The document describes optimizing an automatic generation control (AGC) scheme for a two-area power system using a particle swarm optimization (PSO) tuned fuzzy PID controller. A linearized model of the two-area power system is presented. A fuzzy PID controller is used to regulate the area control error of each area. The gains of the fuzzy PID controller are optimized using PSO to minimize time-domain performance metrics. Simulation results show the PSO tuned fuzzy PID controller provides better dynamic response than a conventional PSO PID controller, with lower error and faster settling time. Comparisons of tie-line power deviation also show improved performance with the PSO tuned fuzzy approach. The paper concludes the proposed method efficiently tunes fuzzy PID parameters
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
An Optimal LFC in Two-Area Power Systems Using a Meta-heuristic Optimization...IJECEIAES
In this study, an optimal meta-heuristic optimization algorithm for load frequency control (LFC) is utilized in two-area power systems. This metaheuristic algorithm is called harmony search (HS), it is used to tune PI controller parameters (퐾 ) automatically. The developed controller (HSPI) with LFC loop is very important to minimize the system frequency and 푝 , 퐾 푖 keep the system power is maintained at scheduled values under sudden loads changes. Integral absolute error (IAE) is used as an objective function to enhance the overall system performance in terms of settling time, maximum deviation, and peak time. The two-area power systems and developed controller are modelled using MATLAB software (Simulink/Code). As a result, the developed control algorithm (HS-PI) is more robustness and efficient as compared to PSO-PI control algorithm under same operation conditions.
DESIGN OF FAST TRANSIENT RESPONSE, LOW DROPOUT REGULATOR WITH ENHANCED STEADY...ijcsitcejournal
Design and implementation of control systems for power supplies require the use of efficient techniques that
provide simple and practical solutions in order to fulfill the performance requirements at an acceptable cost.
Application of manual methods of system identification in determining optimal values of controller settings is
quite time-consuming, expensive and, sometimes, may be impossible to practically carry out. This paper
describes an analytical method for the design of a control system for a fast transient response, low dropout
(LDO) linear regulated power supply on the basis of PID compensation. The controller parameters are
obtained from analytical model of the regulator circuit. Test results showed good dynamic characteristics
with adequate margin of stability. This study shows that PID parameter values sufficiently close to optimum
can easily be obtained from analytical study of the regulator system. The applied method of determining
controller settings greatly reduces design time and cost.
An Adaptive Internal Model for Load Frequency Control using Extreme Learning ...TELKOMNIKA JOURNAL
As an important part of a power system, a load frequency control has to be prepared with a better controller to ensure internal frequency stability. In this paper, an Internal Model Control (IMC) scheme for a Load Frequency Control (LFC) with an adaptive internal model is proposed. The effectiveness of the IMC control has been tested in a three area power system. Results of the simulation show that the proposed IMC with Extreme Learning Machine (ELM) based adaptive model can accurately cover the power system dynamics. Furthermore, the proposed controller can effectively reduce the frequency and mechanical power deviation under disturbances of the power system.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
An Optimal LFC in Two-Area Power Systems Using a Meta-heuristic Optimization...IJECEIAES
In this study, an optimal meta-heuristic optimization algorithm for load frequency control (LFC) is utilized in two-area power systems. This metaheuristic algorithm is called harmony search (HS), it is used to tune PI controller parameters (퐾 ) automatically. The developed controller (HSPI) with LFC loop is very important to minimize the system frequency and 푝 , 퐾 푖 keep the system power is maintained at scheduled values under sudden loads changes. Integral absolute error (IAE) is used as an objective function to enhance the overall system performance in terms of settling time, maximum deviation, and peak time. The two-area power systems and developed controller are modelled using MATLAB software (Simulink/Code). As a result, the developed control algorithm (HS-PI) is more robustness and efficient as compared to PSO-PI control algorithm under same operation conditions.
DESIGN OF FAST TRANSIENT RESPONSE, LOW DROPOUT REGULATOR WITH ENHANCED STEADY...ijcsitcejournal
Design and implementation of control systems for power supplies require the use of efficient techniques that
provide simple and practical solutions in order to fulfill the performance requirements at an acceptable cost.
Application of manual methods of system identification in determining optimal values of controller settings is
quite time-consuming, expensive and, sometimes, may be impossible to practically carry out. This paper
describes an analytical method for the design of a control system for a fast transient response, low dropout
(LDO) linear regulated power supply on the basis of PID compensation. The controller parameters are
obtained from analytical model of the regulator circuit. Test results showed good dynamic characteristics
with adequate margin of stability. This study shows that PID parameter values sufficiently close to optimum
can easily be obtained from analytical study of the regulator system. The applied method of determining
controller settings greatly reduces design time and cost.
An Adaptive Internal Model for Load Frequency Control using Extreme Learning ...TELKOMNIKA JOURNAL
As an important part of a power system, a load frequency control has to be prepared with a better controller to ensure internal frequency stability. In this paper, an Internal Model Control (IMC) scheme for a Load Frequency Control (LFC) with an adaptive internal model is proposed. The effectiveness of the IMC control has been tested in a three area power system. Results of the simulation show that the proposed IMC with Extreme Learning Machine (ELM) based adaptive model can accurately cover the power system dynamics. Furthermore, the proposed controller can effectively reduce the frequency and mechanical power deviation under disturbances of the power system.
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.
Non-integer IMC Based PID Design for Load Frequency Control of Power System t...IJECEIAES
This paper deals with non-integer internal model control (FIMC) based proportional- integral-derivative(PID) design for load frequency control (LFC) of single area nonreheated thermal power system under parameter divergence and random load disturbance. Firstly, a fractional second order plus dead time(SOPDT) reduced system model is obtained using genetic algorithm through step error minimization. Secondly, a FIMC based PID controller is designed for single area power system based on reduced system model. Proposed controller is equipped with single area non-reheated thermal power system. The resulting controller is tested using MATLAB/SIMULINK under various conditions. The simulation results show that the controller can accommodate system parameter uncertainty and load disturbance. Further, simulation shows that it maintains robust performance as well as minimizes the effect of load fluctuations on frequency deviation. Finally, the proposed method applied to two area power system to show the effectiveness.
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.
Fuzzy and predictive control of a photovoltaic pumping system based on three-...journalBEEI
In this work, an efficient control scheme for a double stage pumping system is proposed. On the DC side, a three-level boost converter is employed to maximize the photovoltaic power and to step-up the DC-link voltage. For maximum power point tracking, the classical incremental conductance method is substituted by a fuzzy logic controller. The designed controller estimates the optimal step size which speeds up the tracking process and improves the accuracy of the extracted photovoltaic power. Afterwards, the voltages across the three-level boost converter (TLBC) capacitors are balanced by phase shifting the applied duty ratios. On the motor pump side, a two-level inverter drives the motor pump with the cascaded nonlinear predictive control. The predictive controller is preferred over the conventional field-oriented control because it accelerates the torque response and resists to the change of the engine parameters. The designed controllers are evaluated using MATLAB/Simulink, and compared with the conventional controllers (incremental conductance algorithm and field-oriented control). The robust control scheme of the entire system has increased the hydraulic power by up to 23% during the system start-up and up to 10% in steady state.
Speed Control of DC Motor using PID FUZZY Controller.Binod kafle
speed control of separately excited dc motor using fuzzy PID controller(FLC).In this research, speed of separately excited DC motor is controlled at 1500 RPM using two approaches i.e. PSO PID and fuzzy logic based PID controller. A mathematical model of system is needed for PSO PID while knowledge based rules obtained via experiment required for fuzzy PID controller . The conventional PID controller parameters are obtained using PSO optimization technique. The simulation is performed using the in-built toolbox from MATLAB and output response are analyzed. The tuning of fuzzy PID uses simple approach based on the rules proposed and membership function of the fuzzy variables. Design specification of fuzzy logic controller (FLC) requires fuzzification, rule list and defuzzification process. The FLC has two input and three output. Inputs are the speed error and rate of change in speed error. The corresponding outputs are Kp, Ki and Kd. There are 25 fuzzy based rule list. FLC uses mamdani system which employs fuzzy sets in consequent part. The obtained result is compared on the basis of rise time, peak time, settling time, overshoot and steady state error. PSO PID controller has fast response but slightly greater overshoot whereas fuzzy PID controller has sluggish response but low overshoot. The selection can be done on the basis of system properties and working environment conditions. PSO PID can be used where the response desired is fast like robotics where as fuzzy PID can be used where desired operation is smooth like industries.
Voltage profile Improvement Using Static Synchronous Compensator STATCOMINFOGAIN PUBLICATION
Static synchronous compensator (STATCOM) is a regulating device used in AC transmission systems as a source or a sink of reactive power. The most widely utilization of the STATCOM is in enhancing the voltage stability of the transmission line. A voltage regulator is a FACTs device used to adjust the voltage disturbance by injecting a controllable voltage into the system. This paper implement Nruro-Fuzzy controller to control the STATCOM to improve the voltage profile of the power network. The controller has been simulated for some kinds of disturbances and the results show improvements in voltage profile of the system. The performance of STATCOM with its controller was very close within 98% of the nominal value of the busbar voltage.
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.
Non-integer IMC Based PID Design for Load Frequency Control of Power System t...IJECEIAES
This paper deals with non-integer internal model control (FIMC) based proportional- integral-derivative(PID) design for load frequency control (LFC) of single area nonreheated thermal power system under parameter divergence and random load disturbance. Firstly, a fractional second order plus dead time(SOPDT) reduced system model is obtained using genetic algorithm through step error minimization. Secondly, a FIMC based PID controller is designed for single area power system based on reduced system model. Proposed controller is equipped with single area non-reheated thermal power system. The resulting controller is tested using MATLAB/SIMULINK under various conditions. The simulation results show that the controller can accommodate system parameter uncertainty and load disturbance. Further, simulation shows that it maintains robust performance as well as minimizes the effect of load fluctuations on frequency deviation. Finally, the proposed method applied to two area power system to show the effectiveness.
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.
Fuzzy and predictive control of a photovoltaic pumping system based on three-...journalBEEI
In this work, an efficient control scheme for a double stage pumping system is proposed. On the DC side, a three-level boost converter is employed to maximize the photovoltaic power and to step-up the DC-link voltage. For maximum power point tracking, the classical incremental conductance method is substituted by a fuzzy logic controller. The designed controller estimates the optimal step size which speeds up the tracking process and improves the accuracy of the extracted photovoltaic power. Afterwards, the voltages across the three-level boost converter (TLBC) capacitors are balanced by phase shifting the applied duty ratios. On the motor pump side, a two-level inverter drives the motor pump with the cascaded nonlinear predictive control. The predictive controller is preferred over the conventional field-oriented control because it accelerates the torque response and resists to the change of the engine parameters. The designed controllers are evaluated using MATLAB/Simulink, and compared with the conventional controllers (incremental conductance algorithm and field-oriented control). The robust control scheme of the entire system has increased the hydraulic power by up to 23% during the system start-up and up to 10% in steady state.
Speed Control of DC Motor using PID FUZZY Controller.Binod kafle
speed control of separately excited dc motor using fuzzy PID controller(FLC).In this research, speed of separately excited DC motor is controlled at 1500 RPM using two approaches i.e. PSO PID and fuzzy logic based PID controller. A mathematical model of system is needed for PSO PID while knowledge based rules obtained via experiment required for fuzzy PID controller . The conventional PID controller parameters are obtained using PSO optimization technique. The simulation is performed using the in-built toolbox from MATLAB and output response are analyzed. The tuning of fuzzy PID uses simple approach based on the rules proposed and membership function of the fuzzy variables. Design specification of fuzzy logic controller (FLC) requires fuzzification, rule list and defuzzification process. The FLC has two input and three output. Inputs are the speed error and rate of change in speed error. The corresponding outputs are Kp, Ki and Kd. There are 25 fuzzy based rule list. FLC uses mamdani system which employs fuzzy sets in consequent part. The obtained result is compared on the basis of rise time, peak time, settling time, overshoot and steady state error. PSO PID controller has fast response but slightly greater overshoot whereas fuzzy PID controller has sluggish response but low overshoot. The selection can be done on the basis of system properties and working environment conditions. PSO PID can be used where the response desired is fast like robotics where as fuzzy PID can be used where desired operation is smooth like industries.
Voltage profile Improvement Using Static Synchronous Compensator STATCOMINFOGAIN PUBLICATION
Static synchronous compensator (STATCOM) is a regulating device used in AC transmission systems as a source or a sink of reactive power. The most widely utilization of the STATCOM is in enhancing the voltage stability of the transmission line. A voltage regulator is a FACTs device used to adjust the voltage disturbance by injecting a controllable voltage into the system. This paper implement Nruro-Fuzzy controller to control the STATCOM to improve the voltage profile of the power network. The controller has been simulated for some kinds of disturbances and the results show improvements in voltage profile of the system. The performance of STATCOM with its controller was very close within 98% of the nominal value of the busbar voltage.
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.
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
Comparison of Soft Computing Techniques applied in High Frequency Aircraft Sy...ijeei-iaes
Sinusoidal Current Control strategy for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. The acute analysis of Comparison of the compensation capability based on THD and speedwell be done, and recommendations will be given for the choice of technique to be used. The simulated results using MATLAB model are shown, and they will undoubtedly prove the importance of the proposed control technique of aircraft shunt APF.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
PERFORMANCE COMPARISON OF TWO CONTROLLERS ON A NONLINEAR SYSTEMijccmsjournal
Various systems and instrumentation use auto tuning techniques in their operations. For example, audio
processors, designed to control pitch in vocal and instrumental operations. The main aim of auto tuning is
to conceal off-key errors, and allowing artists to perform genuinely despite slight deviation off-key. In this
paper two Auto tuning control strategies are proposed. These are Proportional, Integral and Derivative
(PID) control and Model Predictive Control (MPC). The PID and MPC controller’s algorithms
amalgamate the auto tuning method. These control strategies ascertains stability, effective and efficient
performance on a nonlinear system. The paper test and compare the efficacy of each control strategy. This
paper generously provides systematic tuning techniques for the PID controller than the MPC controller.
Therefore in essence the PID has to give effective and efficient performance compared to the MPC. The
PID depends mainly on three terms, the P ( ) gain, I ( ) gain and lastly D ( ) gain for control each
playing unique role while the MPC has more information used to predict and control a system.
PERFORMANCE COMPARISON OF TWO CONTROLLERS ON A NONLINEAR SYSTEMijccmsjournal
Various systems and instrumentation use auto tuning techniques in their operations. For example, audio processors, designed to control pitch in vocal and instrumental operations. The main aim of auto tuning is to conceal off-key errors, and allowing artists to perform genuinely despite slight deviation off-key. In this paper two Auto tuning control strategies are proposed. These are Proportional, Integral and Derivative (PID) control and Model Predictive Control (MPC). The PID and MPC controller’s algorithms amalgamate the auto tuning method. These control strategies ascertains stability, effective and efficient performance on a nonlinear system. The paper test and compare the efficacy of each control strategy. This paper generously provides systematic tuning techniques for the PID controller than the MPC controller. Therefore in essence the PID has to give effective and efficient performance compared to the MPC. The PID depends mainly on three terms, the P () gain, I ( ) gain and lastly D () gain for control each playing unique role while the MPC has more information used to predict and control a system.
Stabilization Of Power System Using Artificial Intelligence Based SystemIJARIIT
This paper reviews limitations of traditional control system and modern control system controllers, which are
overcome to some extent using artificial intelligent techniques, such as ANN, Fuzzy Logic, Expert System, Particle Swarm
Optimization, Genetic Algorithm, etc. The review shows that efforts are made towards Power System Stabilizer based on
Artificial Intelligent Techniques, which will give positive impact on the system stabilities and improve system performances.
Performance Evaluation of GA optimized Shunt Active Power Filter for Constant...ijeei-iaes
Sinusoidal Current Control strategy for extracting reference currents for shunt active power filters have been modified using Genetic Algorithm and its performances have been compared. The acute analysis of Comparison of the compensation capability based on THD and speedwell be done, and recommendations will be given for the choice of technique to be used. The simulated results using MATLAB model are shown, and they will undoubtedly prove the importance of the proposed control technique of aircraft shunt APF.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Load frequency control in co ordination with frequency controllable hvdc link...eSAT Journals
Abstract
In this paper decentralized load frequency control (LFC) for suppression of oscillations in multi-area power systems using fuzzy logic
controller was studied. A three area system is considered in which areas 1 and 2 and areas 1 and 3 are connected by HVDC
transmission links and areas 2 and 3 are connected by normal AC tie-line. The performance of the fuzzy logic controller is compared
with the conventional PI controller and the simulation results shows that fuzzy logic controller is very effective enhancing better
damping performance in non-linear conditions.
Keywords: Load Frequency Control, High Voltage Direct Current transmission Link, Proportional Integral Controller,
Fuzzy Logic Control.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
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A011130109
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 1 Ver. III (Jan. – Feb. 2016), PP 01-09
www.iosrjournals.org
DOI: 10.9790/1676-11130109 www.iosrjournals.org 1 | Page
Optimization of Automatic Generation Control Scheme with Pso
Tuned Fuzzy Pid Controller And Comparision With
Conventional Pso Pid Controller
Prashant kumar sharma1
, Mr.Geoffrey eappen2
1,2
(Department of ECE, MATS University Arang(CG), INDIA)
Abstract : Automatic generation control or AGC system is substantial control process that operates perpetually
to balance the generation and load in power system at minimum cost. If any kind of mismatch occurs between
generation and demand causes the variation in the system frequency from its nominal value. This high frequency
deviation may cause system breakdown. In order to maintain the stability, a very fast, reliable and accurate
controller is needed to maintain the system frequency within the range. This paper presents the particle swarm
optimization (PSO) technique to tune the Fuzzy based PID controller gains for the automatic generation control
(AGC) of the interconnected two area power system. Each control area includes the dynamics response of the
power systems. The results reported in this paper present the potency of the particle swarm optimizer in the
tuning of the Fuzzy based PID controller parameter for two area power system. The improvement in the
dynamic response of the power system is verified. The output response of the proposed work is compared with
PSO-PID controller.
Keywords: Automatic Generation Control, Particle Swarm Optimization, Fuzzy PID controller
I. Introduction
The main purpose of power system operation and control is to provide continuous supply of power
with an acceptable range, for all the consumers in the system. The system will be in equilibrium, when there is
symmetry between the power generation and the demand. There are two types of control methods used to attain
reactive power balance i.e. acceptable frequency values and real power balance acceptable voltage profile. The
former is called the automatic load frequency control or automatic generation control [1] and the latter is called
automatic voltage regulator.
The requirement of parallel operation of multi area power systems is that to fulfill requirement with the
increase in size of electrical power system, controlling the frequency of interconnected power system has
becoming the quite challenging[3]. The frequency deviation and tie-line power arise because of unpredictable
load variations, which occur due to a difference between the generated and the demanding power [1].The main
objective of providing an Automatic Generation Control is to maintain system frequency at nominal value.
A. Automatic Generation Control: Fundamentals
The main aim of AGC is to regulate the frequency using for both primary and supplementary controls.
The Automatic Load Frequency Control is used to control the frequency variation by controlling the real power
balance in the power system. The ALFC loop shown in Fig, is known as primary ALFC loop. It achieves the
primary goal of real power balance by adjusting the turbine output to match change in load demand. The
restoration of the frequency to the nominal value requires an additional control loop known as the
supplementary loop. This purpose is to met by using integral controller so that the frequency variation zero [2].
The ALFC with the supplementary or additional loop is generally called the Automatic Generation control.
Fig1: Block diagram representation of AGC unit
2. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 2 | Page
In a single area system, there is no tie-line schedule to be maintained. Thus the function of the AGC is
only to bring the frequency to the nominal value [2].This will be achieved using the supplementary loop which
uses the integral controller to change the reference power setting so as to change the speed set points.
B. Fuzzy Logic
A fuzzy logic system (FLS) is unique system which is able to simultaneously handle numerical data
and linguistic knowledge. It is a kind of nonlinear mapping of an input data (feature) vector into a scalar output,
i.e. it maps numbers into numbers. Fuzzy set theory and fuzzy logic establish the specifics of the nonlinear
mapping [11]. Fuzzy logic, which is the logic on which fuzzy control is based, is much closer in spirit to human
thinking and natural language than the traditional logical systems. Basically, it provides an effective means of
capturing the approximate, inexact nature of the real world. Viewed in this perspective, the essential part of the
fuzzy logic controller (FLC) is a set of linguistic control rules related by the dual concepts of fuzzy implication
and the compositional rule of inference. In essence, then, the FLC provides an algorithm which can convert the
linguistic control strategy based on expert knowledge into an automatic control strategy.
Generally, a FLS is a nonlinear mapping of an input data vector into a scalar output (the vector output
case decomposes into a collection of multi input single output systems). The benefit of FL is that there are many
numbers of possibilities that leads to lots of different mappings. This requires a understanding of FL and the
elements that comprise a FLS.
C. Particle Swarm Optimization (PSO)
Particle Swarm Optimization is a procedure used to explore the search space of a given problem to find
the settings or parameters needs to maximize a particular objective. PSO belongs to the broad class of stochastic
optimization algorithms. PSO is a population based algorithm which exploits a population of individuals to
probe promising regions of the search space. In this process, the population is called a swarm and the
individuals are called particles. Each particle moves with an adaptable velocity for finding space and holds in its
memory it ever considered.
In the global version of PSO the best position ever attained by all individuals of the swarm is
communicated to all the particles. In the local version, each particle is assigned to a neighbour comprising of a
prespecified number of particles. In this case, the best position ever achieved by the particles that comprise the
neighborhood is communicated between them. Finally, because of the PSO algorithm the best fitness value
achieved between all particles in the swarm, called the global best fitness, and the candidate solution that
achieved this fitness, known as the global best position or global best candidate solution. PSO also retains the
track of the all the best values that the particles have achieved so far.
II. System Modeling
A. Linearised model of two area system using SMES
In this paper we have considered a two area system with SMES. The Fuzzy-PID controller used in this
system is being optimized by using PSO. For the dynamic response of the test system 10% load change is
considered.
3. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 3 | Page
controller 1
In1 Out1
controller
In1 Out1
a12
-1
a1
-1
Turbine - Generator 2
In1 Out1
Turbine - Generator 1
In1 Out1
Transfer Fcn
.520354
s
Step1
Step
Scope1
Scope
B2
.425
B1
-K-
1/R2
1
2.4
1/R1
1
2.4
(power system1)
120
20s+1
(power system 2)
120
20s+1
Fig2: linearised model of two area AGC system
B. Fuzzy controller
Fig 3: Fuzzy PID controller
C.Calculation of Area control error
In the control approach each area of an interconnected system tries to regulate its area control error
(ACE) to zero [2].This error signal can be used to generate the Area Control Error (ACE) signal as
erroritieiii
PfBACE
(1)
Where, Bi is the frequency bias factor and Δfi is the frequency deviation in area-i.
D.PSO Tuned Fuzzy PID Controller
The choice of the tolerable values for the scaling factors of each PID-type Fuzzy Controller structure is
often done by a trials-errors hard practice. This optimizing problem becomes difficult and weak without a
4. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 4 | Page
systematic design method. To deal with these problems, the optimization of these scaling factors is projected
like a promising result. This tuning problem can be formulated as the subsequent guarded optimization problem:
{ minimize f (x)
x €D
gl (x) ≤ 0; l = 1, . . . , n }
Where f: Rm
→ R the cost function, D = { x €Dm
; x min ≤ x ≤ x max} the initial search space, which is supposed
containing the desired design parameters, and gl : Rm
→ R the problem’s constraints.
The optimization-based tuning problem consists the finest decision variables x* = (x*1, x*2, x*3, x*4, x*5.......
x*m) representing the scaling factors of a PID-type FC structure, which minimizes the defined cost function,
chosen as the IAE and ISE performance criteria. These cost functions are minimized, using the proposed
constrained PSO algorithm, under various time-domain control constraints like overshoot D, steady state error
Ess, rise time tr and settling time ts of the system’s step response, as shown in the equations (9),.Hence, in the
case of the PID-type Fuzzy Controller structure without self-tuning method, the scaling factors to be minimised
as Ke,Kp,Kd,α and β.The formulated optimization difficulties is defined as follows:
{minimize f(x)
x= (Ke,Kp,Kd,α,β)T
€ R4
+
Subject to
D≤Dmax
; ts≤ts
max
; tr ≤tr
max
; Ess ≤ Ess
max
}
Where Dmax, Essmax,trmax,tsmax are the specified overshoot, steady state, rise and settling times respectively, that
constraint the step response of the PSO-tuned PID-type Fuzzy logic controlled system, and can define some
time-domain templates.
III. PSO Working
The PSO algorithm works simultaneously by maintaining several candidate solutions in search space.
In each iteration of the algorithm, each candidate solution is evaluated with objective function being optimized,
determining the fitness of that solution. Individually candidate solution can be assumed as a particle flying
through the fitness landscape finding the minimum or maximum of objective function. Originally, the PSO
algorithm selects candidate solutions randomly within the search space. Figure below shows the initial state of a
four particle PSO algorithm seeking the global maximum in a one dimensional search space. The PSO algorithm
simply makes usage of the objective function to calculate its candidate solutions and operates upon the resultant
fitness values.
Fig4: Initial PSO State
Every particle maintains its position, composed of the candidate solution and its evaluated fitness, and
its velocity. Additionally, it remembers the best fitness value that it has achieved, referred to as the individual
best position and individual best solution. The PSO procedure consist just three steps to find out the results,
which are repeated until some stopping condition is not obtained.
5. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 5 | Page
1. Calculate the fitness of each particle
2. Update individual and positions and goble best fitnesses
3. Update position and velocity of each particle
First two steps are fairly unimportant [7].
The velocity & position of each particle in the swarm is updated using the following equation [8]:
vi (t+1) = w vi (t) + c1 r1 [xi (t) – xi (t)] + c2 r2 [g (t) – xi (t)]
Fig5: Flow Chart of particle swarm optimization
Iv. Fuzzy Logic Working
The working of the Fuzzy Logic controller can be divided into three parts namely; allocation of the
area inputs, rules associated with the inputs & defuzzifying of the output.
A. Allocation of the Area inputs
Here frequency deviation and error are the two inputs for the fuzzy logic controller. Both the frequency
deviation and error were divided into three control areas based on magnitude and sign. These are Negative (N),
zero (Z), Positive (P) .Here fig 6(a) & 6(b) represents the input functions for the fuzzy logic controller.
Fig6 (a): Membership function plot for input 1
6. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 6 | Page
Fig6 (b): Membership function plot for input 2
B. Rules Associated with Inputs
The rules used for the fuzzy controller are described in the fuzzy rule table shown in Fig. 8. The
mathematical formula applied is the “minmax” rule for “and’ and “or” respectively. This was to reduce the
calculation complexity and time.
S.No INPUT 1 INPUT 2 OUTPUT
1 N N P
2 N P P
3 Z N Z
4 Z P Z
5 P N N
6 P P N
Fig7 (a): Rules Table
Here: - N= Negative, P=Positive, Z=Zero
C. Defuzzifying of the Output Value
The output function of the fuzzy logic is shown in fig 8(b). Here we used 'centroid' method for
defuzzification. In this method of defuzzification we use the centre of the two inputs.
Fig7 (b): Membership function plot for output
V. Simulation Results
Here we compare PSO-PID based system with PSO tuned Fuzzy PID based system
7. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 7 | Page
A. Comparison between PSO-PID & PSO tuned Fuzzy PID system
PSO-PID based Test system:
Firstly simulation is done on a two area power system. Control parameters of Fuzzy PID controller are
tuned by using PSO technique. Result so obtained is shown below:
Fig8: Transient response of PSO based PID controller for a two area unit
PSO tuned Fuzzy PID based Test system:
Optimization was performed on a two area system with PSO tuned Fuzzy PID controller the simulation
result is show below.
Fig9: Transient response of a PSO tuned Fuzzy PID controller
From the above two plots it can be seen that the PSO tuned Fuzzy PID controller has low value for
error and a better settling time as compared to PSO PID controller.
B. Comparison of Tie line power devotion in two area system:
PSO-PID based Test system:
The Tie-line power deviation for PSO-PID Based test system has been shown below.
8. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 8 | Page
Fig10: Tie line deviation response of a PSO based Test system
Fuzzy Logic Based Test system:
Tie line deviation of a PSO tuned Fuzzy PID based test system is shown below:
Fig11: Tie-line deviation for a PSO tuned Fuzzy PID based test system
From Fig 10 & 11 it can be seen that the settling time for the Tie line deviation for PSO tuned Fuzzy
PID based test system is much better as compared to PSO-PID based test system.
III. Conclusion
Significant conclusion of this paper is as follows:
(a)This paper presents design method for determining the Fuzzy PID controller parameters using the PSO
Algorithm.
(b) A comparative study is made between PSO based PID controller and PSO tuned Fuzzy PID based PID
controller. The results show that the proposed approach had superior features, including easy execution, stable
convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller
parameters yields high-quality solution.
(c) Compared with the PSO-PID, the proposed method was indeed more efficient and robust in improving the
step response of an AGC system.
(d)PSO tuned Fuzzy PID based optimization technique have yielded good results.
Reference
Journal Papers:
[1] HasanBevrani&TakashiHiyama,japan -Intelligent Automatic Generation Control.Taylor & francis gp publishing co. -2011
[2] Dr.Sandeep Bhongade,prof H.O.Gupta senior member IEEE-Performance of SMES unit on Artificial Neural Network based multi
area AGC scheme.journal of power electronics & power system.Vol-1.March-2011.
[3] Mohd.Hassan Ali senior member IEEE-An overview of SMES application in energy & power system, Vol. No.1-2010.
9. Optimization Of Automatic Generation Control Scheme With Pso Tuned Fuzzy Pid Controller A…
DOI: 10.9790/1676-11130109 www.iosrjournals.org 9 | Page
[4] Demiroren “Application to self tunning to Automatic Generation Control in power system including SMES unit” ,Newyork.2009
[5] S.P.Ghoshal Dept of Electrical Engg NIT Durgapur-Optimized AGC by SSSC & TCPS in coordination with SMES for two area
HydroHydro power system.International Conference on advances incomputing,Control&telecommunicationtechnologie.2009
[6] James Blondin-Particle Swarm Optimization:A tutorial –sept 9 2009.
[7] FransVan derbergh-Analysis of particle swarm optimizers,University of Pretoria etd-2006
[8] Micheal N. Varhatis-On the computation of all global minimizers in Particle Swarm Optimization ,Vol 8,No.3 –2004.
[9] James Kennedy and Russell Eberhart,-Particle swarm optimization,In Proceedings of the IEEE International Conference on Neural
Networks, volume IV, pages 1942–1948, Piscataway, NJ, 1995.
[10] Yuhui Shi and Russell Eberhart, “A modified particle swarm optimizer”, Proceedings of the IEEE International Conference on
Evolutionary Computation, pages 69–73, 1998.
[11] J. J. Buckley, “Universal fuzzy controllers,” Auromatica, vol. 28, pp. 1245-1248, 1992.
[12] J. Baldwin and N. Guild, “Modeling controllers using fuzzy relations,” Kybemetes, vol. 9, pp. 223-229, 1980.
Chapters in Books:
[13] P.Kundur, Tata Mcgraw-hill Publishing company limited. a hand book on Power System stability & Control (new Delhi 1994)
page no 601-604
Appendix
Table: The system data (capacity of each area is 1000MW)
s.no INPUT 1 INPUT 2 OUTPUT
1 N N P
2 N P P
3 Z N Z
4 Z P Z
5 P N N
6 P P N
Here: - N= Negative, P=Positive, Z=Zero