Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

Model Predictive Control Strategy of Multi-Port Interline DC Power Flow Controller

There are issues with flexible DC transmission system such as a lack of control freedom over power flow.In order to tackle these issues,a DC power flow controller(DCPFC)is incorporated into a multi-terminal,flexible DC power grid.In recent years,a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability.This work proposes a model predictive control(MPC)strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance.Initially,the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed,and the relationship between each regulated variable and control variable is determined;The power flow controller is then discretized,and the cost function and weight factor are built with numerous control objectives.Sub module sorting method and nearest level approximation modulation regulate the power flow controller;Lastly,theMATLAB/Simulink simulation platformis used to verify the correctness of the establishedmathematicalmodel and the control performance of the suggestedMPC strategy.Finally,it is demonstrated that the control strategy possesses the benefits of robust dynamic performance,multiobjective control,and a simple structure.

Asymptotically Optimal Scenario Analysis and Wait-and-See Model for Optimal Power Flow With Wind Power

精确模拟风功率分布的场景,有效求解反映风电随机性的最优潮流模型,对大规模风电并网的经济调度、质量控制及安全运行均具有重要意义。引入风电随机变量,建立最优潮流的＂wait-and-see＂（WS-OPF）模型;采用优于其他指标的瓦瑟斯坦（Wasserstein）距离为衡量指标,形成与风功率概率分布最优近似的场景模型,生成渐近最优场景,获得潮流分布及机组最优出力等指导调度安全经济运行的重要信息。最后,以IEEE 30和IEEE 300系统为例,对比各种距离指标的模拟精度,验证Wasserstein距离的优良性;同时,与不考虑风电随机性的模型相比,WS-OPF模型能更好地模拟风功率随机特性,提供与实际发生的风功率相近的调度方案,从而合理地规避风电接入电网的风险,提高风电的消纳能力。

Modelling Study to Compare the Flow and Heat Transfer Characteristics of Low-Power Hydrogen,Nitrogen and Argon Arc-Heated Thrusters

A modelling study is performed to compare the plasma flow and heat transfer characteristics of low-power arc-heated thrusters （arcjets） for three different propellants： hydrogen, nitrogen and argon. The all-speed SIMPLE algorithm is employed to solve the governing equations, which take into account the effects of compressibility, Lorentz force and Joule heating, as well as the temperature- and pressure-dependence of the gas properties. The temperature, velocity and Mach number distributions calculated within the thruster nozzle obtained with different propellant gases are compared for the same thruster structure, dimensions, inlet-gas stagnant pressure and arc currents. The temperature distributions in the solid region of the anode-nozzle wall are also given. It is found that the flow and energy conversion processes in the thruster nozzle show many similar features for all three propellants. For example, the propellant is heated mainly in the near-cathode and constrictor region, with the highest plasma temperature appearing near the cathode tip; the flow transition from the subsonic to supersonic regime occurs within the constrictor region; the highest axial velocity appears inside the nozzle; and most of the input propellant flows towards the thruster exit through the cooler gas region near the anode-nozzle wall. However, since the properties of hydrogen, nitrogen and argon, especially their molecular weights, specific enthMpies and thermal conductivities, are different, there are appreciable differences in arcjet performance. For example, compared to the other two propellants, the hydrogen arcjet thruster shows a higher plasma temperature in the arc region, and higher axial velocity but lower temperature at the thruster exit. Correspondingly, the hydrogen arcjet thruster has the highest specific impulse and arc voltage for the same inlet stagnant pressure and arc current. The predictions of the modelling are compared favourably with available experimental results.

Modeling load distribution for rural photovoltaic grid areas using image recognition

Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

In recent times,the impact of typhoon disasters on integrated energy active distribution networks(IEADNs)has received increasing attention,particularly,in terms of effective cascading fault path prediction and enhanced fault recovery performance.In this study,we propose a modified ORNL-PSerc-Alaska(OPA)model based on optimal power flow(OPF)calculation to forecast IEADN cascading fault paths.We first established the topology and operational model of the IEADNs,and the typical fault scenario was chosen according to the component fault probability and information entropy.The modified OPA model consisted of two layers:An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model.The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs.Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN.The proposed model also increased the load recovery rate by>24%compared to the traditional OPA model.

A NEW MODELING METHOD FOR MACHINE-FOUNDATION COUPLING SYSTEM AND ITS APPLICATION TO THE CONTROL OF POWER FLOW

A new concept, namely, the equivalent mobility matrix of coupling subsystem is proposed, and the corresponding threesubsystem coupling progressive approach is explored. With the new efficient approach presented, the complexity in dealing with a more complicated dynamic coupling system is greatly reduced. The new modeling method is then combined with the theory of power flow to investigate the dynamics of the overall non rigid isolation system from the viewpoint of energy. The interaction between the resilient machine of its main modes and the resonant behavior of the flexible foundation on power flow transmission is studied. Taking a machine tool mounted on a multi story working plant as an example, the dynamic characteristics of the machine foundation coupling system are analyzed, and their effects on power flow transmission are revealed under various service frequency bands. Some advisable control strategies and the design principle for machinery mounted on flexible structure are proposed.

Power Flow Model for Medium-voltage Distribution Systems Considering Measurement and Structure Characteristics

Medium-voltage distribution systems(MVDSs)mainly consist of a feeder head,lines,distribution transformers,and the equivalent load or power supply interfaced with the distribution transformers.The information of such load or power supply can be measured via the three-wattmeter method(THM)and the two-wattmeter method(TWM).The measurements can be used to perform the control of the power supply and simulate the characteristics of the load,so the models of the load and the power supply need to consider the measurement characteristics.Existing research works on three-phase power flow(PF)just consider the measurement characteristics of THM.Hence,the PF equation of the bus measured via TWM is firstly built.Based on conventional measurements,an accurate and general model of the grounded and ungrounded slack bus is proposed.Furthermore,the influence arising from the connection type and angle shift of distribution transformers on the admittance matrix is considered,and thus a general three-phase transformer model is summarized,which is applicable for all the transformers mentioned herein.Finally,Newton's method is adopted to solve the PF calculation,and the performance of the proposed PF model is demonstrated through designed tests.

A power function model for simulating creep mechanical properties of salt rock

In this paper,a new micro-creep model of salt rock is proposed based on a linear parallel bonded model(LPBM)using the two-dimensional particle flow code(PFC2D).The power function weakening form is assumed to describe the variation of the parallel bonded diameter(PBD)over time.By comparing with the parallel-bonded stress corrosion(PSC)model,a smaller stress fluctuation and smoother creep strain−time curves can be obtained by this power function model at the same stress level.The validity and adaptability of the model to simulate creep deformation of salt rock are verified through comparing the laboratory creep test curves and the Burgers model fitting result.The numerical results reveal that this model can be capable of capturing the creep deformation and damage behavior from the laboratory observations.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

Novel extended C-m models of flow stress for accurate mechanical and metallurgical calculations and comparison with traditional flow models

Here,we developed novel extended piecewise bilinear power law(C-m)models to describe flow stresses under broad ranges of strain,strain rate,and temperature for mechanical and metallurgical calculations during metal forming at elevated temperatures.The traditional C-m model is improved upon by formulating the material parameters C and m,defined at sample strains and temperatures as functions of the strain rate.The coefficients are described as a linear combination of the basis functions defined in piecewise patches of the sample strain and temperature domain.A comparison with traditional closed-form function flow models revealed that our approach using the extended piecewise bilinear C-m model is superior in terms of accuracy,ease of use,and adaptability;additionally,the extended C-m model was applicable to numerical analysis of mechanical,metallurgical,and microstructural problems.Moreover,metallurgy-related values can be calculated directly from the flow stress information.Although the proposed model was developed for materials at elevated temperatures,it can be applied over a broad temperature range.

Simultaneous One-Step Approximations of Real and Reactive Power Flow

This paper presents two new non-iterative approximations of the power flow in a network. Real and reactive power are simultaneously modelled in complex equations. Also, resistances are not set to zero. This is a generalization of the DC approximation, where only real power is modelled with zero line resistance. Hence the proposed approximations are more accurate than the DC approximation. The voltage lag over a link in a short, low voltage, network link is ten times as accurate as with the DC approximation. In the Appendix a new mathematical constant is introduced.

A Data Driven Security Correction Method for Power Systems with UPFC

The access of unified power flow controllers(UPFC)has changed the structure and operation mode of power grids all across the world,and it has brought severe challenges to the traditional real-time calculation of security correction based on traditionalmodels.Considering the limitation of computational efficiency regarding complex,physical models,a data-driven power system security correction method with UPFC is,in this paper,proposed.Based on the complex mapping relationship between the operation state data and the security correction strategy,a two-stage deep neural network(DNN)learning framework is proposed,which divides the offline training task of security correction into two stages:in the first stage,the stacked auto-encoder(SAE)classification model is established,and the node correction state(0/1)output based on the fault information;in the second stage,the DNN learningmodel is established,and the correction amount of each action node is obtained based on the action nodes output in the previous stage.In this paper,the UPFC demonstration project of NanjingWest Ring Network is taken as a case study to validate the proposed method.The results show that the proposed method can fully meet the real-time security correction time requirements of power grids,and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.

A Data-Driven Car-Following Model Based on the Random Forest

The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re- presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em- ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car- following behavior with better performance under multiple performance indicators.

Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

计及低电压穿越及故障全过程动态的双馈风电场等值方法

在分析双馈感应发电机(DFIG)故障各阶段动态的基础上,提出了一种计及低电压穿越控制的双馈风电场等值方法。首先,讨论了低电压穿越控制策略,给出了含双馈风电场的电力系统故障稳态潮流计算方法。在考虑电压暂态过程的条件下,分析了外部系统短路故障后DFIG转子电流的变化机理,指出电压跌落深度及风电功率是影响短路电流的主要因素,可根据动作分界线判断撬棒是否动作。然后,根据初始风速及撬棒状态将风电场中的DFIG分为3群,并对等值DFIG及集电网络进行聚合。为提高等值模型在恢复阶段的精度,根据故障稳态电压对双馈风电场功率恢复曲线进行预估,并在此基础上对等值DFIG恢复速率进行分段修正。最后,对含双馈风电场的算例系统进行了等值计算。仿真结果表明,等值模型较好地保持了原系统在故障各阶段的动态,在保持较高精度的前提下有效简化了系统,提高了计算速度。

涡轮动力模拟器甩油盘热流模型与流场特性研究

甩油盘作为涡轮动力模拟器(TPS)的重要零件,能有效地促进模拟器内循环系统润滑油的回流,提高轴承的润滑性并降低系统温升,为整机安全可靠运行提供了基础性保障。为了解决TPS内循环系统润滑油的回流问题,对TPS甩油盘及其内部循环流道进行了研究。首先,确定了涡轮动力模拟器轴承发热量及散热系数随转速的变化、甩油盘内部流体控制方程和边界条件;然后,构建了一类TPS甩油盘及其内部循环流道的完整内循环热流模型,并对比了轴承处温度仿真结果与实验结果以确保模型的合理性;最后,数值仿真得到了不同转速下内部循环流道的温度场、压力场和速度场。研究结果表明:转速增加能显著引发系统内最高温度上升,甩油盘作用下的内循环流场温度变化较为平稳,典型工况条件下仿真与实验得到的最高温度相对误差最大为12.3%,表明了热流模型的合理性。研究结果对于指导TPS甩油盘结构设计和提高整机运行安全稳定性具有重要的价值。

基于电压补偿的双端直流配电网电压就地协调控制

双端直流配电网是一种双端电源供电的网络结构,可为直流负荷提供更加稳定和可靠的电源电压。然而,负荷的功率波动和不平衡使线路电压跌落增大,可能导致直流负荷的电压不平衡度和电压偏差指标越限,影响直流负荷的正常运行。文中基于电压源型换流器(VSC)外环电压控制,考虑中线电压补偿,提出基于电压补偿等效模型的直流配电网电压偏差及不平衡度联合抑制策略,实现直流配电网电压就地协调控制。首先,建立双端直流配电网潮流模型,将VSC电压下垂控制引入潮流模型,分析不同控制策略下的电压偏差和不平衡度的特性。其次,在此基础上,以电压最低点为分点获得双端电源供电回路压降的简化等效模型,并利用最小二乘法实现等效阻抗参数辨识,构建双端直流配电网的电压补偿等效模型。以参数辨识结果作为电压外环控制输入,提出考虑中线电压补偿的双端直流配电网电压就地协调控制策略。最后,在MATLAB/Simulink中搭建仿真模型,验证了双端直流配电网潮流模型的正确性和控制策略的有效性。

抽蓄电站无压隧洞交叉区体型优化试验研究

交叉无压隧洞交汇区域水流的水力特性复杂,无压隧洞交叉区体型及交汇口水流水力特性的研究对确保水利枢纽的安全运行具有重要的工程意义。基于物理模型试验,对某抽水蓄能电站下水库高落差泄洪排沙洞和放空洞交叉区体型进行优化,最终确定圆弧连接+交叉角21°的交叉区体型。对比分析圆弧连接+交叉角21°的交叉区优化体型下特征试验工况的水力特性。结果表明,6种特征试验工况下,泄洪排沙洞和放空洞交叉口及下游均为急流,明流泄洪且流态较稳定,均在交汇口下游右边直墙处形成最大水深,水深低于直墙高度,满足水面线不宜超过直墙范围的要求。

基于源荷谐波耦合模型的数据驱动概率谐波潮流计算

随着新能源及负荷中电力电子装置的广泛应用,电力系统谐波畸变程度不断增加。同时,新能源及负荷显著的不确定特征,进一步增加了谐波分析难度。为有效评估系统不确定谐波状态,充分挖掘源荷实际运行特征,提出一种数据驱动的概率谐波潮流(probabilistic harmonic power flow,PHPF)计算方法。首先,基于实测数据,建立考虑时变特性的源荷动态谐波耦合矩阵模型(dynamic harmonic coupling matrix model,DHCMM),揭示不同时段内谐波电压与谐波电流的相互耦合关系。然后,利用实测数据挖掘源荷时变不确定特征,采用改进点估计法提取统计特性,克服变量间相互影响引起的估计偏差。最后,提出针对源荷不确定性的PHPF计算方法,对系统中时变不确定谐波进行评估。实验结果表明,基于实测数据的谐波耦合矩阵模型能够有效分析谐波源时变特性,结合源荷时变不确定功率状态,所提PHPF计算方法能够对电力系统谐波进行准确评估。