In order to address the challenge of ensuring convergence and stability in AC/DC hybrid microgrid cluster with self/mutual communication delay, this study proposes a hierarchical control strategy. By leveraging the finite-time consistency algorithm, this strategy facilitates autonomous operation of sub-microgrids and enables mutual aid and assistance within the microgrid cluster. The proposed control strategy consists of two levels: the sub-microgrid level and the microgrid cluster level. Both levels utilize primary control along with a secondary adjustment term based on improved finite-time consistency. This strategy aims to ensure frequency and voltage stability, power equalization in the sub-microgrids, and economic allocation of power in the microgrid cluster. The proposed control strategy improves the traditional finite-time consistency protocol, considers the effect of time delay on system stability. It also introduces the convergence coefficient to strike a balance between system economy and stability objectives. Through a comparison with the traditional consistency algorithm, simulation results demonstrate that the proposed control strategy enhances the robustness of the system, enables coordinated control of frequency and voltage in sub-microgrids, facilitates economic power allocation in the microgrid cluster, and achieves faster convergence speed.

The parallel operation of bidirectional converters is a promising way to increase the power rating, efficiency and reliability, and maintain the power balance in an AC/DC hybrid microgrid. However, zero-sequence circulating current (ZSCC) and active-reactive disturbance current will be generated under the unbalanced conditions of the DC side and AC side. This paper aims to study the performance characteristics of the parallel converters and propose an effective control scheme to control the ZSCC and active-reactive disturbance current. Firstly, the generation mechanism of ZSCC and active-reactive disturbance current are analyzed based on an averaged phase-leg model. The coupling relationship is derived and two categories of disturbance can be found: the primary disturbance caused by the unbalanced condition of one side, and the secondary disturbance generated by the primary disturbance and the unbalanced condition of the other side. On this basis, a decentralized control scheme is proposed to suppress the primary disturbance, thereby eliminating the ZSCC and active power oscillation without the distortion of output current. Finally, simulation cases in PSCAD/EMTDC verify the correctness of the theoretical analysis and the validity of the proposed control scheme.

Robust optimization (RO) is an important method to deal with the uncertainty of wind power. The main challenge is to reduce the redundancy of the uncertainty model, thereby improving the economy of the scheduling plan while ensuring its robustness. In this paper, aiming at the day-ahead robust scheduling problem, the uncertainty model of wind power is improved by fitting 3 typical characteristics. First, the kernel density estimation (KDE) model and the non-parametric Copula model are combined to fit the nonlinear correlation between forecast power and forecast error, and the forecast error boundary constraints are established. Second, by combining the Mallet algorithm, the autoregressive integrated moving average (ARIMA) model, and the t distribution model, the temporal dimension constraints are established to describe the time-series characteristics of wind power. Third, the spatial dimension constraints are established based on the high-dimensional non-parametric regular vine (R-vine) Copula model, and the spatial correlation of multiple wind farms is reflected. Based on the above wind power uncertainty model, a 2-stage day-ahead robust scheduling model is established. The case study shows that the proposed wind power uncertainty model helps to achieve the balance between economics and robustness of the scheduling plan.

为发挥空调负荷的调控潜力,该文将变频空调视为广义储能,提出一种计及温度不确定性的配电网广义储能分层调控策略.首先,建立变频空调广义储能模型,基于广义储能特征参数,将广义储能聚合为多个集群.在此基础上,构建广义储能集群分层调控框架,将广义储能调控分为广义储能集群调控与广义储能群内调控两层.在广义储能集群调控层面,提出配电网广义储能集群分布鲁棒优化调控策略,避免了室外温度不确定性影响用户舒适度;在广义储能群内调控层面,提出了广义储能集群群内功率分配策略,实现广义储能等效荷电状态变化的一致性.最后,基于改进的IEEE 33节点系统开展算例仿真,仿真结果表明所提的调控策略在保障用户舒适度和调控公平性的前提下降低了配电网的运行成本,提高了可再生能源的消纳水平.

Research on distribution network including VSC-connected distributed generation control has arouse more consideration due to the application of DC distributed generation. In this paper, a hierarchical economical MPC control strategy for VSCconnected DG is proposed to realize the efficient and economical control for VSC-AC system. Firstly, the dynamic characteristic of VSC-DG system is analyzed, and the time domain model for this system is established; secondly, the lower layer of overall control strategy is established, which utilizes MPC scheme to realize the track of power to reference value efficiently; then, a consensus protocol is established in upper layer to seek the power reference value for lower layer, realizing the economical distribution of active power; finally, simulation result of hybrid network model verifies the effectiveness of proposed control strategy.

Fault diagnosis is important to stable operation of power systems, and the machine-learning-based fault-diagnosis models were widely studied because of their strong generalization ability. However, the model structures are generally designed according to the topology of power systems. Once there are changes in topology, the system fault characteristics might change, and the models structure and parameters are often required to be adjusted for applying to the new systems. To avoid frequent adjustment work of fault-diagnosis models when system topology changes, we propose a novel fault-diagnosis model for power systems. First, a new data preprocessing using gradient calculation and similarity assessment is presented, and the gradient similarities among the multichannel electrical signals are converted to the visualized similarity images, which are fed to the neural network for further processing. Second, the spatial pyramid pooling (SPP) and hashing classifier (HC) are used in the convolution neural network. With the aid of the SPP and HC techniques, the structure of the fault-diagnosis model can maintain unchanged even though there are topological changes in the power systems. To validate the effectiveness of the proposed model, several state-of-the-art fault-diagnosis models are used for comparison. The results show that the proposed model with unchanged structure is well performed in accuracy and noise immunity, and friendly to the parameters setting.

变压器作为电力系统的重要设备,其故障预防和寿命管理对提升电力系统运行的可靠性具有重要意义.为实现变压器设备故障状态的高效评估,文中从变压器故障机理的关联性和复杂性特征出发,建立了基于多元物理场耦合与模糊数理论的变压器故障演化评估模型.首先,基于变压器故障的关联性特征,建立变压器故障演化网络图.其次,考虑到变压器故障机理复杂以及监测数据有限,采用COMSOL软件进行多元物理场耦合仿真;同时结合模糊数理论,建立变压器故障演化概率计算模型.最后,引入风险熵表征变压器故障演化的不确定性,提出变压器故障演化路径最大概率表达式,并将其转化为线性规划问题,进而得到不同初始风险因素下变压器最大概率故障演化路径.算例分析表明:模型计算结果能够反映变压器故障统计数据特征,可为变压器的状态评估提供参考.

为发挥变频空调(IAC)集群在平抑分布式电源波动方面的调控潜力,该文提出一种变频空调集群多时间尺度模型预测控制策略.首先,根据IAC调控方式的不同,构建基于温度指令调控的等效电机模型与基于直接功率控制的等效储能模型,分别用以实现IAC集群的15min级与1min级控制.在温度指令调控方面,考虑IAC响应目标温度所需的动态过程,利用集中式控制架构实现温度指令的优化整定,并进一步研究了 IAC的有序动作策略以缓解大规模IAC同时动作所带来的功率冲击问题;在直接功率控制方面,计及IAC间的状态差异,采用集中优化、自主响应的方式实现IAC集群的功率优化与快速响应.最后,经算例仿真,验证了该文所提控制策略可实现IAC集群的多时间尺度协同,能够有效平抑分布式电源的功率波动.

为充分发挥温控负荷的调控潜力,以空调负荷为例,对空调负荷建模及集群控制策略进行了研究.首先从热力学模型与电气模型两方面建立了完善的空调负荷模型;其次考虑空调的启停次数以及使用寿命等实际因素,提出了一种基于改进温度优先序列的空调负荷集群群内控制策略;最后分析了负荷电压变化对集群响应功率的影响,基于频率下垂控制,提出了一种考虑功率响应偏差、电压约束以及集群等效荷电状态的空调负荷集群控制策略.基于MATLAB/Simulink的仿真结果表明,所提的空调负荷集群控制策略能够平抑微电网功率波动,同时能够有效降低空调的启停次数,减小对空调使用寿命的影响.

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem.The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation.In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics.On this basis, a scenario generation method for multiple wind farms is proposed.For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCHt) model is proposed to capture the time-series characteristics of wind power generation.For the dependence structure, a timevarying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms.Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated.The effectiveness of the scenarios is evaluated in 3 aspects.The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

To fully adapt to the distributed access of renewable energy, microgrid technology has been developed rapidly. Aiming at the coordination and efficient regulation of distributed resources in microgrid, this paper proposes a distributed autonomous economic control strategy for microgrid considering event triggering mechanism. First, a distributed autonomous economic control architecture is built to provide a distributed operation architecture for optimal regulation of the microgrid. Secondly, a distributed secondary control strategy based on the consensus control theory is established to realize the economic allocation of active power as well as safe and stable operation of the microgrid. On this basis, an event trigger protocol based on the consensus error of the control variables is constructed, which is conductive to reduce redundant communication. The stability of the event trigger protocol is deduced by means of Lyapunov function analysis. The simulation analysis based on the equivalent microgrid verifies that the proposed control strategy can reduce redundant communication and acquire fair distribution of reactive power and active power among DGs, realizing distributed, economical and safe operation of microgrid.

This paper proposes a distributed control strategy based on consistency principle to realize the autonomous economic control of DC distribution network. We first utilize dynamic weight updating method to realize the "plug and play" for DGs. Then we construct the consistent control protocol for distribution network, in which the secondary adjustment of incremental cost and voltage is introduced into the traditional droop control strategy for DG to realize the autonomous and economic control for distribution network, as well as maintaining the stability of the system; in the meantime, by introducing the event triggered mechanism into the consistency control protocol, the number of unnecessary communication in the system is reduced, thus reducing the communication cost between DGs and improving the stability of communication system. Finally, the simulation results based on the distribution network model verify the effectiveness of the proposed method.

as the installation scale of distributed generations in the distribution network continues to increase, the distribution network has more flexible scheduling resources. However, a large number of power electronic devices in distributed power generations pose a threat to the power quality of the distribution network. Based on the frequency coupling matrix, a harmonic optimal power flow model considering harmonic coupling factors is established. Using the method of convex relaxation, the original non-convex model is transformed into a semi-definite programming model that can be solved by mature planning software. An actual township distribution network example is used to verify the rationality of the model proposed.

电力设备在高温、高湿、重载等极端条件下运行,将严重影响电网安全稳定运行和公共安全,亟须研究配电网主设备在多重极端条件下的运行状态影响机理及其寿命预测技术.提出以演化机理与有限元仿真相结合的设备运行状态演化模拟技术,根据设定的外部边界条件,以有限元仿真计算内部各区域场强、压力、温度等指标,融合演化机理Arrihenius公式,得到设备老化率模型,从而实现对配电网设备老化状态与运行寿命的预测评估.

Accurate faulted-phase classification for transmission lines is important to ensure the power systems security, and the machine learning-based methods were widely studied because of their strong generalization ability. However, these methods often require precise marking of fault occurring time. Also, existing methods face the challenges in real-world applications because they are trained using the laboratory samples. In this paper, we propose a novel faulted-phase classification model for the transmission lines. First, the gradient similarities among multi-channel electrical signals are converted to the proposed gradient similarity-based images (GS-images), which are used as the input of neural network. With the aid of this conversion, the fault features are more obvious and there is no need to mark the fault occurring time. Second, cross-domain adaption is introduced to the optimization objective of convolutional neural network (CNN). Through this adaption, the distribution discrepancies of the top-layer features extracted by the neural network between laboratory and real-world fault samples are reduced significantly, thereby increasing the model applicability in diagnosing the real-world faults. To validate the effectiveness of the proposed model, several state-of-the-art faulted-phase classification models are used for comparison. The results show that the proposed model is well-performed in accuracy, noise immunity and real-world applicability.

The grid-connected converter (GCC) is widely used as the interface between various distributed generations and the utility grid. To achieve precise power control for GCC, this paper presents a model predictive direct power control (MPDPC) with consideration of the unbalanced filter inductance and grid conditions. First, the characteristics of GCC with unbalanced filter inductance are analyzed and a modified voltage control function is derived. On this basis, to compensate for the power oscillation caused by unbalanced filter inductance, a novel power compensation method is proposed for MPDPC to eliminate the DC-side current ripple while maintaining sinusoidal grid current. Besides, to improve the control robustness against mismatched filter inductance, a filter inductance identification scheme is proposed. Through this scheme, the estimated value of filter inductance is updated in each control period and applied in the proposed MPDPC. Finally, simulation results in PSCAD/EMTDC confirm the validity of the proposed MPDPC and the filter inductance identification scheme.

以光伏、风电为代表的分布式电源具备较强的不确定性,其规模化、分散式地接入配电网,为系统资源的统一调控带来了严峻挑战.为实现配电网多类型分布式资源的协调调控,提出一种基于自适应步长交替方向乘子法的配电网分布式鲁棒优化调度策略.首先,构建配电网多层级代理系统,为配电网优化调度提供分布式运营架构.其次,建立基于可调鲁棒算法的配电网优化调度模型,提出鲁棒成本系数优化分配方法,实现调度策略保守性及经济性的合理平衡.在此基础上,基于自适应步长交替方向乘子法建立配电网分布式优化模型,并完成调度策略的高效求解.基于改进IEEE-33节点系统的仿真结果表明,所建立的分布式优化模型具备优越的收敛性能,所提鲁棒成本系数优化分配方法可有效提升调度策略的经济性.

The voltage swell (VS) in power system may require high voltage ride through (HVRT) of wind farms (WFs), and the detailed WF simulation model would need huge computational time, and thus is not be suitable for the analysis of HVRT dynamic behaviour of large-scale WFs. In this paper, a novel WF equivalent model with the required accuracy level for HVRT analysis is proposed. Firstly, multiscale entropies (MSEs) of the operational parameters in wind turbines (WTs) are calculated to represent their distinguishability in different HVRT processes, and the time series of several parameters which have obvious distinguishability are selected as the multiview clustering indicators (CIs). To handle the multi-view CIs, a new clustering algorithm namely multi-view incremental transfer fuzzy C means (MVIT-FCM) is proposed. This algorithm integrates the transfer learning technique to increase the stability and accuracy of the WTs clustering. Also, the high-dimensionality of the time series based CIs and the consequent computational burden in clustering are considered, and an incremental technique is applied in MVIT-FCM to handle the large-scale WF modelling. A real WF system is used for case study. The results indicate that the multi-view CIs are very effective for increasing the equivalent accuracies. In addition, with the aid of incremental and transfer learning techniques, MVIT-FCM can acquire stable clustering results and handle large-scale WF accurately and efficiently.

Due to the intermittency and uncertainty natures of wind power, electrical energy storages (EESs) are often equipped in the power systems to reduce the side-effect of wind power fluctuations, and adiabatic compressed air energy storage (A-CAES) is one of EES technologies to smooth the power fluctuation of wind farms (WFs). This paper proposes a coordinated control framework of WF and A-CAES station to achieve frequency response, and discusses the active power distribution scheme among wind turbines (WTs) and A-CAES units during frequency regulation. Firstly, the models of WT and A-CAES used in frequency regulation are presented. Then, considering that the power distribution might go through a long iteration process when the number of WTs in WF is quite large, these WTs are clustered into several groups using a comprehensive multi-view grouping indicator. On the basis of the WTs grouping result and with a defined generalized energy increment (GEI), this paper proposes a discrete consensus based tri-level coordinated frequency control method, which divides the control into three levels, i.e., group level, wind farm level and coordinated level. Through the three levels’ control, the method can reasonably and rapidly distribute the frequency regulation powers among WTs and A-CAES units without being limited by the scale of WF, and the coordination of WF and A-CAES station during frequency regulation is achieved. To demonstrate the effectiveness of the proposed method, a modified WF in Inner Mongolia of China is utilized for case study. Simulation results show that the proposed method is valid in various frequency events and can reach consensus within 4 s in the studied cases, and it is well-performed with different capacities of wind powers and A-CAESs in the power systems. The common communication failures have few influences on the methods, and the frequency nadirs fluctuate lower than 0.1 Hz with time delays in the communications. Compared with centralized and multi-machine equivalent methods, the proposed distributed method can balance the computational speed and the solution accuracy, and thus is beneficial to improve the system frequency nadirs when frequency drops.

为实现交直流混合微电网群在孤岛状态下的自治经济控制,提出一种基于离散一致性原理的分布式控制策略.该控制策略包含子微网控制与微电网群间控制2个层面.在子微网控制层面,通过在传统经济下垂控制中引入成本、频率、电压及无功分配的二次调整项,实现了子微网的自治稳定与功率经济分配;在微电网群间控制层面,通过构造基于成本微增量偏差值的换流站本地控制策略,并进一步引入基于离散一致性的二次调整项,实现了功率在不同子微网间的经济分配.2层控制策略相互配合,共同实现对交直流混合微电网群的分层–分布式自治经济控制.最后,基于所建交直流混合微电网群模型的仿真结果,验证了所提方法的有效性.