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.
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
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.
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
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.
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.
PaperLoad following in a deregulated power system with Thyristor Controlled S...rajeshja
Load following is considered to be an ancillary service in a deregulated power system. This paper investigates
the effect of a Thyristor Controlled Series Compensator (TCSC) for load following in a deregulated
two area interconnected thermal system with two GENCOs and two DISCOs in either areas. Optimal
gain settings of the integral controllers in the control areas are obtained using Genetic Algorithm by
minimizing a quadratic performance index. Simulation studies carried out in MATLAB validates that a
Thyristor Controlled Series Compensator in series with tie-line can effectively improve the load following
performance of the power system in a deregulated environment.
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.
Minimize the load reduction considering the activities control of the generat...IJECEIAES
This study shows how to calculate the minimum load that needs to be reduced to restore the frequency to the specified threshold. To implement this problem, the actual operation of the electricity system in the event of a generator outage is considered. The main idea of this method is to use the power balance equation between the generation and the load with different frequency levels. In all cases of operating the electrical system before and after the generator outage, the reserve capacity of other generators is considered in each generator outage situation. The reduced load capacity is calculated based on the reciprocal phase angle sensitivity or phase distance. This makes the voltage phase angle and voltage value quality of recovery nodes better. The standard IEEE 9-generator 37-bus test scheme was simulated to show the result of the proposed technique.
Advanced deep flux weakening operation control strategies for IPMSM IJECEIAES
This paper proposes an advanced flux-weakening control method to enlarge the speed range of interior permanent magnet synchronous motor (IPMSM). In the deep flux weakening (FW) region, the flux linkage decreases as the motor speed increases, increasing instability. Classic control methods will be unstable when operating in this area when changing load torque or reference speed is required. The paper proposes a hybrid control method to eliminate instability caused by voltage limit violation and improve the reference velocity-tracking efficiency when combining two classic control methods. Besides, the effective zone of IPMSM in the FW is analyzed and applied to enhance stability and efficiency following reference velocity. Simulation results demonstrate the strength and effectiveness of the proposed method.
OPTIMAL TORQUE RIPPLE CONTROL OF ASYNCHRONOUS DRIVE USING INTELLIGENT CONTROL...elelijjournal
The dynamic performance of an asynchronous machine when operated with cascaded Voltage Source Inverter using Space Vector Modulation (SVM) technique is presented in this paper. A classical model of Induction Motor Drive based on Direct Torque Control (DTC) method is considered which displays
appreciable run-time operation with very simple hysteresis control scheme. Direct control of the torque and flux variables is achieved by choosing suitable inverter voltage space vector from a lookup table. Under varying torque conditions the performance of the drive system is verified using MATLAB/Simulink software tool. The ripple content in the torque parameter is significant when traditional PI controller and Fuzzy approach are configured in the proposed system. Finally, by replacing the PI-Fuzzy controller with Hybrid Controller the torque ripple minimization can be achieved during no-load and loaded conditions.
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.
Automatic Generation Control of Multi-Area Power System with Generating Rate ...IJAPEJOURNAL
In a large inter-connected system, large and small generating stations are synchronously connected and hence all stations must have the same frequency. The system frequency deviation is the sensitive indicator of real power imbalance. The main objectives of AGC are to maintain constant frequency and tie-line errors with in prescribed limit. This paper presents two new approaches for Automatic Generation Control using i) combined Fuzzy Logic and Artificial Neural Network Controller (FLANNC) and ii) Hybrid Neuro Fuzzy Controller (HNFC) with gauss membership functions. The simulation model is created for four-area interconnected power system. In this four area system, three areas consist of steam turbines and one area consists of hydro turbine. The components of ACE, frequency deviation (F) and tie line error (Ptie) are obtained through simulation model and used to produce the required control action to achieve AGC using i) FLANNC and ii) HNFC with gauss membership functions. The simulation results show that the proposed controllers overcome the drawbacks associated with conventional integral controller, Fuzzy Logic Controller (FLC), Artificial Neural Network controller (ANNC) and HNFC with gbell membership functionsv
Co-simulation of self-adjusting fuzzy PI controller for the robot with two-ax...TELKOMNIKA JOURNAL
This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuit-hardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.
Computer Simulation of PMSM Motor with Five Phase Inverter Control using Sign...IJECEIAES
The signal processing techniques and computer simulation play an important role in the fault diagnosis and tolerance of all types of machines in the first step of design. Permanent magnet synchronous motor (PMSM) and five phase inverter with sine wave pulse width modulation (SPWM) strategy is developed. The PMSM speed is controlled by vector control. In this work, a fault tolerant control (FTC) system in the PMSM using wavelet switching is introduced. The feature extraction property of wavelet analysis used the error as obtained by the wavelet de-noised signal as input to the mechanism unit to decide the healthy system. The diagnosis algorithm, which depends on both wavelet and vector control to generate PWM as current based manage any parameter variation. An open-end phase PMSM has a larger range of speed regulation than normal PMSM. Simulation results confirm the validity and effectiveness of the switching strategy.
Correlative Study on the Modeling and Control of Boost Converter using Advanc...IJSRD
DC-DC converters are switched power converters. The converters are most widely used in research and industrial applications. The DC-DC Boost Converters are used to step-up the supply voltage given to the plant model. The main advantage of using the Boost Converters is that it works in the low voltage according to the design specifications. In order to regulate the uncontrolled supply of voltage, a controller has to be designed and modeled to stabilize the output voltage. Since the convectional controllers cannot work under dynamic operating conditions, advanced controllers are to be designed to overcome the problems. In this article, the advanced controllers such as NARMA-L2, Fuzzy Logic (FLC) and Sliding Mode Controllers (SMC) are implemented and their responses are compared using MATLAB.
A LEAST ABSOLUTE APPROACH TO MULTIPLE FUZZY REGRESSION USING Tw- NORM BASED O...ijfls
A least absolute approach to multiple fuzzy regression using Tw-norm based arithmetic operations is
discussed by using the generalized Hausdorff metric and it is investigated for the crisp input- fuzzy output
data. A comparative study based on two data sets are presented using the proposed method using shape
preserving operations with other existing method.
PaperLoad following in a deregulated power system with Thyristor Controlled S...rajeshja
Load following is considered to be an ancillary service in a deregulated power system. This paper investigates
the effect of a Thyristor Controlled Series Compensator (TCSC) for load following in a deregulated
two area interconnected thermal system with two GENCOs and two DISCOs in either areas. Optimal
gain settings of the integral controllers in the control areas are obtained using Genetic Algorithm by
minimizing a quadratic performance index. Simulation studies carried out in MATLAB validates that a
Thyristor Controlled Series Compensator in series with tie-line can effectively improve the load following
performance of the power system in a deregulated environment.
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.
Minimize the load reduction considering the activities control of the generat...IJECEIAES
This study shows how to calculate the minimum load that needs to be reduced to restore the frequency to the specified threshold. To implement this problem, the actual operation of the electricity system in the event of a generator outage is considered. The main idea of this method is to use the power balance equation between the generation and the load with different frequency levels. In all cases of operating the electrical system before and after the generator outage, the reserve capacity of other generators is considered in each generator outage situation. The reduced load capacity is calculated based on the reciprocal phase angle sensitivity or phase distance. This makes the voltage phase angle and voltage value quality of recovery nodes better. The standard IEEE 9-generator 37-bus test scheme was simulated to show the result of the proposed technique.
Advanced deep flux weakening operation control strategies for IPMSM IJECEIAES
This paper proposes an advanced flux-weakening control method to enlarge the speed range of interior permanent magnet synchronous motor (IPMSM). In the deep flux weakening (FW) region, the flux linkage decreases as the motor speed increases, increasing instability. Classic control methods will be unstable when operating in this area when changing load torque or reference speed is required. The paper proposes a hybrid control method to eliminate instability caused by voltage limit violation and improve the reference velocity-tracking efficiency when combining two classic control methods. Besides, the effective zone of IPMSM in the FW is analyzed and applied to enhance stability and efficiency following reference velocity. Simulation results demonstrate the strength and effectiveness of the proposed method.
OPTIMAL TORQUE RIPPLE CONTROL OF ASYNCHRONOUS DRIVE USING INTELLIGENT CONTROL...elelijjournal
The dynamic performance of an asynchronous machine when operated with cascaded Voltage Source Inverter using Space Vector Modulation (SVM) technique is presented in this paper. A classical model of Induction Motor Drive based on Direct Torque Control (DTC) method is considered which displays
appreciable run-time operation with very simple hysteresis control scheme. Direct control of the torque and flux variables is achieved by choosing suitable inverter voltage space vector from a lookup table. Under varying torque conditions the performance of the drive system is verified using MATLAB/Simulink software tool. The ripple content in the torque parameter is significant when traditional PI controller and Fuzzy approach are configured in the proposed system. Finally, by replacing the PI-Fuzzy controller with Hybrid Controller the torque ripple minimization can be achieved during no-load and loaded conditions.
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.
Automatic Generation Control of Multi-Area Power System with Generating Rate ...IJAPEJOURNAL
In a large inter-connected system, large and small generating stations are synchronously connected and hence all stations must have the same frequency. The system frequency deviation is the sensitive indicator of real power imbalance. The main objectives of AGC are to maintain constant frequency and tie-line errors with in prescribed limit. This paper presents two new approaches for Automatic Generation Control using i) combined Fuzzy Logic and Artificial Neural Network Controller (FLANNC) and ii) Hybrid Neuro Fuzzy Controller (HNFC) with gauss membership functions. The simulation model is created for four-area interconnected power system. In this four area system, three areas consist of steam turbines and one area consists of hydro turbine. The components of ACE, frequency deviation (F) and tie line error (Ptie) are obtained through simulation model and used to produce the required control action to achieve AGC using i) FLANNC and ii) HNFC with gauss membership functions. The simulation results show that the proposed controllers overcome the drawbacks associated with conventional integral controller, Fuzzy Logic Controller (FLC), Artificial Neural Network controller (ANNC) and HNFC with gbell membership functionsv
Co-simulation of self-adjusting fuzzy PI controller for the robot with two-ax...TELKOMNIKA JOURNAL
This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuit-hardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.
Computer Simulation of PMSM Motor with Five Phase Inverter Control using Sign...IJECEIAES
The signal processing techniques and computer simulation play an important role in the fault diagnosis and tolerance of all types of machines in the first step of design. Permanent magnet synchronous motor (PMSM) and five phase inverter with sine wave pulse width modulation (SPWM) strategy is developed. The PMSM speed is controlled by vector control. In this work, a fault tolerant control (FTC) system in the PMSM using wavelet switching is introduced. The feature extraction property of wavelet analysis used the error as obtained by the wavelet de-noised signal as input to the mechanism unit to decide the healthy system. The diagnosis algorithm, which depends on both wavelet and vector control to generate PWM as current based manage any parameter variation. An open-end phase PMSM has a larger range of speed regulation than normal PMSM. Simulation results confirm the validity and effectiveness of the switching strategy.
Correlative Study on the Modeling and Control of Boost Converter using Advanc...IJSRD
DC-DC converters are switched power converters. The converters are most widely used in research and industrial applications. The DC-DC Boost Converters are used to step-up the supply voltage given to the plant model. The main advantage of using the Boost Converters is that it works in the low voltage according to the design specifications. In order to regulate the uncontrolled supply of voltage, a controller has to be designed and modeled to stabilize the output voltage. Since the convectional controllers cannot work under dynamic operating conditions, advanced controllers are to be designed to overcome the problems. In this article, the advanced controllers such as NARMA-L2, Fuzzy Logic (FLC) and Sliding Mode Controllers (SMC) are implemented and their responses are compared using MATLAB.
A LEAST ABSOLUTE APPROACH TO MULTIPLE FUZZY REGRESSION USING Tw- NORM BASED O...ijfls
A least absolute approach to multiple fuzzy regression using Tw-norm based arithmetic operations is
discussed by using the generalized Hausdorff metric and it is investigated for the crisp input- fuzzy output
data. A comparative study based on two data sets are presented using the proposed method using shape
preserving operations with other existing method.
RISK ASSESSMENT OF NATURAL HAZARDS IN NAGAPATTINAM DISTRICT USING FUZZY LOGIC...ijfls
The assessment of risks due to natural hazards is a major one responsible for risk management and the constant development of Nagapattinam district. The estimation of risk in Nagapattinam district was deduced using fuzzy logic model for the given raw data. A hierarchical fuzzy logic system with six inputs and one output is designed in Matlab software environment using fuzzy logic toolbox and simulink. The simulink investigations are done for five areas in Nagapattinam district. The fuzzy system is developed using the information sources provided by disaster management cell of Nagapattinam district.
Design of Full Order Optimal Controller for Interconnected Deregulated Power ...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This paper presents a new AGC simulation model for deregulated power systems, which
simplifies the design of the controller by concentrating mainly on load disturbances due to contract
violation of DISCOs in the system. In each area of the proposed AGC model thermal, hydro and gas
generators are considered to be part of generation control. Frequency variation due to the bilateral
contract loads is also studied with the help of DPM (DISCO participation matrix) concept. The
MATLAB/SIMULINK model of the proposed model is presented for the contracted load and UN
contracted load. Performance of the designed controller in controlling the frequency of the system
under contracted load and UN contracted load deviation is analyzed using simulation results.
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International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
TORQUE CONTROL OF AC MOTOR WITH FOPID CONTROLLER BASED ON FUZZY NEURAL ALGORITHMijics
Nowadays in the complicated systems, design of proper and implementable controller has a most importance. With respect to ability of fractional order systems in complicated systems identification as a first order fractional system with time delay, usage of fractional order PID has a proper result. From one side flexibility of fractional calculus than integer order has been topics of interest to the researchers. From another side, PMSM motors which are one the AC motor types, has been allocated largely accounted position in industry and used in variety applications. Therefore in this paper torque direct control of PMSM motors with FOPID based on model is proposed. Also fuzzy neural controllers are widely considered. Reason of this is success of fuzzy neural controller in control and identification of uncertain and complicated systems. The proposed method in this paper is combination of FOPID controller with fuzzy neural supervision system which with coefficients setting of this controller, control operation of PMSM will improve. Results of proposed method show the ability of proposed technique in reference signal tracking, elimination of disturbances effects and functional robustness in presence of noise and uncertainty. The results show the error averagely in three condition, nominal form, step disturbance and noise and uncertainly will decrease 11.66% in proposed method (FNFOPID) with Integral Square Error criterion and 7.69% with Integral Absolute Error criterion in comparison to FOPID.
A new design of fuzzy logic controller optimized by PSO-SCSO applied to SFO-D...IJECEIAES
In this article, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization of the FLC, by the hybridization between the particle swarm optimization algorithm (PSO) and the sine-cosine swarm optimization algorithm (SCSO), This new strategy is called FLC-PSCSO. The input-output gains and the geometric shapes of the triangular membership functions of the FLC are the objective functions to be optimized. The optimization of the latter is obtained by minimizing a cost function based on the combination of two criteria, the integral time absolute error (ITAE) and the integral absolute error (IAE). A comparison between the conventional FLC and the proposed FLC-PSCSO is made. The FLC optimized by PSCSO shows a remarkable improvement in the performance of the controlled induction motor.
Damping of Inter-Area Low Frequency Oscillation Using an Adaptive Wide-Area D...Power System Operation
This paper presents an adaptive wide-area damping controller (WADC) based on
generalized predictive control (GPC) and model identification for damping the inter-area low
frequency oscillations in large-scale inter-connected power system. A recursive least-squares algorithm
(RLSA) with a varying forgetting factor is applied to identify online the reduced-order linearlized
model which contains dominant inter-area low frequency oscillations. Based on this linearlized model,
the generalized predictive control scheme considering control output constraints is employed to obtain
the optimal control signal in each sampling interval. Case studies are undertaken on a two-area fourmachine
power system and the New England 10-machine 39-bus power system, respectively.
Simulation results show that the proposed adaptive WADC not only can damp the inter-area
oscillations effectively under a wide range of operation conditions and different disturbances, but also
has better robustness against to the time delay existing in the remote signals. The comparison studies
with the conventional lead-lag WADC are also provided.
Reducing power in using different technologies using FSM architectureVLSICS Design
As in today’s date fuel consumption is important in everything from scooters to oil tankers, power consumption is a key parameter in most electronics applications. The most obvious applications for which power consumption is critical are battery-powered applications, such as home thermostats and security systems, in which the battery must last for years. Low power also leads to smaller power supplies, less expensive batteries, and enables products to be powered by signal lines (such as fire alarm wires) lowering the cost of the end-product. As a result, low power consumption has become a key parameter of microcontroller designs . The purpose of this paper is to summarize, mainly by way of examples,what in our experience are the most trustful approaches to lowpower design. In other words, our contribution should not be intended as an exhaustive survey of the existing literature on low-power esign; rather, we would like to provide insights a designer can rely upon when power consumption is a critical constraint.We will focus on the reduction of power consumption on different technologies for different values of oicapacitance and also compare power saving in technologies.
Type 1 versus type 2 fuzzy logic speed controllers for brushless DC motors IJECEIAES
This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
A hybrid approach for ipfc location and parameters optimization for congestio...eSAT Journals
Abstract
The deregulated power system operation with competitive electricity market environment has been created many challenging tasks to the system operator. The competition with strategic bidding has been resulted for randomness in generation schedule, load withdrawal and power flows across the network. The economic efficiency of electricity market is mainly dependent on network support. In the event of congestion, it is required to alter the base case market settlement and hence the economic inefficiency in terms of congestion cost can occur. In order to anticipate congestion and its consequences in operation, this paper has been considered Interline Power Flow Controller (IPFC).This article proposed a tactical approach for optimal location and then its parameters in Decoupled Power Injection Modeling (DPIM) are optimized using Gravitational Search Algorithm (GSA). The case studies are performed on IEEE 30-bus test system and the results obtained are validating the proposed approach for practical implementations.
Keywords: Deregulated power system, competitive electricity market, congestion management, IPFC, Gravitational Search Algorithm (GSA)
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
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Fuzzy load frequency controller in
1. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
DOI : 10.5121/ijfls.2016.6102 13
FUZZY LOAD FREQUENCY CONTROLLER IN
DEREGULATED POWER ENVIRONMENT BY
PRINCIPAL COMPONENT ANALYSIS
S.Srikanth1
, K.R.Sudha2
And Y.Butchi Raju3
1
Professor , Dept. of Electrical Engg., B.V.C.Engineering College, A.P, India.
2
Professor, Dept. of Electrical Engg., Andhra University(W), Visakhapatnam, India.
3
Assoc. Professor, E.E.E. Dept, Sir. C.R. R. College, A.P, India.
ABSTRACT
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.
KEYWORDS
Deregulated Load Frequency Control (DLFC), PID Controller, Fuzzy PID Controller (FPID), Fuzzy C-
means Controller (FCM), Fuzzy Principal component analysis controller (FPCA)
1. INTRODUCTION
A power system with deregulated load frequency control may consist of Distribution companies
(DISCOMS), Transmission companies (TRANSCOS) and Generation companies (GENCOS).
There is a basic difference between the AGC operation in conventional and deregulation power
system [1, 16]. After deregulation the vertically integrated utilities (VIU) that own the electrical
power generation, transmission and distribution companies amenities provide power at minimum
cost to the consumers, after restructuring processes Generation companies (GENCOS),
Transmission companies (TRANSCOS), Distribution companies (DISCOMs) and Independent
system operators (ISO) are introduced competition in power system[2,3]. Alternative to select
among DISCOMs in their won area, while DISCOMs of an area have the choice to have power
contracts for transaction of power with GENCOs of the same or other area[5,17].
2. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
14
Research on the DLFC problem shows that the Fuzzy Proportional Integral Derivative (FPID)
controller has been proposed to enhance the performance of deregulated power system load
frequency control [10, 11].
The design of a Fuzzy Clustering means (FCM) controller required rule-base from the
phase-plane plots of the inputs given to the fuzzy controller. The ‘closed-loop’ trajectory is
mapping on position space of the inputs. The clusters are shaped in complete position space of the
inputs using Fuzzy C-means. The cluster centers are identified and marked on the phase-plane
plot. These are mapping by the ‘closed–loop trajectory’. Hence the necessary rules are recognized
and the ‘non-cooperative rules’ are eliminated.
The major disadvantage is Fuzzy C-Means algorithm only detects the data classes with the
same super spherical shapes. To overcome the above demerit, a new algorithm is developed fuzzy
Principal component analysis (FPCA) involve a geometric procedure that ‘transforms’ a number
of correlated variables in to a number of ‘uncorrelated variables’ are called ‘principal
components’ [5, 18]. The proposed Fuzzy Principal component analysis Clustering controller
with reduced rule base is compared to FCM and Fuzzy PID controller. The above controller test
in a three area deregulated load frequency control.
2. MODELING OF THREE AREA LOAD FREQUENCY CONTROL IN
DEREGULATED POWER SYSTEM
The three area load frequency control in deregulated power system environment consists of three
power system areas, each power system area with two thermal plants and two DISCOMs as
shown in Fig.1. The detailed schematic diagram of three area deregulated power system six
GENCO with six DISCOMs as shown in Fig.2.
In the open market purchases, any GENCO in one area may supply its DISCOMs and DISCOMs
in other two areas through tie-lines allowing power transfer between all three power system areas.
In a deregulated power system having several GENCOS and DISCOMs, any DISCOM may
contract with any GENCO in another control area independently, is known as mutual transaction
[18]. These transactions are to be carried out through an independent system operator (ISO).
Figure.1. Three area load frequency in deregulated power system
3. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
15
The main purpose of ISO is to control system operator in all GENCOs and DISCOMs, like
Automatic Generation Control. Any DISCOM in a deregulated environment will have the free to
purchase power at competitive price from different GENCOs, which can or cannot have contract
with the won area when the ‘DISCOM’ [9]. In the present paper for the load frequency control
GENCO–DISCOM contracts are represented with ‘DISCOM participation matrix’ (DPM). DPM
effectively provides the participation of a DISCOM in contract with a GENCO. The concept of
‘DISCOM Participation Matrix’ (DPM) is used to express the possible contracts .The number of
rows and columns of DPM matrix is equal to the total number of GENCOs and DISCOMs in the
overall power system, respectively. Each element of the DPM is a fraction of total load power
contracted by a DISCOM from a GENCO and is called a contract participation factor ( cpf). The
total of all the elements in a column in ‘DPM’ is unity.
=
11 12 13 14 15 16
21 22 23 24 25 26
31 32 33 34 35 36
41 42 43 44 45 46
51 52 53 54 55 56
61 62 63 64 65 66
cpf cpf cpf cpf cpf cpf
cpf cpf cpf cpf cpf cpf
cpf cpf cpf cpf cpf cpf
DPM
cpf cpf cpf cpf cpf cpf
cpf cpf cpf cpf cpf cpf
cpf cpf cpf cpf cpf cpf
(1)
Where =
th th
ij th
j DISCOpowerdemandoutof i GENCOinp.uMW
cpf
j DISCOtotalpowerdemand inp.uMW
(2)
Whenever a load demanded by a DISCOM1 changes, it is observed as a local load change in the
area1, which is similar with other areas corresponds to the local loads ∆PD1, ∆PD2, ∆PD3. This
should be reflected in the block diagram of three area power system in deregulated environment
at the point of input to the power system block. Each area two GENCOs, ‘Area Control Error’
(ACE) signal has to be “distributed” among them. The factor that distributes ‘ACE participation
factors’ (apf)
Therefore
∑ ܽ
ୀଵ =1 (3)
Where total number of ‘plants’ are n
The each ‘particular’ set of ‘GENCOs’ are invented to follow the ‘load demanded’ by a
DISCOM, the demand signals must flow from a DISCOM to a particular GENCO specifying
‘corresponding’ load demands. These signals which are absent in traditional AGC system
describes the partial demands and are specified by the cpfs and the per unit MW load of a
DISCOM. The signals take information as to which plants have to track a ‘load demanded’ by
which ‘DISCOM’. In the present case of three areas, the scheduled steady state power flow on the
tie-line is given as in (4) and the tie line power error is expressed as in (5) which is used to
generate the area control error (ACE) . For n-number power system areas, Area Control Error in
th
i area is given in (6)
4. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
16
( )
( )
( )
∆ =
−
tieij
th th
th th
P scheduled
demand of DISCOin j area fromGENCOini area
demand of DISCOini area fromGENCOin j area
(4)
( ) ( ) ( )∆ = ∆ − ∆tieij tieij tieijP error P actual P scheduled (5)
The traditional scenario ‘error signal’ is use to make the respective ‘ACE signals’ as in the.
ACE= B∆f + ∆P tie error (6)
For our case
NGENCO=6=Total number of generation companies
NDISCO =6= Total number of distribution companies
Figure.2. Three area deregulated LFC
5. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
17
3. DESIGN OF FUZZY LOGIC PID (FPID) CONTROLLER
The general model of Fuzzy PID normal Controller and it mainly four important components are
Fuzzification module, Inference mechanism, Knowledge base and defuzzification module.
3.1. Fuzzification Module:
In primary operation is import is fuzzification which include convert all the range of input data
with output of the FLC their corresponding data [12, 13]. The next performance procedure is
dividing the respective input keen on suitable linguistic variables these variables in fuzzification
module depend on triangle shape of the Membership functions (MF).
3.2. Fuzzy Inference mechanism:
Interface mechanism plays a important role in designing FLC. The membership functions
obtained in first step are combined to acquire the firing strength of individual rule [24, 25]. Each
rule characterizes the control goal and control strategy of the field experts by means of a set of
Fuzzy control rules [8, 14]. Then depending on firing strength, the consequent part of each
qualified rule is generated.
Figure.3. Basic model of FPID controller
3.3. Knowledge base:
The knowledge base of an FLC consists of a database, whose basic function is to provide, the
necessary information for the proper functioning of the ‘fuzzification module’, the inference
engine and the ‘de-fuzzification module’. The necessary information includes:
a) ‘Fuzzy membership’ representing the meaning of the ‘linguistic variables’ of the process status
and the ‘control output variables’.
b) Physical domains and their normalized counter-parts together with the normalization (scaling)
factors.
6. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
18
3.4. Defuzzification module:
The following are the functions of the Defuzzification module:
a) It converts the set of modified control output values into a non-fuzzy control output.
b) It performs an output de-normalization which maps the range of values of fuzzy sets to the
normal area
The commonly used strategies for defuzzification are (i) max criterion (ii) the mean of maximum
and (iii) the center of areas. Approach generates the ‘center of gravity’ of the opportunity
distribution of a ‘control action’.
{ }
{ }
Membership value of input output corresponding to the membership value of input
U
membership value of input
×
=
∑
∑ (6)
( )
( )∑
∑=
BiAi,µ
BiAi,ν
U
(7)
3.5. Design of three input MF FPID Controller:
Design of FPID Controller similar to PID controller as below fig 4.
Figure.4.basic model FPID controller
Three variables , ,δ δ δ& && are used as input signals. The coefficients p, d iK K ,K which are called
Fuzzy variables, transform the scaled real values to required values in decision limit. The ‘output
signal’ uK is inject to the ‘summing point’. The normalized inputs of the proposed controllers
namely DE, E, and DEE are equal to p i dK ,K ,K& &&δ δ δ respectively. The three similar fuzzy sets
defining the three inputs of the proposed FLCPID controller are given by equation (8). The inputs
of the fuzzy sets considered are shown in figure .5 and the MF of these are defined by
p N(.), (.)µ µ hand Z (.)µ or 1 1 0(.) (.)and (.)µ µ µ−
( ) ( ) ( ){ }p d iK K K N Negative ,Z Zero ,P Positive= = =& &&δ δ δ (8)
7. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
19
Let the number of linguistic variables and their values are the inputs and their MF is identical. If
the members of the input fuzzy set N,Z and P are X-1(.),X0(.),X1(.) respectively, then the
output function is derived using the following control rules, where i, j and k can take any value
from ( -1,0,+1).
IF DE is ix and E is jx and DEE is kx THEN output is ( )i j k− + +
U
The above fuzzy rule is called a linear control rule because the linear function is employed to
relate the indices of the input fuzzy variables sets to the index of the linguistic variables output
fuzzy set. Based on this concept the rules framed.
3.6. FPID Controller Rules
FPID control rules are linear, the number of ‘membership functions’ of the fuzzy output place
will be equal to ( )3N 2− for N 3≥ , The number of membership functions N of each input. In
the proposed case N=3, hence the output fuzzy set has seven membership functions defined as
follows:
U=ቐ
ܰܤሺܾ݊݁݃ܽ݃݅ ݁ݒ݅ݐሻ, ܰܯሺ݊݁݃ܽ݉ݑ݅݀݁݉ ݁ݒ݅ݐሻ, ܰܵሺ݈݈݊݁݃ܽܽ݉ݏ ݁ݒ݅ݐሻ
ܼሺݎ݁ݖሻ
ܲܤሺܾ݃݅ ݁ݒ݅ݐ݅ݏሻ, ܲܵሺ݈݈ܽ݉ݏ ݁ݒ݅ݐ݅ݏሻ, ܲܯሺ݉ݑ݅݀݁݉ ݁ݒ݅ݐ݅ݏሻ
The triangular membership functions as in Fig (5) are considered and partitioned within the UOD
in the range [-1, +1] for the outputs. The mathematical model of membership function is given as
follows
Figure.5. Membership-Functions Outputs
The portion of the ‘Membership-Functions’ output should be symmetrical about its
essential value and the ‘shape’ of every the members of ‘Membership-Functions’ should be
same [14, 20]. The decisions in fuzzy logic based approach are made by forming series of
rules which relate the inputs to outputs by IF-THEN statements [21]. In this case the
number of control rules to cover all the possible combinations of the three membership
functions of each input variable is 3X3X3(27)(4). These rules are composed as below
table1.
8. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
20
Table 1. 27 Rules for three input member ship functions
Rule DEE DE E Out put Rule DEE DE E Out put
1 P P P NB 15 N Z N PM
2 P P Z NM 16 N N P PS
3 P P N NS 17 N N Z PM
4 P Z P NM 18 N N N PB
5 P Z Z NS 19 Z P P NM
6 P Z N Z 20 Z P Z NS
7 P N P NM 21 Z P N Z
8 P N Z Z 22 Z Z P NS
9 P N N PS 23 Z Z Z Z
10 N P P NS 24 Z Z N PS
11 N P Z Z 25 Z N P Z
12 N P N PS 26 Z N Z PS
13 N Z P Z
27 Z N N PM14 N Z Z PS
4. DESIGN OF FCM CONTROLLER
Fuzzy control normal system requires characterization of the relation between state spaces and the
rules associated with the transient system under control this relation is based on the relative
influence of every rule of the rule base on the direct action produced by ‘fuzzy inference engine’
[6].
A closed loop trajectory can be mapped on the position space [19]. Linguistic trajectory is formed
by the series of rules obtained ‘according’ to the arrange in which they are fired forms. This
corresponds to a certain system trajectory [22]. This provides strategy to attain the necessary rule-
base starting the ‘phase-plane’ plots of the inputs given to the fuzzy controller [23]. The space of
the inputs is mapped on position ‘closed-loop trajectory’. The formed clusters are in complete
location space of the inputs using Fuzzy C-means. The cluster centers are recognized and marked
on the phase-plane plot. The closed–loop ‘trajectory’ with these is mapped. Hence the ‘non-
cooperative’ rules are deleted and the necessary rules are identified.
4.1 Design procedure for FCM Controller
1) The Fuzzy controller is designed normally with 27 rules
2) The ‘Fuzzy C-Means controller’s’ is tuned to the same as fuzzy controller.
3) From the input space of fuzzy controller the ‘phase-plane’ plot is obtained.
4) FCM algorithm using input space is ‘divided’ in to ‘clusters’ and the centers of ‘cluster’
are recognized.
5) The series of rules of the normal ‘fuzzy controller’ is great imposed onto the ‘phase-plane’
plot of the ‘input space’ with ‘cluster centers’ below fig.6.
6) Hence the ‘non-cooperative’ rules are deleted and the necessary rules are ‘identified’
below table2.
9. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
21
Fig.6. phase plane plot to identify required rules
Table.2. Rules for three –input member ship functions for FCM Controller
Rule DEE E DE Out put
26 Z Z N PS
23 Z Z Z Z
20 Z Z P NS
5. DESIGN OF FUZZY PRINCIPAL COMPONENT ANALYSIS (FPCA)
CONTROLLER
The major disadvantage is Fuzzy C-Means algorithm only detects the data classes with the same
super spherical shapes. To overcome the above demerit new algorithm is developed fuzzy
Principal component analysis (FPCA) involves a mathematical procedure that transforms a
number of (possibly) correlated variables in to a (smaller) number of uncorrelated variables are
called principal components [5]. The first principal component accounts for as much of the
variability in the data as possible and each succeeding component accounts for as much of the
remaining variability as possible [10]. The main objectives of FPCA are:
1) New meaningful fundamental variables Identify
2) Determine or to decrease the ‘dimensionality’ of the data set
3) The protrusion of correlated ‘high-dimensional’ data onto a ‘hyper-plane’
There are several equivalent ways of deriving the principal components mathematically. The
‘simplest’ one is by discovery the ‘projections’ which large the ‘variance’ [11]. The initial
‘principal component’ is the path in quality space along which ‘projections’ have the biggest
variance. The next ‘principal component’ is the path which large ‘variance’ among all
‘directions’ orthogonal to the initial. The nth
element is the ‘variance-maximizing’ direction
‘orthogonal’ to the before (n-1) components. There are p principal components in all. Relatively
10. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
22
than large variance, it power sound more promising to appear for the ‘projection’ with the least
regular (‘mean-squared’) distance between the innovative ‘vectors’ and their ‘projections’ on to
the ‘principal components’. This twist out to be corresponding to large the variance. Which the
data points are projected and data is clustered with PCA algorithm indentifies minimum number
of contributed rules.
5.1 Proposed FPCA Algorithm:
1) The common Fuzzy controller is designed normally with 27 rules
2) The ‘FPCA controller’ is tuned to the same as ‘fuzzy controller’.
3) It is the best possible linear design for ‘compressing’ a set of large ‘dimensional’ vectors
into a set of lesser ‘dimensional’ vectors and then reconstructing
4) Form the matrix of squares and products of the features ZT
Z, where scaled report of the
‘matrix’ X& Z is the ‘centered’.
5) The next ‘principal component’ is the direction ‘orthogonal’ to the initial component with
the large variance. Since it is ‘orthogonal’ to the initial ‘eigenvector’, their ‘projections’
resolve be ‘uncorrelated’ and the ‘principal components’ are ‘uncorrelated’ with all other
6) The ‘principal components’ are designed as P= XE, where X is the ‘original data matrix’
of order n× j, of ‘principal components’ P1, P2, P3, P4…
7) The input space is divided into Principal components using PCA and the Principle
components are identified using fig.7.
8) The sequence of rules of the unusual fuzzy controller is recognized by using ‘Principal
components’.
9 Hence the ‘non-cooperative’ rules are deleted and the necessary rules are ‘identified’ below
table3.
Figure 7.Clusters using principal component analysis
11. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
23
Table.3. Rules for three –input member ship functions for FPCA Controller
Rule DEE DE E Out put
20 Z P Z NS
22 Z Z P NS
23 Z Z Z Z
26 Z N Z PS
6. RESULTS AND DISCUSSIONS
The robustness and efficacy of the proposed FPCA controller with minimum rule base is tested
three-area inter connected deregulated power environment for various operating conditions and
are compared with performance of FCM and PID controller.
Case1: In this case each GENCO in each control area participates in AGC, with area
participation factors apf1-apf6 as defined by following:
apf1 = 0.5, apf2 = 1- apf1 = 0.5, apf3 = 0.5, apf4 = 1- apf3 = 0.5, apf5 = 0.6, apf6 = 1- apf5 = 0.4,
Consider that all the DISCOMs contract with the GENCOs for power as per the below DPM.
Suppose that DISCOM3 demands 0.1PU MW power, out of which 0.05PU MW is demanded
0.015PU MW is demanded from GENCO2, 0.02PU MW from GENCO4, and 0.015PU MW
from GENCO5. DISCOM3 does not demand any per unit MW from GENCO1, GENCO3, and
GENCO6. Then row 2 entries in DPM are easily defined as
31 33 36 32 34 35
0.015 0.02
cpf cpf cpf 0,cpf 0.15 ,cpf cpf 0.2
0.1 0.1
= = = = = = = =
0.3 0.25 0 0.4 0.1 0.6
0.2 0.15 0 0.2 0.1 0
0 0.15 0 0.2 0.2 0
0.2 0.15 1 0 0.2 0.4
0.2 0.15 0 0.2 0.2 0
0.1 0.15 0 0 0.2 0
DPM
=
Step increase in load demand in all three areas ∆PD1, ∆PD2, and ∆PD3 is applied in this case.
The frequency deviation in area1 (∆f1) is shown in Fig.8, frequency deviation in area2 (∆f2) is
shown in Fig.9, and frequency deviation in area3 (∆f3) is shown in Fig.10. It can be observed that
the proposed FPCA controller with minimum rule base has better performance in all responses
with respect to overshoot, undershoot and settling time and robustness when compared to FCM
controller and PID controller.
12. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
24
Figure8. Frequency deviation in area 1 with step increase in∆PD1, ∆PD2and ∆PD3
Figure 9.Frequency deviation in area 2 with step increase in∆PD1, ∆PD2and ∆PD3
Fig.10.Frequency deviation in area 3with step increase in∆PD1, ∆PD2and ∆PD3
13. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
25
Case2:
In this case same as the case 1 but included the Generator Rate Constraints (GRC) the rate of
change in the power generating is to be maintained at a specified maximum limit. In regulate to
consider effect of the GRC into account a step load disturbance of 1% in area 1, area2 and area 3,
the MATLAB simulation power system model of a non reheating turbine is changed through a
nonlinear model of Fig.11 with saturation element d = 0.1± P.U/minute is considered. For the
present test system, the generating rate constraints is set to 0.1± by using each limiters in each
GENCO within the AGC controller to provide the control action within set limits. It can be
observed that the proposed FPCA controller with minimum rule base has better performance in all
responses with respect to overshoot, undershoot and settling time and robustness when compared
to FCM controller and PID controller are tested deregulated power system with including GRC.
The outcome in above case are given in Fig12-14
Figure11. Nonlinear turbine model with GRC
Figure12. Frequency deviation in area 1 including GRC with step increase in∆PD1, ∆PD2and ∆PD3
14. International Journal of Fuzzy Logic Systems (IJFLS) Vol.6, No.1, January 2016
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Figure 13.Frequency deviation in area 2 including GRC with step increase in∆PD1, ∆PD2and ∆PD3
Fig.14.Frequency deviation in area 3 including GRC with step increase in∆PD1, ∆PD2 and ∆PD3
Case3:
In this case the contract same as the case1.Also load demand for each DISCO is considered
0.1pu, the bounded variable step load changes in the a load change as un contracted demand in
area 1, area2 and area 3 (Fig15) appears in all control areas where
−0.07ሺݑሻ ≤ ߂ܲ݀݅ ≤ 0.07ሺݑሻ
The purposed for this toward check the robustness load variations up 40% deregulated power
system of above It can be observed that the proposed FPCA controller with minimum rule base has
better performance in all responses with respect to overshoot, undershoot and settling time and
robustness when compared to FCM controller and PID controller. against parametric uncertainties
and variable large load changes.∆pd1 ,∆pd2and ∆pd3(fig22-25) The results in this case are given
in Fig 16-18
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Figure15. Random load for three control areas in ∆PD1, ∆PD2, ∆PD3
Figure 16. Frequency deviation in area1 with Random loading ∆PD1, ∆PD2 and ∆PD3
Fig.17.Frequency deviation in area2 with Random loading∆PD1, ∆PD2and ∆PD3
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6. CONCLUSION
In this paper a new controller Fuzzy Principal component analysis controller (FPCA) is design
minimization of fuzzy rules for load frequency control deregulated power system. The minimum
rules indentify by using principal component analysis locus and identify the clusters centers in
hyper-plane and converts in to the rules for FPCA controller. The FPCA controller tested for load
frequency three area deregulated power system with minimum rules better performance of FCM
Controller. The simulation results are signify the FPCA Controller is good performance in all
operating conditions and mainly consider settling time, percentage of maximum over shoot, and
under shoot. The numerical analysis show that FPCA Controller as better performance as
compare to FCM and PID Controller.
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