Power system stabilization is a major issue in the area of power systems research. The Conventional Power
System Stabilizer (CPSS) parameters are tuned by using Genetic Algorithm to achieve proper damping
over a wide range of operating conditions. The CPSS lack of robustness over wide range of operating
conditions. In this paper type-2 Fuzzy Logic Power System Stabilizer (FLPSS) is presented to improve the
damping of power system oscillations. To accomplish the best damping characteristics three signals are
chosen as in put to FLPSS. Deviation in speed ( ), deviation of speed derivative ( ) and deviation of
power angle ( ) are taken as input to fuzzy logic controller. The proposed controller is implemented for
Single Machine Infinite Bus (SMIB) power system model. The efficacy of the proposed controller is tested
over a wide range of operating conditions. The comparison between CPSS, Type-1 FLPSS and Type-2
FLPSS is presented. The results validate the effective ness of proposed Type-2 FLPSS controller in terms of
less over/under shoot, settling time and enhancing stability over wide range of generator load variations.
Comparative Analysis of Power System Stabilizer using Artificial Intelligence...ijsrd.com
Power system stabilizers (PSSs) are used to enhance the damping during low frequency oscillations. The paper presents study of power system stabilizer using fuzzy logic and neural network to enhance stability of single machine infinite bus system. In this paper basic problem of conventional power system stabilizer for stability enhancement is defined which is traditionally used. Artificial intelligence techniques provide one alternative for stability enhancement and speed deviation (Δw). The proposed method using Artificial intelligence techniques achieves better improvement than conventional power system stabilizer. Fuzzy logic rules were developed for triangular membership function of input and output variables. Neuro controller is implemented and it is compared with reference model. The system is simulated in SIMULINK environment and the performances of conventional, Fuzzy based and Neural network based power system stabilizers are compared.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Performance evaluation of different structures of power system stabilizers IJECEIAES
The electric power from the system should be reliable and economical for consumer’s equipment satisfaction. An electric power system consists of many generators, transformers, transmission lines, loads, etc. For the power system network, dynamic performance and stability are important. The system is lost its stability by some disturbances i.e., load variations, generator failure, prime mover failure, transmission line outage, etc. Whenever load variations in the system, generator rotor speed will vary, means oscillations in the rotor speed, which is deviating from rated speed. The excitation system will control the generator rated line voltage. When fault occurs at any equipment in the system, the system will unstable. If fault occurs at generator, the generator oscillates. To reduce the oscillations and to make the system stable used power system stabilizers (PSS’s). Here, three types of PSS’s are used i.e., PSS1B, PSS2B, PSS4B. Comparisons of three PSS’s are on the multi machine system under some disturbance. From the observations, concluding that PSS4B is quickly control the oscillations in the physical parameters of machine in the system than other power system stabilizers.
The aim of this paper is to prove that fuzzy logic algorithm is a suitable control technique for fast processes such as electrical machines. This theory has been experimented on different kinds of electrical machines such as stepping motors, dc motors and induction machines (with 6 phases) and the experimental results show that the proposed fuzzy logic algorithm is the most suitable control technique for electrical machines since this algorithm is not time consuming and it is also robust between plant parameters variations.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
Comparative Analysis of Power System Stabilizer using Artificial Intelligence...ijsrd.com
Power system stabilizers (PSSs) are used to enhance the damping during low frequency oscillations. The paper presents study of power system stabilizer using fuzzy logic and neural network to enhance stability of single machine infinite bus system. In this paper basic problem of conventional power system stabilizer for stability enhancement is defined which is traditionally used. Artificial intelligence techniques provide one alternative for stability enhancement and speed deviation (Δw). The proposed method using Artificial intelligence techniques achieves better improvement than conventional power system stabilizer. Fuzzy logic rules were developed for triangular membership function of input and output variables. Neuro controller is implemented and it is compared with reference model. The system is simulated in SIMULINK environment and the performances of conventional, Fuzzy based and Neural network based power system stabilizers are compared.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Performance evaluation of different structures of power system stabilizers IJECEIAES
The electric power from the system should be reliable and economical for consumer’s equipment satisfaction. An electric power system consists of many generators, transformers, transmission lines, loads, etc. For the power system network, dynamic performance and stability are important. The system is lost its stability by some disturbances i.e., load variations, generator failure, prime mover failure, transmission line outage, etc. Whenever load variations in the system, generator rotor speed will vary, means oscillations in the rotor speed, which is deviating from rated speed. The excitation system will control the generator rated line voltage. When fault occurs at any equipment in the system, the system will unstable. If fault occurs at generator, the generator oscillates. To reduce the oscillations and to make the system stable used power system stabilizers (PSS’s). Here, three types of PSS’s are used i.e., PSS1B, PSS2B, PSS4B. Comparisons of three PSS’s are on the multi machine system under some disturbance. From the observations, concluding that PSS4B is quickly control the oscillations in the physical parameters of machine in the system than other power system stabilizers.
The aim of this paper is to prove that fuzzy logic algorithm is a suitable control technique for fast processes such as electrical machines. This theory has been experimented on different kinds of electrical machines such as stepping motors, dc motors and induction machines (with 6 phases) and the experimental results show that the proposed fuzzy logic algorithm is the most suitable control technique for electrical machines since this algorithm is not time consuming and it is also robust between plant parameters variations.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
This paper presents interval type-2 fuzzy logic (IT2FL) controller applied on a direct torque controlled (DTC) permanent magnet synchronous motor (PMSM), using digital signal processing (DSP). The simulation of PMSM with space vector pulse widths modulation (SVPWM) inverter presented under several operating condition. To verify the simulation results a hard ware setup is prepared and tested at several operating conditions using dspace 1102 DSP model. The experimental and simulation results are in agreement and the torque dynamic response is very rapid and the system achieves the steady state in a very short time.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This paper presents an analysis by which the dynamic performances of a permanent magnet
brushless dc (PMBLDC) motor drive with different speed controllers can be successfully predicted. The
control structure of the proposed drive system is described. The dynamics of the drive system with a
classical proportional-integral-derivative (PID) and Fuzzy-Logic (FL) speed controllers are presented.
The simulation results for different parameters and operation modes of the drive system are investigated
and compared. The results with FL speed controller show improvement in transient response of the
PMBLDC drive over conventional PID controller. Moreover, useful conclusions stemmed from such a
study which is thought of good use and valuable for users of these controllers
Deregulated Load Frequency Control (DLFC) plays an important role in power systems. The main aim of
DLFC is to minimize the deviation in area frequency and tie-line power changes. Conventional PID
controller gains are optimally tuned at one operating condition. The main problem of this controller is that
it fails to operate under different dynamic operating conditions. To overcome that drawback, fuzzy
controllers have very much importance. The design of Fuzzy controller’s mostly depends on the
Membership Functions (MF) and rule-base over the input and output ranges controllers. Many methods
were proposed to generate and minimize the fuzzy rules-base. The present paper proposes an optimal fuzzy
rule base based on Principal component analysis and the designed controller is tested on three area
deregulated interconnected thermal power system. The efficacies of the proposed controller are compared
with the Fuzzy C-Means controller and Conventional PID controller.
Comparison of Different Design Methods for Power System Stabilizer Design - A...ijsrd.com
In the past two decades, the utilization of supplementary excitation control signals for improving the dynamic stability of power systems has received much attention. In recent years, several approaches based on intelligent control and optimization techniques have been applied to PSS design problem. This paper introduces a review on the techniques applied on the conventional PSS design only. Power System Stabilizer (PSS) is the most cost effective approach of increase the system positive damping, improve the steady-state stability margin, and suppress the low-frequency oscillation of the power system. A PSS has to perform well under operating point variations. This paper introduces a review on the techniques applied on the conventional PSS design only. The techniques could be mainly classified into linear and nonlinear.
Design of power system stabilizer for damping power system oscillationsIOSRJEEE
The problem of the poorly damped low-frequency (electro-mechanical) oscillations of power systems has been a matter of concern to power engineers for a long time, because they limit power transfers in transmission lines and induce stress in the mechanical shaft of machines. Due to small disturbances, power systems experience these poorly damped low-frequency oscillations. The dynamic stability of power systems are also affected by these low frequency oscillations. With proper design of Power System Stabilizer (PSS), these oscillations can be well damped and hence the system stability is enhanced. The basic functions of the PSS is to add a stabilizing signal that compensates the oscillations of the voltage error of the excitation system during the dynamic/transient state, and to provide a damping component when it’s on phase with rotor speed deviation of machine. Studies have shown that PSS are designed to provide additional damping torque, for different operation point normal load, heavy load and leading to improve power system dynamic stability.
NARMA-L2 Controller for Five-Area Load Frequency Controlijeei-iaes
This paper investigates the load-frequency control (LFC) based on neural network for improving power system dynamic performance. In this paper an Artificial Neural Network (ANN)based controller is presented for the Load Frequency Control (LFC) of a five area interconnected power system. The controller is adaptive and is based on a nonlinear auto regressive moving average (NARMA-L2) algorithm. The working of the conventional controller and ANN based NARMA L2 controllers is simulated using MATLAB/SIMULINK package.. The Simulink link results of both the controllers are compared.
Model based PI power system stabilizer design for damping low frequency oscil...ISA Interchange
This paper explores a two-level control strategy by blending a local controller with a centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favorably compared with some controllers prevalent in the literature.
ANN Based PID Controlled Brushless DC drive SystemIDES Editor
Brushless (BLDC) DC motors find many industrial
applications such as process control, robotics, automation,
aerospace etc. Wider usage of this system has demanded an
optimum position control for high efficiency, accuracy and
reliability. Hence for the effective position control, estimation
of dynamic load parameters i.e. moment of inertia and friction
coefficient is necessary. This paper incorporates the estimation
of mechanical parameters such as moment of inertia and
friction coefficient of BLDC motor and load at various load
settings by using simple procedure. To achieve the optimum
position control, PID controller is employed and tuned using
PARR method. ANN training is used for obtaining the
mechanical and PID controller parameters at different load
settings. Closed loop position control system of the BLDC
drive system is created using SIMULINK. Simulation results
of this system are obtained at different load settings. It is
evident from the results that the position control system
responds to the desired position with minimum rise time,
settling time and peak overshoot.
Steady state stability analysis and enhancement of three machine nine bus pow...eSAT Journals
Abstract
Power System stability study is the important parameter of economic, reliable and secure system planning and operation. Studies
are important during the planning and conceptual design stages of the project as well as during the operating life of the plant
periodically. This paper presents the power system steady state stability analysis for IEEE- 9 bus test system and examines
influence of TCPS FACTS device based controller on test system. It is assumed that system under study has been perturbed from a
steady state equilibrium that prevailed prior to the application of the disturbance. If system is stable, we would expect that for
temporary or permanent disturbance, system will acquire initial or new operating state after a transient period. The stability
study is accessed using Lyapunov’s first method. The effectiveness of damping controller in enhancing the steady state stability is
investigated by incorporating available constraints. For analysis MATLAB software is employed. The conclusions have been
drawn here, based on theoretical and mathematical analysis so as to provide an insight and better understanding of steady state
stability of considered multi machine power system.
Key Words: Lyapunov’s first method, Steady-state stability, Phase portrait, FACTS device, supplementary modulation
controller, eigen value, synchronizing power coefficient, IEEE-9 Bus Test System, Load Flow Study, Differential
algebraic equation.
Adaptive Neuro Fuzzy Controller Based Power System Stabilizer for Damping of ...IJMTST Journal
Maximum power transfer and optimal power flow is desired which is affected by the inter area oscillation
of low frequency .To avoid low frequency inter area power oscillations power system stabilizer is added to
the automatic voltage regulators on the generators. Stabilizer provides the means to damp these low
frequency power oscillations .Different types of stabilizers are available to damp power oscillations still a
power system stabilizers can be developed to effectively damp low frequency power oscillations. Without
increasing the system complexity these oscillations can be reduced with the help of developing power system
stabilizer to damp the low frequency inter area power oscillations.
Adaptive Neuro Fuzzy Interface system (ANFIS) is designed to achieve fast and effective power oscillation
damping in two area four machine power system. This work is implemented with MATLAB simulink 2012(b).
Also comparative analysis of low frequency power oscillation damping of ANFIS controller based stabilizer
and conventional PID power system stabilizer is presented in this paper. Comparison of results show that
low frequency inter area power oscillation damping of ANFIS controller based stabilizer is much higher than
PID power system stabilizer. Also damping time provided by ANFIS controller based power system stabilizer
is 75% less in comparison to PID power system stabilizer .
Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Sec...IJECEIAES
Model reference controller is considering as one of the most useful controller to specific performance of systems where the desired output is produced for a given input. This system used the difference between the outputs of the plant and the desired model by comparing them to produce the signals of the control. This paper focus on design a model reference controller (MRC) combined with (type-1 and interval type-2) fuzzy control scheme for single input-single output (SISO) systems under uncertainty and external disturbance. The model reference controller is designed firstly without fuzzy scheme based on an optimal desired model and Lyapunov stability theory. Then a (type-1 and Interval type-2) fuzzy controller Takagi-Sugeno type is combine with the suggested MRC in order to enhance the performer of it, the common parts between the two fuzzy systems such as: fuzzifier, inference engine, fuzzy rule-base and defuzzifier are illustrated. In this paper the proposed controller is applied to controla (SISO) inverted pendulum sustem and the Matlab R2015 software is used to carry out two simulation cases for the overall controlled scheme. The obtained results for the two cases show that the proposed MRC with both fuzzy control schemes have acceptable performance, but it have better performance with the interval type-2 fuzzy scheme.
This paper presents interval type-2 fuzzy logic (IT2FL) controller applied on a direct torque controlled (DTC) permanent magnet synchronous motor (PMSM), using digital signal processing (DSP). The simulation of PMSM with space vector pulse widths modulation (SVPWM) inverter presented under several operating condition. To verify the simulation results a hard ware setup is prepared and tested at several operating conditions using dspace 1102 DSP model. The experimental and simulation results are in agreement and the torque dynamic response is very rapid and the system achieves the steady state in a very short time.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This paper presents an analysis by which the dynamic performances of a permanent magnet
brushless dc (PMBLDC) motor drive with different speed controllers can be successfully predicted. The
control structure of the proposed drive system is described. The dynamics of the drive system with a
classical proportional-integral-derivative (PID) and Fuzzy-Logic (FL) speed controllers are presented.
The simulation results for different parameters and operation modes of the drive system are investigated
and compared. The results with FL speed controller show improvement in transient response of the
PMBLDC drive over conventional PID controller. Moreover, useful conclusions stemmed from such a
study which is thought of good use and valuable for users of these controllers
Deregulated Load Frequency Control (DLFC) plays an important role in power systems. The main aim of
DLFC is to minimize the deviation in area frequency and tie-line power changes. Conventional PID
controller gains are optimally tuned at one operating condition. The main problem of this controller is that
it fails to operate under different dynamic operating conditions. To overcome that drawback, fuzzy
controllers have very much importance. The design of Fuzzy controller’s mostly depends on the
Membership Functions (MF) and rule-base over the input and output ranges controllers. Many methods
were proposed to generate and minimize the fuzzy rules-base. The present paper proposes an optimal fuzzy
rule base based on Principal component analysis and the designed controller is tested on three area
deregulated interconnected thermal power system. The efficacies of the proposed controller are compared
with the Fuzzy C-Means controller and Conventional PID controller.
Comparison of Different Design Methods for Power System Stabilizer Design - A...ijsrd.com
In the past two decades, the utilization of supplementary excitation control signals for improving the dynamic stability of power systems has received much attention. In recent years, several approaches based on intelligent control and optimization techniques have been applied to PSS design problem. This paper introduces a review on the techniques applied on the conventional PSS design only. Power System Stabilizer (PSS) is the most cost effective approach of increase the system positive damping, improve the steady-state stability margin, and suppress the low-frequency oscillation of the power system. A PSS has to perform well under operating point variations. This paper introduces a review on the techniques applied on the conventional PSS design only. The techniques could be mainly classified into linear and nonlinear.
Design of power system stabilizer for damping power system oscillationsIOSRJEEE
The problem of the poorly damped low-frequency (electro-mechanical) oscillations of power systems has been a matter of concern to power engineers for a long time, because they limit power transfers in transmission lines and induce stress in the mechanical shaft of machines. Due to small disturbances, power systems experience these poorly damped low-frequency oscillations. The dynamic stability of power systems are also affected by these low frequency oscillations. With proper design of Power System Stabilizer (PSS), these oscillations can be well damped and hence the system stability is enhanced. The basic functions of the PSS is to add a stabilizing signal that compensates the oscillations of the voltage error of the excitation system during the dynamic/transient state, and to provide a damping component when it’s on phase with rotor speed deviation of machine. Studies have shown that PSS are designed to provide additional damping torque, for different operation point normal load, heavy load and leading to improve power system dynamic stability.
NARMA-L2 Controller for Five-Area Load Frequency Controlijeei-iaes
This paper investigates the load-frequency control (LFC) based on neural network for improving power system dynamic performance. In this paper an Artificial Neural Network (ANN)based controller is presented for the Load Frequency Control (LFC) of a five area interconnected power system. The controller is adaptive and is based on a nonlinear auto regressive moving average (NARMA-L2) algorithm. The working of the conventional controller and ANN based NARMA L2 controllers is simulated using MATLAB/SIMULINK package.. The Simulink link results of both the controllers are compared.
Model based PI power system stabilizer design for damping low frequency oscil...ISA Interchange
This paper explores a two-level control strategy by blending a local controller with a centralized controller for the low frequency oscillations in a power system. The proposed control scheme provides stabilization of local modes using a local controller and minimizes the effect of inter-connection of sub-systems performance through a centralized control. For designing the local controllers in the form of proportional-integral power system stabilizer (PI-PSS), a simple and straight forward frequency domain direct synthesis method is considered that works on use of a suitable reference model which is based on the desired requirements. Several examples both on one machine infinite bus and multi-machine systems taken from the literature are illustrated to show the efficacy of the proposed PI-PSS. The effective damping of the systems is found to be increased remarkably which is reflected in the time-responses; even unstable operation has been stabilized with improved damping after applying the proposed controller. The proposed controllers give remarkable improvement in damping the oscillations in all the illustrations considered here and as for example, the value of damping factor has been increased from 0.0217 to 0.666 in Example 1. The simulation results obtained by the proposed control strategy are favorably compared with some controllers prevalent in the literature.
ANN Based PID Controlled Brushless DC drive SystemIDES Editor
Brushless (BLDC) DC motors find many industrial
applications such as process control, robotics, automation,
aerospace etc. Wider usage of this system has demanded an
optimum position control for high efficiency, accuracy and
reliability. Hence for the effective position control, estimation
of dynamic load parameters i.e. moment of inertia and friction
coefficient is necessary. This paper incorporates the estimation
of mechanical parameters such as moment of inertia and
friction coefficient of BLDC motor and load at various load
settings by using simple procedure. To achieve the optimum
position control, PID controller is employed and tuned using
PARR method. ANN training is used for obtaining the
mechanical and PID controller parameters at different load
settings. Closed loop position control system of the BLDC
drive system is created using SIMULINK. Simulation results
of this system are obtained at different load settings. It is
evident from the results that the position control system
responds to the desired position with minimum rise time,
settling time and peak overshoot.
Steady state stability analysis and enhancement of three machine nine bus pow...eSAT Journals
Abstract
Power System stability study is the important parameter of economic, reliable and secure system planning and operation. Studies
are important during the planning and conceptual design stages of the project as well as during the operating life of the plant
periodically. This paper presents the power system steady state stability analysis for IEEE- 9 bus test system and examines
influence of TCPS FACTS device based controller on test system. It is assumed that system under study has been perturbed from a
steady state equilibrium that prevailed prior to the application of the disturbance. If system is stable, we would expect that for
temporary or permanent disturbance, system will acquire initial or new operating state after a transient period. The stability
study is accessed using Lyapunov’s first method. The effectiveness of damping controller in enhancing the steady state stability is
investigated by incorporating available constraints. For analysis MATLAB software is employed. The conclusions have been
drawn here, based on theoretical and mathematical analysis so as to provide an insight and better understanding of steady state
stability of considered multi machine power system.
Key Words: Lyapunov’s first method, Steady-state stability, Phase portrait, FACTS device, supplementary modulation
controller, eigen value, synchronizing power coefficient, IEEE-9 Bus Test System, Load Flow Study, Differential
algebraic equation.
Adaptive Neuro Fuzzy Controller Based Power System Stabilizer for Damping of ...IJMTST Journal
Maximum power transfer and optimal power flow is desired which is affected by the inter area oscillation
of low frequency .To avoid low frequency inter area power oscillations power system stabilizer is added to
the automatic voltage regulators on the generators. Stabilizer provides the means to damp these low
frequency power oscillations .Different types of stabilizers are available to damp power oscillations still a
power system stabilizers can be developed to effectively damp low frequency power oscillations. Without
increasing the system complexity these oscillations can be reduced with the help of developing power system
stabilizer to damp the low frequency inter area power oscillations.
Adaptive Neuro Fuzzy Interface system (ANFIS) is designed to achieve fast and effective power oscillation
damping in two area four machine power system. This work is implemented with MATLAB simulink 2012(b).
Also comparative analysis of low frequency power oscillation damping of ANFIS controller based stabilizer
and conventional PID power system stabilizer is presented in this paper. Comparison of results show that
low frequency inter area power oscillation damping of ANFIS controller based stabilizer is much higher than
PID power system stabilizer. Also damping time provided by ANFIS controller based power system stabilizer
is 75% less in comparison to PID power system stabilizer .
Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Sec...IJECEIAES
Model reference controller is considering as one of the most useful controller to specific performance of systems where the desired output is produced for a given input. This system used the difference between the outputs of the plant and the desired model by comparing them to produce the signals of the control. This paper focus on design a model reference controller (MRC) combined with (type-1 and interval type-2) fuzzy control scheme for single input-single output (SISO) systems under uncertainty and external disturbance. The model reference controller is designed firstly without fuzzy scheme based on an optimal desired model and Lyapunov stability theory. Then a (type-1 and Interval type-2) fuzzy controller Takagi-Sugeno type is combine with the suggested MRC in order to enhance the performer of it, the common parts between the two fuzzy systems such as: fuzzifier, inference engine, fuzzy rule-base and defuzzifier are illustrated. In this paper the proposed controller is applied to controla (SISO) inverted pendulum sustem and the Matlab R2015 software is used to carry out two simulation cases for the overall controlled scheme. The obtained results for the two cases show that the proposed MRC with both fuzzy control schemes have acceptable performance, but it have better performance with the interval type-2 fuzzy scheme.
Maximum Power Point Tracking Using Adaptive Fuzzy Logic control for Photovolt...IJERA Editor
This work presents an intelligent approach to the improvement and optimization of control performance of a photovoltaic system with maximum power point tracking based on fuzzy logic control. This control was compared with the conventional control based on Perturb &Observe algorithm. The results obtained in Matlab/Simulink under different conditions show a marked improvement in the performance of fuzzy control MPPT of the PV system.
Neural Network-Based Stabilizer for the Improvement of Power System Dynamic P...TELKOMNIKA JOURNAL
This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs)
to improve the oscillation damping and dynamic performance of interconnected multimachine power
system. The scheme was based on the use of a neural network which identifies online the optimal
controller parameters. The inputs to the neural network include the active- and reactive- power of the
synchronous generators which represent the power loading on the system, and elements of the reduced
nodal impedance matrix for representing the power system configuration. The outputs of the neural
network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing
system configuration and operating condition. For a representative power system, the neural network has
been trained and tested for a wide range of credible operating conditions and contingencies. Both
eigenvalue calculations and time-domain simulations were used in the testing and verification of the
performance of the neural network-based stabilizer.
This present paper includes the study Load Frequency Control (LFC) of power systems with several nonlinearities
like Generation Rate Constraint(GRC) and Boiler Dynamics (BD) including Superconducting
Magnetic Energy Storage (SMES) units using Type-2 Fuzzy System (T2FS) controllers . Here, Load
frequency control problem is dealt with a three – area interconnected system of Thermal-Thermal-Hydal
power system by observing the effects and variations of dynamic responses employing conventional
controller, Type-1 fuzzy controller and T2FS controller considering incremental increase of step
pertubations by 10% in the load. The salient advantage of this controller is its high insensitivity to large
load changes and plant parameter variations even in the presence of non-linearities. As the non-linearities
were considered in the system, the conventional and classical Fuzzy controllers does not provide adequate
control performance with the consideration of above nonlinearities. To overcome this drawback T2FS
Controller has been employed in the system. Therefore, the efficacy of the proposed T2FS controller is
found to be better than that of conventional controller and Type-1 Fuzzy controller in cosidreration with
overshoot, settling time and robustness.
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A robust dlqg controller for damping of sub synchronous oscillations in a ser...eSAT Journals
Abstract This paper investigates the use of Discrete Linear Quadratic Gaussian (DLQG) Compensator to damp sub synchronous oscillations in a Thyrisor Controlled Series Capacitor (TCSC) compensated power system. The study is conducted on IEEE First Benchmark Model (FBM) in which, TCSC is modelled as a discrete linear time-invariant modular unit in the synchronously rotating DQ reference frame. This modular TCSC is then integrated with the Linear Time Invariant (LTI) model of the rest of the system. The design of DLQG includes the design of a Kalman filter for full state estimation and a full state feedback for control. Since the order of the controller is as large as the order of the system considered here(27 states), the practical implementation of the controller is difficult. Hence by using Hankels norm approximation technique, the order of the controller is reduced from 27 to 15 without losing the significant system dynamics. The eigen analysis of the system shows that the use of DLQG can damp torsional oscillations as well as the swing mode oscillations simultaneously, which is practically difficult for a conventional sub-synchronous damping controller. The performance of the system with DLQG is appreciable for all operating conditions and it shows the robustness of the controller. Index Terms: Sub-Synchronous Resonance (SSR), Torsional Oscillations, Thyristor Controlled Series Capacitor (TCSC), Discrete Linear Quadratic Gaussian(DLQG)Compensator, Model Order Reduction (MOR).
A robust dlqg controller for damping of sub synchronous oscillations in a se...eSAT Publishing House
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International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
DESIGN OF MPSS AND TCSC DAMPING CONTROLLERS IN MULTI- MACHINE POWER SYSTEM U...Editor IJMTER
The main approach of this paper is to do the small signal analysis in a two area four
machine power system which has coordinated design of both MPSS and TCSC. TCSC controller is
design based on power oscillation damping controller which is used for voltage series compensation
at the voltage sag, voltage swell .Small signal stability analysis is nothing but analysis in power
system for low frequency components in the range of 0.1hz to 2hz.Linear modeling of the power
system is carried out to find out the frequency analysis at the different system components like
transformer, other devices. BFO algorithm is used to minimize an Eigen value based multi-objective
in which it tunes the parameter of the POD controller ,it will be improve the system stability and it
saves the computational cost and in less iterations we can achieve the good optimized POD
parameter values. The time domain simulation in MATLAB/SIMULINK environment carried out on
two area four machine power system over various perturbations shows that superior enhancement in
damping of power system oscillations has been obtained by utilizing proposed PSS-POD coordinated
controllers in comparison with the CPSS-POD.
Sensitive loads are widely used in industrial, which is the main cause of sag-swell and harmonics voltages problems that can affect the power quality. Among the devices that solve such power quality perturbations, the series active power Filter APFS is considered in this paper. Thus, a single phase APFS is developed through an analytic analysis, supported by an experimental validation, where we applied classical proportional integrator PI, fuzzy logic FLC and sliding mode SM controllers to improve the dynamic response of the APFS. In addition, a comparative study between these control strategies has made in order to mitigate voltage sag-swell and especially harmonics, where the SMC has showed more effective and robust results compared to PI and FLC and proved by the Total harmonic distortion THD ratio. Results of the proposed controllers are simulated in MATLAB simulink® and validated through experimental tests applied on our system prototype.
Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzz...IJECEIAES
Intelligent control included ANFIS and type-2 fuzzy (T2FLS) controllers grown-up rapidly and these controllers are applied successfully in power system control. Meanwhile, small signal stability problem appear in a largescale power system (LSPS) due to load fluctuation. If this problem persists, and can not be solved, it will develop blackout on the LSPS. How to improve the LSPS stability due to load fluctuation is done in this research by coordinating of PSS based on ANFIS and T2FLS. The ANFIS parameters are obtained automatically by training process. Meanwhile, the T2FLS parameters are determined based on the knowledge that obtained from the ANFIS parameters. Input membership function (MF) of the ANFIS is 5 Gaussian MFs. On the other hand, input MF of the T2FLS is 3 Gaussian MFs. Results show that the T2FLS-PSS is able to maintain the stability by decreasing peak overshoot for rotor speed and angle. The T2FLS-PSS makes the settling time is shorter for rotor speed and angle on local mode oscillation as well as on inter-area oscillation than conventional/ ANFIS-PSS. Also, the T2FLS-PSS gives better performance than the other PSS when tested on single disturbance and multiple disturbances.
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Stability enhancement of power system
1. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
DOI : 10.5121/ijfls.2016.6103 31
STABILITY ENHANCEMENT OF POWER SYSTEM
USING TYPE-2 FUZZY LOGIC POWER SYSTEM
STABILIZER
K.R.Sudha1
and I.E.S.Naidu2
1
Professor, Electrical Engineering Department, Andhra university
2
Research Scholar, Electrical Engineering Department, Andhra university
ABSTRACT
Power system stabilization is a major issue in the area of power systems research. The Conventional Power
System Stabilizer (CPSS) parameters are tuned by using Genetic Algorithm to achieve proper damping
over a wide range of operating conditions. The CPSS lack of robustness over wide range of operating
conditions. In this paper type-2 Fuzzy Logic Power System Stabilizer (FLPSS) is presented to improve the
damping of power system oscillations. To accomplish the best damping characteristics three signals are
chosen as in put to FLPSS. Deviation in speed ( ), deviation of speed derivative ( ) and deviation of
power angle ( ) are taken as input to fuzzy logic controller. The proposed controller is implemented for
Single Machine Infinite Bus (SMIB) power system model. The efficacy of the proposed controller is tested
over a wide range of operating conditions. The comparison between CPSS, Type-1 FLPSS and Type-2
FLPSS is presented. The results validate the effective ness of proposed Type-2 FLPSS controller in terms of
less over/under shoot, settling time and enhancing stability over wide range of generator load variations.
KEYWORDS
Power System Stabilizers, Eigen values, Type-1and Type-2 Fuzzy Logic Control System
1. INTRODUCTION
Power system is a typical multi-variable, nonlinear and dynamical system consisting of
synchronous alternators, transmission lines, transformers, switching relays and compensators. For
keeping the terminal voltage magnitude of synchronous generator within limits, the automatic
voltage regulators (AVRs) are adopted in generator excitation system. AVRs introduce negative
damping torques, because of which the stability is adversely affected [1]. The power system
exhibits electromechanical oscillations because of load variation. These oscillations should be
damped to acceptable limitation failing which may result in instability. These oscillations can be
damped by using Power System Stabilizers. PSSs generate supplementary signals to excitations
system to suppress these oscillations [2].
The CPSSs are led-lag phase compensators tuned using a linearized model of power system for
specific operating points to provide the desired damping characteristics [3-4]. Because of
nonlinear characteristics of power systems, CPSS is not capable to adapt large changes in
operating conditions [5]. Adaptive power system stabilizers are proposed to deal with the
variation of operating conditions [6-7]. The Fuzzy Logic Power System Stabilizer (FLPSS) was
2. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
32
developed to improve the dynamic stability of power systems under wide range of operating
conditions. FLPSS shows better performance in dynamic stability compared with CPSS. The
constructed FLPSSs rely on expert knowledge which usually consists of uncertainties to certain
degree. Therefore, the corresponding fuzzy membership functions parameters [8-12]. This fuzzy
logic is also known as type-1 fuzzy logic.
Although the FLPSS have improved power systems stability, recent research has shown the
limitations of Type-1 fuzzy logic theory in considering large uncertainties and unexpected
disturbances. In order to overcome these limitations, Type-2 fuzzy logic methods are developed
[12–15]. The Type-1 fuzzy logic is further be modified to Type-2 fuzzy by applying grading to
the membership functions which to form Type-2 fuzzy sets. A Type-2 fuzzy set can be envisaged
as a three dimensional set and results in an extra degree of freedom for handling uncertainties
[16]. Because of this feature, a robust FLPSS is designed using Type-2 fuzzy logic [17]. The
proposed controller is simulated for a SMIB and compared with conventional controller and
Type-1 fuzzy controller under various operating conditions [18]. Results show that the Type-2
fuzzy controllers guarantee the robust performance over a wide range of operating conditions.
2. POWER SYSTEM MODEL
The power system under study is a single machine connected to an infinite bus through a tie-line.
The infinite bus is represented by the thevenin equivalent of a large inter connected power
system. The machine is outfitted with a static exciter. The non-linear model of the system is
described using following differential equations [1].
= (1)
= (2)
=
(3)
= (4)
The above equations can be linearized around an operating point for small deviations and is
considered from [19].
2.1. Conventional Power System Stabilizers
For the system considered to analyze the enhancement of stability margin, the limit cycles are
controlled by designing an adaptive lead-lag PSS, whose parameters tuned are 1 2K,T ,T and
Washout time constant, wT is considered as 10 seconds. The single stage PSS with Wash-Out is
in the form
PSSU G ω= ×∆ (5)
11
1 1 2
w
PSS
w
sT sT
G K
sT sT
+
= × ×
+ +
(6)
The parameters of Power System stabilizer (PSS) can be derived from conventional methods or
Meta heuristic methods [19-20].
3. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
33
2.2 Type-1 Fuzzy Logic Power System Stabilizer
The analytical structure of three input three output T1 FLPSS similar to PID Controller is
designed heuristically with 27 rules listed in Table 1 [18].
Table 1 : Rules for three input three membership functions
Rule DE DEE E output Rule DE DEE E output
1 P P P NB 14 N N N PB
2 P P N NS 15 N N Z PM
3 P P Z NM 16 N Z P Z
4 P N P NM 17 Z Z Z PS
5 P N Z Z 18 Z Z N PM
6 P N N NS 19 Z P P NM
7 Z Z P NM 20 Z P N Z
8 N Z Z NS 21 Z P Z NS
9 N Z Z Z 22 Z N P ZE
10 N P P NM 23 Z N N PM
11 N P Z Z 24 Z N Z PS
12 N P N PS 25 Z Z P NS
13 N N P PS 26 Z Z Z Z
27 Z Z N NS
In the Type-1 FLPSS controller, the gains are converted to adaptive gains by introducing FLC at
the input of the PID Controller. The parameters are tuned by using a systematic approach [18].
2.3 Type-2 fuzzy logic controllers
Fuzzy logic system using ordinary fuzzy sets, inference and logic is known as Type-1 fuzzy
system [21-22]. A fuzzy logic system using Type-2 fuzzy sets and fuzzy logic and inference is
called a Type-2 fuzzy system. A third dimension and footprint of uncertainty is incorporated in
Type-2 fuzzy system. Thus, under the state of high uncertainty the type-2 fuzzy logic controller
can perform better than its type-1 counterpart [23]. The type-2 fuzzy logic stabilizer includes a
Fuzzifier, a rule base, fuzzy inference engine and an output processor. The block diagram
representation of type-2 fuzzy logic controller is shown in Figure1.
Figure 1 Type-2 Fuzzy Logic System
4. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
34
The grade of membership of type-1 fuzzy sets is crisp and that of type-2 fuzzy is fuzzy, hence
Type-2 fuzzy sets are ‘fuzzy–fuzzy’ sets. The membership grade of a type-2 fuzzy logic set is a
fuzzy itself [16]. Membership function in interval type-2 fuzzy set can be represented by using as
an area called Footprint of Uncertainty (FOU). Working of Type-2 fuzzy is same as the working
of Type-1 fuzzy. Type-2 fuzzy is an interval fuzzy system where fuzzy operation is taken as two
Type-1 membership functions, Upper Membership Function (UMF) and Lower Membership
Function (LMF), to produce the firing strength. This UMF and LMF will limit the FOU. Figure 2
represents Interval type-2 membership function.
Figure 2. Type-2 interval membership function
A type-2 fuzzy set comprises of two individual membership functions known as primary and
secondary. Hence, both primary and secondary membership functions will be in the interval [0,
1]. Since, the FOU is designed over an interval, Type-2 fuzzy logic controller can also be referred
as interval Type-2 fuzzy logic controller. The effect of considering their FOU over an interval
gives the 3-dimensional effect and an extra degree of freedom for handling uncertainties in the
Power system stability. The implementation of interval type-2 membership functions and
operators is done by using the IT2FLS toolbox [16].
Type-1 fuzzy system uses ordinary fuzzy sets and inference, whereas type-2 fuzzy system uses
type-2 fuzzy sets and inference [14]. Type-1 fuzzy controllers have been developed and applied to
practical problems.
Defuzzification is a process of mapping from fuzzy logic control action to a non-fuzzy (crisp)
control action using centroid method. Figure 3 describes the process of Defuzzification of
interval Type-2 fuzzy system using centroid method. The Inference system uses a fuzzy reasoning
mechanism to derive a fuzzy output.
5. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
35
Figure 3 Defuzzification process of an interval Type-2 fuzzy logic system
2.4 Test System
The System under study in is a thermal generating station consisting of four 555MVA, 24KV, and
60Hz units. The network reactance’s are in p.u. on 2220 MVA, 24 KV base(referred to LT side of
step-up transformer). Resistances are assumed to be negligible.
Equivalent generator parameters in p.u:
dX = 1.81, dX ′= 0.3, qX = 1.76, doT ′ = 8sec, H = 3.5MJ/MVA, Vt = 1.0
Exciter: Ek = 25, ET = 0.05Sec.
3. RESULTS AND DISCUSSION
The design of type-2 FLPSS for damping of oscillations in SMIB test system is presented in this
paper. The efficacy of the controller is tested for multiple operating conditions. The results
obtained are compared with CPSS and Type-1 FLPSS. Controller Parameters for CPSS and
FLPSS are obtained by using Genetic Algorithm. The parameters obtained for CPSS are K=
10.75, T1= 0.485 and T2=0.05. The gains obtained for FLPSS (FPID) are kp= 1.01, kd=20.6837
and ki=0.4676.
The response in speed deviation of SMIB at light load of P+jQ=0.5+j0 is represented in Figure4.
The settling time for type-2 FLPSS is 1.307 Sec. The settling times obtained with CPSS and type-
1 FLPSS are 3.036sec and 1.57sec respectively.
6. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
36
Figure 4 Speed deviation for P+jQ= 0.5+j0
The peak overshoot for type-2 FLPSS, type-1 FLPSS and CPSS are 3.54x10-
4, 4.577x10-4
and
5.024x10-4
respectively. The peak undershoot for type-2 FLPSS, type-1 FLPSS and CPSS are -
9.357x10-8
, -6.998x10-5
and -1.806 x10-4
respectively. Type-2 FLPSS exhibits lower values of
peak overshoot and undershoots compared to type-1 FLPSS and CPSS.
The response in rotor angle deviation of SMIB at light load of P+jQ= 0.5+j0 is given in Figure5.
Settling time for type-2 FLPSS is 1.09Sec. the settling times obtained with CPSS and type-
1FLPSS are 2.609sec and 1.513sec respectively.
Figure 5 Rotor angle deviation for P+jQ= 0.5+j0
7. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
37
The peak overshoot for type-2 FLPSS, type-1 FLPSS and CPSS are 0.04607, 0.04953 and
0.04747 respectively. The type-2 FLPSS settles abruptly without any undershoot. The peak
undershoot for type-1 FLPSS and CPSS are 0.02894 and 0.02264 respectively. Type-2 FLPSS
exhibits lower values peak overshoot compared to type-1 FLPSS and CPSS.
The efficacy of the controller is tested by subjecting the SMIB system to a normal load of
P+jQ=1+j0. The response in speed deviation is given in Figure 6. The settling time with type-2
FLPSS is 1.616Sec and is relatively less compared to settling times obtained with type-1 FLPSS
and CPSS (1.756 and 3.06 respectively).
Figure 6 Speed deviation for P+jQ= 1+j0
The peak overshoot for type-2 FLPSS, type-1 FLPSS and CPSS are 3.199x10-4
, 3.99x10-4
and
4.477x10-4
respectively. The peak undershoot for type-2 FLPSS, type-1 FLPSS and CPSS are -
2.727x10-6
, -6.523x10-5
and -1.926x10-4
respectively. Type-2 FLPSS have lower values of
peak overshoot and undershoots compared to type-1 FLPSS and CPSS.
The responses in rotor angle deviation for normal load are given in Figure 7. The settling time
with type-2 FLPSS is 1.566sec and is less compared to the settling time obtained with type-1
FLPSS and CPSS (1.832sec and 2.942sec).
Figure 7 Rotor angle deviation for P+jQ= 1+j0
8. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
38
The peak over shoots for type-2 FLPSS, type-1 FLPSS and CPSS are 0.03445, 0.03739 and
0.03711 respectively. Type-2 FLPSS settles without any undershoot. The peak under shoot for
type-1 and CPSS are 0.03256 and 0.02434 respectively. Type-2 FLPSS exhibits better response
with respect to Settling time, peak overshoot and undershoot compared to type-1 FLPSS and
CPSS.
The SMIB is subjected to heavy load of P+jQ= 1.5+j0.5. The response in speed deviation and
rotor angle deviations are given in Figure 8 and figure 9 respectively. Type-2 FLPSS shows better
performance in settling time, peak undershoot and peak overshoot.
Figure 8 Speed deviation at P+jQ = 1.5+j0.5
Figure 9 Rotor angle deviation at P+jQ= 1.5+j0.5
9. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
39
Table 2 shows the settling time, peak overshoot and peak undershoot in frequency deviation with
different controllers under wide operating conditions. From the table it is observed that type-2
FLPSS makes the speed deviation to Settles faster with lower peak overshoot and undershoot
compared to type-1 FLPSS and CPSS.
Table 2: Speed deviation of SMIB for different operating conditions
Table 3 shows the settling time, peak overshoot and peak undershoot in rotor angle deviation with
different controllers under wide operating conditions. From the table it is observed that type-2
FLPSS makes rotor angle deviation Settles faster with lower peak overshoot and undershoot
compared to type-1 FLPSS and CPSS.
Response
Characteristics
Response
Characteristics
CPSS Type1 FPSS Type2 FPSS
0.5+j0
Settling Time (Sec) 3.026 1.57 1.307
Peak Overshoot
(rad/sec)
5.021 x10-4 4.577x10-4
3.54x10-4
Peak Undershoot
(rad/sec)
-1.806 x10-4 -6.998x10-5
-9.357x10-8
0.7+j0.3
Settling Time (Sec) 3.542 1.947 1.226
Peak Overshoot
(rad/sec)
4.688x10-4
4.307x10-4
3.482x10-4
Peak Undershoot
(rad/sec)
-2.383x10-4
-1.363x10-4
-1.356x10-5
0.7-j0.3
Settling Time (Sec) 3.066 2.109 1.976
Peak Overshoot
(rad/sec)
4.7x10-4
4.153x10-4
3.132x10-4
Peak Undershoot
(rad/sec)
-1.752x10-4
-3.167x10-5
-----
1+j0
Settling Time (Sec) 3.06 1.756 1.616
Peak Overshoot
(rad/sec)
4.477x10-4
3.99x10-4
3.199x10-4
Peak Undershoot
(rad/sec)
-1.926x10-4
-6.523x10-5
-2.727x10-6
1.5+j0.5
Settling Time (Sec) 4.24 2.513 1.508
Peak Overshoot
(rad/sec)
4.084x10-4
3.703x10-4
3.007x10-4
Peak Undershoot
(rad/sec)
-2.333x10-4
-1.218x10-4
-1.659x10-5
1.5-j0.2
Settling Time (Sec) 3.659 2.225 1.617
Peak Overshoot
(rad/sec)
4.533x10-4
4.015x10-4
3.067x10-4
Peak Undershoot
(rad/sec)
-1.802x10-4
-5.185x10-5
-----
10. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
40
Table 3: Rotor Angle deviation of SMIB for different operating conditions
3. CONCLUSIONS
In this paper a type-2 FLPSS is implemented to increase the dynamic stability of SMIB system.
Different operating points were used to test robustness of the proposed controller. The efficacy in
damping of oscillations of proposed controller is compared with type-1 FLPSS and CPSS. Type-2
FLPSS makes the system settle faster compared to type-1 FLPSS and CPSS. Results show that
even when operating conditions change, type-2 FLPSS provides good damping and improves
system performance.
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Operating
condition
Response
Characteristics
CPSS Type1 FPSS Type2 FPSS
0.5+j0
Settling Time (Sec) 2.609 1.513 1.09
Peak Overshoot (rad) 0.04747 0.04953 0.04607
Peak Undershoot (rad) 0.03403 0.04354 ----
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0.7-j0.3
Settling Time (Sec) 2.844 1.976 1.848
Peak Overshoot (rad) 0.0407 0.04248 -----
Peak Undershoot (rad) 0.02925 0.04042 -----
1+j0
Settling Time (Sec) 2.942 1.832 1.566
Peak Overshoot (rad) 0.03711 0.03739 0.03445
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1.5+j0.5
Settling Time (Sec) 3.767 1.996 1.576
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Peak Undershoot (rad) 0.0148 0.02036 ------
1.5-j0.2
Settling Time (Sec) 2.225 2.1 2
Peak Overshoot (rad) 0.03829 0.3903 0.3571
11. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
41
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