Static VAR Compensator Parameters Research Articles

SVC Parameters Optimization Using a Novel Integrated MCDM Approach

Nowadays, multi-criteria decision-making (MCDM) methods are used widely in many fields of research and applications. Many studies have shown that MCDM approaches are effective in determining the optimal solution to a variety of symmetrical and asymmetrical problems with numerous parameters. This article investigates a novel approach using multi criteria decision making (MCDM) to optimize the parameters of static var compensator (SVC) and power system stabilizers (PSS). The proposed technique integrates similarity membership function reduction algorithm (SMFRA), removal effects of criteria (REC) and combined compromise solution (CoCoSo). In the first stage, (SMFRA) is employed to select the most dominant controller parameters in the optimization process. Secondly, the weights of the reduced parameters are computed based on (REC). Finally, (CoCoSo) method searches for the optimal setting parameters. A detailed sensitivity analysis is presented to evaluate the obtained results. It is found that the suggested integrated technique is time saving, easily implemented and of low computation burden, which can successfully be implemented to solve a wide range of issues, both comparable and dissimilar.

Multi-Objective Real-Time Tuning of SVC Used in Electrified Traction Systems.

Electric train system is a very large load for the power network. This load consumes a large amount of reactive power. In addition, it causes a massive unbalance to the network, which results in many problems such as voltage drops, high transmission losses, reduction in the transformer output ability, negative sequence current, mal-operation of protective relays, etc. In this paper, a novel real-time optimization approach is presented to adjust the static VAR compensator (SVC) for the traction system to realize two objectives; current unbalance reduction and reactive power compensation. A multi-objective optimization technique entitled non-dominated sorting genetic algorithm (NSGA-II) is used to fulfill the regarded objectives simultaneously. A comprehensive simulator has been designed for electric train network modeling that is able to adjust the parameters of SVC in an optimum manner at any time and under any circumstances. The results illustrate that the provided method can efficiently reduce the unbalancing in current as well as supply the demanded reactive power with acceptable precision.

Online estimation of control parameters of FACTS devices for ATC enhancement using artificial neural network

The deregulated electricity sector needs an improvement in the Available Transfer Capability (ATC) towards the maintenance of power network at balanced condition and to utilize the system in effective manner. Independent System Operator (ISO) maintains the ancillary services by ensuring the reliability of the power system. One of the major functions of ancillary service provider is to maintain the voltage and power flow at stable level. To improve the ATC, both the line power flow and bus voltage profile have to be modified and it is taken care by the ISO. The major limiting criterion for ATC is bus voltage profile. It is well known that the device Thyristor Controlled Series Compensation TCSC which is one of the Flexible AC Transmission System (FACTS) devices can modify the line flow by adjusting the line reactance and Static VAR compensator (SVC) can improve the bus voltage profile by injecting reactive power to the bus. In this research, an Artificial Neural Network (ANN) based estimation of control parameter of FACTS devices such as TCSC and SVC for ATC enhancement is used. The proposed approach uses two different ANN network to find the different TCSC and SVC control parameters to improve the ATC values without violating its voltage constraints for real time transactions. The ANN algorithms such as Radial Basis Function (RBF) as well as Back Propagation Algorithm (BPA) were used to find the TCSC and SVC Parameters and the results are compared. The proposed methods are demonstrated through Reliability Test System (RTS) of IEEE 24 bus. The simulation output represents the suitability of the anticipated method for Real Time estimation of FACTS devices control parameter settings for ATC Enhancement.

Swarm Intelligence Approach for Angle Stability Improvement of PSS and SVC-Based SMIB

This paper discusses the effect of multi-objective function in angle stability improvement for a single machine connected to an infinite bus system (SMIB). Minimum damping ratio, ξmin and maximum damping factor, σmax which are commonly used to indicate oscillatory stability condition in power system are merge in certain ratio to produce a multi-objective function, FMO. This new index brings the advantages of the two indices without compromising the weakness of the index involved. In this study, FMO is applied to tune parameters of static var compensator with proportional-integral-derivative controller (SVC-PID) to improve damping efficiency in SMIB. The result is compared with a system connected to power system stabilizer attached with lead lag controller (PSS-LL). The parameters of SVC-PID and PSS-LL are optimized by particle swarm optimization method. Validation based on speed response, phase plane and determination of eigenvalues confirms that the proposed FMO is more effective for solving angle stability problems compared to single objective function.

Voltage Stability Enhancement by Coordination of SVC and TCSC Using Particle Swarm Optimization

The Flexible AC Transmission System (FATCS) plays a vital role in power system performance enhancement. They attributes to accomplish faster and having answers for many issues in power system study. With regards to this, a particles warm optimization (PSO) algorithm is implemented so that to design the coordinated parameters of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). Transient stability of a power system is based on the generator relative rotor angles obtained from time domain simulations outputs. A self-sufficient model of IEEE fourteen bus systems has been given with full detail and voltage stability analysis is done by considering load changing at a bus. PSO applied here is built on searching the values of L and C of SVC, then one can achieve desired and fine coordination. PSO is available with few modification, as change in PSO range & number, difference in selection criteria technique, control technique etc, with due respect to normalize PSO. The results are compared and found that coordination of FACTS devices with each other promises the efficiency of the suggested method for revamping Power flows. When function of TCSC is impelled by some curb, adjustable SVC can supply additional support to upgrade the gross performance.

Comparative study of the effects of SVC and TCSC on the small signal stability of a power system with renewables

This study analyzed the effects of applications of flexible alternating current transmission system (FACTS) devices, such as a thyristor controlled series capacitor (TCSC) and static var compensator (SVC) to handle the oscillations due to wind penetration in an existing network, and to exploit the AC lines to their thermal limits. A small signal stability analysis of an interconnected power system with wind penetration in the presence of FACTS and a power system stabilizer (PSS) was studied under a diverse range of operating conditions. The coordinated tuning of SVC and PSS parameters as well as a TCSC in the presence of a wind energy conversion system was analyzed. The salp swarm algorithm, a recently developed meta-heuristic approach, was used for tuning TCSC, SVC, and PSS parameters. The supremacy of TCSC over SVC for improving system stability was demonstrated under a diverse range of operating conditions.

Coordination of SVC and TCSC for Management of Power Flow by Particle Swarm Optimization

The Flexible AC Transmission System (FACTS) plays a vital role in power system performance enhancement. They attributes to accomplish faster and having an answer for many issues in power system study. With regards to this, a particle swarm optimization (PSO) algorithm is implemented so that to design the coordinated parameters of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). With behavioral nonlinearities of the power system, linear methods cannot be used to design coordinated parameters of controllers. Simulation is carried out on IEEE-14 bus system with help of MATLAB. PSO applied here is built on searching the values of L and C of SVC, then one can achieve desired and fine coordination. PSO is available with few modification, as change in PSO range & number, difference in selection criteria technique, control technique etc., with due respect to normalize PSO. The results are compared and found that coordination of FACTS devices with each other promises the efficiency of the suggested method for revamping Power flows. When function of TCSC is impelled by some curb, adjustable SVC can apply additional support to upgrade the gross performance.

Multi-Objective Evolutionary Programming for Static VAR Compensator (SVC) in Power System Considering Contingencies (N-m)

<span>Static VAR Compensators (SVCs) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting capacitive or inductive current components at the midpoint of interconnection line or in load areas. This device is capable of minimizing the overall system losses and concurrently improves the voltage stability. A line index, namely <em>SVSI</em> becomes indicator for the placement of SVC and the parameters of SVCs are tuned by using the multi-objective evolutionary programming technique, effectively able to control the power. The algorithm was tested on IEEE-30 Bus Reliability Test System (RTS). Comparative studies were conducted based on the performance of SVC in terms of their location and sizing for installations in power system.</span>

Steady state and dynamic performance of self-excited induction generator using FACTS controller and teaching learning-based optimization algorithm

PurposeThe paper aims to present an application of teaching learning-based optimization (TLBO) algorithm and static Var compensator (SVC) to improve the steady state and dynamic performance of self-excited induction generators (SEIG).Design/methodology/approachThe TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with different types of prime mover. For a constant speed prime mover, the TLBO algorithm attains the optimal capacitance to have rated load voltage at different loading conditions. In the case of variable speed prime mover, the TLBO methodology is used to obtain the optimal capacitance and prime mover speed to have rated load voltage and frequency. The SVC of fixed capacitor and controlled reactor is used to have a fine tune in capacitance value and control the reactive power. The parameters of SVC are obtained using the TLBO algorithm.FindingsThe whole system of three-phase induction generator and SVC are established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 7.5 kW and 1.5 kW) using the TLBO algorithm and SVC. An experimental setup is built-up using a 1.5 kW three-phase induction machine to confirm the theoretical analysis. The TLBO results are matched with other meta heuristic optimization techniques.Originality/valueThe paper presents an application of the meta-heuristic algorithms and SVC to analysis the steady state and dynamic performance of SEIG with optimal performance.

SVC damping controller design based on novel modified fruit fly optimisation algorithm

Existing reactive power systems do not readily provide support or anti-disturbance capabilities. This study was conducted to explore the predisposing factor and suppression measures of low-frequency oscillation in large-scale wind power cluster systems by establishing a wind farm cluster mode with wind power fluctuation in a DIgSILENT/power factory. Considering the multiple time scale and the operating characteristics of cluster system, a novel modified fruit fly optimisation algorithm (nMFOA) combined with probabilistic sensitivity indices is proposed to coordinate and optimise static VAR compensator (SVC) damping controller parameters to enhance the power system stability of the wind farm cluster. Adverse effects in the SVC damping controller are eliminated via the nMFOA with probabilistic eigenvalue, which can be used to effectively coordinate and optimise SVC parameters. The proposed scheme was tested on a certain wind farm cluster in Hami, Xinjiang Province.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

Power system stabiliser and flexible alternating current transmission systems controller coordinated design using adaptive velocity update relaxation particle swarm optimisation algorithm in multi‐machine power system

In this study, power system stabiliser (PSS), static VAR compensator (SVC), and thyristor controlled series capacitor (TCSC) controllers are simultaneously used to increase the stability and damping of the oscillations of a multi-machine power system. By SVC, the transferrable power in the steady state can be increased and the voltage profile can be controlled along the line. TCSC can regulate line impedance and therefore, increase the transferred power of the system. PSS is added to excitation system to enhance the damping of electric power system during low frequency oscillations. SVC, TCSC, and PSS can combine with each other for further damping and stability; therefore, their coordination is very important. Then, adaptive velocity update relaxation particle swarm optimisation algorithm is used to optimise the parameters of PSS, SVC, and TCSC simultaneous controllers. The robustness of the proposed algorithm has been tested on 16-machine 68-bus power system.

OPTIMAL PERFORMANCE OF SELF EXCITED INDUCTION GENERATOR USING TEACHING LEARNING-BASED OPTIMIZATION ALGORITHM AND STATIC VAR COMPENSATOR

The paper presents an application of Teaching Learning-Based Optimization (TLBO) algorithm to improve the performance of self-excited induction generators (SEIG). Two control methods of SEIG have been studied. The first method, the TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with constant prime mover speed. The drawback of this method is the generator frequency decreases with load and to overcome this disadvantage, the other control method is proposed. In the proposed method, the TLBO is used to obtain optimal capacitance and prime mover speed to have rated load voltage and frequency. The Static VAR Compensator (SVC) of fixed capacitor and controlled reactor is used to control the reactive power. The parameters of SVC are obtained by using TLBO algorithm. The performance of the SEIG at different loads and prime mover speeds using TLBO algorithm is realized. A whole system of three phase induction generator and SVC is established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 10 hp and 2hp). An experimental setup is built-up using a 2 hp induction motor to confirm the theoretical analysis. Good agreement between results confirms and signifies the viability of the proposed TLBO-based methodology.

MODELO SIMPLIFICADO DE COMPENSADOR ESTÁTICO DE POTENCIA REACTIVA EN EL FLUJO DE CARGAS CON INYECCIÓN DE CORRIENTE

This paper describe Newton-Raphson current injection power flow model of a Static Var Compensator (SVC) to reduce the complexities of the computer load flow program codes of a network containing this device. This model is based on power mismatch approach. The SVC controlled buses in the network are converted to PV buses with zero active power generation at the required voltage magnitudes. The power mismatch equations for these connected buses are included and updated in NR current injection load flow algorithm. While, the load buses are represented by current mismatch equations. Moreover, the parameters of SVC can be calculated using an iterative process and the final value is updated after the convergence is achieved. The developed SVC model enhances the reusability of load flow program by avoiding modifications in the Jacobian matrix. This model is considered the simplest and the best representation of shunt control devices. The feasibility of the SVC model has been validated on the WSCC 9-bus and IEEE 57-bus transmission systems.

Improving Transient Stability with Multi-objective Allocation and Parameter Setting of SVC in a Multi-machine Power System

ABSTRACTThis paper aims to select the optimal location, size, and setting parameters of SVC (Static Var Compensator) controller using MOPSO (multi-objective particle swarm optimization) to improve the power system stability in a multi-machine power system. Though power system stabilizers are mandatory requirements for damping of oscillations, its performance still gets affected by changes in network configurations, load variations, etc. Hence, installations of SVC have been suggested in this paper to achieve appreciable damping of system oscillations. However, the performance of SVC highly depends upon its parameters, suitable location, and sizes in the power network. In order to find the solution, this problem is formulated as a multi-objective function. Then, MOPSO is used to find the best answers. In this paper, two objectives are considered to improve both the transient stability of the system and the SVC investment cost. The proposed method was tested on the 10-machine 39-busbar New England test system. The results of the nonlinear simulation show that the optimum sizing, siting, and parameter setting of SVC can improve greatly the transient stability of the multi-machine power system.

Intelligent fuzzy-based reactive power compensation of an isolated hybrid power system

In this paper, an isolated wind-diesel hybrid power system model is considered for its on-line reactive power compensation. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between the reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, variable source of reactive power such as static VAR compensator (SVC) is used. The different tunable parameters of the studied hybrid power system model are optimized by a novel opposition-based gravitational search algorithm (OGSA). Gravitational search algorithm (GSA) is based on the law of gravity and the interaction between the masses. In GSA, the searcher agents are a collection of masses and their interactions are based on the Newtonian laws of gravity and motion. To further improve the optimization performance of the GSA, opposition-based learning is employed for population initialization and also for generation jumping. The performance analysis of a Sugeno fuzzy logic (SFL) based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. Time-domain simulation of the investigated power system model reveals that the proposed OGSA-SFL yields on-line, off-nominal optimal SVC parameters resulting in on-line optimal terminal voltage response.

Modeling and seeker optimization based simulation for intelligent reactive power control of an isolated hybrid power system

Seeker optimization algorithm (SOA) is a novel heuristic population-based search algorithm based on the concept of simulating the act of human searching. In SOA, the acts of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, effectiveness of the SOA has been tested for optimized reactive power control of an isolated wind–diesel hybrid power system model. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for the purpose of power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, a variable source of reactive power such as static VAR compensator (SVC) is used. The SG is equipped with IEEE type-I excitation system and dual input power system stabilizer (PSS) like IEEE-PSS3B. The performance analysis of a Takagi–Sugeno fuzzy logic (TSFL)-based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. In time-domain simulation of the investigated power system model, the proposed SOA–TSFL yields on-line, off-nominal coordinated optimal SVC and PSS parameters resulting in on-line optimal reactive power control and terminal voltage response. The performance of the proposed controller, with the influence of signal transmission delay, has also been investigated.

New Coordinated Design of SVC and PSS for Multi-machine Power System Using BF-PSO Algorithm

Nowadays, flexible AC transmission system (FACTS) devices are increasingly used in power systems. They have remarkable effects on increasing the damping of the system. In this paper, a bacterial-foraging oriented by particle swarm optimization (BF- PSO) algorithm is employed to design the coordinated parameters of power system stabilizer (PSS) and static VAR compensator (SVC). With regard to nonlinearities of power system, linear methods can’t be used to design coordinated parameters of controllers. In this paper, nonlinear model of power system and SVC is used to design parameters of PSS and SVC. For this purpose, this design problem is firstly converted to an optimization problem and then the BF-PSO algorithm is used to solve it. Simulations are carried out on four machine 11 bus power system in MATLAB software. The results confirm the efficiency of the proposed method for stabilizing the power system oscillations. Comparing BF-PSO algorithm with other intelligent methods, (PSO, BFA) verifies better performance of the BF-PSO method.

Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability problem

This paper aims to select the optimal location and setting parameters of SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Compensator) controllers using PSO (Particle Swarm Optimization) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) associated with generators are mandatory requirements for damping of oscillations in the power system, its performance still gets affected by changes in network configurations, load variations, etc. Hence installations of FACTS devices have been suggested in this paper to achieve appreciable damping of system oscillations. However the performance of FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper the PSO based technique is used to investigate this problem in order to improve the small signal stability. An attempt has also been made to compare the performance of the TCSC controller with SVC in mitigating the small signal stability problem. To show the validity of the proposed techniques, simulations are carried out in a multimachine system for two common contingencies, e.g., load increase and transmission line outage. The results of small signal stability analysis have been represented employing eigenvalue as well as time domain response. It has been observed that the TCSC controller is more effective than SVC even during higher loading in mitigating the small signal stability problem.

Harmony search algorithm for SVC controller tuning in voltage support mode

Static Var Compensator (SVC) in one of the most widely used FACTS devices in industry and real world power systems. The problem of finding optimal values of SVC parameters has been studied for years. Many different methods have been carried out to design SVC controllers. But trying to find better controller is still remains. In this scope, Harmony Search (HS) Algorithms method as a meta-heuristic optimization method is considered for tuning the parameters of SVC. A multi machine electric power system is used to test viability of SVC in voltage support under disturbances. Simulation results on a multi machine power system show the ability of SVC in control of voltage as well as stability enhancement. The results are carried out by numerical simulations by using MATLAB software.