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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 **power** **system** and analysis, which **controllers** (I, PI, **PID** and Fuzzy **PID**) give better **performance** and stability of the **system** [3].

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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** [1]. Therefore, Load Frequency **Control** (LFC) is very important for **power** **system** 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 [2]. 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 **control** **performance** over a wide range of operating conditions. So to keep the **system** **performance** 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.

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ABSTRACT: The **Automatic** **Generation** **Control** is the main **control** in **power** **system** to match the **generation** with demand. **Power** **system** 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 **power** **system** is one. Similarly, thermal plant and hydro plant are considered as separate areas and they are connected with tie-line to form **two** **area** 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 **two** **area** **power** **system** 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 **two** **area** **power** **system**. The results are compared to determine the **performance** of the **system** with SMES and different **controllers** **using** SIMULINK.

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implementation of fuzzy logic controller to solve **automatic** **power** **generation** **control** problem in **two**-**area** hydrothermal **power** **system**. 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.

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This work presents an efficient method based on a modified fuzzy PI **control** with parallel fuzzy PD **control** for **automatic** **generation** **control** (AGC) of a **two**-**area** **power** **system**. This describes the **control** schemes required to operate the **two**-**area** **power** **system** in the steady state. The model of a **two**-**area** **power** **system** is established **using** the equations describing dynamic behaviour of a **two**-**area** **power** **system** and **control** schemes in Matlab-Simulink program respectively. The performances of different **controllers** for variable inputs are compared for the same **two** **area** **power** **system**. 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**.

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Many investigations in of **power** **system** 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.[4] 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.[7]. Due its high potential for global optimization, GA has received great attention in **control** **system** 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.

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From the investigations have been carried out to design an optimal **automatic** **generation** controller to enhance the stability and preserve the security of the **system** [10]. Dynamic **performance** of all conventional classical **controllers** [4] like Integral, P, PI, **PID** **controllers** and soft controller(Fuzzy-Tuned Controller ) [13]. 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.

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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 [14]-[15]. **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** [13].

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(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.

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Abstract: In this paper, an adaptive fuzzy logic **control** for **automatic** **generation** **control** of interconnected **two** **area** Hydro-Thermal **System** **using** 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 **two** **area** interconnected **power** **system**. 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 **two** **area** interconnected **power** **system**.

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In this paper result, the effectiveness of the TLBO algorithm has been tested for **Automatic** **Generation** **Control** (AGC) of an interconnected **power** **system**. We used linear and nonlinear model of **two** **area** 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

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Abstract- This paper presents the GRC & AGC techniques which are useful for the study for the methods of artificial intelligence for the **automatic** **generation** **control** of interconnected **power** systems. In the given paper, a **control** line of track is established for interconnected three **area** **power** **system** **using** **generation** 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.

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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 **power** **system**. 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.

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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 **Automatic** **Generation** **Control** is taken into consideration which is then used for adjusting the output **power** of various generators at different **power** plants in an electric **power** **system**. 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.

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rated sudden capacitive load change (with the FLBPSS), (a)-rotor. angle, (b)-speed deviation 152[r]

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This paper is intended in investigating the **Automatic** **Generation** **Control** (AGC) problem of a de- regulated **power** **system** **using** Adaptive Neuro Fuzzy controller. Here, three **area** **control** 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 **control** **area**, 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 **power** **system**. 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.

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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 **control** **area** 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 [10].

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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[1]. The LFC issues have been tackled with by the various researchers in different time through AGC regulator, excitation controller design and **control** **performance** 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 [2].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 [3] which offers many benefits in the **area** of nonlinear **control** problems, particularly when the **system** is operating over the nonlinear operating range.

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