Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy

This study presents a binary particle swarm optimization (BPSO) based methodology for the optimal placement of phasor measurement units (PMUs) when using a mixed measurement set. The optimal PMU placement problem is formulated to minimize the number of PMUs installation subject to full network observability and to maximize the measurement redundancy at the power system buses. In order to ensure full network observability in an electric power network the topology-based algorithm is used and Several factors considered; such as the available data from existing conventional measurements, the number and location of zero injection buses, the number and location of installed PMUs and of course, the system topology. The efficiency of the proposed method is verified by the simulation results of IEEE 14-bus, 30-bus, 57-bus-118 bus systems, respectively. The results show that the whole system can be observable with installing PMUs on less than 25% of system buses. For verification of our proposed method, the results are compared with some newly reported methods which show the method as a novel solution to obtain redundant measurement system with the least number of phasor measurement units.     

Fault-tolerant placement of phasor measurement units based on control reconfigurability

This paper formulates and solves the problem of placing additional phasor measurement units (PMUs) into a power grid, with focus on enhancing the tolerance to loss of a specified number of PMUs anywhere in the grid under a placement constraint based on a previously defined concept called control reconfigurability. Control reconfigurability imposes a requirement on the minimum Hankel singular value for all anticipated fault scenarios. This requirement causes the placement of PMUs to benefit the use of analytic redundancy in the controlled dynamic grid with respect to anticipated PMU outages, thus enhances the grid resilience. The same principle is applicable to placing additional control devices into the grid. A simple power network model is used to explain the fault-tolerant PMU placement method, and the particle swarm optimization algorithm is employed to minimize the number of additional PMUs placed without violating the required control reconfigurability. The results of placing PMUs for the IEEE 118-bus system are presented.     

Observer-based controller for Lipschitz nonlinear systems

An observer-based controller for Lipschitz nonlinear systems is presented. The necessary and sufficient condition to ensure stability of state-observer as well as nonlinear system with state-feedback control law is derived. According to the separation principle, the closed-loop stability is guaranteed based on dual problems concerning stability of state-observer and stability of state-feedback parts. Also, the stability region for locally Lipschitz nonlinearities is obtained. A practical synthesis approach to achieve controller parameters is then given which yields the closed-loop convergence with less conservative results. The effectiveness of the proposed synthesis method is finally demonstrated by simulation results.     

Multi-objective biogeography based optimization for optimal PMU placement

The paper proposes a multi-objective biogeography based optimization (MO-BBO) algorithm to design optimal placement of phasor measurement units (PMU) which makes the power system network completely observable. The simultaneous optimization of the two conflicting objectives such as minimization of the number of PMUs and maximization of measurement redundancy are performed. The Pareto optimal solution is obtained using the non-dominated sorting and crowding distance. The compromised solution is chosen using a fuzzy based mechanism from the Pareto optimal solution. Simulation results are compared with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Non-dominated Sorting Differential Evolution (NSDE). Developed PMU placement method is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm.     

Power system observability and dynamic state estimation for stability monitoring using synchrophasor measurements

Growing penetration of intermittent resources such as renewable generations increases the risk of instability in a power grid. This paper introduces the concept of observability and its computational algorithms for a power grid monitored by the wide-area measurement system (WAMS) based on synchrophasors, e.g. phasor measurement units (PMUs). The goal is to estimate real-time states of generators, especially for potentially unstable trajectories, the information that is critical for the detection of rotor angle instability of the grid. The paper studies the number and siting of synchrophasors in a power grid so that the state of the system can be accurately estimated in the presence of instability. An unscented Kalman filter (UKF) is adopted as a tool to estimate the dynamic states that are not directly measured by synchrophasors. The theory and its computational algorithms are illustrated in detail by using a 9-bus 3-generator power system model and then tested on a 140-bus 48-generator Northeast Power Coordinating Council power grid model. Case studies on those two systems demonstrate the performance of the proposed approach using a limited number of synchrophasors for dynamic state estimation for stability assessment and its robustness against moderate inaccuracies in model parameters.     

Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

The invention of Phasor Measurement Units (PMUs) produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible. PMUs are used in transmitting data to Phasor Data Concentrators (PDC) placed in control centers for monitoring purpose. A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid. This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data. The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid. As a result, detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical. A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper. The proposed method consists of three modules namely, offline Gaussian Mixture Model (GMM), online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies. The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset, adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies. The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.     

基于时间最优的快速平息区间扰动的紧急直流功率控制策略

为了快速平息交直流互联电网的区间扰动振荡,基于等值两机两区域系统推导了2阶振荡系统的时问最优紧急直流功率支援控制策略.首先采用时间最优控制将相点快速驱动到平衡点附近,然后采用非线性控制对相点进行平衡点镇定控制.为消除直流功率的跃变和高频非线性"颤振"及防止"超调"现象,对开关曲线作了改进,并根据控制相平面推导实现最少次开关切换的直流功率控制量.最后通过仿真验证了该策略的有效性.     

基于粒子群优化方法的电力系统状态向量估计模型

电力系统状态向量估计是电力系统能量管理系统的重要组成部分;在电力系统实时监控中,传统的基于最小二乘法的状态向量估计方法,存在估计值与实际电力系统中的参数值相差较大的问题,基于此提出了一种适用于电力系统实时监测的有效状态估计模型;该模型采用了一种基于直角坐标系的加权最小二乘法,由一组与测量量和状态变量相关的非线性方程组描述,使用预测-校正迭代技术求解状态估计器模型;利用粒子群算法优化同步相量测量单元(phasor measurements unit,PMU)仪表的分配,增强了算法的有效性;该模型被应用于IEEE14总线和IEEE-30总线测试系统;结果表明,与传统算法相比,所开发的电力系统状态向量估计模型在执行时间、准确性和迭代次数方面均有明显的优势,所提出的估计模型对于实时监控应用具有很好的应用前景.     

双馈风力发电机系统的自抗扰神经网络的励磁控制

在双馈发电机传统控制方式的基础上, 将自抗扰控制技术和BP神经网络相结合结合, 应用于双馈风力发电机并网运行的控制上, 提出了一种新的双馈风力发电机并网运行控制方案. 该控制方案具有内外两个控制环, 内环通过BP神经网络实现双馈风力发电机的转子d-q轴电流控制, 外环通过自抗扰技术实现双馈风力发电机定子侧的有功、无功控制. 由于自抗扰控制器利用一阶跟踪微分器和扩张状态观测器对系统扰动进行动态跟踪补偿, 在此基础上输出双馈电机转子交--直轴电流的参考值, 然后将该参考值作为BP神经网络训练样本的输入, 训练后的BP神经网络可以更好地逼近实际转子电压输出量. 论文设计并实现了该方案的具体控制算法. 仿真测试表明: 该控制方案具有优良的动态性能, 对系统的内外扰动具有较强的鲁棒性, 在没有精确的发电机参数情况下依然可实现并网系统的稳定运行.     

基于自适应评价UPFC神经网络控制器设计

对含UPFC(统一潮流控制器)的电力系统提出一种新型的非线性最优神经网络控制器。启发式动态规划(HDP)是自适应评价设计(ACDs)体系中的一员,采用HDP来设计UPFC神经网络控制器。和传统的PI控制器相比,这种神经网络控制器能够提供非线性最优控制。仿真结果表明,此种控制器具有很好的控制效果。     

Parameterized power domination complexity

The optimization problem of measuring all nodes in an electrical network by placing as few measurement units (PMUs) as possible is known as Power Dominating Set. Nodes can be measured indirectly according to Kirchhoff's law. We show that this problem can be solved in linear time for graphs of bounded treewidth and establish bounds on its parameterized complexity if the number of PMUs is the parameter.     

Differential evolution approach for optimal reactive power dispatch

Differential evolution based optimal reactive power dispatch for real power loss minimization in power system is presented in this paper. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a mixed integer nonlinear optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling. The formulation also checks for the feasibility of the optimal control variable setting from a voltage security point of view by using a voltage collapse proximity indicator. The algorithm is tested on standard IEEE 14, IEEE 30, and IEEE 118-Bus test systems. To show the effectiveness of proposed method the results are compared with Particle Swarm Optimization and a conventional optimization technique – Sequential Quadratic Programming.     

Optimal control of nonlinear power systems by an imbedding method

Realistic models of power systems involve high-order non-linear differential equations. The application of optimal control theory in optimizing such systems involves a high-order nonlinear 2-point boundary-value problem whose solution is very cumbersome. In this paper the imbedding method of solving a free-end, fixed-time optimal control problem is applied to a sixth-order nonlinear power system. The optimal designs for both open and closed loop plants are presented. The results of this application are compared with those by the hybrid linearization method considered by other authors. The results indicate that the 2 methods follow quite closely.     

基于稳定传输零点补偿器的动态输出观测器反馈控制

针对被控系统不满足实现动态输出观测器反馈控制两个充分条件的情况,提出稳定传输零点补偿器的概念.将零点补偿器与被控系统串联,使得整个系统拥有稳定的传输零点.这样就保证可以实现动态输出观测器反馈控制,从而使系统具有很高的鲁棒稳定性.通过实验将基于稳定传输零点补偿器的动态输出观测器反馈控制系统与全维状态观测器反馈控制系统进行...     

A wide area measurement based neurocontrol for generation excitation systems

Power system is a highly interconnected nonlinear system that needs optimal and accurate control for continuous operation. Large power transfer through long transmission line between different electrical areas, stressed system and adverse interaction between local controllers, may give rise to slow frequency inter-area oscillations. The inter-area modes may not be visible from local measurements and hence it is useful to use remote measurement based centralized supplementary control. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control to local controllers such as automatic voltage regulators, power system stabilizers, etc. to damp out inter-area oscillations. This paper presents a design and real time implementation of a nonlinear neural network based optimal wide area controller using adaptive critic design (ACD). The real time implementation of a power system model is carried out on a real time digital simulator (RTDS). The performance of the WACS as a power system stability agent is studied using a two-area power system under different operating conditions and contingencies. The WACS shows improvement in the damping of inter-area mode with the use of supplementary excitation control. In addition, results show that the designed controller can provide robust performance under small communication delay in remote signal transmission.     

同步发电机非线性励磁控制的逆系统方法

本利用非线性控制的逆系统方法，将大型同步发电机非线性励磁控制问题变换为一个相应的线性最优控制问题，设计出便于实现的非线性励磁控制规律。经仿真研究表明，所设计的控制律显地改善了电力系统暂态过程的动态响应，较大提高了静态稳定极限，有效地提高了电力系统的暂态稳定水平。     

Control of Grid Connected Photovoltaic Systems with Microinverters: New Theoretical Design and Numerical Evaluation

This paper addresses the problem of controlling grid connected photovoltaic (PV) systems that are driven with microinverters. The systems to be controlled consist of a solar panel, a boost dc–dc converter, a DC link capacitor, a single‐phase full‐bridge inverter, a filter inductor, and an isolation transformer. We seek controllers that are able to simultaneously achieve four control objectives, namely: (i) asymptotic stability of the closed loop control system; (ii) maximum power point tracking (MPPT) of the PV module; (iii) tight regulation of the DC bus voltage; and (iv) unity power factor (PF) in the grid. To achieve these objectives, a new multiloop nonlinear controller is designed using the backstepping design technique. A key feature of the control design is that it relies on an averaged nonlinear system model accounting, on the one hand, for the nonlinear dynamics of the underlying boost converter and inverter and, on the other, for the nonlinear characteristic of the PV panel. To achieve the MPPT objective, a power optimizer is designed that computes online the optimal PV panel voltage used as a reference signal by the PV voltage regulator. It is formally shown that the proposed controller meets all the objectives. This theoretical result is confirmed by numerical simulation tests.     

Maximum wind power tracking for PMSG chaos systems – ADHDP method

Aiming at the chaotic behavior of PMSG under certain parameters, the new ADHDP method based on Cloud RBF neural network is proposed to track the point of maximum wind power, which can make the system out of chaos and track the point of maximum power stably, and the optimal control problems of the complex nonlinear system can be solved effectively. This method is realized by using the optimal power–speed curve and the vector control principle, and by adjusting the stator output voltage to control the electromagnetic torque, then the rotor speed of wind turbine can operate at the optimal speed that corresponds to the point of maximum power, meanwhile, the measure of wind speed also can be avoided. The simulation results show the effectiveness of the proposed method.     

Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers

This study presents a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) for the solution of optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the total voltage deviations are to be minimized separately. In order to evaluate the performance of the proposed algorithm, it has been implemented on IEEE 30-, 57- and 118-bus test power systems and the optimal results obtained are compared with those of the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving the ORPD problem of power system.     

Direct Power Control of Dfig by Using Nonlinear Model Predictive Controller

This paper proposes a nonlinear model predictive direct power control (PDPC) strategy for a double fed induction generator (DFIG)‐based wind energy generation system. Active and reactive power variations of DFIG are calculated based on machine rules, and a nonlinear model of DFIG is given. A nonlinear model predictive controller (NMPC) is presented based on the useful cost function and constraint that it results in more proximity between simulations and reality. The power and current ripples are reduced and the optimal rotor voltage is generated based on an objective function and the constraints. The rotor voltage vector is calculated in the synchronous reference frame and transferred into the rotor reference frame. Simulation results of a 2 MW DFIG system show good performance of the proposed method during variation of active and reactive powers, machine parameters, and wind speed. Also, the transient responses of active and reactive powers are within a few milliseconds.