trained by optimizing the number of hidden layers and the number of neurons on each layer. Using the remaining 10% data, normal predictions are made using ANN-VOF and the most used YOUNG and HEIGHT-FUNCTION methods. The RMSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.022/0.045 respectively. In the reconstruction of a sinusoidal surface, the MSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.018/0.041. It is demonstrated that the ANN-VOF method has better performance for interface normal prediction. The proposed method has a simple computational logic and does not need to deal with complex geometric topology, which lays the foundation for application in other more complex grids.