Computation of the controllable swing mode spectrum of FACTS and HVDC in large power systems

This paper presents computation of swing modes of a large power system that could be significantly affected by power swing damping controllers in FACTS or HVDC devices at a given location. Modal controllability is a suitable measure to isolate these modes for analysis. Computation of the controllable swing mode spectrum is useful, especially in situations where the controller structure and feedback signals are not frozen (e.g., at the planning stage). This paper proposes two important steps that allow us to map the problem of finding highly controllable swing modes to the problem of finding the swing modes that have high transfer function residues (for which efficient algorithms are available). The steps are: (a) normalization of the eigenvectors corresponding to different modes and (b) identification of specific feedback signals for each type of FACTS/HVDC device such that the modal observability and modal controllability are tightly coupled. Once the mapping is done, a computationally efficient method like the Subspace Accelerated Dominant Pole Algorithm [16] (SADPA) can be adapted to find the highly controllable swing modes. The effectiveness of this approach is demonstrated by case studies of FACTS and HVDC devices in a 16-machine system and the Indian power grid.     

Robust low-order controller design for multi-modal power oscillation damping using flexible AC transmission systems devices

Damping of multi-modal oscillation through supplementary control of a single flexible AC transmission system (FACTS) device is illustrated here. This often requires multiple feedback signals in a centralised multi-input single-output framework for which extension of the classical control design approaches is not straight forward. Past contributions have either focused on decentralised design of low-order PSS in an SISO or MIMO framework; or alternatively, on robust control design techniques which of course, result in higher order controllers. An attempt to design a fixed (low)-order controller, which is robust and is able to damp multiple swing modes with a single FACTS device is presented. The control design problem is formulated as a multi-objective parameter optimisation and solved using a standard evolutionary optimisation technique. Possible post-contingency operating conditions are considered explicitly during the design phase itself to reduce the conservativeness. The present exercise is a step forward towards use of wide area measurement systems for closed-loop supplementary control (around the primary voltage and/or power flow control loop) of the FACTS devices to improve the transfer capacity of the existing corridors.     

N -21 security in optimal power flow control applied to limited areas

Blackouts in recent years have demonstrated that a reliable and secure power system is a key component of an efficient economy. Therefore control devices such as flexible AC transmission system devices (FACTS) are placed in the system and utilised to improve the security of the system. A method to determine appropriate settings for these devices is optimal power flow control. As the area of influence of a FACTS device is usually limited, it is sufficient to include only a reduced area in the optimisation problem. Here, such an optimal power flow problem is formulated where the considered area is defined using sensitivity analysis. To include N 2 1 security as an objective, a current injection method is applied, which facilitates the determination of the system state in the case of a line outage, without having to carry out a full-load flow simulation.     

Self-tuning flexible ac transmission system controllers for power oscillation damping: a case study in real time

Design and real-time implementation of a self-tuning flexible ac transmission system (FACTS) controller is illustrated for power oscillation damping. Although the system model is not required for self-tuning control design, it is shown to perform similar to a model-based design. For parameter estimation, the classical recursive least square (RLS) is supplemented by a random walk (RW) with a switched structure and compared to standard variable forgetting factor (VFF) approach. It is shown that the RW improves the accuracy and convergence of the estimated system parameters, which is critical to self-tuning control following large disturbances. The performance is validated in real time using a commercial real-time simulation platform. The control computation time is shown to be considerably less than the typical sampling time used for power oscillation damping applications demonstrating the feasibility of self-tuning FACTS controllers in practice.     

Placement of FACTS controllers using modal controllability indices to damp out power system oscillations

Flexible ac transmission systems (FACTS) controllers are being used to damp out the power system oscillations. The effectiveness of these controllers depends on their optimal location in the power system network. A controllability index has been proposed to find the optimal location of the FACTS controllers to damp out the inter-area mode of oscillations. Three types of FACTS controllers have been considered, which include static var compensator, thyristor-controlled series compensator and unified power flow controller. The proposed controllability index is based on the relative participation of the parameters of FACTS controllers to the critical mode. A simple approach of computing the controllability indices has been proposed, which combines the linearised differential algebraic equation model of the power system and the FACTS output equations. The placements of FACTS controllers have been obtained for the base case as well as for the critical contingency cases. The effectiveness of the proposed method is demonstrated on New England 39-bus system and 16-machine, 68-bus system     

Probabilistic technique for sizing FACTS devices for steady-state voltage profile enhancement

A novel and simple stochastic-based approach to determine the optimal sizing of multiple flexible AC transmission systems (FACTS) devices in a power system for steady-state voltage profile enhancement is presented. In this context, investigations have been conducted on a published test system taking into consideration the uncertainty of the system load and generator scheduling. Two FACTS schemes are considered, namely a thyristor controlled series capacitor (TCSC) and two static synchronous compensators (STATCOMs) and a unified power flow controller (UPFC) and a STATCOM. The TCSC and UPFC are employed in the system to adjust the natural power sharing of two different parallel transmission lines and therefore enable the maximum transmission capacity to be utilised. Risk indices to estimate the likelihood that the voltage magnitude at a certain bus falls below a desired value is also presented.     

Self-adaptiveness in particle swarm optimisation to enhance available transfer capability using thyristor-controlled series compensation (TCSC)

Available transfer capability (ATC) is one of the challenging criteria under the functioning of the deregulated power system. The high demand for improving ATC is generally met using flexible alternating current transmission system (FACTS) devices in the power system. However, it suffers from serious crisis during determination of the optimal location and compensation stage of FACTS. The present study uses thyristor-controlled series compensation (TCSC) devices in order to compensate for the limitation of FACTS. Further, a novel self-adapted particle swarm optimisation (SAPSO) algorithm is proposed in this study for enhancing ATC. Experiments are carried on three benchmark bus systems such as IEEE 24, IEEE 30 and IEEE 57. Performance and statistical analyses are carried out by comparing the proposed SAPSO with the conventional PSO. Eventually, the study proves the effectiveness of the proposed method in case of ATC enhancement.     

Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings

In the present milieu, changes in regulations and the opening of power markets have manifested in the form of large amount of power transfer across transmission lines with frequent changes in loading conditions based on market price. Since conventional distance relays may consider power swing as a fault, tripping because of such malfunctioning would lead to serious consequences for power system stability. A frequency domain approach for digital relaying of transmission line faults mitigating the adverse effects of power swing on conventional distance relaying is presented. A wavelet-neuro-fuzzy combined approach for fault location is also presented. It is different from conventional algorithms that are based on deterministic computations on a well-defined model for transmission line protection. The wavelet transform captures the dynamic characteristics of fault signals using wavelet multi-resolution analysis (MRA) coefficients. The fuzzy inference system (FIS) and the adaptive-neuro-fuzzy inference system (ANFIS) are both used to extract important features from wavelet MRA coefficients and thereby to reach conclusions regarding fault location. Computer simulations using MATLAB have been conducted for a 300 km, 400 kV line and results indicate that the proposed localisation algorithm is immune to effects of fault inception, angle and distance. The results contained here validate the superiority of the ANFIS approach over the FIS for fault location.     

Neural networks for adaptive control coordination of PSSs and FACTS devices in multimachine power system

The paper develops a new design procedure for online control coordination which leads to adaptive power system stabilisers (PSSs) and/or supplementary damping controllers of flexible ac transmission system (FACTS) devices for enhancing the stability of the electromechanical modes in a multimachine power system. The controller parameters are adaptive to the changes in system operating condition and/or configuration. Central to the design is the use of a neural network synthesised to give in its output layer the optimal controller parameters adaptive to system operating condition and configuration. A novel feature of the neural-adaptive controller is that of representing the system configuration by a reduced nodal impedance matrix which is input to the neural network. Only power network nodes with direct connections to generators and FACTS devices are retained in the reduced nodal impedance matrix. The system operating condition is represented in terms of the measured generator power loadings, which are also input to the neural network. For a representative power system, the neural network is trained and tested for a wide range of credible operating conditions and contingencies. Both eigenvalue calculations and time-domain simulations are used in the testing and verification of the dynamic performance of the neural-adaptive controller.     

A combined model predictive control and time series forecasting framework for production-inventory systems

Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored, by proposing a complete management framework for production-inventory systems that is based on MPC and on a neural network time series forecasting model. The proposed framework is tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on the overall control performance. To this end, the proposed method is compared with several alternative forecasting approaches that are implemented on the same industrial dataset. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem that is solved in real time.     

Deterministic oscillatory search: a new meta-heuristic optimization algorithm

The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. The results show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.     

非线性系统高阶微分反馈控制

提出了不依赖系统模型的高阶微分反馈控制思想，控制目标是系统输出及其微分和高阶微分逼近设定输入及其微分和高阶微分，极大地提高了对控制品质要求。基于这种思想设计了能高品质地提取量测信号的微分和高阶微分的高阶微分器（HOD, highorder differentiator），该HOD参数少，容易调节，并给出其稳定性、收敛性和滤波特性的证明；另外，对带有未知扰动、模型未知的非线性SISO和MIMO系统分别设计了基于HOD的高阶微分反馈自适应控制器(HODFC, high order differentials feedback adaptive controller)，给出了闭环系统稳定性和鲁棒性分析，并且实现了线性化解耦控制。     

非线性悬吊质量摆对输电塔减振控制的研究

针对悬吊质量摆减控制研究中小摆角线性分析的局限性,本文提出考虑悬吊质量摆大摆角非线性特性来计算结构体系动力响应的方法,将传统分析中的悬吊质量摆的线性刚度修正为非线性。以一输电塔为例,通过线性和非线性计算结果对比可知,当摆角过大时线性计算方法不满足线性小摆角条件,而非线性计算方法则可很好模拟质量摆在大摆角下的响应。通过参数分析讨论了正弦激励周期、摆长及质量比对输电塔减震率的影响。选取三条不同场地地震记录,计算输电塔附加质量摆体系的地震响应,结果表明在不同场地条件下,输电塔均具有明显的减震效果。     

Application of support vector machines for fault diagnosis in power transmission system

Post-fault studies of recent major power failures around the world reveal that mal- operation and/or improper co-ordination of protection system were responsible to some extent. When a major power disturbance occurs, protection and control action are required to stop the power system degradation, restore the system to a normal state and minimise the impact of the disturbance. However, this has indicated the need for improving protection co-ordination by additional post-fault and corrective studies using intelligent/knowledge-based systems. A process to obtain knowledge-base using support vector machines (SVMs) is presented for ready post-fault diagnosis purpose. SVMs are used as Intelligence tool to identify the faulted line that is emanating and finding the distance from the substation. Also, SVMs are compared with radial basis function neural networks in datasets corresponding to different fault on transmission system. Classification and regression accuracies are is reported for both strategies. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighbouring line connected to the same substation. This may help to improve the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. To validate the proposed approach, results on IEEE 39-Bus New England system are presented for illustration purpose.     

OPF-based security redispatching including FACTS devices

An Optimal Power Flow (OPF)-based security-driven redispatching procedure to archive an appropriate security level is provided. The proposed procedure is particularly suited for security redispatching by an independent system operator. This procedure uses full ac equations and explicitly considers security limits through a stressed loading condition. Furthermore, a variety of FACTS devices can be incorporated in the redispatching problem to enhance system security. Several case studies based on the IEEE 24-bus system and on a real size model of the Italian system are analysed and discussed.     

Optimization and predictive control of a vapor compression cycle under transient pulse heat load

There are well established control methods to stabilize the vapor compression cycle (VCC) about a given operating point. However, it is challenging to design such local controllers to handle large transient heat flux disturbances, due to complex coupling and constraints, and potential violation of critical heat flux (CHF) which could lead to the damaging dryout condition. Since VCCs are locally stable with relatively slow dynamics, model predictive control (MPC) is ideally suited to address these challenges. MPC solves a constrained receding horizon minimization problem under known transient heat disturbance. The objective function is a combination of the exit evaporator wall temperature, which provides an indication for the onset of partial dryout, energy consumption, and control input effort. This paper presents results from the application of MPC to a VCC testbed in our laboratory. We show that MPC can significantly increase the robustness with respect to transient disturbances by moving the system to an advantageous operating point in anticipation of known disturbances.     

Structure-preserved power-frequency slow dynamics simulation of interconnected ac/dc power systems with AGC consideration

A structure-preserved power-frequency slow dynamics simulation model is suggested for interconnected ac/dc power systems with automatic generation control (AGC) consideration, which will be applied to study relevant emergency control in future so that the bulk system viability crisis caused by load-frequency slow dynamics can be released. In the model, the network structure of interconnected power systems is entirely preserved, and the multi-area dynamic load flow (DLF) is developed for simulation. The generator speed governor and rotor dynamics, load-frequency characteristics, simplified models for high voltage direct current (HVDC) transmission and flexible ac transmission systems (FACTS) device thyristor controlled series capacitor (TCSC) suitable for long-term dynamics are considered with their AGC interfaces kept for future emergency-AGC study. However, at this stage, the sub-problem of reactive power and voltage is neglected for modelling simplicity and dc load flow is thus used for network solution. The concept of area centre of inertia (ACOI) is used based on the assumption of uniform frequency in each control area similar to that of the conventional single-area DLF calculation. The application of ACOI concept is attractive because the signal can be obtained from wide-area measurement systems (WAMSs) in real time and used to enhance long-term frequency stability through advanced control in future. The computer test results from 2-area 4-machine and IEEE 30-bus power systems demonstrated the validity and effectiveness of the suggested model and corresponding algorithm.     

光电跟踪系统力矩波动的自抗扰控制

为解决光电跟踪伺服系统受电机力矩波动影响产生的速度波动问题,提出了一种改进的自抗扰控制策略进行力矩波动补偿。该算法主要由两部分组成:通过扩张状态观测器辨识出系统扰动,然后将该扰动前馈到系统控制量中去,构成复合校正系统;反馈通道中采用两参数的比例微分控制器,可以保证系统的稳定性和良好的动态特性。仿真分析和实验结果表明:与同等闭环控制带宽的PI控制器相比,自抗扰控制器可以提高系统对扰动力矩的抑制能力,采用自抗扰补偿时,速度误差的峰值由1.88%降低到0.65%,速度误差的均方根值由0.8%降低到0.2%。实验结果证明提出的方法能够有效降低电机力矩波动的影响,提高速度平稳性。     

MPC在扬子20000m~3/h空分设备中的应用

简介模型预测控制 (MPC)概念 ;介绍了扬子 2 0 0 0 0m3 /h空分设备所配MPC的组成及工作方式 ,MPC中CV～MV之间的关系 ,以及MPC投用要点 ;阐述了扬子 2 0 0 0 0m3 /h空分设备投用MPC的效果和MPC的改进之处。