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:

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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 **load** **frequency** 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, **adaptive** **controllers** 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 **power** **system** 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**

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In literature, control strategies based on conventional and fuzzy logic controller are proposed [5]. 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 [9], a **load** **frequency** control using a conventional PID controller is applied and it is emphasized that the controller **performance** is better than others. However, if a **power** **system** structure has nonlinear dynamics and parts, the **system** operating **point** varies and conventional **controllers** needing **system** model must not be used. In Reference [5], 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 [2], for a single **area** **system** 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 [10], a robust decentralized control strategy is used for **Load** **frequency** control for four **area** **power** systems to obtain robust stability and better performances. In References [1, 8], **power** **system** **load** **frequency** control is realized by fuzzy logic controller.

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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 **power** **system** changes, and hence systems may experience deviations in nominal **system** **frequency** and scheduled **power** exchanges to other **area**, which may yield undesirable effects. To maintain **load** **frequency** 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 **system** **frequency** like generation rate constraints, speed governing techniques, **load** disturbances [1-2]. **Load** **frequency** 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 **enhance** **power** **system** stability. The **power** exchange between controlling areas are based on their contracts and other **power** generation constraints [3-5].

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[10] J. Nanda, J.S. Kakkarum, Automatic generation control with fuzzy logic c **controllers** considering generation constraints, in: Proceeding of 6th InternationalConference on Advances in **Power** **System** 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.

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Abstract: This paper deals with the automatic generation control of inter connected **multi** **area** grid network. The first purpose of the AGC is to balance the full **system** generation against **system** **load** and losses so the specified **frequency** and **power** interchange with neighboring systems are maintained. Any pair between generation and demand causes the **system** **frequency** 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. **System** **performance** 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.

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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.

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The main objective of **load** **frequency** 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 **system** **frequency** from its nominal **frequency** (50Heartz)[1].Because of increasing & decreasing **load**, the real **power** & reactive **power** balance is harmed, thus **frequency** & voltage get deviated from nominal value[2].Thus high **frequency** deviation may results in **system** collapse[1].This necessitates proper designing of an controller in order to give an accurate & fast response to maintain the **system** parameters at nominal value[2].A PID controller is used for the design & analysis of the proposed model [5].In this paper the PSO algorithm used to determine the optimal PID controller parameters for LFC in a two **area** **power** **system**[3].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[5]. 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[2].

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In the event of an interconnected **power** **system**, 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 **Load** **Frequency** Control (LFC) **area** is to maintain the right **frequency**, besides the de- sired **power** output (megawatt) in the interconnected **power** **system** and to monitor the change in tie line **power** between control areas. Thus, an LFC scheme incorporates an appropriate control **system** for an interconnected **power** **system**. 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 **set** **point** 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 **power** **system** 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 **power** **system** [7]. 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 **power** **system** [8]-[23].

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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**-**area** **power** 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 [12]. Necessary and sufficient conditions on the subsystems were derived in [13] under which the observers could be designed. In [14] 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 [15].

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Abstract—The characteristics output of solar panels are nonlinear and change with the environmental factors, so we need a controller named maximum **power** **point** 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.

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Big **power** systems have regions representing coherent generators called control areas. These different areas are interrelated through tie- lines. Inter **area** **power** 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.

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Coon’s [6], Astrom and Hagglund[7] 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 **load** **frequency** control [8]. In this study, it is used to determine the parameters of a PID controller according to the **system** dynamics

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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

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In interconnected **power** **system** **load** **frequency** control has been used extensively. This study presents an application of a fuzzy gain scheduled proportional and integral (FGPI) controller for **load**-**frequency** **area** electrical interconnected **power** **system**. The main aim is to design a FGPI controller that can ensure good **performance**. The paper present analysis on dynamic **performance** of **Load** **Frequency** Control (LFC) of three **area** interconnected thermal reheat **power** **system** 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

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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 **power** **system** with several conventional synchronous generators. If this **power** **system** 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 **Load** **Frequency** 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**-**area** **power** **system** [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

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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 [4]. 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).

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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.

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Conferring to Ref. [3], conventional PID control systems shan’t extent to great result. A gain forecasting controller is used [2]. 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 [4]. Fuzzy methods were used for control of **load**-**frequency** in **power** systems by Chang and Fu [2] and Akalın and coworkers [6]. 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**.

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