An improved passivity-based control strategy for providing an accurate coordination in a AC/DC hybrid microgrid

This paper proposes an improved passivity-based control (PBC) technique to provide accurate coordination between AC and DC sides of a grid-tied AC/DC hybrid microgrid. The proposed PBC is based on the passive properties of the system and the energy exchange between the subsystems. To this end, three mathematical formulations for the converters are driven in the first step. Then, using the formulations, the impedance injection is applied to shape the error dynamics of all DC/DC and DC/AC converters. The DC link-connected DC/AC converter is used to help the AC side for maintenance of the power quality factors such as, the grid frequency and voltage magnitude in the presence of any unbalanced power load sharing. The first contribution of the paper is the part of impedance injection to the DC/AC converter, which aims to provide more decoupling and faster convergence rate for both the steady states and the dynamical conditions. Secondly, a Lyapunov function is defined for converters’ error dynamics that provide more accurate statement of the proposed damping injections. Besides, as an attractive control feature, decoupling properties of the suggested method are investigated. Then, through achieving several curves, various operating conditions of DC/DC and DC/AC converters under varying the system errors and proposed damping injections are evaluated as well. For the purpose of validation, the system under study is simulated in the MATLAB/SIMULINK software package against a variety of system errors and converters’ operational conditions.     

新型动态电压恢复器在双馈风力发电机中的应用

在不对称电网故障下,采用基于同相位补偿法改进控制策略的新型动态电压恢复器（DVR）,提高双馈风力发电机（DFIG）的低电压穿越能力。通过两个改进的正负序检测器分别对网侧和机侧电压分量进行分解,消除不平衡故障下的负序分量的影响;利用四个PI控制器分别调节每一相序,注入串联电压,保证双馈风力发电机电压可以达到正常值;通过旋转这个串联电压,注入无功功率到电网中,保证系统的稳定运行,仿真结果证明了所提控制策略的有效性。     

Analysis of integrated STATCOM-SMES based on three-phase three-level multi-pulse voltage source inverter for high power utility applications

This paper is aimed to investigate the operating characteristics of a static synchronous compensator (STATCOM) integrated with superconducting magnetic energy storage (SMES) for high power applications in the transmission network level. The STATCOM controller topology comprises multi-level multi-pulse neutral-point clamped-type (NPC) voltage source inverters (VSIs) using the harmonics cancellation technique, and incorporates a SMES coil. An innovative two-quadrant multi-level dc-dc converter is proposed to effectively interface the STATCOM with the superconducting coil using a buck-boost topology with neutral point voltage control capabilities; thus enabling to simultaneously control both active and reactive power exchange with the high voltage power system. A detailed analysis of major system variables is presented, including analytical results and digital simulations using the MATLAB/Simulink environment. Moreover, a three-level control scheme is designed, including a full decoupled current control strategy in the d-q reference frame with a novel controller to prevent the STATCOM dc bus capacitors voltage drift/imbalance and an enhanced power system frequency controller.     

Evolutionary algorithm-based fuzzy PD control of spillway gates of dams

In this paper, an evolutionary algorithm (EA)-based fuzzy proportional-derivative (PD)-type controller is employed to reservoir control of dams with the purpose of operating spillway gates during any flood of any magnitude, which is not predictable beforehand. EA is used to evolve the main parameters of the fuzzy PD controller. The use of the EA, in conjunction with a systematic neighborhood structure for the determining of fuzzy rule-base parameters, leads to a significant improvement in the performance of the controller. The major objective of the controller is to achieve better system performance over the conventional control methods. In order to demonstrate the high performance of the presented method, we simulate the control system using different probable inflow hydrographs of various magnitudes. The simulation results indicate that the EA-based fuzzy PD controller not only performs an accurate and efficient solution, but also exhibits more desirable and reliable results than the conventional approaches.     

Event triggering power sharing control for AC/DC microgrids based on P -F droop curve method

The power sharing of AC/DC micro-grids is researched in this paper. The proposed strategy mainly include two parts: the primary power event triggering control with secondary control and an adaptive quasi sliding mode voltage control in inner-loop. Firstly, a event triggering power sharing control (ETPSC) based on $P?F$ droop curve is developed to regulate the voltage and frequency of AC and voltage of DC with the aim of the proportional power sharing between AC and DC micro-grids. The triggered threshold of ETPSC can be chosen to decide the transmitted power between AC and DC micro-grids. When the difference power between AC and DC micro-grids is less than the triggered threshold of power flow, there is no power sharing between AC and DC micro-grids, which can less the number of switching the power flow direction and the transmitted line power loss. The ETPSC has a great robust for the disturbances of load and improve the stability of the system. An adaptive quasi-sliding-mode control,which is implemented easily and flexibly with small computational burden and only based on input/output (I/O) measurement data but not the model any more, is used to control voltage in inner-loop. The effectiveness of the proposed control schemes is demonstrated by some numerical simulations and experimental results.     

船用电池组管理系统设计

介绍了一种用STM32单片机为主控芯片,实现船用电池组管理系统的设计方案,给出了系统的工作原理、硬件设计和软件设计方案。该控制系统以逆变器和均衡电路为核心,实现正弦交流电输出,延长电池组的供电时间和电池的寿命。     

Grid and plate currents in a grid-controlled mercury vapor tube

An oscillographic and quantitative investigation of the grid and plate currents of a grid-controlled mercury vapor tube, operating in an a.c. circuit, as functions of grid voltage and resistance, plate voltage and phase displacement between grid and plate voltages.It is shown that the direction of grid current depends not only upon the instantaneous polarity of the grid but also upon the magnitude of the grid voltage and upon the plate current. The grid current wave may consist of positive loops only, of positive and negative loops of varying areas or of equal areas, and of negative loops only. The various factors affecting the form of grid current wave are considered in detail.Inverse plate current occurs whenever the grid current is concurrent with the negative half-cycle of the plate voltage. The minimum value of inverse plate current occurs when there is no grid current coincident with the negative half-cycle of the plate voltage. It is caused by the lag of deionization of the mercury vapor furnishing a small amount of ionization when the plate voltage becomes instantaneously negative. With a constant average grid current the magnitude of the inverse plate current varies approximately sinusoidally with phase displacement between grid and plate voltages.     

Event-triggered asynchronous periodic distributed secondary control of microgrids under DoS attacks

A novel distributed secondary voltage and frequency control strategy is proposed with the Zeno-free event-triggered scheme for an island alternating current (AC) microgrid under Denial-of-Service (DoS) attacks. A DoS attack compensation mechanism and an event-triggered mechanism on the basis of the checking scheme are developed. Then, a secure event-checked based event-triggered secondary control method is explored to guarantee the tracking performance of the microgrid under DoS attacks. Further, some linear matrix inequalities (LMIs)-based sufficient conditions are derived to design the controller. What’s more, the proposed asynchronous periodic triggering method can efficiently save communication resources and further reduce the update number of the controller. Finally, the efficiency of this work is verified by an islanded AC microgrid with comparisons.     

Nonlinear dynamic investigation and anti-bifurcation control of a boiler-turbine unit via dual-mode fuzzy model predictive control strategy

The increase in renewable energy sources connected to the grid has increased flexibility requirements in the operation of thermal power plants. Because of severe nonlinearity and various disturbances, the dynamic behavior of the boiler-turbine unit will change significantly at different operating conditions. Further, modeling uncertainties cofound definitive knowledge about the changes, exacerbating efforts to control. In this paper, a general disturbed model of the boiler-turbine unit for control design is determined through the nonlinear dynamic analysis on the concepts of bifurcation and limit cycles behavior rather than given by an artificial chosen. Then, a dual-mode fuzzy predictive control strategy is proposed on discrete-time Takagi-Sugeno fuzzy model. Online optimal problem of the approach is solved to accomplish the fast-tracking task; to achieve the anti-bifurcation control objective, a novel minimal robust positively invariant set corresponding with a local controller is offline obtained in order to change the undesired nonlinear behavior into stable limit cycles. With the input-to-state stability theory, the proposed control strategy is theoretically proved and given in terms of linear matrix inequalities. Simulation carried out on the boiler-turbine unit demonstrates that the proposed dual-mode control strategy achieves the disturbance suppression ability and the improvement of the nonlinear dynamic behavior.     

An optimal Fuzzy-logic based frequency control strategy in a high wind penetrated power system

This paper introduces a new load frequency control (LFC) model in the presence of high wind power penetration level. The main issue in a wind-penetrated power system is to maintain the system frequency in a normal operating band which is specified by the given system grid codes. Essentially, the power system equilibrium point changes following a contingency, and in this case, the high penetration of wind farms makes it harder to regain an acceptable system equilibrium points through conventional control applications. In order to overcome the aforesaid problem, a new Fuzzy-logic controller is designed optimally in this paper using the artificial bee colony (ABC) algorithm. In this approach, the ABC algorithm tunes the membership function parameters of the Fuzzy controller to acquire a good-enough performace of the proposed strategy. More importantly, the proposed Fuzzy-logic controller is blessed with robustness, simplicity, and reliability in order to ameliorate the frequency deviation. It is worth saying that the stability analysis is presented in this paper as well as the noise analysis of the proposed method. The research results indicates how effectively wind farm could participate in the system frequency control through inertial control, primary frequency control, and supplementary frequency control.     

New modeling approach of secondary control layer for autonomous single-phase microgrids

In a microgrid (MG) topology, the secondary control is introduced to compensate for the voltage amplitude and frequency deviations, mainly caused by the inherent characteristics of the droop control strategy. This paper proposes an accurate approach to derive small signal models of the frequency and amplitude voltage at the point of common coupling (PCC) of a single-phase MG by analyzing the dynamics of the second-order generalized integrator-based frequency-locked loop (SOGI-FLL). The frequency estimate model is then introduced in the frequency restoration control loop, while the derived model of the amplitude estimate is introduced for the voltage restoration loop. Based on the obtained models, the MG stability analysis and proposed controllers’ parameters tuning are carried out. Also, this study includes the modeling and design of the synchronization control loop that enables a seamless transition from island mode to grid-connected mode operation. Simulation and practical experiments of a hierarchical control scheme, including traditional droop control and the proposed secondary control for two single-phase parallel inverters, are implemented to confirm the effectiveness and the robustness of the proposal under different operating conditions. The obtained results validate the proposed modeling approach to provide the expected transient response and disturbance rejection in the MG.     

基于车辆导航的一种改良动态最短路径算法

车辆导航的一个基本问题是如何在一个即时的动态交通网中找到最优路径,现有的算法不是太复杂,就是不能很好地处理当移动物体的位置及交通环境同时发生变化所造成的复杂环境.本文推荐一种A*算法的变种-A*终身计划(Lifelong Planning A*,LPA*),并在该算法的基础上进行了改良,提出了采用特定椭圆修剪不必要的查询节点,以提高动态搜索的速度.     

新建高校电力网科研工作规划

新建高校电力网迅速发展,科研创新是重要一环。必须确保正常用电。变压器与负载电力负荷科学合理匹配,捕捉各种参数、选择供电电源、电压、高低压配电。防雷等系统寻拟关联参数依据合理设计。另一方面新建高校电力网潮流设计计算线路中,科学使用基尔霍夫电流、电压理论工具,在交、直流等不同复杂电路设计中遵循规律的精确构建正确框架。对它适应普通高校电力网科研工作规划起到重要作用。     

基于电流型多电平变流器的单相光伏并网系统

采用单相电流型多电平变流器作为光伏并网发电系统的电路接口,可以降低开关器件的开关频率和电流应力,同时可以使电网侧电流获得更好的谐波特性。为获取更好的谐波性能,电流型多电平逆变器部分采用POD-PWM调制,并通过直流侧电流反馈实现分流电感电流的均衡控制。最后,通过锁相环控制,使多电平逆变器输出的电流跟踪电网电压,实现并网。基于PSIM仿真环境,设计了单相五电平电流型光伏并网逆变系统,对上述方案的正确性和可行性进行了验证。     

High-resolution probabilistic load forecasting: A learning ensemble approach

High-resolution probabilistic load forecasting can comprehensively characterize both the uncertainties and the dynamic trends of the future load. Such information is key to the reliable operation of the future power grid with a high penetration of renewables. To this end, various high-resolution probabilistic load forecasting models have been proposed in recent decades. Compared with a single model, it is widely acknowledged that combining different models can further enhance the prediction performance, which is called the model ensemble. However, existing model ensemble approaches for load forecasting are linear combination-based, like mean value ensemble, weighted average ensemble, and quantile regression, and linear combinations may not fully utilize the advantages of different models, seriously limiting the performance of the model ensemble. We propose a learning ensemble approach that adopts the machine learning model to directly learn the optimal nonlinear combination from data. We theoretically demonstrate that the proposed learning ensemble approach can outperform conventional ensemble approaches. Based on the proposed learning ensemble model, we also introduce a Shapley value-based method to evaluate the contributions of each model to the model ensemble. The numerical studies on field load data verify the remarkable performance of our proposed approach.     

Improved equalization and joint iterative detection

Intersymbol interference (ISI) degrades the bit error rate (BER) performance of sequential detectors such as maximum likelihood sequence detection (MLSD) and the maximum a posteriori (MAP) algorithm due to minimum distance reduction. This paper describes a novel rotation method that can increase the minimum distance and results in significant improvement in the BER performance for binary PSK and 4QAM systems when operating over severe ISI channels. Simulation results show that this method can improve the BER performance of systems employing MLSD and MAP turbo equalization by up to 4.6 dB compared to existing approaches including known precoding schemes.     

A model-free distributed cooperative frequency control strategy for MT-HVDC systems using reinforcement learning method

The performance of the existing frequency control strategies for MT-HVDC systems relies on the accuracy of the mathematical models. However, it is hard to obtain the exact mathematical models of MT-HVDC systems. To deal with this challenge, this paper presents a distributed cooperative frequency control strategy for MT-HVDC systems by using a reinforcement learning method, i.e., value iteration algorithm. Specifically, the proposed control strategy is data driven and hence model free. Besides, the proposed control strategy is distributed in the sense that each AC area only requires the local and neighboring information, rather than the information of all the AC areas. Moreover, the proposed control strategy makes the connected AC areas compensate load disturbances together by sharing their power reserves via HVDC grids, which greatly decreases the operation costs of MT-HVDC systems. The performance of the proposed control strategy is validated by cases study.     

一种变电站内无功电压优化控制的求解方法

为了实现变电站内无功电压优化控制,提出了采用禁忌搜索算法进行无功电压优化控制问题的求解。以最大限度地降低功率损耗、提高电压质量和减少变压器的调节次数及电容器组的投切次数为目标,建立了相应的数学模型,并考虑系统潮流、控制变量、状态变量、器件动作次数的约束条件。采用数值仿真的方法进行了验证,仿真结果表明提出的方法可以得到最优解,解的质量较高。     

Pattern-based NN control for uncertain pure-feedback nonlinear systems

Implementing human-like learning and control for nonlinear dynamical systems operating in different control situations is an important and challenging issue. This paper presents a pattern-based neural network (NN) control strategy for nonlinear pure-feedback systems via deterministic learning (DL). Firstly, an appropriately designed adaptive neural dynamic surface controller is proposed to achieve the finite time tracking control. By analyzing the recurrent property of NN input signals, a partial persistent excitation (PE) condition for radial basis function (RBF) network is established, the implicit desired control dynamics under different control situations are accurately identified via DL in the case that the dimension of NN input is reduced. And a set of pattern-based experienced actual and virtual controllers is constructed using the learned knowledge. Secondly, to classify different control situations, when the system is operating in different control situations but controlled by current normal experienced controller, the dynamics of each subsystem are accurately identified via DL, n sets of dynamical estimators are constructed using the learned knowledge. Thirdly, in the recognition phase, n sets of residuals are achieved by comparing each set of estimators with the monitored system, sudden change in the control situation is rapidly recognized based on the principle of the earliest occurrence of the minimum residual. Finally, in the control phase, according to the recognition result, the correct experienced actual and virtual controllers will be selected to control the plant, guaranteed stability and superior control performance are achieved without any further re-adaptation online. Simulation studies are given to verify the proposed scheme can not only acquire and memorize knowledge like humans, but also reuse the learned knowledge to achieve rapid recognition and control of current control situation.     

Distributed data driven control for multi-agent consensus with unknown system dynamics

This study investigates the consensus tracking problem for unknown multi-agent systems (MASs) with time-varying communication topology by using the methods of data-driven control and model predictive control. Under the proposed distributed iterative protocol, sufficient conditions for reducing tracking error are analyzed for both time invariable and time varying desired trajectories. The main feature of the proposed protocol is that the dynamics of the multi-agent systems are not required to be known and only local input-output data are utilized for each agent. Numerical simulations are presented to illustrate the effectiveness of the derived consensus conditions.