Distribution Line Longitudinal ProtectionMethod Based on Virtual Measurement Current Restraint

As an effective approach to achieve the“dual-carbon”goal,the grid-connected capacity of renewable energy increases constantly.Photovoltaics are the most widely used renewable energy sources and have been applied on various occasions.However,the inherent randomness,intermittency,and weak support of grid-connected equipment not only cause changes in the original flow characteristics of the grid but also result in complex fault characteristics.Traditional overcurrent and differential protection methods cannot respond accurately due to the effects of unknown renewable energy sources.Therefore,a longitudinal protection method based on virtual measurement of current restraint is proposed in this paper.The positive sequence current data and the network parameters are used to calculate the virtual measurement current which compensates for the output current of photovoltaic(PV).The waveform difference between the virtual measured current and the terminal current for internal and external faults is used to construct the protection method.An improved edit distance algorithm is proposed to measure the similarity between virtual measurement current and terminal measurement current.Finally,the feasibility of the protection method is verified through PSCAD simulation.

Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.

Single Failure Routing Protection Algorithm in the Hybrid SDN Network

Loop free alternate(LFA)is a routing protection scheme that is currently deployed in commercial routers.However,LFA cannot handle all single network component failure scenarios in traditional networks.As Internet service providers have begun to deploy software defined network(SDN)technology,the Internet will be in a hybrid SDN network where traditional and SDN devices coexist for a long time.Therefore,this study aims to deploy the LFA scheme in hybrid SDN network architecture to handle all possible single network component failure scenarios.First,the deployment of LFA scheme in a hybrid SDN network is described as a 0-1 integer linear programming(ILP)problem.Then,two greedy algorithms,namely,greedy algorithm for LFA based on hybrid SDN(GALFAHSDN)and improved greedy algorithm for LFA based on hybrid SDN(IGALFAHSDN),are proposed to solve the proposed problem.Finally,both algorithms are tested in the simulation environment and the real platform.Experiment results show that GALFAHSDN and IGALFAHSDN can cope with all single network component failure scenarios when only a small number of nodes are upgraded to SDN nodes.The path stretch of the two algorithms is less than 1.36.

Transmission Lines Distance Protection Using Differential Equation Algorithm and Hilbert-Huang Transform

This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various factors, such as the distributed capacitance, the transient response characteristics of current transformer and voltage transformer, etc. In order to overcome this problem, the proposed scheme applies HHT to improve the apparent impedance estimated by DEA. Empirical mode decomposition (EMD) is used to decompose the data set from DEA into the intrinsic mode functions (IMF) and the residue. This residue has monotonic trend and is used to evaluate the impedance of faulty line. Simulation results show that the proposed scheme improves significantly the accuracy of the estimated impedance.

Efficient Routing Protection Algorithm Based on Optimized Network Topology

Network failures are unavoidable and occur frequently.When the network fails,intra-domain routing protocols deploying on the Internet need to undergo a long convergence process.During this period,a large number of messages are discarded,which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers(ISP).Therefore,improving the availability of intra-domain routing is a trending research question to be solved.Industry usually employs routing protection algorithms to improve intra-domain routing availability.However,existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures,which increases the cost of the network and impedes the methods deployed in practice.To address the issues,this study proposes an efficient routing protection algorithm based on optimized network topology(ERPBONT).ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source purposes.Firstly,the backup path with the minimum path coincidence with the shortest path is described as an integer programming problem.Then the simulated annealing algorithm ERPBONT is used to find the optimal solution.Finally,the algorithm is tested on the simulated topology and the real topology.The experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path,and significantly improves the routing availability.

XGBoost Algorithm under Differential Privacy Protection

Privacy protection is a hot research topic in information security field.An improved XGBoost algorithm is proposed to protect the privacy in classification tasks.By combining with differential privacy protection,the XGBoost can improve the classification accuracy while protecting privacy information.When using CART regression tree to build a single decision tree,noise is added according to Laplace mechanism.Compared with random forest algorithm,this algorithm can reduce computation cost and prevent overfitting to a certain extent.The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data.

Privacy Protection for Blockchains with Account and Multi-Asset Model

The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.

The Influence of Abnormal Data on Relay Protection

The parameters of abnormal data are defined and their influence on the original data and Fourier Algorithm is studied. A formula is proposed to quantify how the abnormal data influences the amplitude calculated by Fourier Algorithm. Two simulation models are established in Matlab to study the influence of abnormal data on relay protection. The simulation results show that the abnormal data can make distance protection extend the fault’s influence and make over current protection start by error.

基于区块链共识激励机制的新型联邦学习系统

随着云存储、人工智能等技术的发展,数据的价值已获得显著增长。但由于昂贵的通信代价和难以承受的数据泄露风险迫使各机构间产生了“数据孤岛”问题,大量数据无法发挥它的经济价值。虽然将区块链作为承载联邦学习的平台能够在一定程度上解决该问题,但也带来了三个重要的缺陷:1)工作量证明(Proof of Work,POW)、权益证明(Proof of Stake,POS)等共识过程与联邦学习训练过程并无关联,共识将浪费大量算力和带宽;2)节点会因为利益的考量而拒绝或消极参与训练过程,甚至因竞争关系干扰训练过程;3)在公开的环境下,模型训练过程的数据难以溯源,也降低了攻击者的投毒成本。研究发现,不依靠工作量证明、权益证明等传统共识机制而将联邦学习与模型水印技术予以结合来构造全新的共识激励机制,能够很好地避免联邦学习在区块链平台上运用时所产生的算力浪费及奖励不均衡等情况。基于这种共识所设计的区块链系统不仅仍然满足不可篡改、去中心化、49%拜占庭容错等属性,还天然地拥有49%投毒攻击防御、数据非独立同分布(Not Identically and Independently Distributed,Non-IID)适应以及模型产权保护的能力。实验与论证结果都表明,本文所提出的方案非常适用于非信任的机构间利用大量本地数据进行商业联邦学习的场景,具有较高的实际价值。

基于多目标优化的变电站消防资源配置算法

针对变电站消防资源分配与分布不均匀的问题,提出了一种基于多目标优化的变电站消防资源配置算法。该算法采用电力生产场所的道路网作为变电站消防责任区划分的依据,并结合各划分区域的消防需求构建消防资源配置模型。根据该模型的特点,在帕累托最优解的约束下提出了一种基于模拟退火的多目标优化算法,来求解该消防资源配置模型。仿真与实验结果表明,所提出的消防资源配置模型更符合实际变电站各场所分布的特点,且所提出的多目标优化算法得到的消防资源分配结果能在消防资源有限的条件下,实现最大的配置效益和快速的消防响应。

基于动作时限曲线拟合思想的含DG型配电网后备保护新方法

分布式电源(distributed generation,DG)的接入可能会改变电力系统的短路电流特性,从而影响到反时限过流保护的选择性和配合关系。为此,提出一种基于动作时限曲线拟合思想的含DG型配电网后备保护新方法。从配电网主保护和后备保护的动作时间差入手,建立曲线拟合模型,并运用梯度下降法对反时限过流保护的动作时限曲线进行优化。通过在优化整定计算中考虑保护的选择性以及DG接入所带来的影响,使优化参数适用于配电网中各分段位置。然后,在配电网中设置两相、三相短路故障,并根据故障位置、类型进行整定参数再调整,最终得到参数的全局最优解。基于PSCAD/EMTDC仿真软件进行仿真分析,验证了所提方法的有效性和实用性。

数据引流下异构传感器网络能量空洞防护算法

异构传感器网络在工作时,靠近传感器基站位置的节点会由于接收转发次数较多而出现能量空洞现象。为有效对此现象实施防护,保障网络安全运行,提出数据引流下异构传感器网络能量空洞防护算法。构建网络节点部署模型,对传感器网络节点实施节点优化部署;计算网络节点能耗对网络实施能量均衡控制,获取异构传感器网络节点的簇内传输跳数;结合获取的节点簇内跳数以及能量均衡结果开展节点接收数据的的引流分配,平衡传感器网络层内节点能量消耗,实现传感器网络的能量空洞防护。实验结果表明,使用上述方法开展传感器网络能量空洞防护时,数据转发接收能耗较低,防护方法性能高,防护效果好。

基于有限元法的油气田集输管网阴极保护布局优化

在利用有限元法建立外加电流阴极保护模型的基础上,借助Sobol灵敏度分析,确定了显著影响某油气田集输管网阴极保护效果的关键参数,并通过粒子群优化(PSO)算法对其系统布局进行优化。结果表明:由于外加电流阴极保护模型的模拟电位与测试桩的测定电位误差较小,故该模型能够较好地反映出油气田集输管网的电位分布现状;改变防护涂层电阻率等一阶敏感性系数较高的因素会对阴极保护效果产生较大影响,土壤电阻率、防护涂层电阻率以及管道埋深与其他参数之间可能存在明显交互作用;当利用PSO算法对油气田集输管网的阴极保护效果进行全局寻优时,该模型经过47700次粒子进化迭代后的最佳阴极保护系统有效覆盖率高达99.14%,优化效果显著。

基于深度强化学习和隐私保护的群智感知动态任务分配策略

在移动群智感知(Mobile Crowd Sensing,MCS)中,动态任务分配的结果对提高系统效率和确保数据质量至关重要。然而,现有的大部分研究在处理动态任务分配时,通常将其简化为二分匹配模型,该简化模型未充分考虑任务属性与工人属性对匹配结果的影响,同时忽视了工人位置隐私的保护问题。针对这些不足,文章提出一种基于深度强化学习和隐私保护的群智感知动态任务分配策略。该策略首先通过差分隐私技术为工人位置添加噪声,保护工人隐私;然后利用深度强化学习方法自适应地调整任务批量分配;最后使用基于工人任务执行能力阈值的贪婪算法计算最优策略下的平台总效用。在真实数据集上的实验结果表明,该策略在不同参数设置下均能保持优越的性能,同时有效地保护了工人的位置隐私。

改进巴氏距离算法下混合级联直流输电纵联阻抗保护优化

为提高纵联保护在LCC-MMC混合级联高压直流输电系统中的鲁棒性,提出一种基于纵联阻抗和改进巴氏距离算法的高压直流输电纵联保护方案。该方案将巴氏距离算法与纵联阻抗保护相结合,对被保护线路首末两端电气量的差异进行量化,并通过量化后的数据,进行区内外故障的判定,然后基于能量函数构建了故障选极判据,保证了保护的可靠性;在此基础上,通过分形维数构建了改进的自适应滑窗,提高了保护的速动性。最后,基于Matlab与PSCAD/EMTDC平台建立了“白江工程”±800 kV LCC-MMC混合级联高压直流输电系统模型,并对所提改进后的保护方案进行验证。仿真结果表明所提保护方案能够快速可靠地判别区内外故障并正确完成故障选极,且有着较好的耐受过渡电阻能力。

基于全同态加密优化的云数据隐私保护方法

为了提升云数据的安全性,改善数据加密时间长以及加密效果较差等问题,提出一种基于全同态加密优化的云数据隐私保护方法。通过生成对抗网络模型学习原始数据中的重要特征,确保合成的数据和初始数据之间具有较高的相似度,使其能够满足差分隐私特征。引入PKI对数据所有者和用户身份认证处理,完成密钥的产生和分发,根据差分隐私特征将用户所有者的数据划分为不同类型,获取用户访问特征向量。构建全同态加密机制,同时引入代理重加密机制,通过密钥转换完成数据加密处理,最终实现云数据全同态加密优化。实验对比结果表明,上述加密方案能够快速完成数据加密处理,有效确保数据的安全性。

基于首行波曲率半径的交直流互联电网交流输电线路单端量保护

交直流互联电网中逆变侧交流输电线路故障特征与纯交流系统具有较大的差异性,传统交流输电线路的保护方案已经不再适用于逆变侧交流输电线路.首先,基于逆变侧交流输电线路发生区内、外故障时拓扑结构的不同,分别推导了两种情况下线模故障分量电流首行波的表达式,理论上明确了交流输电线路在发生区内、外故障时首行波波形特征的差异性.其次,采用小波变换模极大值法和Levenberg-Marquardt算法提取首行波曲率半径,构造了利用首行波曲率半径进行故障区域识别的高可靠性单端量保护方案.最后,在PSCAD/EMTDC中搭建交直流互联电网模型,使用MATLAB对保护方案进行验证.仿真结果表明:所提保护方案快速有效,可靠性高,耐受过渡电阻的能力较强,具有良好的抗噪声能力.

基于国密SM2算法的车联网无证书隐私保护认证协议

作为大多数智能交通系统的基础,车载自组网(Vehicular Ad-Hoc Network,VANET)能够有效改善交通管理和提升道路安全性,因此受到广泛关注。然而,由于无线通信环境的开放性,VANET中的各个节点易遭受隐私泄露攻击。许多条件隐私保护认证(Conditional Privacy-Preserving Authentication,CPPA)协议被提出来解决隐私泄露问题,但这些协议主要基于传统的签名算法,这使得它们存在复杂的证书管理或密钥托管问题。为解决以上问题,本文以国密SM2签名算法为基础提出了一种适合VANET环境中无证书条件隐私保护认证协议,该协议在降低了通信开销的同时也避免了证书管理和密钥托管问题。此外,协议还保证了当出现恶意车辆用户时,可信中心可以追踪用户的真实身份并撤销该用户。安全证明和分析表明该协议具有较高的安全性并且满足消息完整性、匿名性、可追踪性和可撤销性等安全要求。最后,与最近的三种同类隐私协议对比,仿真实验结果表明该协议在通信量和计算量上优于其他方案。

基于区块链隐私保护的工业网络输出指令安全控制算法设计

当前工业网络输出指令加密机制多为目标式和独立式,导致指令安全控制效果不理想,丢包率增加,为此提出基于区块链隐私保护的工业网络输出指令安全控制算法。设计工业网络指令安全控制辨识目标,将此目标导入初始的安全指令控制加密程序中,设置一个基础的控制指令。以其作为引导,进行安全控制目标的扩展及生成,对指令的内容进行多层级加密。在指令加密处理的基础上,根据输出指令安全控制指标及参数的限制,设计不同的安全控制标准和双向控制条件,将安全控制辨识目标导入当前的区块链双向控制结构中,形成一个具体细化的控制结构。在控制结构中,利用区块链技术将区块控制程序与重叠指令的控制程序关联起来,实现动态化的指令输出安全控制。实验结果表明:这种安全控制算法能够对全部工业网络输出指令数据进行加密处理,能够最大程度上保证工业网络输出指令隐私安全,丢包率在5.2%以下,控制正确率较高,具有较高的实际应用价值。