Aiming at the problems of poor real-time performance and low time-frequency resolution in existing detection algorithms for composite power quality disturbances,a real-time power quality disturbance detection method based on improved adaptive S-transform(IAST)is proposed.A globally adaptive Gaussian window is constructed as the kernel function of the IAST,allowing the effective window length and frequency spectrum to adapt dynamically with the detection frequency.This avoids the need for frequent switching of window parameters to improve time-frequency resolution,thereby reducing algorithm complexity.The window parameters are optimized with the objective of enhancing signal energy concentration,ensuring accurate time-frequency positioning of various types of disturbances.An automatic thresholding method is used to determine the dominant frequency points of the actual disturbance signals,which are then subjected to time-frequency transformation to further improve computational efficiency.Simulation and experimental results show that,compared with existing algorithms for detecting composite power quality disturbances,the proposed method offers superior real-time performance,strong time-frequency resolution,and low computational complexity,making it suitable for accurate real-time detection of complex power quality disturbances.
A wind-PV-thermal-energy storage system(WPTESS)is bundled and transmitted using AC collection,MMC-HVDC transmission,and AC grid connection.Among them,short-circuit faults at the AC sending line will generate new fault characteristics.Firstly,the fault current characteristics are analyzed by combining the fault ride-through control strategies of the WPTESS and MMC-HVDC.When the voltage drops to a range between 0.2 p.u.and 0.9 p.u.,the mechanism by which the fault current phase angle on both sides of the AC sending line is affected by the degree of voltage drop and the upper limit control of the amplitude limiting link is clarified through theoretical derivation.Using phasor analysis,it is revealed that the fault current phase angle on the WPTESS side is less than 75°,and on the MMC-HVDC side is less than 45°.The fault current amplitude characteristics under grounding faults and phase-to-phase faults are analyzed using fault sequence network.Secondly,considering the short-circuit ratio and using Thevenin's equivalent method,the reasons for the reduction of fault current phase angle due to the increase in system short-circuit capacity caused by thermal power integration are explored.In addition,the characteristics of the fault current amplitude of a wind-PV-energy storage system varying proportionally with input power are analyzed.Finally,the correctness of the theoretical analysis is verified through RTDS simulation.
Accurately describing the error characteristics of wind power output prediction is helpful for the rational allocation of system reserve capacity and the optimization of day-ahead scheduling plans.This paper proposes a day-ahead optimization scheduling method for power systems considering wind power ramping reserve requirements.First,based on the wind power ramping segment,the ramping characteristics are extracted,and a two-dimensional interval of ramping amplitude-predicted power is established.The adaptive kernel density estimation method is used to fit the probability distribution of wind power prediction errors.Then,based on the distribution of wind power prediction errors,the system reserve requirements are determined.Looking for minimal comprehensive operating costs of reserve costs and risk costs,a continuous-time day-ahead optimization scheduling model is established.Next,the Bernstein polynomial interpolation solution space transform is adopted to complete model conversion,thereby optimizing reserve capacity,unit combination,and output plans.Finally,a case study verifies that the established wind power prediction error distribution model can accurately describe the stochastic characteristics of wind power.The proposed day-ahead scheduling method can effectively allocate system reserve capacity,ensuring operational safety and economic efficiency.
With the construction of large-capacity long-distance high-voltage direct current transmission projects and the large-scale integration of renewable energy,frequency security of the power system is facing severe challenges.For fast and accurate online assessment of frequency security,a data-driven model based on a metric learning(ML)and generative adversarial network(GAN)is proposed.First,the key frequency security indicators are selected as the outputs of the model,and an input feature set is constructed.Then,the improved Wasserstein generative adversarial network(WGAN)based on the Wasserstein distance metric is used to learn the distribution information of historical operation scenarios of power systems.This generates operational scenarios covering typical modes to build the training sample set.Considering the inapplicability of a single machine learning model to frequency security assessment with complicated operational modes of power systems,a combined assessment model for assessment composed of multiple sub-models is constructed based on metric learning for a kernel regression(MLKR)method.Finally,a simplified Shandong power system example is used to verify the effectiveness of the proposed method.
Long control link delays can lead to negative damping in the impedance range of flexible DC transmission systems based on modular multi platform converters(MMC).These can easily interact with distributed capacitors in the AC system and cause high-frequency oscillations.Although existing active damping suppression strategies can effectively suppress high-frequency oscillations,they will deteriorate the impedance characteristics of the mid frequency range and increase the risk of mid frequency oscillations.To effectively suppress mid-to-high frequency oscillations,this paper proposes a passive damping suppression strategy by paralleling a passive impedance reshaping device at the AC side bus of the MMC.First,an MMC simplified model is derived and its accuracy is verified.Secondly,impedance reshaping device parameters are designed based on the Nyquist stability criterion and minimum power loss constraint to achieve impedance reshaping of the MMC system.Finally,the impedance method is applied to compare the impedance characteristics of the MMC system before and after reshaping,and the correction effect of the impedance reshaping device on the impedance of the MMC system is analyzed.An MMC electromagnetic transient simulation model with a passive impedance reshaping device is built in PSCAD/EMTDC.The results show that this suppression strategy can effectively suppress the oscillation phenomenon of the MMC system in the mid-to-high frequency range,with little impact on the steady-state characteristics of the system,and low fundamental frequency power loss.
With the expansion of the power grid and the increase of the proportion of new energy sources,the uncertainty and random factors of the power grid increase,endangering the safe operation of the system.It is particularly important to find out the critical links of vulnerability in the power grid to ensure the reliability of the power grid operation.Aiming at the problem that the identification speed of the traditional critical link of vulnerability identification methods is slow and difficult to meet the actual operation requirements of the power grid,the improved graph attention network(IGAT)based identification method of the critical link is proposed.First,the evaluation index set is established by combining the complex network theory and the actual operation data of power grid.Secondly,IGAT is used to dig out the mapping relationship between various indicators and critical links of vulnerability during the operation of the power grid,establish the identification model of critical links of vulnerability,and optimize the original graph attention network considering the training accuracy and efficiency.Thirdly,the original data set is obtained through simulation,and the identification model is trained,verified and tested.Finally,the model is applied to the improved IEEE 30-node system and the actual power grid,and the results show that the proposed method is feasible,and the accuracy and speed are better than that of traditional methods.It has certain engineering utilization value.
Aiming at the problem that the bus voltage in a low-inertia DC microgrid is prone to be affected by internal power fluctuations, an adaptive virtual inertia control strategy for a grid-connected converter of a DC microgrid based on an improved model prediction is proposed. Firstly, the adaptive analog virtual synchronous generator (AVSG) is introduced into the voltage outer loop by combining the inertial parameters with the voltage change rate, and the flexible adjustment of the inertial parameters is realized. Secondly, the improved model predictive control is introduced into the current inner loop to realize the fast-tracking of the given current value and improve the dynamic characteristics of the control system. Finally, a system model is established based on Matlab/Simulink for simulation. The results show that compared with the traditional virtual inertia control strategy, the proposed control strategy has smaller bus voltage fluctuation amplitude and better dynamic performance; when a 10 kW load mutation occurs, the magnitude of bus voltage drop is reduced by 60%, and the voltage recovery time is shortened by 30%. The proposed control strategy can effectively improve the stability of DC bus voltage and the operation ability of the system under asymmetric conditions.
With the gradual increase in the proportion of new energy generation, grid-forming control with dynamic regulation and support capability of frequency and voltage has become a research hotspot. However, the current-limiting control of the converter reduces the transient stability margin and leads to the complex evolution of the transient instability mode, which poses a great challenge to the stable operation of new energy units. In this paper, the influence of different current limiting strategies on the transient stability of the single-loop voltage-magnitude controlled grid-forming converter is analyzed, and three typical instability evolution modes are summarized. Further, the influence law of virtual inertia and damping parameters on the transient stability is analyzed by critical clearing angle (CCA), and critical clearing time (CCT), the mechanism of abnormal phenomena of virtual inertia and damping parameters on transient stability is revealed based on the equal area criterion, and the conditions for abnormal phenomena are clarified, finally, the transient instability evolution modes and the mechanism of abnormal phenomena is verified by electromagnetic transient time domain simulation.
针对电力变压器状态评价过程中存在的不确定性,提出一种基于云相似度和证据融合的电力变压器状态评价方法.首先,考虑到各指标在状态等级边界处的随机性与模糊性问题,采用云模型来建立状态评价的基本框架.其次,为兼顾状态等级划分的严明性和模糊性,采用改进的云熵优化算法确定云模型的熵值.然后,考虑到指标数据本身的不确定性,利用正向云发生器和云合成算法生成各试验项目待识别云和标尺云,并采用模糊贴近度计算二者间的相似度作为证据源的基本概率分配.最后,采用考虑证据可信度和不确定度的冲突证据修正方法修正证据源,并融合不同证据以判断电力变压器的最终状态.经实例验证,相较于传统方法,所提方法能够有效处理状态评价过程中的不确定性,评价结果符合电力变压器的真实情况,对电力设备状态评价有一定参考价值.
针对现货环境下的储能容量成本回收问题,提出了一种考虑容量支撑贡献的储能容量补偿定价方法.首先,通过分析储能顶峰放电时的等效容量,提出基于不同时长储能容量折损系数的容量补偿机制.然后,建立含储能参与的现货市场出清模型来模拟储能运行状态,并建立包含市场随机场景的储能等效容量模型.通过缺供负荷概率指标来计算不同时长储能的容量折损系数和容量电价,使得对储能的容量补偿能够反映储能对系统的实际容量贡献.最后,通过算例验证了该方法能够对不同时长的储能进行有效的容量补偿.
为有效提升台风天气下主动配电网的韧性,提出同时考虑分级减载和同级负荷削减的主动配电网韧性提升方法.首先,综合考虑台风天气下强风和暴雨对配电网的影响,通过建立 Batts 台风风场模型和暴雨压强模型实现了对配电网元件故障率的量化分析,进而采用蒙特卡洛(Monte Carlo)法模拟台风天气下的主动配电网故障场景,并利用系统信息熵进行场景筛选,确定故障规模.其次,提出了同时考虑最大化一级负荷存活量与故障孤岛中同级负荷削减逻辑的主动配电网分级减载策略.然后,提出包括综合鲁棒性、一级负荷损失速度和损失率、总负荷曲线面积缺失比的 4 个主动配电网韧性评估指标,并通过遗传-粒子群融合算法(hybrid GA and PSO algorithm,GA-PSO)对配电网韧性评估模型进行高效求解.最后,基于Matlab 2020a仿真平台建立某实际配电网和IEEE 118节点测试系统算例,验证了提出的考虑分级减载的台风天气下主动配电网韧性评估方法的正确性和有效性.
随着新型电力系统发展,输电监测文本数据呈现出体量大、增速快等特点,且因行业数据传输协议私有化,导致数据检索性能低,影响输电线路实时决策分析.因此提出了基于MapReduce的输电监测数据智能检索模型.首先,改进了SimHash算法,实现输电线路在线监测文本数据检索向量的高效提取.并引入多属性决策以及综合评分机制,实现目标数据的精准检索,提升数据的检索精度及查全率.其次,针对数据体量大、增速快的特点,设计了基于MapReduce的电力数据检索模型.最后,通过电网实例对比分析,验证了所提方法的检索精度、查全率及检索效率.
针对单相级联H桥整流器(cascaded H-bridge rectifier,CHBR)输入输出瞬时功率不平衡导致直流侧电压中含有二倍频纹波,造成网侧电流低次谐波污染的问题,提出了一种嵌入N次陷波滤波器的混合控制策略.首先根据CHBR数学模型和双闭环控制策略,分析CHBR网侧电流谐波产生的机理及推导其谐波分布规律.其次在dq旋转坐标系下的前馈解耦控制基础上,引入瞬态直接电流控制思想.然后分析了N次陷波滤波器对网侧电流谐波的抑制效果,给出N次陷波滤波器参数设计和离散方法.接着详细讨论了N次陷波器嵌入电流内环控制的设计方法,该方法有效抑制了网侧电流的3、5、7次等低次谐波,减少了PI控制器的负荷.最后,实验结果表明所提控制策略具有更小的电压超调和更短的动态响应时间,明显降低了网侧电流畸变率.
针对电压行波传感器二次侧故障行波信号不能真实反映电网一次行波波形特征的问题,提出了一种基于L1正则化反演的电压行波高精度检测方法.首先,分析了行波传感器的非理想传变特性,揭示了一、二次行波信号的波形差异性.在此基础上,提出利用小波包变换对观测信号进行多尺度分解,并对各频段信号分别进行反演的方法,从而减小由行波传感器引起的畸变误差.其次,在反演模型中引入L1正则化约束对模型进行稀疏性刻画,使反演结果更能体现真实故障波形特征.最后,利用快速迭代收缩阈值算法(fast iterative shrinkage-thresholding algorithm,FISTA)进行迭代求解,将各分量的反演波形线性叠加,实现故障行波信号的精确还原.仿真和实验结果表明:与直接反演相比,所提方法能够实现故障行波在时域和频域上的高精度真实测量,在微弱故障和噪声环境下也能获得较为精确的反演结果,具有一定的工程应用价值.
随着光伏、储能等分布式电源广泛接入,导致配电网区域电压波动频繁.针对分布式电源分散接入带来的不确定性问题,提出了一种计及分布式电源集群不确定性的配电网分散鲁棒电压控制方法.将大规模分布式电源聚合成相互关联的集群,对电压控制进行分区域调节.首先,针对配电网结构复杂和分布式电源点多面广的问题,设计一种基于改进Louvain算法的配电网集群划分方案,利用模块度函数并兼顾了集群的电压灵敏度和分布式电源调控容量.然后,由于分布式电源接入后配电网潮流更加复杂多变,在划分集群的基础上提出一种考虑不确定性的分散鲁棒控制方法,协同各分布式电源集群的调控能力,抑制由于模型参数及功率波动等不确定性导致的配电网电压波动.最后,通过算例分析验证了所提方法的可行性和有效性.
基于模块化多电平换变流器(modular multilevel converter,MMC)的光伏直流升压并网系统为大容量并网提供了更多的可能.而光伏升压系统直流侧发生故障时暂态过程复杂,故障电流上升迅速且峰值过高.针对此问题,首先对系统直流侧双极短路故障时的直流升压变换器与MMC换流站进行了故障过程分析,并通过故障回路分别计算出了可靠闭锁下流经短路点的故障电流.然后针对MMC换流站的故障电流,依据其控制原理,提出基于电压变化的主动限流控制策略.该控制通过引入电压变化量动态改变桥臂参考电压,从而限制故障电流.最后通过PSCAD仿真模型验证了故障分析结果与限流效果,经检验,该控制策略可以有效减小断路器的开断电流以及桥臂过流峰值.
针对内置式永磁同步电机(interior permanent magnet synchronous motor,IPMSM)由于内部参数变化、外部扰动等各种不确定性因素导致控制性能不佳的问题,提出一种无模型超螺旋快速终端滑模控制方法.首先,建立考虑 IPMSM 不确定性的新型超局部模型,结合超螺旋算法和快速终端切换函数设计无模型超螺旋快速终端滑模控制器,确保系统状态有限时间收敛,并有效减小抖振.其次,设计扩展滑模扰动观测器精准估计超局部模型中的未知部分,并前馈补偿给设计的控制器,进一步提升系统的抗干扰能力和跟踪性能.最后,通过与PI控制和传统无模型滑模控制进行仿真实验对比,验证了该方法具有更快的收敛速度和更强的鲁棒性.
在低碳经济背景下,为提高向大用户直供能的灵活性,解决多主体在有限理性下共建共享的非对称性行为决策问题,提出多新能源场站联合投资低碳综合微网氢储能的演化行为分析方法.首先,利用异质性新能源场站发电与大用户用能的时空互补特性,提出多主体共享低碳综合微网氢储能的基本框架,使各发电主体形成"自平衡+直供电+余量上网"模式,在增强新能源友好并网能力与供用能灵活性的同时降低投资成本.然后,考虑多主体在参与氢储能共建共享时的非对称行为决策,基于复制者动态方程与演化稳定定理,对多新能源场站在有限理性下的多投资策略进行演化行为分析.最后,以我国西北部新能源汇集区域为算例对发电侧在联合投资中的合作行为进行推演,结果表明政府可通过对间断性发电特征的电站给予适当补助,或细化电价设置来激励异质性新能源场站的联合投资行为,从而促进氢储能的规模化发展;新能源场站则可通过提高装机容量来保证自身定值收益.
为了使微电网控制系统中PI控制器的参数能够更好地适应可再生能源的随机性和波动性,提出了基于自适应步长的四分区多策略果蝇优化算法(fruit fly optimization algorithm,FOA)对PI参数进行实时优化.首先,以风光燃储微电网不同微源控制系统中的变换器为控制对象,建立微电网整体控制系统模型,基于此模型实时调整PI参数.然后,根据不同果蝇个体的适应度值将果蝇种群分为 4 个区,同时考虑 4 个区果蝇收敛性以及多样性的差异,设计不同的自适应更新策略.最后,采用所提算法对各微源控制过程中的PI参数进行寻优,与其他3种智能算法进行对比,验证了所提算法的可行性和优越性.仿真结果表明,所提算法可以使系统变换器响应速度更快,输出更加稳定.
电动汽车(electric vehicle,EV)的大规模发展,使得交通网转变为电动汽车和燃油车(gasoline vehicle,GV)随机混合出行的复杂网络;配电网也转变为供需双侧不确定、"源-网-站-储"高效互动的低碳型电网.在新型交通-配电网耦合系统下,亟需研究交通和配电网的联合规划方法,以满足EV时空充电需求,保障交通和配电网的安全可靠运行.为此,基于EV 和 GV的实时能耗特性差异,建立了混合交通流分配模型,以充分反映 GV对 EV出行和充电行为的影响,进而建立了混合交通流下的交通网多阶段规划模型.此外,考虑储能型柔性开关(energy storage integrated soft open points,E-SOP)的运行特性,协同"源-网-站-储",建立了E-SOP影响的配电网多阶段规划模型.结合交通-配电网物理和"流量-功率"耦合约束,实现了交通-配电网的联合规划.根据 Sioux Falls交通网和 IEEE 69 节点配电网组成的耦合系统算例测试,验证了所提联合规划模型比独立规划的总成本减少了7.21%,并且E-SOP的接入能减少系统无功出力和电压波动.
