The **multimachine** power system in Fig. 1 has the operating points for Gen 1 : P 1 = 1094 MW, Q 1 = -94 MVAR and Gen 2 : P 2 = 1500 MW, Q 2 = 0 MVAR. At this operating point, the nine combined **parameters** of the **UPFC** shunt and series branch controllers are optimized for transient stability **using** the **PSO** algorithm. The five **PSO** particles initial settings for a given run are shown in Table I. The **PSO** **parameters** used in the simulation are w = 0.8 and c 1 = c 2 = 2. After ten iterations with the **PSO** algorithm, the **optimal** **parameters** (g best ) are found and shown in the last row of Table I. The **PSO** process was carried out over 20 trial runs. Overall, **parameters** close to the

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modifications in **parameters**, LDIW-**PSO** can get better optimum fitness convergence speed, stability and robustness [17]. In Global-Local Best Inertia weight (Global-w), the weight 'w' neither assumes constant value nor a linearly decreasing time-varying value instead, it depends on local best and global best values of the particles in each iteration [18]. Chatterjee and Siarry proposed a new variant of **PSO**, which employs a nonlinear variation of inertia weight. This nonlinear variation has been adopted to employ aggressive, coarse tuning during initial iterations and mild, fine tuning during later iterations so that the optimum solution can be approached with better accuracy [19]. Agees Kumar and Kesavan Nair have proved that the Adaptive Inertia weight (Adaptive-w) **PSO** based PID controller can coordinate various performance indices of the system and provide an effective tool for trade-off analysis among convergence, stability and robustness [20].

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system. It is important for maintaining voltage to deliver real power through power lines. By reactive power compensation we can **control** the power factor and reduce the consumption of electricity. Reactive power compensation have two aspects. Voltage support means voltage fluctuation reduction at the given terminal of transmission line. Load compensation involves power factor improvement, balance of active power drawn from supply, improvement in voltage regulation and elimination of current harmonics etc. There are mainly two types of compensation in use:-Series compensation and shunt compensation. System **parameters** are modified by these to enhance VAR compensation. This results in improvement of stability of the ac system by raising maximum active power to be transmitted. The flexible AC transmission system (FACTS) are now recognized as a viable solution for controlling transmission voltage, power flow, dynamic response ,etc. and represent a new era for transmission systems. These adjust **parameters** like governing the power system like voltage, current, phase angle, impedance and frequency. Although primary purpose of the shunt FACTS devices is supporting bus voltage by injection (or absorption ) of reactive power, they also have capability of improving transient stability by increasing (or decreasing) power transfer capability as machine angle increases (or decreases), that is achieved by operation of shunt FACTS devices in the capacitive (or inductive) mode. STATCOM is a member of FACTS family and is connected in shunt with system. It is capable of enhancing voltage security. However, owing to the considerable cost of the FACTS device involved, it is important to find the **optimal** location and sizing (rating) of the device in a power system to obtain maximum benefits of the devices

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The process **control** techniques in the industry have made great advances during the past decades. A no of **control** methods such as adaptive **control**, neural **control**, and fuzzy **control** have been studied. Among them, the best known is the proportional-integral- derivative (PID) controller, which has been widely used in the industry because of its simple structure and robust performance in a wide range of operating conditions. Unfortunately, it has been quite difficult to tune properly the gains of PID controllers because many industrial plants are often burdened with problems such as high order, time delays, and nonlinearities. It is hard to determine **optimal** or near **optimal** PID **parameters** with the classic tuning method (Ziegler-Nichol’s method for instance). For these reasons, it is highly desirable to increase the capabilities of PID controllers by adding new features.

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and **PSO** are employed to find the optimized **parameters** of the controllers. The structure of controller is selectable here and the fixed-structure robust PI controller is designed. Simulation results show that the controller designed by the proposed approach has a good performance and robustness properties as well as a simple structure of low order. The remainder of this paper is organized as follows. Section II represents the modelling of MIMO Electro-hydraulic Servo system. In Section III, conventional Η loop shaping and the proposed technique are illustrated along with the

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Abstract- This paper presents the development of simple and efficient models for suitable location of unified power flow controller (**UPFC**), with static point of view, for congestion management. Two different objectives have been considered and the results are compared. Installation of **UPFC** requires a two-step approach. First, the proper location of these devices in the network must be ascertained and then, the settings of its **control** **parameters** optimized. The effectiveness of the proposed methods is demonstrated.

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A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency **Control** (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with **UPFC** (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended **control** method are proved by altering the **parameters** of the system from their nominal values and by the implementation of random loading in the system.

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It is possible to deal with the thermal limitations by renovating the transmission lines and opting for a higher current rating conductor. Nonetheless, this solution does not necessarily guarantee voltage within the acceptable boundaries or the flow and the controllability of the power, but it is feasible by line compensation. Electro-mechanical devices used for line compensation cannot achieve rapid compensation and are prone to the wear quicker than the static equipments. The solid-state based technology, FACTS, provides the **control** of one or more **parameters** to magnify the loading capability and to develop controllability. As the current in a transmission line has the property to be controlled, it is possible to use a FACTS device for flowing power across the line during normal or disturbed conditions. This returns to the ability of FACTS devices to **control** all power flow **parameters**, namely phase angle, bus voltage and line impedance. In other words, the FACTS technology gives the possibility to maintain acceptable voltage magnitude and power flow.

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Fuzzy theory was first proposed and investigated by Prof. Zadeh in 1965. The Mamdani Fuzzy Inference System (FIS) was presented to **control** a steam engine and boiler combination by linguistic rules. Fuzzy logic is expressed by means of IF-THEN rules with the human language. In the design of a fuzzy logic controller, the mathematical model is not necessary. Therefore the Fuzzy Logic Controller (FLC) is of good robustness [1]. Owing to its easy application, it has been widely used in industry. However, the rules and the membership functions of a fuzzy logic controller are based on expert experience or knowledge database. Much work has been done on the analysis of fuzzy **control** rules and membership function **parameters** [14]. The **PSO** (particle swarm optimization) algorithms are used to get the **optimal** values and **parameters** of our FLC. The **PSO** is based on a metaphor of social interaction. It searches a space by adjusting the trajectories of individual vectors, called ‘particles’, as they are conceptualized as moving as points in multidimensional space. The individual particles are drawn stochastically towards the positions of their own previous best performances and the best previous performance of their neighbors. Of these is the **PSO** algorithms are applied to choose membership functions and fuzzy rules [15]. However, the expert experiences or knowledge are still necessary for the ranges of membership functions. In this paper, a novel strategy is proposed for designing the **optimal** fuzzy controller.

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In this paper, the author presents an application of the **PSO** in tuning **parameters** of a fuzzy logic controller (FLC) for stabilizing the ball position of the maglev system. FLC have been successfully applied to **control** many objects that are high nonlinear or open-loop unstable but finding **parameters** such as scaling gains and membership function is difficult. Normally, they are identified by trial and error method. Recently, some **optimal** methods have been used to find these **parameters**. In [5], authors used **PSO** algorithm to determine Fuzzy **control** rules in order to avoids falling in local optimum by setting variable inertia weight and learning factors. Simulation results show that the proposed approach achieves better dynamic performance compared with conventional proportional and integral (PI) controller in four-area reheat thermal and hydro AGC system. In [6] and [7], authors used **PSO** to optimize FLC for speed **control** of Quasi-Z Source DC/DC converter fed drive and Self-Balancing Two-Wheeled Robot. Results show that effectiveness of **PSO**-based fuzzy **control**. In [6], the proposed method has been tested and examined for two types of DC/DC converters fed DC drive. The results of fuzzy **PSO** were compared with that of conventional fuzzy and genetic algorithm based fuzzy controller. The fuzzy **PSO** based controller was found to be more viable as it gave minimum rise time, settling time, steady state error and also ripples in the armature current was found to be minimized. In [7], the proposed novel LMI-based **PSO** algorithm not only stabilizes the self-balance **control** system of two-wheeled robot but also seeks the best **control** gains.

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Since the purpose of the **control** scheme is to receive a minimum steady-state error, the harmonic reference signal r is set to zero. First, supply harmonic current is detected. Then, the expectation **control** signal of the inverter is revealed by the adaptive fuzzy dividing frequency controller. The stability of the system is achieved by a proportional controller, and the perfect dynamic state is received by the generalized integral controller. The fuzzy adjustor is set to adjust the **parameters** of proportional **control** and generalized integral **control**. Therefore, the proposed harmonic current tracking controller can decrease the tracking error of the harmonic compensation current, and have better dynamic response and robustness.

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PID controllers have been at the heart of **control** engineering practice for seven decades. The PID controllers have a wide range of applications in industrial **control** because of their simple **control** structure. The PID controllers need of less plant information than a complete mathematical model. The controller attempts to minimize the error by adjusting the process **control** input. The PID controller calculation (algorithm) involves three constant **parameters** called the proportional (P), integral (I), derivative (D) values, these value can be interpreted in terms of time. The transfer function of PID controller is:

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Day by day electrical power consumption has been increasing very rapidly. It must be necessary to supply to all the consumers with quality and reliability. So the exact prediction of load is not possible but only estimated that the generation must be equal to load all the times. Due to variation in load low frequency oscillation, electromechanically oscillations are unavoidable characteristics of power system. FACTS devices can be use to damp these low frequency oscillations. In this paper, result obtained for the dynamic **control** of the power transmission, damping oscillations with **UPFC** based on theory & computer simulation through PSAT software. The objective of this paper is to find location of **UPFC** in a **Multimachine** power system tested on IEEE-14 bus system, relation between system **parameters** and effect of oscillation.

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concepts like elitism, fast non-dominated sorting approach and diversity maintenance along the Pareto-**optimal** front. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been successfully applied to various multi-objective engineering optimization problems [19, 20, 22 & 23-29]. Multi-objective algorithm NSGA-II has been used for the tuning of TCSC based damping controller by considering speed deviation and **control** signal as objectives [24]. The NSGA-II still falls short in maintaining lateral diversity and obtaining Pareto-front with high uniformity. To overcome this shortcoming, controlled elitism concept, which can maintain the diversity of non-dominated front laterally, has been proposed [19]. Also to obtain Pareto-front with high uniformity, Luo et al. have proposed DCD based diversity maintenance strategy [25]. Jeyadevi et al. have suggested Modified NSGA-II by incorporating **control** elitism and DCD features to ensure better convergence and diversity for solving multi-objective **optimal** reactive power dispatch problem [23]. Lakshminarasimman et al. have applied MNSGA-II for the **optimal** placement of mobile antenna [24]. Rajkumar et al. have also applied MNSGA-II for Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators [26]. Piraisoodi et al. have applied MNSGA-II for **optimal** nonlinear controller design in boiler turbine system [31]. In the present proposed work, MNSGA- II has been considered for the **optimal** design of **UPFC** damping controller ( E ) . Minimizing ISE of the error signal and input **control** signal (u) gives the optimum performance of the proposed **UPFC** controller under nominal, light and heavy loading conditions compared to other optimization techniques [32,33. 34]. For the purpose of understanding the benefits of multi-objective algorithms, the **UPFC** damping controller is also tuned with single objective algorithm **PSO** along with Integral Squared Error

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mathematical operators in order to adjust it to the minimum **parameters** required. In this method, minimum **parameters** are needed for adjustment. Furthermore, the executive function of algorithm will not be lost when the dimensions of research space are developed. **PSO** method is one of the new species of evolutionary methods whose application potential in optimization problems with continuous functions has been proved. In this way, move toward the **optimal** point, based on two data sets is done. One of the best-point of information obtained from each of the initial population [2, 3].

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This paper discusses various aspects of unified power flow controller (**UPFC**) **control** modes and settings and evaluates their impacts on the power system reliability. **UPFC** is the most versatile flexible ac transmission system device ever applied to improve the power system operation and delivery. It can **control** various power system **parameters**, such as bus voltages and line flows. The impact of **UPFC** **control** modes and settings on the power system reliability has not been addressed sufficiently yet. A power injection model is used to represent **UPFC** and a comprehensive method is proposed to select the **optimal** **UPFC** **control** mode and settings. The proposed method applies the results of a contingency screening study to estimate the remedial action cost (RAC) associated with **control** modes and settings and finds the **optimal** **control** for improving the system reliability by solving a mixed- integer nonlinear optimization problem. The proposed method is applied to a test system in this paper and the **UPFC** performance is analyzed in detail.

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In order to investigate the feasibility of the proposed technique, a large number of power systems of different sizes and under different system conditions have been tested. It should be pointed out that the results are under so- called normal power flow, i.e. the **control** **parameters** of **UPFC** are given and **UPFC** is operated in an closed -loop form. All the results indicate good convergence and high accuracy achieved by the proposed method. In this section, the IEEE 5-bus system and a 14-bus practical system have been presented to numerically demonstrate its performance. It have been used to show quantitatively, how the **UPFC** performs. The original network is modified to include the **UPFC**. This compensates the line between any of the buses. The **UPFC** is used to regulate the active and reactive power flowing in the line at a pre specified value. The load flow solution for the modified network is obtained by the proposed power flow algorithm and the Matlab program is used to find the **control** setting of **UPFC** for the pre specified real and reactive power flow between any buses and the power flow between the lines are observed the effects of **UPFC**. The same procedure is repeated to observe the power flow between the buses. (Depending on the pre specified value of the active and reactive power the **UPFC** **control** setting is determined after the load flow is converged.).

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Table I compares the obtained values of ASPs with literature values. ASPs of Eight phase synthesized sequence sets with three transmitting antennas (L=3) , and Sequence length varying from N= 40 to 128 are tabularized and Table II compares the obtained values of ASPs with literature values. Auto correlation side lobe peaks of four transmitting antennas (L=4) synthesized sequence sets ,and Sequence length various from N= 7 to 117. Fig. 1 and Fig.2 illustrates the Max (ASP) values of L=3 and L=4 designed **using** Particle swarm optimization algorithm compared with literature values .

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The null and the first moments of the distribution function of labor objects in terms of the state characterize the magnitude of interoperational stocks and the rate of processing of labor objects from operations of the technological route and are the main **parameters** of the management of the production line. The limiting transition from the kinetic description of the state of objects of labor to the stream description of the processing of objects of labor is accomplished. Integration of the kinetic equation by the states of the objects of labor made it possible to construct a closed system of balance equations for the **parameters** of the production line. The task of **optimal** **control** of the flow **parameters** of the production line has been set. The balance equations for the moments of the distribution function of objects of labor by states determine the constraint equations in the **control** problem.

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This is the steady state condition that is the prefault condition. Transient stability is more in this condition. From Fig. 3. the IEEE 14-bus network built **using** the PSAT Simulink library. Once defined in the Simulink model, one can load the network in PSAT and solve the power flow. Power flow results can be displayed in a GUI and exported to a file in several formats including Excel and LaTeX. PSAT also allows displaying bus voltages and power flows within the Simulink model of the currently loaded system. Notice that PSAT uses vectorized computations and sparse matrix functions provided by MATLAB, so that computation times increase slowly as the network size increase. Net power flow computation times for a variety of tests network, with different solvers, namely NR method and fast decoupled power flows. Result was obtained **using** the command line version of PSAT.

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