Top PDF Optimal Control Parameters for a UPFC in a Multimachine Using PSO

Optimal Control Parameters for a UPFC in a Multimachine Using PSO

Optimal Control Parameters for a UPFC in a Multimachine Using PSO

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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Comparative study and implementation of multi objective pso algorithm 
		using different inertia weight techniques for optimal control of a CSTR 
		process

Comparative study and implementation of multi objective pso algorithm using different inertia weight techniques for optimal control of a CSTR process

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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Optimal Placement and Sizing of STATCOM using PSO

Optimal Placement and Sizing of STATCOM using PSO

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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Optimal Design of PID Controller for a CSTR System Using BF PSO

Optimal Design of PID Controller for a CSTR System Using BF PSO

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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Structure Specified Optimal Robust H∞ Loop Shaping Control of Multivariable Electro-Hydraulic Servo System using GA and PSO

Structure Specified Optimal Robust H∞ Loop Shaping Control of Multivariable Electro-Hydraulic Servo System using GA and PSO

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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OPTIMAL LOCATION OF UPFC IN POWER SYSTEM USING SYSTEM LOSS SENSITIVITY INDEX

OPTIMAL LOCATION OF UPFC IN POWER SYSTEM USING SYSTEM LOSS SENSITIVITY INDEX

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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Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

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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Enhancement of Voltage Stability through Optimal Location of UPFC

Enhancement of Voltage Stability through Optimal Location of UPFC

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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Induction Motor Control Using PSO-ANFIS

Induction Motor Control Using PSO-ANFIS

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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Tuning Parameters of Fuzzy Logic Controller using PSO for Maglev System

Tuning Parameters of Fuzzy Logic Controller using PSO for Maglev System

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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Comparative Study of Reducing the Fault Current and Overvoltage in a Distribution System in Autonomous Micro Grids Operation Using PSO UPFC & SFCL

Comparative Study of Reducing the Fault Current and Overvoltage in a Distribution System in Autonomous Micro Grids Operation Using PSO UPFC & SFCL

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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Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller

Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller

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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Location of UPFC in A Multimachine System for Damping Power Oscillation

Location of UPFC in A Multimachine System for Damping Power Oscillation

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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Optimal Tuning Of UPFC Damping Controller Using Single And Multi-Objective Evolutionary Algorithms

Optimal Tuning Of UPFC Damping Controller Using Single And Multi-Objective Evolutionary Algorithms

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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Solving optimal control problems by PSO-SVM

Solving optimal control problems by PSO-SVM

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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Power Flow Control and Analysis of Transformerless Upfc

Power Flow Control and Analysis of Transformerless Upfc

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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Optimal Power Flow in Transmission System by Using UPFC

Optimal Power Flow in Transmission System by Using UPFC

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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EIGHT PHASE OPTIMAL SEQUENCE DESIGN FOR MIMO RADAR USING PSO

EIGHT PHASE OPTIMAL SEQUENCE DESIGN FOR MIMO RADAR USING PSO

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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On the formulation of the problem of optimal control of production parameters using a two level model of the production process

On the formulation of the problem of optimal control of production parameters using a two level model of the production process

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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Transient Stability Analysis of Multimachine System Using Statcom

Transient Stability Analysis of Multimachine System Using Statcom

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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