基于一种改进型逆凸不等式的混合攻击下多区域时滞电力系统的分布式控制

本文研究了混合攻击下多区域时滞电力系统的分布式协调控制问题。首先,考虑了一种基于混合攻击下（包括拒绝服务攻击和欺骗攻击）的多区域时滞电力系统,在拒绝服务攻击下,时滞电力系统的各个区域之间的通信将恶化为一种切换残余拓扑结构,它的时间特性建模成了模型依赖的平均停留时间的模型。在以负反馈控制为目标的欺骗攻击下,将其建模成了基于符号函数的模型。然后,为了在低惯性和混合攻击下获得时滞电力系统的弹性性能,采用了负荷频率控制与虚拟惯性控制相结合的弹性分布式策略,并将时滞电力系统的负荷频率控制问题转换为切换非线性切换系统控制问题。再通过采用合适的Lyapunov-Krasovskii泛函、改进型逆凸不等式、线性矩阵不等式技术以及切换系统方法,得到了多区域时滞电力系统的稳定性定理。并基于此定理,设计了负荷频率控制和虚拟惯性控制下的分布式控制增益。最后,建立仿真,针对三区域电力系统进行了仿真,仿真结果体现了本文所提出来的稳定性定理和设计的分布式控制增益的优势和有效性。     

基于压缩采样的电力系统负荷频率自动控制研究

为降低新能源接入对电力系统的影响，研究了基于压缩采样的电力系统负荷频率自动控制方法。利用匹配追踪算法对采集到的新能源发电输出功率信号进行稀疏表示，并将稀疏表示后的信号投射至测量矩阵得到低维的测量值。在测量值范围内，运用L₁范数优化方法得到重构后新能源发电输出功率。将该功率与期望功率的偏差作为扰动项，接入基于比例积分(PI)控制器的负荷频率控制模型。利用变论域方法调节PI控制器输入与输出论域。根据模糊推理定义伸缩因子，构建变论域模糊PI控制器使之作用于负荷频率控制模型，完成电力系统负荷频率的自动控制。试验结果证明：该方法控制后的输出功率稳定，负荷扰动下区域控制偏差和频率偏差小，电力系统负荷频率自动控制的精度最高可达99%,具有实用性。     

电力系统小波神经网络负荷频率控制器的研究

将小波网络用于电力系统负荷频率辨识和控制中，建立了非线性的电力系统负荷频率控制LFC模型，用递归NARMA模型的小波网络辩识器对LFC模型进行了辩识，利用Akaike’s的最终预测误差准则FPE和信息准则AIC，进行了隐层节点数目和反馈阶次的计算，用辩识结果建立了NARMA模型的小波网络的控制器，对LFC模型进行控制，理论和仿真表明辩识和控制模型可取得较好效果。     

基于S函数的分布式电网并网方法仿真研究

以燃气内燃机驱动的同步电机作为分布式电源,对小型孤岛分布式微型电网并网进行了研究,根据同步电机的四阶基本方程提出利用PI控制方法,将频率和相位差反馈到励磁电压,对其进行励磁控制,以改变分布式孤岛微型电网的频率与相位,使孤岛微型电网与大电网近似满足并网条件。在不能调节分布式电源有功增加孤岛微电网频率的情况下,在不切除负荷、不中断供电的情况下,采用上述方法。通过励磁控制,能够降低微电网电压,减少负荷消耗功率,提高微网频率。在MATLAB中编写S函数进行仿真,可以证明在保持原动机功率不变的情况下,调节PI参数,可使孤岛微型电网与无穷大电网的频率和相位保持一致。结果验证了方法的可行性。     

二维Logistic分数阶微分方程的离散化过程

针对二维Logistic分数阶微分方程的求解问题，引进了一种离散化方法对其进行离散求解。首先，将二维Logistic整数阶微分方程推广到分数阶微积分领域；其次，分析相应具有分段常数变元的二维Logistic分数阶微分方程并应用提出的离散化方法对模型进行数值求解；然后，根据不动点理论讨论该合成动力系统不动点的稳定性，给出了在参数空间内二维Logistic分数阶系统发生第一次分岔的边界方程；最后，借助Matlab对模型进行数值仿真，并结合Lyapunov指数、相图、时间序列图、分岔图探讨模型更多复杂的动力学现象。仿真结果显示，所提方法成功对二维Logistic分数阶微分方程进行离散。     

基于滑模控制的含风储多域电力系统负荷频率控制

建立含风储多域互联电力系统负荷频率控制(LFC)模型,同时考虑系统参数不确定性、储能系统和传统机组控制信道延时问题.为提高系统鲁棒性,降低储能系统的容量配置,针对含风储的LFC模型,设计滑模负荷频率控制器,并提出滑模负荷频率控制器和储能协调的控制策略.算例分析表明,所提出的协调控制策略在新能源大规模渗透和系统负荷波动情况下能够有效减小系统频率偏差和区域控制偏差,同时降低储能系统的配置容量,提高电力系统安全稳定运行的经济性.     

分数阶B样条小波域的图像变分去噪

分数阶B样条具有分数阶逼近,可以更好地刻画图像纹理部分。将分数阶B样条小波推广到二维领域,利用分数阶B样条小波进行图像阈值去噪,提出了分数阶B样条小波域图像去噪的变分模型。同传统小波函数与全变差结合模型比较,分数阶B样条小波在保持纹理和去噪方面得到了明显改进。     

考虑通信时滞和采样周期的电力系统负荷频率控制

针对电力系统负荷频率稳定控制问题,本文提出了一种时滞/采样相关的离散负荷频率控制(LFC)方案.首先,考虑通信网络传输时滞和反馈信号采样周期对系统的影响,建立闭环电力系统LFC模型.然后,基于建立的LFC模型,利用双边闭环Lyapunov泛函和LMI技术,提出了低保守性的时滞/采样相关稳定准则和控制器设计方法,确保所提控制方案能在一个较大的通信时滞和采样周期条件下保持电力系统稳定运行.最后,通过单区域和三区域电力系统验证所提方法的有效性.仿真结果表明,所设计LFC方案比现有其他LFC方案的控制性能更佳,鲁棒性更强,并且能在一定大小的通信时滞条件下提升电力系统的动态性能.     

基于BF-PSO的船舶电站柴油机分数阶控制器

针对船舶电力系统的频率稳定性问题,对船舶电站柴油机调速系统设计了分数阶PIλDμ控制器。采用细菌觅食-粒子群混合优化(BF-PSO)算法对分数阶PIλDμ控制器参数进行优化整定,解决了分数阶PIλDμ控制器整定参数多、设计复杂的问题。对分别采用分数阶PIλDμ控制器和传统整数阶PID控制器的柴油机调速系统进行了仿真和对比。结果表明,在同等条件下优化得到的分数阶PIλDμ控制器能够有效抑制模型参数摄动,鲁棒性更强,具有更好的控制效果。     

风电介入下的多区域互联电力系统分布式经济模型预测负荷频率控制

针对风电介入下的多区域互联电力系统,提出一种分布式经济模型预测负荷频率控制策略.通过将大规模互联电力系统分解成若干个动态耦合的子系统,这些子系统能够利用网络交流并共享信息,使得各区域的控制器实现各自优化问题的求解.同时,在满足状态约束和控制输入约束的前提下,遵循传统火力发电优先、风力发电配合的原则,通过在线求解优化问题,实现风电介入下的多区域互联电力系统的负荷频率控制.为了提高系统整体运行经济性,所提出的分布式经济模型预测控制器将负荷调频成本、燃料消耗成本以及风力发电成本等经济性指标考虑在内.仿真结果表明,在阶跃负荷扰动下,所设计的控制器不仅可以满足调频要求,在降低计算负担和提高经济性能方面也具有一定优势.     

基于云计算的多因素电力负荷坏数据自动检测方法

电力负荷坏数据对电网具有严重的破坏性,为了提高对电力负荷坏数据的检测能力,提出基于云计算的多因素电力负荷坏数据自动检测方法。采用云计算模型进行多因素电力负荷坏数据的分布式重组和集成运算,构建多因素电力负荷坏数据的云网格分布模型,在云网格空间中采用主成分特征分析方法进行多因素电力负荷坏数据特征检测,在双极型直流配电网中实现对多因素电力负荷坏数据的共模分量计算,提取电力负荷坏数据的能量谱特征量,根据负荷用电特性、潮流分布及其容量等参数,实现对多因素电力负荷坏数据的特征检测。仿真结果表明,采用该方法进行多因素电力负荷坏数据检测的自动性较好,检测准确率较高。     

A Model Predictive Scheduling Algorithm in Real-Time Control Systems

The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.      

Resilient distributed model-free adaptive predictive control for multiarea power systems under denial of service attack

This paper proposes a novel load frequency control (LFC) strategy for power systems based on distributed model-free adaptive predictive control. First, a power system dynamic model is established by the input and output signals of the power system. Then, a distributed model-free adaptive predictive control algorithm is established for the power system under denial-of-service attack by the established power system dynamic model, and a predictive compensation algorithm is designed to compensate the impact of DoS attack. Based on the designed distributed model-free adaptive predictive control algorithm, the frequency tracking error of the multiarea power system is bounded. The scheme is independent of the structure of the power system and does not need to measure any state signals of the power system, relying only on the input and output data of the power system. The results of simulations demonstrate the effectiveness and superiority of the design.     

Demand response scheduling to support distribution networks operation using rolling multi-period optimization

This paper presents an approach of demand response (DR) scheduling with thermostatically controlled loads (TCL) in a distribution grid with high penetration of distributed generations (DG). In this approach, household TCL are employed as flexibility resources to support the distribution network for mitigating voltage or congestion constraints. A two-stage rolling optimization based control scheme is proposed to determine the optimal operating status of flexible loads using the forecast of generation and demand in the distribution system. The proposed methodology is conducted in a distribution test feeder with realistic scenarios. The simulation results have shown the usefulness and efficiency of the proposed method in improving the network operation and increasing the hosting capacity of DG.     

基于调控云的电网供区分析方法

为了解决调控云平台下电网大规模数据集的电网供区分析及其效率的问题,文中设计了基于调控云模型的电网供区分析方法.首先,本文定义了调控云电网供区数据模型.其次,在此数据模型基础上设计了一种电网供区分析方法并利用上述方法实现了电网供区分析展示应用.最后,针对某地区电网的调控云真实的厂站模型及拓扑数据进行实验验证,实验结果表明本文提出的基于调控云模型的电网供区计算方法能较好的分析出电网供区,满足实际电网调度的需求.     

Distributed generation system control strategies with PV and fuel cell in microgrid operation

Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. In this paper the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads. Real and reactive power (PQ) control and droop control are developed for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. Two PI controllers were used in the PQ controller, and a novel heuristic method, artificial bee colony (ABC), was adopted to tune the PI parameters. DGs can be controlled by droop control both under grid-connected and islanded modes. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. Through voltage, frequency, and power characteristics in the simulation under different scenarios, the proposed control strategies have demonstrated to work properly and effectively. The simulation results also show the effectiveness of tuning PI parameters by the ABC.     

基于多重知识推演的电力遥控防误自动闭锁方法研究

电力遥控受到伺服系统的逐阶反馈因素作用产生误操作,提出基于多重知识推演的电力遥控防误自动闭锁控制方法。构建系统的硬件结构模型,采用二维超精密定位方法进行电力遥控的自动闭锁执行器数学模型分析,构建电力遥控防误自动闭锁的动力学模型,以D/A和A/D转换器的固有频率作为输入特征量进行电力遥控防误自动闭锁的多重知识推演,采用滑模积分控制方法进行电力遥控防误操作的伺服位置、电流载荷和压力等参数估计,根据参数反馈结果进行辨识模型设计,根据多重知识推演结果实现电力遥控多状态开关可靠性建模和自动闭锁控制。仿真结果表明,采用该方法进行电力遥控防误自动闭锁控制的稳定性和容错性能较好,灵敏度较高,且具有较高的控制精度。     

Distributed Consensus‐Based Optimization of Multiple Load Aggregators for Secondary Frequency Control

Responsive loads can act as spinning reserve which is incorporated with the conventional AGC units for the improvement of frequency reliability. In this paper, a distributed consensus‐based optimal strategy of responsive load aggregators and AGC units is presented to provide secondary frequency control in the power system. Specifically, two‐step optimal model is proposed. In the first step, Lambda‐Iteration method is used to yield the total active power command for load group and AGC group. In the second step, the distributed optimal algorithm with θ‐logarithmic barrier cost function is proposed to allocate the active power command to multiple load aggregators and AGC units while guaranteeing consensus among individual incremental costs. In achievement of such a consensus, two virtual pinners are utilized to generate the regulation signal only shared with a part of load aggregators and AGC units, respectively. Moreover, the communication topology among load aggregators is a strongly connected digraph, the same as to the AGC units. Finally, simulation results show that the system frequency can be greatly improved, while the total cost of the whole system can be minimized with both equality and inequality constraints.     

Parameter identification of aggregated thermostatically controlled loads for smart grids using PDE techniques

This paper develops methods for model identification of aggregated thermostatically controlled loads (TCLs) in smart grids, via partial differential equation (PDE) techniques. Control of aggregated TCLs provides a promising opportunity to mitigate the mismatch between power generation and demand, thus enhancing grid reliability and enabling renewable energy penetration. To this end, this paper focuses on developing parameter identification algorithms for a PDE-based model of aggregated TCLs. First, a two-state boundary-coupled hyperbolic PDE model for homogenous TCL populations is derived. This model is extended to heterogeneous populations by including a diffusive term, which provides an elegant control-oriented model. Next, a passive parameter identification scheme and a swapping-based identification scheme are derived for the PDE model structure. Simulation results demonstrate the efficacy of each method under various autonomous and non-autonomous scenarios. The proposed models can subsequently be employed to provide system critical information for power system monitoring and control.