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.
Simulation DC Motor Speed Control System by using PID Controllerijtsrd
Speed control system is the most common control algorithm used in industry and has been universally accepted in industrial control. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motor is very important as any small change can lead to instability of the closed loop system. The aim of this thesis is to show how DC motor can be controlled by using PID controller in MATLAB. The development of the PID controller with the mathematical model of DC motor is done using automatic tuning method. The PID parameter is to be test with an actual motor also with the PID controller in MATLAB Simulink. In this paper describe the results to demonstrate the effectiveness and the proposed of this PID controller produce significant improvement control performance and advantages of the control system DC motor. Mrs Khin Ei Ei Khine | Mrs Win Mote Mote Htwe | Mrs Yin Yin Mon ""Simulation DC Motor Speed Control System by using PID Controller"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25114.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/25114/simulation-dc-motor-speed-control-system-by-using-pid-controller/mrs-khin-ei-ei-khine
Due to extensive use of motion control system in industry, there has been growing research on proportional-integral-derivative (PID) controllers. DC motors are widely used various areas of industrial applications. The aim of this paper is to implement efficient method for controlling speed of DC motor using a PID controller based. Proposed system is implemented using arduino microcontroller and PID controller. Motor speed is controlled through PID based revolutions per minute of the motor. This encoder data will be send through microcontroller to Personal Computer with PID controller implemented in MATLAB. Results shows that PID controllers used provide efficient controlling of DC motor.
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.
Hybrid fuzzy-PID like optimal control to reduce energy consumptionTELKOMNIKA JOURNAL
The electric motor is one of the appliances that consume considerable energy. Therefore, the control method which can reduce energy consumption with better performance is needed. The purpose of this research is to minimize the energy consumption of the DC motor with maintaining the performance using Hybrid Fuzzy-PID. The input of the Fuzzy system is the error and power of the system. Where error is correlated with matric Q and power is correlated with matric R. Therefore, adjusting the fuzzy rule on error and power is like adjust matrices Q and R in LQR method. The proposed algorithm can reduce energy consumption. However, system response is slightly decrease shown from ISE (Integral Square Error). The energy reduction average is up to 5.58% while the average of ISE increment is up to 1.89%. The more speed variation in the system, the more energy can be saved by the proposed algorithm. While in terms of settling time, the proposed algorithm has the longest time due to higher computation time in the fuzzy system. This performance can be increased by tuning fuzzy rules. This algorithm offers a solution for a complex system which difficult to be modeled.
This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).
Simulation DC Motor Speed Control System by using PID Controllerijtsrd
Speed control system is the most common control algorithm used in industry and has been universally accepted in industrial control. One of the applications used here is to control the speed of the DC motor. Controlling the speed of a DC motor is very important as any small change can lead to instability of the closed loop system. The aim of this thesis is to show how DC motor can be controlled by using PID controller in MATLAB. The development of the PID controller with the mathematical model of DC motor is done using automatic tuning method. The PID parameter is to be test with an actual motor also with the PID controller in MATLAB Simulink. In this paper describe the results to demonstrate the effectiveness and the proposed of this PID controller produce significant improvement control performance and advantages of the control system DC motor. Mrs Khin Ei Ei Khine | Mrs Win Mote Mote Htwe | Mrs Yin Yin Mon ""Simulation DC Motor Speed Control System by using PID Controller"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25114.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/25114/simulation-dc-motor-speed-control-system-by-using-pid-controller/mrs-khin-ei-ei-khine
Due to extensive use of motion control system in industry, there has been growing research on proportional-integral-derivative (PID) controllers. DC motors are widely used various areas of industrial applications. The aim of this paper is to implement efficient method for controlling speed of DC motor using a PID controller based. Proposed system is implemented using arduino microcontroller and PID controller. Motor speed is controlled through PID based revolutions per minute of the motor. This encoder data will be send through microcontroller to Personal Computer with PID controller implemented in MATLAB. Results shows that PID controllers used provide efficient controlling of DC motor.
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.
Hybrid fuzzy-PID like optimal control to reduce energy consumptionTELKOMNIKA JOURNAL
The electric motor is one of the appliances that consume considerable energy. Therefore, the control method which can reduce energy consumption with better performance is needed. The purpose of this research is to minimize the energy consumption of the DC motor with maintaining the performance using Hybrid Fuzzy-PID. The input of the Fuzzy system is the error and power of the system. Where error is correlated with matric Q and power is correlated with matric R. Therefore, adjusting the fuzzy rule on error and power is like adjust matrices Q and R in LQR method. The proposed algorithm can reduce energy consumption. However, system response is slightly decrease shown from ISE (Integral Square Error). The energy reduction average is up to 5.58% while the average of ISE increment is up to 1.89%. The more speed variation in the system, the more energy can be saved by the proposed algorithm. While in terms of settling time, the proposed algorithm has the longest time due to higher computation time in the fuzzy system. This performance can be increased by tuning fuzzy rules. This algorithm offers a solution for a complex system which difficult to be modeled.
This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).
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.
Parallel distribution compensation PID based on Takagi-Sugeno fuzzy model app...IJECEIAES
This paper presents a new technique for a Takagi-Sugeno (TS) fuzzy parallels distribution compensation-PID'S (TSF-PDC-PID'S) to improve the performance of egyptian load frequency control (ELFC). In this technique, the inputs to a TS fuzzy model are the parameters of the change of operating points. The TS fuzzy model can definite the suitable PID control for a certain operating point. The parameters of PID'S controllers are obtained by ant colony optimization (ACO) technique in each operating point based on an effective cost function. The system controlled by the proposed TSF-PDCPID’S is investigated under different types of disturbances, uncertainty and parameters variations. The simulation results ensure that the TSF-PDC-PID'S can update the suitable PID controller at several operating points so, it has a good dynamic response under many types of disturbances compared to fixed optimal PID controller.
In this paper, the closed loop speed controller parameters are optimized for the permanent magnet synchronous motor (PMSM) drive on the basis of the indirect field-oriented control (IFOC) technique. In this derive system under study, the speed and current controllers are implemented using the fractional order proportional, integral, and derivative (FOPID) controlling technique. FOPID is considered as efficient techniques for ripple minimization. The hybrid grey wolf optimizer (HGWO) is applied to obtain the optimal controllers in case of implementing conventional PID as well as FOPID controllers in the derive system. The optimal controller parameters tend to enhance the drive response as ripple content in speed and current, either during steady state time or transient time. The drive system is modeled and tested under various operating condition of load torque and speed. Finally, the performance for PID and FOPID are evaluated and compared within MATLAB/Simulink environment. The results attain the efficacy of the operating performance with the FOPID controller. The result shows a fast response and reduction of ripples in the torque and the current.
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.
Data-based PID control of flexible joint robot using adaptive safe experiment...journalBEEI
This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
Implementation of closed loop control technique for improving the performance...IJERA Editor
this review paper presents closed loop control techniques for controlling the inverter working under different load or KVA ratings. The control strategy of the inverter must guarantee its output waveforms to be sinusoidal with fundamental harmonic. For this purpose, close loop current control strategies such as H∞ repetitive controller, dual closed-loop feedback control, Adaptive Voltage Control, SRFPI controller, Optimal Neural Controller, etc. have been used to meet the power quality requirements imposed by IEEE Interconnection Standards. Based on present scenario regarding energy crises, immediate action is the use of different renewable energy sources (RESs) . Out of RESs, solar is gaining more attention. It is very important to design and developed a system which should be efficient enough to utilize the extracted energy for different types of load and feeding of energy into utility grid. Since experimentation and comparison of such inverter models on hardware being relatively expensive, the latest computing tool like MATLAB are considered to be a better alternative to simulate the outcomes of such expensive systems. The proposed closed loop control technique for the inverter working under linear and nonlinear system will be implemented in MATLAB/SIMULINK working platform and results will be analyzed to check its benefits.
Tuning PID Controller Parameters for Load Frequency Control Considering Syste...IJERA Editor
In this paper, parameters of PID controller and bias coefficient for Load Frequency Control (LFC) are designed using a new approach. In the proposed method, the power system uncertainties and nonlinear limitations of governors and turbines ,i.e. Valve Speed Limit (VSL)and Generation Rate Constraint (GRC), are taken into account in designing. Variations of uncertain parameters are considered between -40% and +40% of nominal values with 5% step .In order to design the proposed PID controller ,a new objective function is defined. MATLAB codes are developed for GA based PID controller tuning, the results of which are used to study the system step response. All these are through in Simulink based background.
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.
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.
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.
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.
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.
Parallel distribution compensation PID based on Takagi-Sugeno fuzzy model app...IJECEIAES
This paper presents a new technique for a Takagi-Sugeno (TS) fuzzy parallels distribution compensation-PID'S (TSF-PDC-PID'S) to improve the performance of egyptian load frequency control (ELFC). In this technique, the inputs to a TS fuzzy model are the parameters of the change of operating points. The TS fuzzy model can definite the suitable PID control for a certain operating point. The parameters of PID'S controllers are obtained by ant colony optimization (ACO) technique in each operating point based on an effective cost function. The system controlled by the proposed TSF-PDCPID’S is investigated under different types of disturbances, uncertainty and parameters variations. The simulation results ensure that the TSF-PDC-PID'S can update the suitable PID controller at several operating points so, it has a good dynamic response under many types of disturbances compared to fixed optimal PID controller.
In this paper, the closed loop speed controller parameters are optimized for the permanent magnet synchronous motor (PMSM) drive on the basis of the indirect field-oriented control (IFOC) technique. In this derive system under study, the speed and current controllers are implemented using the fractional order proportional, integral, and derivative (FOPID) controlling technique. FOPID is considered as efficient techniques for ripple minimization. The hybrid grey wolf optimizer (HGWO) is applied to obtain the optimal controllers in case of implementing conventional PID as well as FOPID controllers in the derive system. The optimal controller parameters tend to enhance the drive response as ripple content in speed and current, either during steady state time or transient time. The drive system is modeled and tested under various operating condition of load torque and speed. Finally, the performance for PID and FOPID are evaluated and compared within MATLAB/Simulink environment. The results attain the efficacy of the operating performance with the FOPID controller. The result shows a fast response and reduction of ripples in the torque and the current.
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.
Data-based PID control of flexible joint robot using adaptive safe experiment...journalBEEI
This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
Implementation of closed loop control technique for improving the performance...IJERA Editor
this review paper presents closed loop control techniques for controlling the inverter working under different load or KVA ratings. The control strategy of the inverter must guarantee its output waveforms to be sinusoidal with fundamental harmonic. For this purpose, close loop current control strategies such as H∞ repetitive controller, dual closed-loop feedback control, Adaptive Voltage Control, SRFPI controller, Optimal Neural Controller, etc. have been used to meet the power quality requirements imposed by IEEE Interconnection Standards. Based on present scenario regarding energy crises, immediate action is the use of different renewable energy sources (RESs) . Out of RESs, solar is gaining more attention. It is very important to design and developed a system which should be efficient enough to utilize the extracted energy for different types of load and feeding of energy into utility grid. Since experimentation and comparison of such inverter models on hardware being relatively expensive, the latest computing tool like MATLAB are considered to be a better alternative to simulate the outcomes of such expensive systems. The proposed closed loop control technique for the inverter working under linear and nonlinear system will be implemented in MATLAB/SIMULINK working platform and results will be analyzed to check its benefits.
Tuning PID Controller Parameters for Load Frequency Control Considering Syste...IJERA Editor
In this paper, parameters of PID controller and bias coefficient for Load Frequency Control (LFC) are designed using a new approach. In the proposed method, the power system uncertainties and nonlinear limitations of governors and turbines ,i.e. Valve Speed Limit (VSL)and Generation Rate Constraint (GRC), are taken into account in designing. Variations of uncertain parameters are considered between -40% and +40% of nominal values with 5% step .In order to design the proposed PID controller ,a new objective function is defined. MATLAB codes are developed for GA based PID controller tuning, the results of which are used to study the system step response. All these are through in Simulink based background.
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.
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.
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.
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.
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.
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.
DESIGN OF CONTROL STRATEGIES FOR THE LOAD FREQUENCY CONTROL (LFC) IN MULTI AR...IAEME Publication
This paper features the Differential Evolution (DE) by controller parameters tuning algorithm
and also an application of a multi source power system to a Load Frequency Control (LFC) by
having several sources of power generation techniques. At first, a single area multi-source power
system using integral controllers for every unit is taken and DE procedure is implemented to attain
the controller parameters. Several mutation procedures of DE are estimated and the control
parameters of DE for best obtained procedure are tuned by implementing numerous runs of
algorithm for every change in parameter. Multi-area multi-source power system is also discussed
and a HDVC link is also taken in accordance with the current AC tie line for the internal
connection between the areas. The two variables of Integrals which are to be enhanced using tuned
DE algorithm are proportional integral and proportional integral derivative.
Convergence Parameter Analysis for Different Metaheuristic Methods Control Co...IJPEDS-IAES
This paper is an extension of our previous work, which discussed the
difficulty in implementing different methods of resistance emulation
techniques on the hardware due to its control constant estimation delay. In
order to get rid of the delay this paper attempts to include the meta-heuristic
methods for the control constants of the controller. To achieve the minimum
Total Harmonic Disturbance (THD) in the AC side of the converter modern
meta-heuristic methods are compared with the traditional methods. The
convergence parameters, which are primary for the earlier estimation of the
control constants, are compared with the measured parameters, tabulated and
tradeoff inference is done among the methods. This kind of implementation
does not need the mathematical model of the system under study for finding
the control constants. The parameters considered for estimation are
population size, maximum number of epochs, and global best solution of the
control constants, best THD value and execution time. MatlabTM /Simulink
based simulation is optimized with the M-file based optimization techniques
like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo
Search Algorithm, Gravity Search Algorithm, Harmony Search Algorithm
and Bat Algorithm.
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
A new approach for Tuning of PID Load Frequency Controller of an Interconnect...Editor IJMTER
Load frequency control is one of the important issues in electrical power system
design/operation and is becoming much more significant recently with increasing size,
changing structure and complexity in interconnected power system. This paper deals with a
new approach of PID tuning and their dynamic responses are compared with PID classical
Controller when both controllers are applied to three area interconnected Thermal-ThermalHydro. The dynamical response of the load frequency control problem in an interconnected
power system is improved by designing PID controller using Pessen Integral Rule (similar to
Ziegler Nichols -2nd method). The results indicate that the proposed controller gives the better
performance.
Current predictive controller for high frequency resonant inverter in inducti...IJECEIAES
In the context of this article, we are particularly interested in the modeling and control of an induction heating system powered by high frequency resonance inverter. The proposed control scheme comprises a current loop and a PLL circuit. This latter is an electronic assembly for slaving the instantaneous phase of output on the instantaneous input phase, and is used to follow the rapid variations of the frequency.To further improve the transient dynamics of the studied system and in order to reduce the impact of measurement noise on the control signal, a generalized predictive control has been proposed to control the current of the inductor. We discussed the main steps of this command, whose it uses a minimization algorithm to obtain an optimal control signals, its advantages are: its design is simple, less complexity and direct manipulation of the control signal. The results have shown the effectiveness of the proposed method, especially in the parameters variation and/or the change of the reference current.
Automatic Generation Control of Two Equal Areas with Traditional ControllersIJPEDS-IAES
Automatic generation control (AGC) is major issue in power system whose main purpose is to maintain the frequency and tie line power flow during normal period in an interconnected system. Thus, It is the responsibility of the Power system engineers to ensure that adequate power is delivered to the load reliably and economically so that nominal condition will be re-established The main focus is to maintain the value of frequency in its prescribed limit. We use renewable source which are important as well as non renewable sources at the level of extinct. As renewable alone cannot reliable then we use interconnection of both for making the system reliable and more efficient. This research paper is devoted to explore the interconnection of the automatic generation control of hydro power system and wind system. The wind system is comprised with governor dead band, generation rate constraint and turbine dynamics where as the hydro system is comprised with generation rate constraint. The traditional PI controller does not have adequate control performance with the consideration of nonlinearities and turbine dynamics. To overcome this drawback, PID controllers helps in solving optimization problems by exploitation of random search. It will provide the system security also for maintain the frequency in prescribed limit with the help of simulation.
The Proportional-Integral-Derivative (PID) controller is the most popular control strategy in the process industry. The popularity can be attributed to its simplicity, better control performance and excellent robustness to uncertainties that is found through the research work on such controllers so far. This paper presents the design and tuning of a PID controller using Fuzzy logic for industrial induction heating systems with LLC voltage source inverter for controlling the induction heating power. The paper also compares the performance of the Fuzzy PID controller with that of a conventional PID controller for the same system. The system and the controllers are simulated in MATLAB/SIMULINK. The results show the effectiveness and superiority of the proposed Fuzzy PID controller.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
Pa3426282645
1. Aswini Kumar Patel, Mr Dharmendra Ku. Singh, Mr Binod Kumar Sahoo / International
Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2628-2645
2628 | P a g e
Automatic generation control of a two unequal area thermal
power system with PID controller using Differential Evolution
Algorithm
Aswini Kumar Patel, Mr Dharmendra Ku. Singh, Mr Binod Kumar Sahoo,
M.Tech(P.S.E), Dr.C.V.R.I, H.O.D,. (EEE) Asst. Professor
Of Sc. &Tech. Dr.C.V.R.Iof Sc&Tech . I.T. E.R.
.Kargi Road, Kota, Bilaspur. Kargi Road, Kota, Bilaspur,, Bhubaneswar.
Abstract
The early aim of automatic generation
control is to regulate the power output of the
electrical generator with in a prescribed area in
response to changes in system frequency, tie line
loading, so as to maintain the scheduled system
frequency and interchange with the other areas
with predetermined limits. Automatic generation
control of a multi-area power system provides
power demand signals for its power generators to
control frequency and tie line power flow due to
the large load changes or other disturbances.
Occurrence of large megawatt imbalance causes
large frequency deviations from its nominal value
which may result instability in the power system.
To avoid such situation emerging control to
maintain the system frequency using differential
evolution based proportional integral-derivative
(PID) controller has been used in this paper.
Differential evolution (DE) is one of the most
power full stochastic real parameter optimization
in current use. Differential evolution based
optimization gains give better optimal transient
response of frequency and tie line power changes
compared toLozi-map based chaotic algorithm
(LCOA).
Key words: DE: Differential Algorithm, AGC:
Automatic Generation Control, ACE: Area Control
Error, SLP: Step Load Perturbation,LFC: Load
Frequency Control,
I. Introduction
An interconnected power system can
generated, transport and distribute the electrical
energy with the main objective of these power system
is to supply electric energy with its system nominal
frequency and terminal voltage. According to the
power system control theory the nominal frequency
depends upon the balance between the generated
power and the consumed real power[1].If the amount
of the generated power is less than the amount of
demand power, then the speed and frequency of
generator units is going to be decreased ,and vice
versa .So for that reason the frequency deviation
occurred in the power system in order to maintain
that balance. For this purpose a megawatt frequency
controller or automatic generation
control(AGC)concept is used.The early aim of the
Automatic generation control(AGC)is to regulate the
power output of the electrical generator within a
prescribed area in response to changes in system
frequency,tie-line loading ,so as to maintain the
scheduled system frequency and interchange with the
other areas with predetermined limits[2,3]. Generally
the load frequency control is accomplished by two
different control action of the primary speed control
and supplementary speed control in an interconnected
power system. The primary speed control performs
the initial readjustment of the frequency.By its action,
the various generator in the control area track a load
variation and share it in proportion to their
capacities.The speed of these response is only limited
by the natural time lags of the turbine,governor and
system it-self.The supple-mentary speed control takes
over the fine adjustment of the frequency by the
resetting the frequency error to zero through an PI
controller[2].The main drawback of this
supplementary controller is that the dynamic
performance of the system is highly dependent on the
selection of its gain.A high gain may deteriorate the
system performance having large oscillation and in
most cases it causes instability[2,4].Thus the
integrator must be set to a level that provides a
compromise a desirable transient recovery and low
over shoot in the dynamic response of the overall
system preventing instability[5,6].For the better
performance of PID control optimized constraints
have to be adopted.To get the optimized value we are
having different optimization techniques such as
classical, optimal, genetic algorithm,fuzzy
logic,artificial neural network etc, for the design of
supplementary controller.Talaq, suggested an
adoptive fuzzy gain scheduling method for
conventional PI controllerin[11].In[12] Pingkang
optimized the gains of the PI and PID controller
through real coded genetic algorithm in a two area
power system. Abdel-Majid and Abedo purposed a
usage of PSO for the same purpose[13].In[14] Yesil
suggested the self-tuning fuzzy PID type controller
for AGC.In[15]Gozde and Taplamacioglu
purposedthe usage of craziness based PSO algorithm
2. Aswini Kumar Patel, Mr Dharmendra Ku. Singh, Mr Binod Kumar Sahoo / International
Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2628-2645
2629 | P a g e
for AGC system for an interconnected power
system.In[30]M.Farahani, S.Ganjefar, M.Alizadeh
suggested a method of using PID controllers for a
two area unequal thermal system tuned by Lozi-mape
based chaotic optimization algorithm.
In[31]Storn and Price proposed a meta-
heuristic evolutionary algorithm non as differential
evolution algorithm.In order to solve˝ many
parameter‖ based higher order optimization
problems; DE can be considered as a power full and
well efficient population based stochastic search
technique.
2: Proposed methodology
The contribution of this paper is the
performance analysis of differential evolution
algorithm in tuning of a PID controller applied in
AGC.
2.1:PID controller
Basically, different process industries use PID
controller as the most popular feedback control
mechanism. Till date from half of this century, it is the
most popular feedback controller which is being used in
the process industries. This can be considered as an
excellent controller that can help in providing excellent
performance of the process plant. It is due to their easy
and simple implementation in various control
applications.
Fig.1. Simulink representation of PID controller
The PID as given in fig. 1 controller consists
of three basic modes: proportional, integral,
derivative modes respectively. A proportional
controller gain (𝐾𝑝)reduces the rise time but does not
eliminate the steady-state error, integral
gain (𝐾𝑖)eliminates the steady state error but
resulting a worse transient response, derivative
gain (𝐾𝑑 ) increases the stability of the system and
improves the transient response and reduces
overshoot[29]. These values of above gains are
obtained by hit and trial method based on the plant
behaviour and experiences. The equation below
represents the transfer function of PID (Laplace
Domain) is given by:
𝑇𝐹𝑃𝐼𝐷 = 𝐾𝑝 +
𝐾𝑖
𝑠
+ 𝐾𝑑 𝑠 (1)
In time domain, the output of PID controller is given
by;
u (t)=𝐾𝑝. 𝑒 𝑡 + 𝐾𝑖 𝑒(𝜏
𝑡
0
)𝑑𝜏 + 𝐾𝑑
𝑑𝑒 𝑡
𝑑𝑡
(2)
Where e(t) is error signal and u(t) is control signal.
In the design of PID controllers(two in
number) for this work the six gains are selected in
such a way that the desired response obtained of the
closed loop system which refers that the system
should have a minimum settling time and a very less
value of overshoot as well as undershoots with less
oscillations due to a 20% Step Load Perturbation.
In the PID controllers the control inputs are
𝐴𝐶𝐸𝐼and 𝐴𝐶𝐸2whereas 𝑢1 and 𝑢2 are the outputs
respectively. On relating the inputs and outputs of the
system 𝑢1 and 𝑢2are given as;
𝑢1 = 𝐴𝐶𝐸𝐼 𝐾𝑝1
+
𝐾𝑖1
𝑠
+ 𝐾𝑑1
𝑠 (3)
𝑢2 = 𝐴𝐶𝐸2 𝐾𝑝2
+
𝐾𝑖2
𝑠
+ 𝐾𝑑2
𝑠 (4)
The value of Area Control Error (ACE) is the sum of
Bias Factor and Tie line power deviation. The Area
Control Error in both the areas consists of Tie line
power error of the intermediate area and the
Frequency error, given by;
𝐴𝐶𝐸1 = 𝐵1 + ∆𝑃𝑡𝑖𝑒 (5)
3. Aswini Kumar Patel, Mr Dharmendra Ku. Singh, Mr Binod Kumar Sahoo / International
Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2628-2645
2630 | P a g e
𝐴𝐶𝐸2 = 𝐵2 + ∆𝑃𝑡𝑖𝑒 (6)
In this work, the constraint of setting the gains of PID
controller is a major problem. Therefore, the PID
gains should be in limits; i.e.
𝐾𝑝 𝑚𝑖𝑛
≤ 𝐾𝑝 𝑖
≤ 𝐾𝑝 𝑚𝑎𝑥
𝐾𝑖 𝑚𝑖𝑛
≤ 𝐾𝑖 𝑖
≤ 𝐾𝑖 𝑚𝑎𝑥
𝐾𝑑 𝑚𝑖𝑛
≤ 𝐾𝑑 𝑖
≤ 𝐾𝑑 𝑚𝑎𝑥
Where i is the number of controller gain
(here𝑖 = 2, due to two controllers).𝐾𝑝 𝑚𝑖𝑛
,𝐾𝑖 𝑚𝑖𝑛
,𝐾𝑑 𝑚𝑖𝑛
are the minimum values of controller parameters and
𝐾𝑝 𝑚𝑎𝑥
, 𝐾𝑖 𝑚𝑎𝑥
, 𝐾𝑑 𝑚𝑎𝑥
are the maximum allowable
values for the controller parameters. Henceforth, in
this work the PID parameters are constrained within
[0, 2].
2.2:Differential Evolution
In the earlier section, the need for use of PID
controller is shown and in order to tune different
parameters of the PID controller Evolutionary
Algorithms comes into picture. DE can be said as
such a meta-heuristic evolutionary algorithm which
was developed by Storn and Price[31].In order to
solve ―many parameter‖ based higher order
optimization problems; DE can be considered as a
powerful and well efficient population based
stochastic search technique.
The main flow of steps of Differential
evolution algorithm in a simple manner is shown in
fig.2.The DE algorithm consists of the four basic
steps-initialization of a population of search variable
vectors, mutation, crossover or recombination, and
finally selection.
Initialization
Mutation
Crossover
Selection
Based on the
conditions
Randomly generate a
population with desired
dimension of problem
Generate a mutation vector
corresponding to the target
vector
Generate a trial vector
corresponding to the target
vector based on Binomial
crossover technique
Retain the best values(either in
the form of target vector or
trial vector)
Fig. 2. Stages of Differential Evolution algorithm
2.2.1 Initialization
The values of the control parameters of DE:
scale factor F, crossover rate Cr, and the population
size NP are initialised. Initially a population of NP
individuals is initialized with the generation set given
as 𝐺 = 0,1, … … . , 𝐺 𝑚𝑎𝑥 .
(7)
For a D dimensional optimization problem, the
candidate solutions are taken to be
𝑋𝑖,𝐺 = {𝑥𝑖,𝐺
1
… … … … , 𝑥𝑖,𝐺
𝐷
},where the value
of 𝑖 = [1, 𝑁𝑃].
𝑋𝑖,𝐺is in a range of
{𝑋 𝑚𝑖𝑛 < 𝑋𝑖,𝐺 < 𝑋 𝑚𝑎𝑥 }.𝑋 𝑚𝑖𝑛 𝑎𝑛𝑑 𝑋max have values
in the range of {𝑥1
𝑚𝑖𝑛
,……,𝑥 𝐷
𝑚𝑖𝑛 } and
{𝑥1
𝑚𝑎𝑥
,……,𝑥 𝐷
𝑚𝑎𝑥 } respectively.
2.2.2 Mutation
Mutation stands for sudden change. In DE-
literature, a parent vector from the current generation
G is called target vector, a mutant vector is generated
through the mutation operation is known as donor
vector and finally an offspring formed by
recombining the donor with the target vector (𝑋𝑖,𝐺) is
called trial vector. For each target vector, a mutation
vector 𝑉𝑖,𝐺 is generated with the help of following
strategy:
Strategy 1 DE/rand/1:
𝑉𝑖,𝐺 = 𝑋 𝑟1
𝑖
,𝐺
+ 𝐹. 𝑋 𝑟2
𝑖
,𝐺
− 𝑋 𝑟3
𝑖
,𝐺
(8)
𝑟1
𝑖
, 𝑟2
𝑖
, 𝑎𝑛𝑑 𝑟3
𝑖
are generated randomly in between
[1,NP] and are mutually exclusive integers are not
4. Aswini Kumar Patel, Mr Dharmendra Ku. Singh, Mr Binod Kumar Sahoo / International
Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2628-2645
2631 | P a g e
repeated. F is the scaling factor in the range of [0.1
1.0].
2.2.3 Crossover
The donor vector exchanges its components
with the target vector 𝑋𝑖,𝐺under this operationto form
the trial vector
𝑈 𝑖,𝐺
= [ 𝑢1,𝑖,𝐺, 𝑢2,𝑖,𝐺, 𝑢3,𝑖,𝐺 … . . , 𝑢 𝐷,𝑖,𝐺]
.The DE uses two kinds of cross over methods—
exponential (or two-point modulo) and binomial(or
uniform).
Now for each target vector𝑋𝑖,𝐺, a trial vector 𝑈𝑖,𝐺 is
generated. Crossover in DE can be binomial or even
exponential. Here for the generation of trial vector
𝑈𝑖,𝐺 binomial crossover is used. The steps for
crossover are:
𝑈𝑖,𝑗,𝐺 = 𝑉𝑖,𝑗 ,𝐺if (𝑗𝑟𝑎𝑛𝑑 ≤ 𝐶𝑅) or (j=𝑗𝑟𝑎𝑛𝑑 )
𝑋𝑖,𝑗,𝐺if (𝑗𝑟𝑎𝑛𝑑 > 𝐶𝑅) or (j≠ 𝑗𝑟𝑎𝑛𝑑 )
𝑗𝑟𝑎𝑛𝑑 is a random number between 0 and 1 multiplied
with the dimension of the optimization D. Crossover
Rate (CR) is chosen in between [0.1 1.0]. In this
work the values of F and CR are 0.8 and 0.5
respectively.
2.2.4 Selection
In order to maintain a constant population
size over the length of consistently increasing
generations, this step selects the survival of the target
or the trial vector based on equations (7) and (8) thus
passing it to the next generation, i.e., at 𝐺 = 𝐺 +
1.The target vector is compared with the trial vector
and the better value between these two is used in the
further generation.
If 𝑓(𝑈𝑖,𝐺) ≤ 𝑓(𝑋𝑖,𝐺) 𝑡ℎ𝑒𝑛 𝑋𝑖,𝐺+1 = 𝑈𝑖,𝐺, 𝑓(𝑋𝑖,𝐺+1) = 𝑓(𝑈𝑖,𝐺) (9)
If 𝑓(𝑈𝑖,𝐺) > 𝑓(𝑋𝑖,𝐺) 𝑡ℎ𝑒𝑛 𝑋𝑖,𝐺+1 = 𝑋𝑖,𝐺 (10)
The iteration of the DE steps terminates when either
the number of generations is exhausted or when the
best value of fitness of the objective function does
not have an appreciable change.
Initialization
Evaluate the fitness values of each particle
Repeat {
Mutation
Crossover
Selection
Evaluate the new individuals.
} Until (stopping criterion is met)
3:System investigated
5. Aswini Kumar Patel, Mr Dharmendra Ku. Singh, Mr Binod Kumar Sahoo / International
Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2628-2645
2632 | P a g e
1B
-
-
-
+PID
11
1
gsT 11
1
tsT
-
+
-
SLP
112
1
DsH
1
1
R
s
T
+
-
+
-
PID
-
+
-
SLP
222
1
DsH
2
1
R
2B
Controller
Governor Turbine
1f
2f
tieP
Area 1
Area 2
Controller
2
1
1
gsT
21
1
tsT
Governor Turbine
1u
2u
ACE1
ACE2
Fig 3. The control diagram of an interconnected unequal thermal system [30]
The Automatic Generation Control system
investigated in this work as shown in fig. 3 consists
of two generating areas, i.e.; Area 1 and Area 2 both
comprising of two unequal thermal systems [30]. The
use of both the unequal systems in the interconnected
system can be considered as a simple model for
general power system around the world which
basically consists of thermal as a primary means for
the generation of power to cater the demands of the
ever-growing consumption. In order to understand
the control actions at the power plants for LFC,
taking the boiler–turbine–generator combination into
consideration of a normal thermal generating unit.
Most steam turbo generators (STG) now in service
are equipped with turbine speed governors.
Moreover, to make the approach more realistic both
the areas taken into consideration have different time
constants of different control parameters.
4:Results and discussion
As per the Literature and many other past
works it has been observed that many researchers
have considered to give the disturbance or to provide
a small step load perturbation (SLP) only in one area
so to optimize the gains of the controller. Similarly in
this work a step disturbance of 20% or 0.2 p.u. is
provided in Area 1 and thus the parameters of PID
controllers are tuned in accordance to it. The PID
controller is tuned with meta- heuristic, Differential
Evolution algorithms and the results are compared
against the past Lozi map-based chaotic optimization
algorithm (LCOA) based work [30].
Based on the PID parameters dynamic responses are
observed i.e., frequency deviation in Area 1(∆𝑓1),
frequency deviation in Area 2(∆𝑓2) and the tie line
power deviation (∆𝑃𝑡𝑖𝑒 ). Fig. 4, Fig. 5 and Fig. 6
shows the curves of variations of∆𝑓1, ∆𝑓2 and∆𝑃𝑡𝑖𝑒
respectively with optimal controller gains obtained
corresponding to the differential evolution algorithms
with SLP provided at Area-1.For similar SLP, the
variations in the output of the Governor is compared
in Fig.7. The settling time (𝑇𝑠) of ∆𝑓1, ∆𝑓2
and∆𝑃𝑡𝑖𝑒 corresponding to the differential evolution
algorithms used LCOA algorithm are depicted in
Table 2. Table3 mentions about the maximum peak
overshoot (𝑂𝑠ℎ ) of ∆𝑓1, ∆𝑓2 and∆𝑃𝑡𝑖𝑒 corresponding
to the DE algorithms and LCOAalgorithm.The details
of the maximum values of undershoot
(𝑈𝑠ℎ )aredenoted in Table 4.
Results show that the PID controllers‘ tuned
DE algorithm for an AGC system achieves better
dynamic performance as compared to LCOA
algorithms under normal condition as well as with a
step load perturbation of 20%.
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Table; 1
Algorithm PID controller gains of area 1 PID controller gains of area 2
Kp1 Ki1 Kd1 Kp2 Ki2 Kd2
DE 1.9138 1.8981 0.9063 0.8035 1.6833 0.8820
LCOA 0.939 0.7998 0.5636 0.5208 0.4775 0.7088
Fig. 4.Comparisons of dynamic responses Frequency deviations (∆𝑓1) of Area-1.
Fig. 5.Comparisons of dynamic responses Frequency deviations ( ∆𝑓2) of Area-2.
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Fig.6.Comparisons of dynamic responses Tie-line power deviations (∆𝑃𝑡𝑖𝑒 ).
Fig.7.Comparisons of dynamic responses of Governor output variations due to DE & LCOA.
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Fig.8.Comparisons of dynamic responses of Governor output variations due to DE & LCOA.
4.1 Analysis of settling time
Table 2
Settling times (0.02% band) corresponding DE and LCOA algorithms.
Settling time DE LCOA [30]
For frequency deviation in area1 3.9300 9.23
For frequency deviation in area2 6.2600 11.44
For tie line power deviation 19.0900 23.97
Based on Table 2, it can be made clear that the system tuned with DE algorithm has a settling time (𝑇𝑠)
for ∆𝑓1, ∆𝑓2 and∆𝑃𝑡𝑖𝑒 lesser than that of the system tuned with LCOA algorithms. The settling time for
frequency deviation in Area 1 for DE algorithm is about 57.4% lesser than that LCOA algorithm tuned system.
Similarly, the settling time for frequency deviation in Area 2 for DE algorithm is about 45.3% lesser than that
LCOA algorithm tuned system. For the deviation in tie line power the settling time for DE algorithm is 20%
lesser than that of LCOA tuned system[30]. Thus; in all respects and all cases the settling time of the dynamic
responses of the deviations of ∆𝑓1, ∆𝑓2 and∆𝑃𝑡𝑖𝑒 for the system tuned with DE algorithm are quite lesser in
comparison to the LCOA algorithm. In order to give a clear pictorial representation fig.9 shows the comparison
of the settling times for the DE and LCOA algorithms for the deviations in frequencies and tie line power
deviations.
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Fig. 9..Comparison of settling times for DE and LCOA algorithm.
4.2Analysis of maximum peak overshoot
Table 3
Maximum Peak overshoots corresponding to DE and LCOA algorithms (*10−4
).
Overshoot x10-4
DE LCOA [30]
For frequency deviation in area1 2.1377 2.5766
For frequency deviation in area2 0 0
For tie line power deviation 0 0
From Table 3, it can be seen that the frequency deviation in area 2 and the tie line power deviation do
not have any peak overshoot or positive value of peak. Mainly the peak overshoot is present for the frequency
deviation in Area 1 which has a lower value for the system tuned with DE algorithm. The maximum overshoots
for frequency deviation in Area 1 for DE algorithm is about 17% lesser than that of LCOAalgorithm tuned
system. To verify this analysis a column graph is depicted in fig 10.In this it is clear that the system tuned with
DE gives a lesser value of peak overshoot as compared to LCOA algorithms.
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Fig. 10. Comparison of peak overshoots for DE and LCOA algorithm.
4.3 Analysis of maximum peak undershoot
Table 4
Maximum Peak undershoots corresponding to DE and LCOA algorithms (*10−4
).
Undershoot x10-4
DE LCOA [30]
For frequency deviation in area1 -54.3827 -70.9301
For frequency deviation in area2 -6.7042 -11.0361
For tie line power deviation -75.8542 -134.2559
Table 4 specifies the values of maximum values of peak undershoot for the dynamic responses
corresponding to DE and LCOA algorithms. In all the responses i.e.,∆𝑓1, ∆𝑓2 and∆𝑃𝑡𝑖𝑒 the values of peak
undershoots are minimum in case of DE tuned system. The values obtained with the DE system are quite lesser
in compared to LCOA algorithms. The peak undershoot (𝑼 𝒔𝒉) for frequency deviation in Area 1 for DE
algorithm is about 23.3% lesser than that LCOA algorithm tuned system. Similarly,(𝑼 𝒔𝒉) for frequency
deviation in Area 2 for DE algorithm is about39.25% lesser than LCOA algorithm tuned system. For the
deviation in tie line power the undershoot(𝑼 𝒔𝒉) for DE algorithm is about43.5%lesser than that of LCOA tuned
system . Fig. 11 shows the comparisons for the values of undershoots for the system
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Fig.11. Comparison of peak undershoots for DE and LCOA algorithm.
From all the above analysis, facts and figures it is cleared that the DE tuning approach gives better dynamic
responses as compared to LCOA tuning algorithm Thus, keeping all other factors, analysis and comparison it
can be well said that this system when tuned by DE approach gives the best dynamic response.
5.VARIATION IN THE STEP LOAD PERTURBATION (SLP)
The nominal value of Step Load Perturbation used in this work is 20% or 0.2 p.u. The value of SLP is
varied in the ranges of ± 50% in steps of 20% in order to estimate the response of the interconnected power
system to different values and types of disturbances i.e., SLP. The settling time along with the values of
maximum peak overshoot and undershoot of the transient responses due to the variation of SLP applied in areas
1and as far as in all possible cases is given in Table 5,Table 6 and Table 7. From the analysis, it can be depicted
that the transient response of the curve takes a time of about 21sec,s to completely zero itself even if the
disturbance of 30% or 0.3 p.u. is provided at both the areas simultaneously. Fig 12-17 shows the curves for SLP
variation in area 1, the variation of the curves from the normal value of response when SLP is varied in the areas
1. Also, from Table 7and Table 8; it can be clearly analysed that the values of overshoots as well as undershoot
lies within a allowable range. Thus, it can be said that irrespective of variation in the values of SLP the system
does not show any undesirable results.
5.1.SLP Variation applied to Area 1
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Fig.12. Frequency Deviation in Area 1 due to SLP in area 1.
Fig.13. Frequency Deviation in Area 1 due to SLP in area 1.
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Fig.14. Frequency Deviation in Area 2 due to SLP in area 1.
Fig.15. Frequency Deviation in Area 2 due to SLP in area 1.
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Fig.16. Tie-line power deviation due to SLP in area 1.
Fig.17. Tie-line power deviation due to SLP in area 1.
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Table 5
Settling time (0.02% band) of frequency deviations and tie-line power changes due to variation in
parameters in the areas 1 in the range on -50% to +50% with SLP given only at Area 1.
Parameters % age deviation
sT for frequency deviation
in area1
sT for frequency
deviation in area2
sT for tie line power
deviation (in pu)
1gT -50% 4.7200 6.3700 19.2000
-30% 4.5900 6.3200 19.1600
-10% 3.8500 6.2800 19.1100
10% 3.9700 6.2400 19.0700
30% 4.0800 6.1900 19.0200
50% 5.8600 6.1800 18.9700
1tT -50% 4.9700 6.5200 19.3900
-30% 4.6900 6.4200 19.2700
-10% 2.2700 6.3100 19.1500
10% 4.1900 6.2100 19.0300
30% 4.5200 6.1300 18.900
50% 4.8200 5.9700 18.7700
Table 6
Maximum overshoots of frequency deviations and tie-line power changes due to variation in
parameters in areas1 in the range on -50% to +50% with SLP given only at Area 1.
Parameters % age deviation
shO for frequency
deviation in area1
shO for frequency
deviation in area2
shO for tie line power
deviation (in pu)
1gT -50% 2.0495 0 0
-30% 2.0312 0 0
-10% 2.0017 0 0
10% 4.1254 0 0
30% 9.2868 0 0
50% 14.7803 0 0
1tT -50% 2.0355 0 0
-30% 2.0165 0 0
-10% 1.9900 0 0
10% 5.2863 0 0
30% 12.3728 0 0
50% 19.4919 0 0
Table 7
Maximum undershoots of frequency deviations and tie-line power changes due to variation in parameters in
areas 1 in the range on -50% to +50% with SLP given only at Area 1.
Parameters % age deviation
shU for frequency
deviation in area1
shU for frequency
deviation in area2
shU for tie line power
deviation (in pu)
1gT -50% -45.0130 -5.9597 -70.3897
-30% -48.9729 -6.2091 -71.3591
-10% -56.0471 -6.5256 -73.9870
10% -56.0471 -6.8932 -77.9570
30% -59.1938 -7.2932 -82.5994
50% -62.1271 -7.7108 -87.5517
1tT -50% -40.5065 -5.1529 -66.6938
-30% -46.6195 -5.6688 -67.1584
-10% -51.9414 -6.3423 -71.7302
10% -56.7024 -7.0730 -80.1779
30% -61.0409 -7.8158 -89.0313
50% -65.0412 -8.5532 -97.9099
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5.2.ROBUSTNESS ANALYSIS
The system tuned by Differential Evolution algorithm was analysed for robustness by varying the
Time Constants of the control parameters of both the unequal areas in the steps of 25% in the range of -50% to
+50%.The variation of the parameters are basically carried out on the Governor and Turbine time constants of
both the thermal areas.
Table:8
Settling time (0.02% band) of frequency deviations and tie-line power changes due to variation in
parameters in both the areas in the range on -50% to +50% with SLP given only at Area 1& 2.
Parameters % age deviation
sT for frequency deviation
in area1
sT for frequency
deviation in area2
sT for tie line power
deviation (in pu)
1gT -50% 4.7200 6.3700 19.2000
-25% 4.5500 6.3100 19.1500
25% 4.0500 6.2000 19.0300
50% 5.8600 6.1800 18.9700
1tT -50% 4.9700 6.5200 19.3900
-25% 4.6400 6.3900 19.2400
25% 4.4400 6.1500 18.9300
50% 4.8200 5.9700 18.7700
2gT -50% 3.8200 6.2700 19.1100
-25% 3.8600 6.2800 19.1000
25% 4.0300 6.1600 19.0800
50% 4.1900 5.9500 19.0700
2tT -50% 3.7700 6.1600 19.1300
-25% 3.8300 6.2300 19.1100
25% 4.1200 6.1800 19.0700
50% 4.6600 6.1800 19.0500
Table:9
Maximum overshoots of frequency deviations and tie-line power changes due to variation in parameters in both
the areas in the range on -50% to +50% with SLP given only at Area 1& 2.
Parameters % age deviation
shO for frequency
deviation in area1
shO for frequency
deviation in area2
shO for tie line
power deviation (in
pu)
1gT -50% 2.0495 0 0
-25% 2.0247 0 0
25% 7.9526 0 0
50% 14.7803 0 0
1tT -50% 2.0355 0 0
-25% 2.0159 0 0
25% 10.5942 0 0
50% 19.4919 0 0
2gT -50% 2.0850 0 0
-25% 2.1073 0 0
25% 2.1725 0 0
50% 2.2061 0 0
2tT -50% 2.0702 0 0
-25% 2.0990 0 0
25% 2.1716 0 0
50% 2.1924 0 0
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Table:10
Maximum undershoots of frequency deviations and tie-line power changes due to variation in parameters in
both the areas in the range on -50% to +50% with SLP given only at Area 1& 2.
Parameters % age deviation
shU for frequency
deviation in area1
shU for frequency
deviation in area2
shU for tie line power
deviation (in pu)
1gT -50% -45.0130 -5.9597 -70.3897
-25% -49.9150 -6.2824 -71.8483
25% -58.4228 -7.1911 -81.3999
50% -62.1271 -7.7108 -87.5517
1tT -50% -40.5065 -5.1529 -66.6938
-25% -48.0125 -5.8273 -67.1849
25% -59.9892 -7.6304 -86.8079
50% -65.0412 -8.5532 -97.9099
2gT -50% -54.3831 -5.9824 -75.9023
-25% -54.3829 -6.3570 -75.8557
25% -54.3826 -7.0123 -75.8750
50% -54.3826 -7.2844 -75.9064
2tT -50% -54.3832 -5.7041 -75.8746
-25% -54.3829 -6.2529 -75.8297
25% -54.3826 - -7.0793 -75.9001
50% -54.3826 -7.3988 -75.9507
Therefore, by considering all above analysis
the system it can beseen that the rate of deviations is
quite less such that the system tuned with DE
algorithm can be said as a robust one.
Conclusion
The DE optimization algorithm is used in
this paper to obtained the optimum gains of the PID
controller for the LFC problem. At first, the
optimization algorithm is explained in detail. Then, a
two areapower system is investigated. The simulation
results emphasise the effectiveness of the DE. It is
found that the frequency after a disturbance have
minimum overshoot and oscillation. Also, simulation
results demonstrated that the PID controllers capable
to guarantee the robust stability and performance
under a wide range of uncertaintiesand load
changes.A comparative study is carried out between
the DE & LCOA algorithms. The comparison results
show that the DE PID can provide a better
performance than LCOA PID.
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Appendix
The system parameters are as follows (frequency=60
Hz, MVA base =1000) [2]:
Area 1: H=5, D=0.6, Tg=0.2, Tt=o.5, =0.05,B1=20.6,
Area 2: H=4, D=0.9, Tg=0.3, Tt=0.6, R=0.0625,
B2=16.9,