the different generating units available in each area. However, decreasing the frequency deviation to zero is out of the scope of the primary frequency control and it is realized by means of the secondary frequency control. In fact, to reduce the steady state frequency error to zero and also maintain AC tie-lines power at the scheduled values, a controller is needed to be used for each power plant. As shown in Fig. 1 , the area control error (ACE) is used as the input of these controllers which are responsible for changing the position of governor in order to regulate the mechanical power and consequently retain the power balance such that the frequency error and AC tie-line power error are both restored to zero. At the last frequency control stage, tertiary control which usually operates manually, activates tertiary control reserves to free up the secondary control reserves for the next disturbances. Generally, a turbine is used in power plants to convert the natural energy of steam, water, gas, etc. to the mechanical power. The amount of the mechanical power is controlled by changing the position of the governor valve. The models implemented for the power plants in this study are described as follows ( Mohanty et al., 2014; Challa and Rao, 2010; Ibraheem et al., 2014 ). The reheat thermal unit turbine is modeled by a second order transfer function:
Increasing demand for electrical power, complexity and nonlinearity of electrical power systems as well as the need for supply of electricity with high stability and reliability emphasize the importance of the loadfrequency control (LFC) in power systems. Furthermore, controlling the system in emergency situations and sudden load changes such as short-term interruption is necessary to prevent frequency deviations. Conventional controllers such as PI and PID are designed to best operate under specific operating conditions, while their control performance is reduced if operating conditions vary under sudden load changes; adjusting parameters of conventional controllers might be improper for the new operating points. Therefore, for controlling frequency and dynamic performance of a generator in a wide range of operating conditions, adaptivecontrollers are suitable. The use of a fuzzy controller to regulate the voltage and frequency of the generator has become more popular during recent years [ 1 – 5 ]. Firstly, they are independent of the system model and do not allow the complexities of the dynamic system model to be included in the design process. Secondly, the fuzzy controllers operation depends on the human experts which have made this type of controller very popular in the industry [ 6 – 10 ]. Since the powersystem has uncertain nonlinear dynamics, the controllers with constant gain do not perform well under variable and uncertain loading conditions. Therefore, some methods such as artificial neural network, fuzzy logic and fuzzy neural networks have been used for frequency
In literature, control strategies based on conventional and fuzzy logic controller are proposed . Several authors suggest variable-structure systems, various adaptive control techniques and Riccati equation approach for load a frequency controller design [6, 7]. There are many studies about different control strategies having advantages and disadvantages [1, 2, 5, 8-10]. In Reference , a loadfrequency control using a conventional PID controller is applied and it is emphasized that the controller performance is better than others. However, if a powersystem structure has nonlinear dynamics and parts, the system operating point varies and conventional controllers needing system model must not be used. In Reference , a modified dynamic neural networks controller is proposed. It is determined that the proposed controller offers better performance than conventional neural network controller. In Reference , for a single areasystem and two areas interconnected power systems, artificial intelligence techniques are purposed for the automatic generation control and the comparison is performed between intelligent controllers and the conventional PI and PID controllers. In Reference , a robust decentralized control strategy is used for Loadfrequency control for four areapower systems to obtain robust stability and better performances. In References [1, 8], powersystemloadfrequency control is realized by fuzzy logic controller.
The successful operation of interconnected power systems requires the matching of total generation with total load demand and associated system losses. With time, the operating point of a powersystem changes, and hence systems may experience deviations in nominal systemfrequency and scheduled power exchanges to other area, which may yield undesirable effects. To maintain loadfrequency with in the desirable limits various controlling strategies like classical methods, adaptive and variable structure methods, robust control approaches, intelligent techniques and digital control schemes are available. Various factors affect the systemfrequency like generation rate constraints, speed governing techniques, load disturbances [1-2]. Loadfrequency control is a wide problem so, to provide better controlling aspects the system fragmented as a control areas. The control areas have group of generating stations or generating units. In such a control area the power generating plants are alike or different. Generally same plants are grouped for easy controlling. Different plants can be grouped in a single area. The control area maintain the frequency with in the permissible limits by generating required amount of power to meet power demand at every instant of time. The control areas are inter connected with the tie-lines. To exchange power from one area to another area, to operate generating stations most economical manner, to enhancepowersystem stability. The power exchange between controlling areas are based on their contracts and other power generation constraints [3-5].
 J. Nanda, J.S. Kakkarum, Automatic generation control with fuzzy logic c controllers considering generation constraints, in: Proceeding of 6th InternationalConference on Advances in PowerSystem Control Operation and Managements, Hong Kong, November 2003.
The simplified SFR model of the system is as shown in Fig.2, where all parameters are in per unit on an MVA base equal to the total rating of all generating units. The model behaviour depends on the following factors; the gain, Km , the damping factor ,D, the inertia constant H, the average reheat time constant, TR , and the high pressure power fraction of the reheat turbines, FH. The range of TR is about 6 to 12 seconds and tends to dominate the response of the largest fraction of turbine power output. Therefore we ignore all the time constants. The value of H is on the order of 3 to 6 seconds for a typical large unit and is always multiplied by two, which increases its effect.
Abstract: This paper deals with the automatic generation control of inter connected multiarea grid network. The first purpose of the AGC is to balance the full system generation against systemload and losses so the specified frequency and power interchange with neighboring systems are maintained. Any pair between generation and demand causes the systemfrequency to deviate from regular worth. So high frequency deviation could result in system collapse. This necessitates associate correct and quick acting controller to take care of constant nominal frequency. The limitations of the conventional controls are slow and lack of efficiency in handling system non- linearity. This leads to develop a control technique for AGC. In this paper both conventional PI and FUZZY controller approach of automatic generation control has been examined. Fuzzy based AGC has been used for all optimization purposes. Systemperformance has been evaluated at various disturbances such as, load disturbances, grid disturbances and both load and grid disturbances. Various responses due to conventional and proposed FUZZY based AGC controllers have been compared at load disturbances, grid disturbances and both load and grid disturbances.
In power systems having thermal plants, power generation can be changed only at a specified maximum rate. The generation rate for reheat turbines is very low. If these constrains are not considered, the system is likely to chase large momentary disturbances. This results in undue wear and tear of the controller. It is, therefore, extremely important to understand the influence of Generation Rate Constraint (GRC) in the AGC problem. The GRCs result in larger deviations in ACEs as the rate at which generation can change in the area is constrained by the limits imposed. Therefore, the duration for which power needs to be imported increases considerably as compared to the case where generation rate is not constrained.
The main objective of loadfrequency control & automatic generation control is to maintain the proper balance between the system generations against load & losses, so that the desired frequency & power flow interchange with the neighboring system is maintained. If any mismatch occurs between generation & load, then it may result in deviation of the systemfrequency from its nominal frequency (50Heartz).Because of increasing & decreasing load, the real power & reactive power balance is harmed, thus frequency & voltage get deviated from nominal value.Thus high frequency deviation may results in system collapse.This necessitates proper designing of an controller in order to give an accurate & fast response to maintain the system parameters at nominal value.A PID controller is used for the design & analysis of the proposed model .In this paper the PSO algorithm used to determine the optimal PID controller parameters for LFC in a two areapowersystem.The PSO algorithm is developed to obtain suitable control parameters to achieve the optimum performance. A unique objective function is also formulated considering the transient specifications. This method had superior features like, stable convergence characteristics, easy implementation & good computational efficiency. The simulation results demonstrate the effectiveness of the designed system in terms of reduced settling time, overshoot & oscillations.
In the event of an interconnected powersystem, any small sudden load change in any of the areas causes the variation of the frequencies of each and every area and likewise at that place is a fluctuation of power in tie line. The primary objective of LoadFrequency Control (LFC) area is to maintain the right frequency, besides the de- sired power output (megawatt) in the interconnected powersystem and to monitor the change in tie line power between control areas. Thus, an LFC scheme incorporates an appropriate control system for an interconnected powersystem. It is heaving the capability to bring the frequencies of each area and the tie line powers back to original setpoint values or very nearer to setpoint values effectively after the load change because of the of conventional controllers. Nevertheless, the conventional controllers are having some demerits like; they are very sluggish in functioning. They do not care about the inherent nonlinearities of different powersystem compo- nents; it is very hard to decide the gain of the integrator setting according to changes in the operating point. The artificial intelligence control system delivers many advantages over the conventional integral controller. They are a lot faster than integrated controllers, and besides they give better stability response than integral controllers. Several control strategies for LFC of power systems have been proposed to maintain the frequency and tie-line power flow at their scheduled power values during normal and distributed conditions. Classical controllers are offered for LFC of powersystem . Also, various soft computing algorithms based controllers such as GA, PSO, Craziness PSO, Bacterial Foraging Optimization, Differential Evaluation, etc., are applied and found the superiority of the powersystem -.
The classical scheme for decentralized state feedback control is based on the assumption that all states of the subsystems are available [10-11]. In large-scale systems, especially multi-areapower systems, however, this assumption is not usually realistic. Therefore, a state estimator has to be designed. This estimator exploits the model of each subsystem and its actual inputs and outputs to produce a good estimation of unknown states of the system. Interactions between subsystems are another uncertainties that make the complexity of controller design in large-scale systems. In the classical scheme for decentralized control, the interactions are unknown for the local observer or controller. Therefore the reconstruction of interactions plays an important role in the local observers and controllers to achieve less conservative performance. The main idea of this paper is to introduce a scheme to estimate the interactions in a decentralized approach. The decentralized observation problem was first considered in . Necessary and sufficient conditions on the subsystems were derived in  under which the observers could be designed. In  an output-decentralization and stabilization scheme were proposed, which could be directly used to construct asymptotic state estimators for linear large-scale systems. The problem of robustness of a Luenberger observer applied to a given large-scale system was addressed in .
Abstract—The characteristics output of solar panels are nonlinear and change with the environmental factors, so we need a controller named maximum powerpoint tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this study we present the two most popular controllers refers to traditional approach based on the perturbation & observation (P&O) methods and incremental conductance (INC). Then we explore a new intelligent controller based on fuzzy logic. The obtained results under various conditions of functioning have shown the good tracking and rapid response to change in different meteorological conditions of intelligent controller compare with the conventional one.
Big power systems have regions representing coherent generators called control areas. These different areas are interrelated through tie- lines. Inter areapower exchange and abnormal condition power transfer is the main objective of these tie lines. Abnormal condition include the outage of generator, mismatch in frequency that leads to blackout. Area control error represents the error in frequency to be corrected. Conventional system uses the proportional and integral action that operate the turbine governor so that the generated power follow the load variations.
Coon’s , Astrom and Hagglund and many other traditional techniques. Although new methods are proposed for tuning the PID controller, their usage is limited due to complexities arising at the time of implementation. Since, Particle Swarm Optimization algorithm is an optimization method that finds the best parameters for controller in the uncertainty area of controller parameters and obtained controller is an optimal controller, it has been used in almost all sectors of industry and science. One of them is the loadfrequency control . In this study, it is used to determine the parameters of a PID controller according to the system dynamics
The main thing is to be done in the closed loop system with PID controller is to elect the mathematical values of Proportional, Integral and Derivative deciding parameters known as tuning of controller. There are many tuning methods are available to the PID controllers like manual tuning which requires experienced workforce, Ziegler- Nichols method which is said to an aggressive method and online tuning process, Cohen-coon method which is an offline method and only first order process can be determined nicely by it etc., In these methods it is very urgent to get exact transfer function of the system only than it is possible to practice the traditional methods for tuning the PID controller. But in real-world it is very challenging to attain the exact process controlling using traditional tuning methods to tune the PID controllers due to the persistence of high ambiguity in the modelling of practical systems. A precise type of mathematical model is required like first order plus dead time for tuning the process model by traditional methods. Soft computing methods can be the good solution for the problem of precise tuning as these techniques has superiority in solving the complicated and lengthy calculations and even those problems that are mathematically untrack able. The few examples of soft computing methods are Neural Network, Fuzzy Logic, Particle Swarm Optimization etc., But recent JAYA optimization methods has some aids over others. To understand it
In interconnected powersystemloadfrequency control has been used extensively. This study presents an application of a fuzzy gain scheduled proportional and integral (FGPI) controller for load-frequencyarea electrical interconnected powersystem. The main aim is to design a FGPI controller that can ensure good performance. The paper present analysis on dynamic performance of LoadFrequency Control (LFC) of three area interconnected thermal reheat powersystem by the use of Fuzzy telligence. The fuzzy rules are developed to ensure there is minimum frequency deviation occur when load is changed. The proposed controller limits the frequency deviations effectively as compared to conventional controller. The results has been verified
Understanding of the frequency dynamics of power systems is essential for several issues including frequency control design and estimation of the system inertia through measurements. Consider a powersystem with several conventional synchronous generators. If this powersystem is subjected to a disturbance such as a sudden outage of a generator, then dynamical changes in the system start instantaneously. These dynamics are mainly caused by the instantaneous power imbalance between the instantaneous generation and consumption of electric power. Consequently, the remaining synchronous generators are subjected to acceleration and deceleration effects. Due to the strong connection between the mechanical and electrical frequency, the changes in the rotor speed results in changes in the electrical frequency. Eventually, the power balance and the frequency are restored in the system has sufficient capacity to compensate the lost generation. This control action is called primary Automatic LoadFrequency Control (ALFC). In this direction, the load-frequency control (LFC) is one of important control problems in concerning the integration of wind power turbine in a multi-areapowersystem [2, 8, 18, 20, 21].The increasing need for electrical energy in the twenty-first century, as well as limited fossil fuel reserves, very high transportation and fuel cost and the increasing concerns with environmental issues for the reduction of carbon dioxide (CO2) and other greenhouse gasses, causes fast development in the area of renewable energy sources (RESs). One of the adaptive and nonlinear intelligent control techniques that can be effectively applicable in the frequency control design is reinforcement
Methods to mitigate voltage swings and current unbalances in use currently ar completely different from those of the past. Originally, separate devices were accustomed attain management of voltage and to produce reactive power for current reconciliation or power issue correction. one in all the well-known technologies to handle compensation of reactive power is that the synchronous compensator, that may be a electric motor running on no load and solely supply reactive current so as to manage voltage. though the machine itself is strong and capable of handling overcurrent, its dynamic response is slow and its value is high, and thanks to its rotating elements, maintenance necessities ar expensive . differently to compensate reactive power may be a automatically switched bank of capacitors however these also are too slow. associate degree recent technique for dominant power generation embrace the automated Generation management (AGC) that consists of observance the distinction between the demand and also the generated power employing a speed governor and different signals to come up with feedback and judge if the rotary engine desires additional or less steam/water to drive the torsion of the ability generator. Another device for voltage management is that the Tap-Changer management, that works as a electrical device with completely different settings which will be adjusted on-line or offline looking on the kind to regulate voltage and consequently power flow. additionally, a Phase-Shifting electrical device are often used for power flow management. These comparatively “old” devices are often adjusted to regulate the flow of power in terms of section, magnitude or each . a number of the disadvantages of those recent systems embrace their restricted vary, restricted controllability, their speed of operation, that is usually slow, and that they gift wear and maintenance issues. Newer technologies to handle voltage management and reactive power compensation have appeared as a consequence of latest developments within the semiconductor sector within the last 2 or 3 decades. These arrangements that mix power natural philosophy and power systems ar referred to as versatile AC Transmission Systems (FACTS).
The second category includes the controllers that use on- line learning. Chen has investigated on-line learning for adaptive control, although his method is only applicable to single input, single output linearized systems. It is shown that the learning process makes this controller an adaptive one. On-line learning has been successfully used for underwater vehicle control as reported. The proposed learning algorithm and the network architecture provides stable and accurate tracking performance. For the on-line learning method, the mathematical formulation of the process under the control is needed.
Conferring to Ref. , conventional PID control systems shan’t extent to great result. A gain forecasting controller is used . Gain forecasting is a controller model method used in the non- linear systems. In it, the control variables is changed promptly, as variables valuation isn’t needed. It is at ease to catch than programmed change or variation of the controller parameters. But the transient response may be uneven because of unexpectedness in the system parameters. Also, precise linear time invariant prototypes at variable operating marks can’t be gained . Fuzzy methods were used for control of load-frequency in power systems by Chang and Fu  and Akalın and coworkers . Both established some fuzzy rules for the P and PI gains discretely. In this paper, the system result is enhanced by the gain rules, which are selected alike. By the proposed controller, the overshoots and settling time are efficient than the other controllers.