This paper studies how random phase （namely, noise-perturbed phase） effects the dynamical behaviours of a simple model of power system which operates in a stable regime far away from chaotic behaviour in the absence...This paper studies how random phase （namely, noise-perturbed phase） effects the dynamical behaviours of a simple model of power system which operates in a stable regime far away from chaotic behaviour in the absence of noise. It finds that when the phase perturbation is weak, chaos is absent in power systems. With the increase of disturbed intensity σ, power systems become unstable and fall into chaos as σ further increases. These phenomena imply that random phase can induce and enhance chaos in power systems. Furthermore, the possible mechanism behind the action of random phase is addressed.展开更多
The use of power systems as close to their operating limits can cause instability if a disturbance is occurred. The damping of the system’s oscillations can be obtained by conventional means such as voltage and speed...The use of power systems as close to their operating limits can cause instability if a disturbance is occurred. The damping of the system’s oscillations can be obtained by conventional means such as voltage and speed regulation but also by Flexible AC Transmission System devices (FACTS). These devices are increasingly used in power systems. This paper presents a systematic procedure for modelling and simulation of a single-machine infinite-bus power system installed with a Static VAR Compensator (SVC). So the impact of the SVC on power system stability can be reasonably evaluated. Genetic algorithm (GA) optimization technique is applied to design robust power system stabilizer and SVC-controllers for single-machine infinite-bus (SMIB) and is employed to search for optimal controller parameters.展开更多
The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various faul...The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.展开更多
Selection of better optimized unified power flow controller （UPFC） control inputs along with simultaneous coordinated design of power system stabilizer （PSS） is a challenge in the present scenario of power systems...Selection of better optimized unified power flow controller （UPFC） control inputs along with simultaneous coordinated design of power system stabilizer （PSS） is a challenge in the present scenario of power systems. Hence, in this paper, four sets of experiments performed are presented. First set of experiments are without disturbance scenario where switching is done using linear quadratic regulators （LQR＇s）. Second set is for power systems with disturbances using linear quadratic gaussian （LQG）. Switching control algorithms presented here are tested on the single machine infinite bus （SMIB） linearised Phillips Heffron model of power system using MATLAB/SIMULINK~ platform.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10862001,10947011 and 70571017)
文摘This paper studies how random phase （namely, noise-perturbed phase） effects the dynamical behaviours of a simple model of power system which operates in a stable regime far away from chaotic behaviour in the absence of noise. It finds that when the phase perturbation is weak, chaos is absent in power systems. With the increase of disturbed intensity σ, power systems become unstable and fall into chaos as σ further increases. These phenomena imply that random phase can induce and enhance chaos in power systems. Furthermore, the possible mechanism behind the action of random phase is addressed.
文摘The use of power systems as close to their operating limits can cause instability if a disturbance is occurred. The damping of the system’s oscillations can be obtained by conventional means such as voltage and speed regulation but also by Flexible AC Transmission System devices (FACTS). These devices are increasingly used in power systems. This paper presents a systematic procedure for modelling and simulation of a single-machine infinite-bus power system installed with a Static VAR Compensator (SVC). So the impact of the SVC on power system stability can be reasonably evaluated. Genetic algorithm (GA) optimization technique is applied to design robust power system stabilizer and SVC-controllers for single-machine infinite-bus (SMIB) and is employed to search for optimal controller parameters.
文摘The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.
文摘Selection of better optimized unified power flow controller （UPFC） control inputs along with simultaneous coordinated design of power system stabilizer （PSS） is a challenge in the present scenario of power systems. Hence, in this paper, four sets of experiments performed are presented. First set of experiments are without disturbance scenario where switching is done using linear quadratic regulators （LQR＇s）. Second set is for power systems with disturbances using linear quadratic gaussian （LQG）. Switching control algorithms presented here are tested on the single machine infinite bus （SMIB） linearised Phillips Heffron model of power system using MATLAB/SIMULINK~ platform.
