Research on power electronic transformer applied in AC/DC hybrid distribution networks

The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.

Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays

A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed, which is composed of constant coupling, coupling discrete time-varying delay and coupling distributed timevarying delay. All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion, which reflects a more realistic dynamical behaviour of coupled systems in practice. Based on a simple adaptive feedback controller and stochastic stability theory, several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays. Finally, numerical simulations illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.

Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

In this work,an Artificial Neural Network(ANN)based technique is suggested for classifying the faults which occur in hybrid power distribution systems.Power,which is generated by the solar and wind energy-based hybrid system,is given to the grid at the Point of Common Coupling(PCC).A boost converter along with perturb and observe(P&O)algorithm is utilized in this system to obtain a constant link voltage.In contrast,the link voltage of the wind energy conversion system(WECS)is retained with the assistance of a Proportional Integral(PI)controller.The grid synchronization is tainted with the assis-tance of the d-q theory.For the analysis of faults like islanding,line-ground,and line-line fault,the ANN is utilized.The voltage signal is observed at the PCC,and the Discrete Wavelet Transform(DWT)is employed to obtain different features.Based on the collected features,the ANN classifies the faults in an effi-cient manner.The simulation is done in MATLAB and the results are also validated through the hardware implementation.Detailed fault analysis is carried out and the results are compared with the existing techniques.Finally,the Total harmonic distortion(THD)is lessened by 4.3%by using the proposed methodology.

Aperiodic Sampled-Data Control of Distributed Networked Control Systems Under Stochastic Cyber-Attacks

This paper examines the stabilization problem of a distributed networked control system under the effect of cyberattacks by employing a hybrid aperiodic triggering mechanism.The cyber-attack considered in the paper is a stochastic deception attack at the sensor-controller end. The probability of the occurrence of attack on a subsystem is represented using a random variable. A decentralized hybrid sampled-data strategy is introduced to save energy consumption and reduce the transmission load of the network. In the proposed decentralized strategy, each subsystem can decide independently whether its state should be transmitted to the controller or not. The scheme of the hybrid triggering mechanism for each subsystem composed of two stages: In the first stage, the next sampling instant is computed using a self-triggering strategy. Subsequently, in the second stage, an event-triggering condition is checked at these sampling instants and the control signal is computed only if the event-triggering condition is violated. The self-triggering condition used in the first stage is dependent on the selection of eventtriggering condition of the second stage. Finally, a comparison of the proposed approach with other triggering mechanisms existing in the literature is presented in terms of the sampling instants,transmission frequency and performance measures through simulation examples.

A Fully Distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization

This paper develops a fully distributed hybrid control framework for distributed constrained optimization problems.The individual cost functions are non-differentiable and convex.Based on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization methods.The proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump set.With the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different solutions.Numerical simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

The Evolving Bipartite Network and Semi-Bipartite Network Models with Adjustable Scale and Hybrid Attachment Mechanisms

The bipartite graph structure exists in the connections of many objects in the real world, and the evolving modeling is a good method to describe and understand the generation and evolution within various real complex networks. Previous bipartite models were proposed to mostly explain the principle of attachments, and ignored the diverse growth speed of nodes of sets in different bipartite networks. In this paper, we propose an evolving bipartite network model with adjustable node scale and hybrid attachment mechanisms, which uses different probability parameters to control the scale of two disjoint sets of nodes and the preference strength of hybrid attachment respectively. The results show that the degree distribution of single set in the proposed model follows a shifted power-law distribution when parameter r and s are not equal to 0, or exponential distribution when r or s is equal to 0. Furthermore, we extend the previous model to a semi-bipartite network model, which embeds more user association information into the internal network, so that the model is capable of carrying and revealing more deep information of each user in the network. The simulation results of two models are in good agreement with the empirical data, which verifies that the models have a good performance on real networks from the perspective of degree distribution. We believe these two models are valuable for an explanation of the origin and growth of bipartite systems that truly exist.

Hybrid Flow Model of Cyber Physical Distribution Network and an Instantiated Decentralized Control Application

With the access to large amounts of renewable energy sources(RES),operation uncertainty of distribution networks increases significantly.Fortunately,adopting advanced information and communication technology,a cyber-physical distribution network(CPDS)provides the possibility to solve this problem via aggregative management of decentralized controllable loads.However,information flow in cyber space deeply interacts with energy flow in physical space,leading to a complexity in modeling,design and analysis of the whole control process.To deal with this problem,a general hybrid flow model of CPDS is first proposed in this paper.In this model,the control process is abstracted into interactions among three types of cyber nodes through cyber branches.The mathematic model of cyber nodes and branches is developed as well as that of the controlled physical object for hybrid flow computation.Then,based on the hybrid model,an instantiated application to compensate feeder power deviation caused by RES fluctuation through aggregative control of large-scale air-conditioners(ACs)is investigated.In this application,coordinative control of the AC cluster is achieved through a decentralized control strategy with very little communication cost and very good privacy protection.Results of numerical examples verify the correctness and flexibility of the hybrid flow model in reflecting interactions between cyber flow and energy flow as well as system operations.The proposed decentralized control strategy of the AC cluster is also proven to be effective and robust in FCE compensation.

混合量子-经典算法的配电网灾后拓扑重构方法

为及时有效地制定配电网拓扑重构策略以提升负荷快速恢复能力,基于量子计算的优越性,提出混合量子-经典(HQC)算法的弹性配电网灾后拓扑重构方法。首先,构建基于HQC算法的灾后配电网拓扑重构模型,以实现实际场景、优化问题、嵌入算法相应模块在量子计算和经典计算环境下的协作交互过程。然后,将配电网拓扑重构问题构造为无约束离散优化子问题和有约束连续优化子问题,提出量子退火嵌入式交替方向乘子(QA-ADMM)算法,将离散子问题等效映射成量子可解释的伊辛模型后,部署在D-Wave量子退火计算机中,并与经典计算机中连续子问题迭代求解,采用自适应惩罚因子调节机制加速算法收敛。最后,通过IEEE 14、33、69、123以及改进的205节点的不同规模配电系统,分析验证了QA-ADMM算法的有效性、稳定性与可扩展性。结果表明,惩罚因子、目标函数惩罚项系数、量子退火中采样读取次数会影响所提算法的精度和收敛速度;优化问题规模扩大后,所提混合量子-经典算法计算优势更加明显,205节点配电系统算例下,计算效率较经典计算可提升约34%。

基于直流环节互联的配电网重构技术研究现状及展望

在“碳达峰、碳中和”背景下,大量分布式电源、电力电子设备接入到电网中,使网络结构面临复杂化,故需要寻求更优的配电网重构方案。为满足直流环节互联配电网可控需求,使网架拓扑结构与系统运行紧密耦合,进而实现组网灵活运行是现阶段研究重点。首先,对基于直流环节互联的交直流混合配电网展开分析;然后对考虑优化运行和故障恢复的几种常用的配电网目标函数及约束条件做了进一步讨论,并从经典算法、启发式算法、其他算法3方面探讨了其在直流环节互联配电网重构中的应用;总结了基于直流环节互联的配电网重构在优化运行和故障恢复面临的一系列挑战和技术需求;最后,展望了基于直流环节互联的配电网重构未来发展方向并给出了建议。

计及混合博弈配电网与氢能系统的多综合能源服务商优化调度

针对多个综合能源服务商(IESP)协同运行中的能源交易问题,在考虑系统内风光不确定性问题的前提下,通过配电网运营商(DSO)制定电价,并协调IESP联盟的购、售电计划以及IESP成员之间的能源交互计划。以典型风光场景的历史出力数据为基础,以综合范数为约束条件,提出基于数据驱动的IESP联盟分布鲁棒优化调度模型。构建以DSO为上层领导者、IESP联盟为下层跟随者的主从博弈模型以及以下层IESP联盟为主的合作博弈模型;在下层IESP联盟中建立氢能系统模型,并考虑需求响应与碳交易机制;基于纳什谈判理论将合作博弈模型分解为联盟成本最小化问题与支付效益最大化问题。结合二分法、列与约束生成算法、交替方向乘子法对模型进行求解。算例结果表明:相较于传统的主从博弈模型,所提混合博弈模型能够实现IESP联盟的联合运行成本最小化,所建氢能系统能有效地减少碳排放,具有良好的经济效益;IESP联盟基于各成员的贡献大小实现了合作收益的公平分配。

Coordinated voltage regulation of hybrid AC/DC medium voltage distribution networks

In a hybrid AC/DC medium voltage distribution network, distributed generations(DGs), energy storage systems(ESSs), and the voltage source converters(VSCs)between AC and DC lines, have the ability to regulate node voltages in real-time. However, the voltage regulation abilities of above devices are limited by their ratings. And the voltage regulation efficiencies of these devices are also different. Besides, due to high r/x ratio, node voltages are influenced by both real and reactive power. In order to achieve the coordinated voltage regulation in a hybrid AC/DC distribution network, a priority-based real-time control strategy is proposed based on the voltage control effect of real and reactive power adjustment. The equivalence of real and reactive power adjustment on voltage control is considered in control area partition optimization, in which regulation efficiency and capability are taken as objectives.In order to accommodate more DGs, the coordination of controllable devices is achieved according to voltage sensitivities. Simulations studies are performed to verify the proposed method.

Two-stage Optimal Dispatching of AC/DC Hybrid Active Distribution Systems Considering Network Flexibility

The increasing flexibility of active distribution systems(ADSs)coupled with the high penetration of renewable distributed generators(RDGs)leads to the increase of the complexity.It is of practical significance to achieve the largest amount of RDG penetration in ADSs and maintain the optimal operation.This study establishes an alternating current(AC)/direct current(DC)hybrid ADS model that considers the dynamic thermal rating,soft open point,and distribution network reconfiguration(DNR).Moreover,it transforms the optimal dispatching into a second-order cone programming problem.Considering the different control time scales of dispatchable resources,the following two-stage dispatching framework is proposed.d dispatch uses hourly input data with the goal(1)The day-ahea of minimizing the grid loss and RDG dropout.It obtains the optimal 24-hour schedule to determine the dispatching plans for DNR and the energy storage system.(2)The intraday dispatch uses 15-min input data for 1-hour rolling-plan dispatch but only executes the first 15 min of dispatching.To eliminate error between the actual operation and dispatching plan,the first 15 min is divided into three 5-min step-by-step executions.The goal of each step is to trace the tie-line power of the intraday rolling-plan dispatch to the greatest extent at the minimum cost.The measured data are used as feedback input for the rolling-plan dispatch after each step is executed.A case study shows that the comprehensive cooperative ADS model can release the line capacity,reduce losses,and improve the penetration rate of RDGs.Further,the two-stage dispatching framework can handle source-load fluctuations and enhance system stability.

Battery Storage Configuration of AC/DC Hybrid Distribution Networks

The upscaling requirements of energy transition highlight the urgent need for ramping up renewables and boosting system efficiencies.However,the stochastic nature of excessive renewable energy resources has challenged stable and efficient operation of the power system.Battery energy storage systems(BESSs)have been identified as critical to mitigate random fluctuations,unnecessary green energy curtailment and load shedding with rapid response and flexible connection.On the other hand,an AC/DC hybrid distribution system can offer merged benefits in both AC and DC subsystems without additional losses during AC/DC power conversion.Therefore,configuring BESSs on an AC/DC distribution system is wellpositioned to meet challenges brought by carbon reductions in an efficient way.A bi-level optimization model of BESS capacity allocation for AC/DC hybrid distribution systems,considering the flexibility of voltage source converters(VSCs)and power conversion systems(PCSs),has been established in this paper to address the techno-economic issues that hindered wide implementation.The large-scale nonlinear programming problem has been solved utilizing a genetic algorithm combined with second-order cone programming.Rationality and effectiveness of the model have been verified by setting different scenarios through case studies.Simulation results have demonstrated the coordinated operation of BESS and AC/DC hybrid systems can effectively suppress voltage fluctuations and improve the cost-benefit of BESSs from a life cycle angle.

50 GHz optical true time delay beamforming in hybrid optical/mm-wave access networks with multi-core optical fiber distribution

We propose and experimentally validate an optical true time delay beamforming scheme with straightforward integration into hybrid optical/millimeter（mm）-wave access networks. In the proposed approach, the most complex functions, including the beamforming network, are implemented in a central office, reducing the complexity and cost of remote antenna units. Different cores in a multi-core fiber are used to distribute the modulated signals to high-speed photodetectors acting as heterodyne mixers. The mm-wave carrier frequency is fixed to 50 GHz（VBand）, thereby imposing a progressive delay between antenna elements of a few picoseconds. That true time delay is achieved with an accuracy lower than 1 ps and low phase noise.

Day-ahead scheduling based on reinforcement learning with hybrid action space

Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.

Reverse Droop Control-based Smooth Transfer Strategy for Interface Converters in Hybrid AC/DC Distribution Networks

Hybrid AC/DC distribution networks are promising candidates for future applications due to their rapid advancement in power electronics technology.They use interface converters(IFCs)to link DC and AC distribution networks.However,the networks possess drawbacks with AC voltage and frequency offsets when transferring from grid-tied to islanding modes.To address these problems,this paper proposes a simple but effective strategy based on the reverse droop method.Initially,the power balance equation of the distribution system is derived,which reveals that the cause of voltage and frequency offsets is the mismatch between the IFC output power and the rated load power.Then,the reverse droop control is introduced into the IFC controller.By using a voltage-active power/frequency-reactive power(U-P/f-Q)reverse droop loop,the IFC output power enables adaptive tracking of the rated load power.Therefore,the AC voltage offset and frequency offset are suppressed during the transfer process of operational modes.In addition,the universal parameter design method is discussed based on the stability limitations of the control system and the voltage quality requirements of AC critical loads.Finally,simulation and experimental results clearly validate the proposed control strategy and parameter design method.

考虑SOC的混合储能功率分配与自适应虚拟惯性控制

电力电子化的直流配电网存在低惯性问题,不利于系统稳定运行。混合储能设备可向电网提供虚拟惯性,但不同类型的储能之间存在功率协调问题,并且储能的荷电状态(state of charge, SOC)对虚拟惯性的调节也有约束作用。针对上述问题,提出了一种自适应时间常数的分频控制策略,时间常数根据混合储能系统(hybridenergy storage system, HESS)的SOC而动态调整以改变功率分配。首先,通过分析储能SOC与虚拟惯性的关系,并考虑储能充放电极限问题,研究兼顾SOC、电压变化率以及电压幅值的自适应虚拟惯性控制策略,提高系统惯性。然后,建立控制系统的小信号模型,分析虚拟惯性系数对系统的影响。最后,基于Matlab/Simulink搭建直流配电网仿真模型,验证了所提控制策略能合理分配HESS功率,提高超级电容器利用率,改善直流电压与功率稳定性。

考虑负荷停电差异化的配电自动化终端混合优化配置方法

成本低且安装灵活的故障指示器(FI)常与具备远程遥控能力的馈线远程终端单元(FRTU)协同完成配电网的故障定位和隔离,以缩减负荷停电时间。在配电网各开关位置或分支线路上,有选择性地部署配电自动化终端的FRTU和FI,可减少故障停电损失费用。为此,提出了考虑各供电负荷停电差异化的FRTU和FI的混合优化配置新方法。首先,根据待分析负荷、故障点、上级110 kV/35 kV变电站以及联络开关的相对位置,考虑FRTU和FI协同作用下的故障定位、故障隔离和供电恢复全过程,建立基于FRTU和FI混合配置方案的各供电负荷差异化停电时间关系式。然后,构建了以FRTU和FI的全生命周期成本费用和故障停电损失费用之和最小为目标的混合优化配置数学模型,并完成优化求解。最后,在CSG 53节点测试系统上验证了所提方法的有效性。

基于CNN-LSTM混合神经网络的高速铁路地震响应预测

为了更好地挖掘高速铁路在地震时的响应信息,提高光纤光栅监测的效率及预测精度,该文针对地震响应数据的时序性及非线性的特点,提出卷积神经网络(CNN)和长短期记忆(LSTM)网络的混合神经网络模型预测方法.通过在高速铁路简支梁桥上布设准分布式光纤光栅采集地震时轨道板、钢轨、底座板、箱梁的响应数据,在每根光纤上布置7个光栅,利用两边光栅的响应数据预测中间点的光栅响应,将采集位置、历史数据及地震波形等信息作为特征图输入.利用CNN提取特征,再将提前提取出来的特征数据以时序方式作为LSTM网络的输入数据,最后LSTM网络进行地震应变响应预测.实验结果表明,LSTM网络在3层时效果最好,CNN-LSTM方法具有较高的预测精度,根均平方误差(R_(RMSE))、平均绝对误差(R_(MAE))、决定系数(R^(2))分别达到了0.3753、0.2968、0.9371.

高比例分布式可再生能源交直流混合配电网运行调控:科学问题与技术框架

随着分布式可再生能源发电与中低压直流组网技术的快速发展,未来配电网必然向复杂的交直流混合形态演进,其运行调控技术面临许多新的挑战。在高比例分布式可再生能源交直流混合配电网(HR-HDN)运行调控技术发展需求分析的基础上,提炼出HR-HDN运行调控技术的3个科学问题,并以多类型可控设备协同、源-网-荷-储高度互动化和市场化需求为研究主线,从稳定控制、故障主动防御与恢复、电力市场机制辅助调控以及“平台+数据”数字化集成调控4个方面,提出了HR-HDN的运行调控技术框架。最后,对HR-HDN运行调控关键技术的未来发展进行了展望。