AutomaticGenerationControl (AGC) is used for regulating the electrical power supply in two-areapowersystem and changing the system frequency and tie-line load. The performance of AGC has to be tuned properly so that the performance can be optimized. In this project, modified evolutionary particle swarm optimisation (MEPSO) -time varying acceleration coefficient (TVAC) is proposed for an AGC of two-areapowersystem to optimize its performance by tuning parameters of the PIDcontrollers. Comparison of the performance by using the proposed algorithm and other algorithms was made to identify which algorithm is better in controlling the performance of the AGC. The AGC in two-areapowersystem was constructed and simulated by using MATLAB R2017b software. From the simulation results, it was found that with the same number of PIDcontrollers, the performance of AGC optimised by usingMEPSO- TVAC algorithm is better in terms of overshoot and fitness value than using EPSO and PSO algorithms. Also, usingMEPSO-TVAC algorithm, the performance of AGC by usingtwoPIDcontrollers is better in terms of rise time and settling time than using one PID controller. Therefore, via implementation of optimisation method, the performance of AGC can be improved by varying the parameters of PID controller.
In a generating unit load is supplied by an isolated system. The change in load brings about the change in speed and magnitude of frequency.Frequency change with load is represented by the droop characteristics of the governor.When change in the system occurs, mainly work of the supplementary control reset of frequency to nominal value. This can be accomplished by adding a reset control to the governor. Reset control action means frequency error to be zero by supplementary controller. So, different controllers use in powersystem and analysis, which controllers (I, PI, PID and Fuzzy PID) give better performance and stability of the system .
Nowadays, electricity generation is very important because of its increasing necessity. The dynamic behavior of the system depends on disturbances and on changes in the operating point. The quality of the generated electricity in power stations is depending on the system output, it has to be of constant frequency and should maintain the scheduled power . Therefore, Load Frequency Control (LFC) is very important for powersystem in order to supply reliable and quality electric power. The conventional controllers such as PI, PID can give control action for one particular operating condition, where as in real situation the parameters change from time to time. So it is difficult to arrange the required gains to achieve zero frequency deviation. Hence there is a necessity to provide automatic correction. However research is going on and several methods are developed to overcome this difficulty . A number of control techniques have been employed in the design of load frequency controllers in order to achieve better dynamic performance. Comparing the various types of load frequency controllers, the most common and widely employed is the conventional proportional (PI) controller. Conventional controller is simple for implementation but gives large frequency deviation. Most of state feedback controllers based on linear optimal control theory have been proposed to achieve better performance. Fixed gain controllers are designed at nominal operating conditions and fail to provide best controlperformance over a wide range of operating conditions. So to keep the systemperformance near to its optimum it is desirable to track the operating conditions and use updated parameters to compute the control. Adaptive controllers with self adjusting gains settings have been proposed for LFC to achieve the function compared to PI Controller.
ABSTRACT: The AutomaticGenerationControl is the main control in powersystem to match the generation with demand. Powersystem size and the type of load, makes the AGC more important. In this work Hydro plants and thermal plants are taken into consideration. Two models are developed using MATLAB/SIMULINK. Single area thermal powersystem is one. Similarly, thermal plant and hydro plant are considered as separate areas and they are connected with tie-line to form twoarea hydro thermal system. When the systems are subjected to load change of 1%, there is variation in frequency and tie-line power which can be reduced by using secondary controller. PID controller is used in this paper as secondary controller. The different controller parameters for single area and twoareapowersystem are tuned by Z–N method. The concept of SMES unit applied to AGC has also been made. Apart from the secondary controller, Superconducting Magnetic Energy Storage device is used for frequency control in twoareapowersystem. The results are compared to determine the performance of the system with SMES and different controllersusing SIMULINK.
implementation of fuzzy logic controller to solve automaticpowergenerationcontrol problem in two-area hydrothermal powersystem. The AGC performance is compared with intelligent fuzzy logic control with conventional controllers like PI, PID and PR under step load Disturbance. The conventional controller Gains for PI and PID(kp, ki,kd) is obtained by analyzing the transfer function using Ziegler Nicholas Methods. The intelligent fuzzy controller simulation is run to observe the performance of the system During 1% step load disturbance. The simulation result show that the fuzzy controller is better than the conventional PI, PID and PR controllers in terms of Better Dynamic response and steady error.
This work presents an efficient method based on a modified fuzzy PI control with parallel fuzzy PD control for automaticgenerationcontrol (AGC) of a two-areapowersystem. This describes the control schemes required to operate the two-areapowersystem in the steady state. The model of a two-areapowersystem is established using the equations describing dynamic behaviour of a two-areapowersystem and control schemes in Matlab-Simulink program respectively. The performances of different controllers for variable inputs are compared for the same twoareapowersystem. The dynamic response of the load frequency control problem are studied using MATLAB simulink software. The results indicate that the proposed Fuzzy logic controller exhibits better performance.
Many investigations in of powersystem have been reported and a number of control schemes like Proportional and Integral (PI), Proportional, Integral and Derivative (PID) and optimal control have been proposed to achieve improved performance [1 - 3]. The conventional method exhibits relatively poor dynamic performance as evidenced by large overshoot and transient frequency oscillations. These conventional fixed gain controllers based on classical control theories in literature are insufficient because of change in operating points during a daily cycle.[5,6]. Several new optimization techniques like Genetic Algorithm (GA), PSO, Ant Colony Optimization (ACO), Simulated Annealing (SA) and Bacterial Foraging have emerged in the past two decades that mimic biological evolution, or the way biological entities communicate in nature.. Due its high potential for global optimization, GA has received great attention in controlsystem such as the search of optimal PID controller parameters. The natural genetic operations would still result in enormous computational efforts. The premature convergence of GA degrades its performance and reduces its search capability. Particle swarm optimization (PSO), first introduced by Kennedy and Eberhart, is one of the modern heuristics algorithms. It was developed through simulation of a simplified social system, and has been found to be robust in solving continuous non-linear optimization problems.
From the investigations have been carried out to design an optimal automaticgeneration controller to enhance the stability and preserve the security of the system . Dynamic performance of all conventional classical controllers  like Integral, P, PI, PIDcontrollers and soft controller(Fuzzy-Tuned Controller ) . A more recent and powerful evolutionary computational technique Particle Swarm Optimization (PSO) is used here for simultaneous optimization of several parameters for both primary and secondary control loops of the governor with different types of classical controller and soft controllers. This classical controller and soft controller are tried and their performance compared so as to assess the best controller. Sensitivity analysis has been carried out too for the best controller.
In order to improve the performance and stability of these control loops, proportional- integral-derivative (PID) controllers are normally used. But these fixed gain controllers fail to perform under varying load conditions and hence provide poor dynamic characteristics with a large settling time, overshoot and oscillations. In order to achieve a better dynamic performance, system stability and sustainable utilization of generating systems, PID gains must be well tuned -. Two main variables that change during transient power load are area frequency and tie line power interchanges. The concept of Load frequency control (LFC) is directly related to the aforementioned variables since the task is to minimize these variations. The key factor is to maintain the steady state deviations at zero. In this respect, effective measures like Active Disturbance Rejection Control (ADRC) have been developed that allow practical control .
(lag-lead) controller. The comparison is carried out under two different kinds of operating points: (i). Total real power of load P = 0.7 p.u, Total reactive power of load Q = 0.8 p.u, Terminal voltage Vt = 1.05 p.u, and (ii). Total real power of load P = 0.8 p.u, Total reactive power of load Q = 0.9 p.u, Terminal voltage Vt = 1.05 p.u) and power disturbances. Here, for illustration, the first set of operating point is considered. With conventional SSSC based damping (lag-lead) controllers, one installed between bus 5 and bus 7 and another between bus 6 and bus 9 respectively, the system response curves due to a power (or torque) disturbance of ǻT m = 0.01 p.u and disturbance clearing time of 50 seconds are shown in Figures 5-10. From these Figures, it is observed that the system damping in Area 1 and Area 2 is poor and the system is highly oscillatory. Therefore, it is necessary to install ANFIS based SSSC controllers in order to have good damping performance. The fuzzy rules are trained using ANFIS technology.
Abstract: In this paper, an adaptive fuzzy logic control for automaticgenerationcontrol of interconnected twoarea Hydro-Thermal Systemusing This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a twoarea interconnected powersystem. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller. The study was designed for a twoarea interconnected powersystem.
In this paper result, the effectiveness of the TLBO algorithm has been tested for AutomaticGenerationControl (AGC) of an interconnected powersystem. We used linear and nonlinear model of twoarea non-reheat thermal system equipped with Proportional-Integral derivative (PID) controller is considered initially for the design and analysis purpose. We use, a conventional Integral Time multiply Absolute Error (ITAE) based objective function is considered and the performance of TLBO algorithm is compared with hBFOA-PSO and GA. The contrast of Teaching Learning –Based Optimization (TLBO) is employed to look for optimum controller parameters to reduce the time domain objective feature. By means of contrast with the GA PID, hBFOA-PSO PID method and TLBO PID, the effectiveness of the proposed TLBO PID is verified over different running situations, and device parameters variations
Abstract- This paper presents the GRC & AGC techniques which are useful for the study for the methods of artificial intelligence for the automaticgenerationcontrol of interconnected power systems. In the given paper, a control line of track is established for interconnected three areapowersystemusinggeneration rate constraints (GRC) &Artificial Neural Network (ANN). The ANN controller is simulated using MATLAB/SIMULINK technique. The waveforms of both (i.e. with & without) controllers are compared with 1% step load conditions.
If the generated rates are included in the state vector, the system order will be altered. Instead of augmenting them, while solving the state equations, it may be verified at each step if the GRCs are violated. Another way of considering the GRCs for all the areas is to add limiters to governors as shown in figure below, i.e. the maximum rate of valve opening or closing is restricted by limiters.
When there is sudden load change in any interconnected area, the frequency and tie-line power are affected. It is essential to minimize these errors for economic and reliable operation of powersystem. So the integral control- ler is studied here to meet the stated demand. Integral square error and integral time absolute error has been con- sidered as performance indices in this study. Controller designed here minimizes the change in frequency in all the three area. Change in tie line power should also be minimized because if there is any load change in any area, then the extra power required can be got from the other area but for this tie line should be capable of transmit- ting this extra power but as the agreement done by the systems tie-line has a pre specified capacity, so the con- trol action should take place in area where the change has occurred to keep the change in tie-line power mini- mum. So for this change in tie-line power is also considered in performance index. It has been found out that integral time square error (ITAE), if utilized as performance index rather than Integral of squared error, produc- es fast optimized value of integral gain. It is further observed that reduction of R (speed droop) reduces fre- quency error. With high R, low damping of oscillations is produced & low R, high damping of oscillations is produced. Use of subcritical gain setting gives sluggish non oscillatory response of control loop which means integral of Δ f (t) and time error is relatively large. If all parameters are considered same, then freq. Drop will be 1/3 rd of that which would be experience if the control areas were operating alone.
ABSTRACT: This paper presents that the use of electricity and its need has been increased. Thus, in order to ensure the consumers with reliable and continuous supply during the increase of demand, need of interconnection between the different generating areas are required and due to this reason the concept of AutomaticGenerationControl is taken into consideration which is then used for adjusting the output power of various generators at different power plants in an electric powersystem. A controller based on Particle Swarm Optimization has been designed in order to keep the frequency deviations due to change in load in either of the area at a minimum level i.e. by keeping the frequency at its set value of 5o Hz and power transfer through tie-line uniform.
This paper is intended in investigating the AutomaticGenerationControl (AGC) problem of a de- regulated powersystemusing Adaptive Neuro Fuzzy controller. Here, three areacontrol structure of Hydro-Thermal generation has been considered for different contracted scenarios under di- verse operating conditions with non-linearities such as Generation Rate Constraint (GRC) and Backlash. In each controlarea, the effects of the feasible contracts are treated as a set of new input signals in a modified traditional dynamical model. The key benefit of this strategy is its high in- sensitivity to large load changes and disturbances in the presence of plant parameter discrepancy and system nonlinearities. This newly developed scheme leads to a flexible controller with a sim- ple structure that is easy to realize and consequently it can be constructive for the real world powersystem. The results of the proposed controller are evaluated with the Hybrid Particle Swarm Optimisation (HCPSO), Real Coded Genetic Algorithm (RCGA) and Artificial Neural Network (ANN) controllers to illustrate its robustness.
L. A. Zadeh presented the first paper on fuzzy set theory in 1965. Since then, a new language was developed to describe the fuzzy properties of reality, which are very difficult and sometime even impossible to be described using conventional methods. Fuzzy set theory has been widely used in the controlarea with some application to dc-to-dc converter system. A simple fuzzy logic control is built up by a group of rules based on the human knowledge of system behavior. Matlab/Simulink simulation model is built to study the dynamic behavior of dc-to-dc converter and performance of proposed controllers. Furthermore, design of fuzzy logic controller can provide desirable both small signal and large signal dynamic perfor mance at same time, which is not possible with linear control technique. Thus, fuzzy logic controller has been potential ability to improve the robustness of dc-to-dc converters. The basic scheme of a fuzzy logic controller is shown in Fig 5 and consists of four principal components such as: a fuzzification interface, which converts input data into suitable linguistic values; a knowledge base, which consists of a data base with the necessary linguistic definitions and the control rule set; a decision-making logic which, simulating a human decision process, infer the fuzzy control action from the knowledge of the control rules and linguistic variable definitions; a de-fuzzification interface which yields non fuzzy control action from an inferred fuzzy control action .
provide high level of power quality while maintaining both voltage and frequency within tolerance limits. Subjected to any disturbance, As a result the deviation occurs about the operating point such as nominal system frequency, scheduled power exchange to the other areas which is undesirable. The LFC issues have been tackled with by the various researchers in different time through AGC regulator, excitation controller design and controlperformance with respect to parameter variation/uncertainties and different load characteristics.Several control strategy such as integral control, optimal control, variable control have been used to control the frequency and to maintain the scheduled regulation between the interconnected areas. One major advantage of integral controller is that it reduces the steady state error to zero, but do not perform well under varying operating conditions and exhibits poor dynamic performance. An optimization of feedback controller and Proportional-Integral-Derivative (PID) controller is focused in .Due to non-linearity in various segregated components and design of the controller. The further research in LFC has been carried out by use of various soft computing techniques. Artificial neural network controller (ANN) is implemented in paper  which offers many benefits in the area of nonlinear control problems, particularly when the system is operating over the nonlinear operating range.