Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.     

电站系统中PSS与SVC联合运行的稳定分析

针对电力系统装置相互协调运行问题,对配有PSS的励磁发电机与SVC联合运行的系统,分析了其数学模型,研究了电力系统稳定器(PSS)与静止无功补偿器(SVC)在工厂用电系统近距离联合运行中的交互影响问题,指出了在两者同时运行的情况下SVC控制器的设计需要考虑PSS放大倍数的影响。并提出了设计SVC的比例积分系数和PSS超前滞后环节以及线性二次高斯最优控制方法,动态特性仿真表明可以提高工厂用电系统SVC和PSS的稳定性。最后通过对系统仿真计算从而对理论方法进行了验证。     

PSS与SVC多目标协调设计

随着电力系统的发展,元器件／装置相互间协调运行问题引起人们更多的关注,为此提出一种基于多目标进化算法（MOEA）的控制器设计方法,并在一多机电力系统中,给出了采用该方法进行多台电力系统稳定器（PSS）及静止无功补偿器（SVC）的协调控制设计的实例。仿真结果表明,所提出的协调控制器设计方法是有效的。PSS与SVC的协调运行可以有效地阻尼电力系统中的模态振荡和稳定节点电压。     

Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm

In this work we present a novel approach in order to improve the power system stability, by designing a coordinated structure composed of a power system stabilizer and static synchronous series compensator (SSSC)-based damping controller. In the design approach various time delays and signal transmission delays owing to sensors are included. This is a coordinated design problem which is treated as an optimization problem. A new hybrid particle swarm optimization and gravitational search algorithm (hPSO–GSA) algorithm is used in order to find the controller parameters. The performance of single-machine infinite-bus power system as well as the multi-machine power systems are evaluated by applying the proposed hPSO–GSA based controllers (PSS and damping controller). Various results are shown here with different loading condition and system configuration over a wide range which will prove the robustness and effectiveness of the above design approach. From the results it can be observed that, the proposed hPSO–GSA based controller provides superior damping to the power system oscillation on a wide range of disturbances. Again from the simulation based results it can be concluded that, for a multi-machine power system, the modal oscillation which is very dangerous can be easily damped out with the above proposed approach.     

Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm

A robust coordination scheme to improve the stability of a power system by optimal design of multiple and multi-type damping controllers is presented in this paper. The controllers considered are power system stabilizer (PSS) and static synchronous series compensator (SSSC)-based controller. Local measurements are provided as input signals to all the controllers. The coordinated design problem is formulated as an optimization problem and differential evolution (DE) algorithm is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Nonlinear simulation results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach.     

Strict-Tabu Based Eigenvalue Optimization of Power System Stabilizer

A Strict-Tabu (S-Tabu) optimization of power system stabilizer (PSS) parameters has been developed and presented in this paper. To achieve good damping characteristics over a wide range of operating conditions, S-Tabu is used to search for the best eigenvalue of the PSS and later installed into the phase lead-lag controller. The implementation studies are carried out on both singlemachine, infinite-bus, and multimachine power systems, respectively. Simulation studies for a variety of disturbances on the power system with optimized PSS demonstrate its effectiveness in improving system performance. Comparison studies are also performed to show the advantages of the proposed controller over the nonoptimized Conventional PSS (CPSS).     

Tuning a static var compensator controller over a wide range of load models using an artificial neural network

A novel approach using an artificial neural network (ANN) for tuning a static var compensator (SVC) controller over a wide range of load models is presented in this paper. To enhance power system damping over a wide range of load models, it is desirable to adapt the SVC controller gain in real time based on load models. To do this, online measurements of load parameters which are representative of load models are chosen as the input signals to the neural network. The output of the neural network is the desired gain of the SVC controller. The neural network, once trained by a set of input-output patterns in the training set, can yield a proper SVC controller gain under any load model. Simulation results show that the tuning gain of a SVC controller using the ANN approach can provide better damping of the power system over a wide range of load models than the fixed-gain controller.     

Tuning of power system stabilizers using genetic algorithms

Several techniques exist for developing optimal controllers. This paper investigates the tuning of power system stabilizers (PSS) using genetic algorithms (GA). A digital simulation of a linearized model of a single-machine infinite bus power system at some operating point is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the time-multiplied absolute value of the error performance indices are considered in the search for the optimal PSS parameters. In order to have good damping characteristics over a wide range of operating conditions, the PSS parameters are optimized off-line for a selected set of grid points in the real power (P)-reactive power (Q) domain. The optimal settings thus obtained can then be stored and retrieved on-line to update the PSS parameters based on measurements of the generator real and reactive power. Time domain simulations of the system with GA-tuned PSS show the improved dynamic performance under widely varying load conditions.     

Coordinated design of probabilistic PSS and SVC damping controllers

This paper presents an application of probabilistic theory to the coordinated design of power system stabilizers (PSSs) and FACTS controllers, taking static VAr system (SVC) as an example. The aim is to enhance the damping of multi electro-mechanical modes in a multimachine system over a large and pre-specified set of operating conditions. In this work, conventional eigenvalue analysis is extended to the probabilistic environment in which the statistical nature of eigenvalues corresponding to different operating conditions is described by their expectations and variances. Probabilistic sensitivity indices (PSIs) are used for robust damping controller site selection and for optimization objective functions. A probabilistic eigenvalue-based objective function is employed for coordinated design of PSS and SVC controller parameters. The effectiveness of the proposed controllers is demonstrated on an 8-machine system.     

Energy-based coordinated nonlinear control of synchronous generator and static var compensator

Using the Hamiltonian function method, this paper investigates the coordinated control of the synchronous generator and static var compensator (SVC) in power systems with constant power loads. First, via a proper variable transformation and pre-feedback control, the dissipative Hamiltonian realization of the power system is completed. Then, based on the obtained dissipative Hamiltonian realization, an energy-based coordinated stabilization controller is constructed. The guaranteed L₂ performance property of the closed loop system is analyzed as well. Simulation results indicate that the proposed coordinated control scheme can effectively improve both the transient stability and voltage regulation performance of the power system.     

Coordinated Design of Power System Stabilizers and Static Phase Shifters Using Genetic Algorithm

Coordinated design of a power system stabilizer (PSS) and a static phase shifter (SPS) using genetic algorithm (GA) is investigated in this paper. The design problem of PSS and SPS controller is formulated as an optimization problem. An eigenvalue-based objective function to increase the system damping is proposed. Then, GA is employed to search for optimal controller parameters. Different control schemes have been proposed and tested on a weakly connected power system with different disturbances, loading conditions, and parameter variations. It was observed that although the PSS enhances the power system stability, the SPS controller provides most of the damping and improves the voltage profile of the system. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and system parameter variations.     

发电厂厂用电系统中PSS与SVC稳定的离散分析

针对电力系统装置间相互协调运行的问题,对配有电力系统稳定器(PSS)的励磁系统发电机与装有静止无功补偿器(SVC)联合运行的发电厂厂用电系统,分别建立了PSS、SVC和带励磁系统的发电机数学模型并将其离散化,深入研究了PSS与SVC在厂用电系统近距离联合运行中的交互影响问题及提高SVC和PSS运行稳定性的方法。指出了在两者同时运行的情况下SVC控制器的设计需要考虑PSS放大倍数的影响,对SVC的比例、积分系数的改进设计的动态特性仿真表明,可以提高厂用电系统中SVC和PSS联合运行的稳定性。通过大量的仿真实验对离散分析的结果进行了验证。     

A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design

A novel hybrid approach involving Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization Algorithm (BFOA) called Bacterial Swarm Optimization (BSO) is illustrated for optimal Power System Stabilizers (PSSs) design in a multimachine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual’s best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning PSSs compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power system stability over a wide range of loading conditions and various disturbances.     

Adaptive control of a synchronous machine using the auto-searchingmethod

A novel adaptive control scheme is presented for the design of a power system stabilizer (PSS) and a static VAr (reactive volt-ampere) compensator (SVC). The developed adaptive proportional-integral (PI) controllers, whose gains are adjusted in real time using the online-measured system operating conditions and a look-up table stored in computer memory, can offer better damper effects for generator oscillations over a wide range of operating conditions than conventional PI controllers with fixed gain settings. The effects of the adaptive PSS and the adaptive SVC on generator damping and terminal voltage profile are examined by computer simulation of system dynamic responses to a major disturbance     

BFOA based design of PID controller for two area Load Frequency Control with nonlinearities

This paper presents an application of the novel artificial intelligent search technique to find the parameters optimization of nonlinear Load Frequency Controller (LFC) considering Proportional Integral Derivative controller (PID) for a power system. A two area non reheat thermal system is considered to be equipped with PID controller. Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controller parameters to minimize the time domain objective function. The performance of the proposed technique has been evaluated with the performance of the conventional Ziegler Nichols (ZN) and Genetic Algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PID controller. By comparison with the conventional technique and GA, the effectiveness of the proposed BFOA is validated over different operating conditions, and system parameters variations.     

Robust SVC controller design for improving power system damping

The design of a robust controller for a static VAr compensator (SVC) to improve the damping of a power system is presented. The main contributions of the paper are to formulate and to solve the power system damping control problem using robust optimization techniques, and to synthesize the controller with explicit consideration of the system operating condition variations. Nonlinear simulations using the PSCAD/EMTDC software packages have been conducted to evaluate the performance of the closed-loop system. The results have indicated that the designed controller can provide positive damping to the system under a wide range of operating conditions     

Coordinated fuzzy logic control between SVC and PSS to enhance stability of power systems

This paper presents a new switching control scheme for static var compensator (SVC) using fuzzy logic control rules to enhance the overall stability of electric power systems. In addition, the coordination with power system stabilizers (PSS) is also considered to achieve a wider stable region. An SVC is set on one of the busbars in the transmission system, where the real power flow signal is utilized at the location of the SVC to determine the firing angle of the thyristor switch. The switching control scheme is simple so as not to require heavy computation on the microcomputer based switching controller. The PSSs are also set on the generators in the study system. Simulation results show the effectiveness of the proposed fuzzy logic switching control scheme for the SVC. The coordination between SVC and PSS is also effective to enlarge the stable region.     

基于多目标进化算法的TCSC与SVC控制器协调设计

以可控串补(TCSC)与静止无功补偿器(SVC)两种FACTS装置为研究对象,通过理论分析 指出了TCSC与SVC联合运行时不同控制目标间的矛盾,并通过一多机系统时域仿真实例,介绍 了TCSC与SVC控制器之间存在的交互影响。仿真表明:单独设计且运行良好的SVC与TCSC 控制器,并不能保证两者同时投运工况下控制器的性能。将FACTS功能控制器的协调问题转化 为一多目标优化问题,采用多目标进化算法(MOEA)优化控制器参数,得到一组Pareto优化解集, 时域仿真验证了协调控制器的控制效果良好。     

A hybrid neuro-fuzzy power system stabilizer for multimachine powersystems

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters in real-time based on machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a single machine infinite bus system and a multimachine power system subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of conventional (CPSS). The results show the capability of the proposed FBFN PSS to enhance the system damping of local modes of oscillations over a wide range of operating conditions. The decentralized nature of the proposed FBFN PSS makes it easy to install and tune     

基于MSSA的PSS与UPFC-POD参数和 UPFC位置协调优化

针对风火打捆(wind-thermal-bundled, WTB)系统在受到干扰时可能由于阻尼不足而出现的低频振荡现象以及较高的网损会导致运行成本的增加和阻碍“双碳”目标实现的问题,提出了一种电力系统稳定器(power system stabilizer, PSS)与统一潮流控制器(unified power flow controller, UPFC)附加功率振荡阻尼控制器(power oscillation damping, POD)参数和UPFC安装位置协调优化策略方法。首先,基于Matlab构建了风火打捆外送系统和控制器模型。然后,利用多目标樽海鞘优化算法(multi-objective salp swarm algorithm, MSSA),将协调优化问题转化为多目标优化问题。目标函数设计中考虑了UPFC装置的调节特性。最后,采用IEEE 4机2区系统和16机5区系统进行多种工况下的仿真。仿真结果显示,协调优化后的控制器可以提高系统阻尼,维持发电机转速的稳定,抑制低频振荡引起的系统有功、电压等的波动,同时降低了系统的有功网损,提高了系统稳定性和运行经济性。MSSA在工程问题上的应用得到了补充。