Placement Of Static Var Compensator Research Articles

Optimal placement of TCSC and SVC for reactive power planning using Whale optimization algorithm

Abstract In the present work, Whale optimization algorithm (WOA), Differential evolution (DE), Grey wolf optimization (GWO), Quasi-opposition based Differential Evolution (QODE) and Quasi-opposition based Grey wolf optimization (QOGWO) algorithm has been applied for the solution of reactive power planning with FACTS devices i.e., Thyristor controlled series compensator (TCSC) and Static Var compensator (SVC). WOA is a recently developed nature-inspired meta-heuristic algorithm based on hunting behaviour of Humpback Whales; DE is a stochastic real-parameter optimization technique comprising of genetic parameters namely - mutation & cross-over; and GWO is a nature-inspired meta-heuristic algorithm based on hunting behaviour of Grey wolf. Standard IEEE 30 and IEEE 57 bus test system has been adopted for the testing purposes. Location of TCSC has been determined by the power flow analysis method and location of SVC has been determined by the voltage collapse proximity indication (VCPI) method. Further, WOA, GWO, DE, QODE and QOGWO algorithms have been applied to find the optimal setting of all control variables including TCSC, the series type and SVC, the shunt kind of FACTS device in the test system which minimizes active power loss and system operating cost while maintaining voltage profile within permissible limit. The superiority of the proposed WOA technique has been illustrated by comparing the results obtained with all other techniques discussed in the present problem. ANOVA test has also been conducted to show the statistical analysis between different techniques. The proposed approach shows lesser number of iterations which does not gets trapped in the local minima and offers promising convergence characteristics.

Power System Performance Improvement by Optimal Placement and Sizing of SVC using Genetic Algorithm

The power system loss minimization becomes more important as the need of power generation is more recent days. The loss minimization improves the voltage profile which improves the loadability of the system. In many types of Flexible AC Transmission System (FACTS) devices Static Var Compensators (SVC) are cost vise it is affordable and it improves the system performance with lesser size. Here SVC is optimally placed in a test system of 30 bus system. Genetic algorithm is used to find the optimal results.

Power System Performance Improvement by Optimal Placement and Sizing of SVC using Genetic Algorithm

The power system loss minimization becomes more important as the need of power generation is more recent days. The loss minimization improves the voltage profile which improves the loadability of the system. In many types of flexible AC transmission system (FACTS) devices static var compensators (SVC) are cost vise it is affordable and it improves the system performance with lesser size. Here SVC is optimally placed in a test system of 30 bus system. Genetic algorithm is used to find the optimal results.

A novel approach for determining optimal number and placement of static var compensator device to enhance the dynamic performance in power systems

Increasing in the scale and complexity of interconnected power systems was because of the growth of electricity demands, leading to multiple electromechanical oscillations despite having support of the power system stabilizer (PSS). Therefore, the dynamic performance of the existing networks should enhance. One type of flexible alternating current transmission system devices, namely the static var compensator (SVC), can be installed at the buses to maintain and/or control particular parameters of the electrical power system by exchanging capacitive and/or inductive current. However, the performance of SVC device highly depends upon its parameters, sizes, and suitable number and location in the power network. Hence, the optimal location for SVC has become a key issue; in this paper, we propose a novel approach for the suitable number and location for SVC by using critical energy analysis based on the Gramian matrices that the solution framework applied an algorithm based on the Lyapunov equations and the balanced realization technique. The optimal placement is determined by (1) analyzing the small-signal stability to seek number of feasible locations and (2) comparing these feasible locations by analyzing the transient stability through various simulation cases. The effectiveness of the proposed method is compared with the other optimal method and verified on the 39-bus New England system by the simulation results based on the power system simulation engineering (PSS/E) and MATLAB programs. The obtained result shows that the SVC is installed at the single or multi-location having the total maximum Gramian energy.

Technique of Optimal Placement of SVC for Voltage Collapse Mitigation

Every transmission system has voltage stability limit which may lead to voltage collapse if an undetected bulk transmission network is operated close to its operating limit. This research work uses continuation power flow method to identify voltage collapse point for Bangladesh Power System Network (BPSN). For the identification of weak buses, an analytical based technique of tangent factor has been presented. The risk of voltage collapse has been mitigated by placing SVCs at the weak buses of the system using load flow analysis. Moreover, sensitivity based approach has been introduced to determine optimal location of Static VAR Compensator (SVC) for the voltage security enhancement. A comparative analysis has been documented considering the size of reactive power to improve the loading factor of the overall network at the end of this work.

Congestion management in deregulated power system using hybrid cat-firefly algorithm with TCSC and SVC FACTS devices

Purpose The loading and power variations in the power system, especially for the peak hours have abundant concussion on the loading patterns of the open access transmission system. During such unconditional state of loading the transmission line parameters and the line voltages show a substandard profile, which depicts exaction of congestion management of the power line in such events. The purpose of this paper is to present an uncomplicated and economical model for congestion management using flexible AC transmission system (FACTS) devices. Design/methodology/approach The approach desires a two-step procedure, first by optimal placement of thyristor controlled series capacitor (TCSC) and static VAR compensator (SVC) as FACTS devices in the network; second tuning the control parameters to their optimized values. The optimal location and tuning of TCSC and SVC represents a hectic optimization problem, due to its multi-objective and constrained nature. Hence, a reassuring heuristic optimization algorithm inspired by behavior of cat and firefly is employed to find the optimal placement and tuning of TCSC and SVC. Findings The effectiveness of the proposed model is tested through simulation on standard IEEE 14-bus system. The proposed approach proves to be better than the earlier existing approaches in the literature. Research limitations/implications With the completed simulation and results, it is proved that the proposed scheme has reduced the congestion in line, thereby increasing the voltage stability along with improved loading capability for the congested lines. Practical implications The usefulness of the proposed scheme is justified with the computed results, giving convenience for implementation to any practical transmission network. Originality/value This paper fulfills an identified need to study exaction of congestion management of the power line.

Optimal Placement of SVC Incorporating Installation Cost

As the FACTS controllers are an inequitable part of power system due to their fast and very flexible control. With the enhancement of FACTS technology, their role in the marginal cost determination should be considered taking their cost function into account. The aim of this paper is to compare the optimal performance of static VAR compensator (SVC) in voltage stability enhancement problem. During last decade, FACTS devices are broadly used for maximizing the margin of voltage stability and loadability of existing power system transmission networks. To get the optimal system, cost analysis might be necessary. In the cost analysis of SVC, authors analyzed the installation cost of SVC devices in USD, the cost of SVC in USD/KVAR and the average value of installation cost for given systems. Here, the voltage stability enhancement problem is solved applying Gravitational Search algorithm incorporating SVC. The following conditions are investigated with the incorporation of SVC: the role of SVC for improving the voltage profile, loss minimization and the approximate analysis on cost recovery and payback period with SVC in voltage stability enhancement problem. With this motivation, the location of SVC is finalized through weak bus identification methods. Voltage stability indices namely Fast Voltage Stability Index (FVSI) is utilized to identify weak buses in the systems. For calculation of the Size of SVC, an optimization routine Gravitational Search Algorithm (GSA) is established. The main purpose of optimization is to minimize the FVSI. The proposed approach has been tested on three standard IEEE bus systems with different loading scenarios. General Terms: Flexible AC Transmission System (FACTS), Marginal Cost, Voltage Stability Assessment, Static VAR Compensators.

Optimal Placement of SVC Using Fuzzy and Firefly Algorithm

<p>Voltage stability is major phenomena in any power system network for reliability and continuity operation. But the tight operation of power system due to overloading or fault on the system which is evitable and major threat to the power system. So it is necessary to maintain the voltages within the constraints at the overloading conditions also by placing of Static VAR Compensator (SVC) at optimal locations. New approaches are used to find the placement and size of the SVC at different locations. Fuzzy is used to find the location and the size of the SVC is fined by the Firefly algorithm. This paper considers different loading conditions of the power system network (125,150,175over loading conditions). From the results we can conclude that the power losses are reduced and the voltages can be maintained within the limits .IEEE 14 bus, IEEE 30 bus system is taken for the implementing the above techniques.</p>

Placement of SVC for Multi-objective Function using RCGA

This paper addresses the optimal placement of static var compensators (SVCs) in a power system network in such a manner that power loss, voltage deviation and installation cost is minimized. Voltage deviation is minimized by improving voltage profile. The aim of this study is to minimizing the power loss, voltage deviation and installation cost under critical contingency and increasing load condition in power system network. The multi-objective function is carried out in this study. RCGA is used to solve the optimization problem in this paper which is one of the heuristic methods. Real Coded Genetic Algorithm optimization helps in determining the location of the SVC. The proposed approach has been tested on IEEE-30 Bus test system with different objectives. It has also been observed, we can apply this algorithm to larger systems with computational difficulties. The obtained results show that the suggested method of SVC placement is effective in reducing the real power loss, voltage deviation and installation cost during normal as well as critical contingency cases. General Terms Power Loss, SVC, Critical Contingency, Real Coded Genetic Algorithms.

Hybrid GA–PSO for optimal placement of static VAR compensators in power system

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.

Optimal placement of SVC for transmission loss minimisation using self-adaptive firefly algorithm

Transmission loss minimisation is an important research issue, which can be minimised by reactive power compensation. Static var compensator (SVC) is a popular flexible alternating current transmission system (FACTS) device for reactive power support in power system. A new strategy has been proposed for optimal placement of SVC in power systems with a view to minimise the transmission loss. The proposed approach uses a self-adaptive firefly algorithm (SAFA) and identifies the optimal location for SVC placement and their parameters. The SVC placement strategy is applied on IEEE 14, IEEE 30 and IEEE 57-bus systems and their results are presented to demonstrate its effectiveness.

SVC Placement for Voltage Profile Enhancement Using Self-Adaptive Firefly Algorithm

Static VAR Compensator (SVC) is one of the shunt type FACTS devices for providing reactive power support in power systems network and its placement representing the location and size has significant influence on enhancement of voltage profile. This paper presents a firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing voltage deviation at the load buses to enhance the load bus voltages. This method uses a self-adaptive scheme for tuning the firefly parameters. The proposed strategy is tested on three IEEE test systems. The obtained results are promising and show the effectiveness of the proposed approach.

Optimal SVC Placement in Electric Power System Using NSGA-II

This paper proposes non dominated sorting genetic algorithm (NSGA-II) with a feature of adaptive crowding distance for obtaining optimal location and sizing of Static Var Compensators (SVC) to minimize real power losses (RPL) and load bus voltage deviation (VD) considering critical contingencies. The voltage security of the power system is also analyzed for all placements of SVCs at respective location computed. Finally, after compression of optimal power loss, load bus voltage deviation along with voltage security following by simulation of critical contingency the most appropriate location and sizing of SVC is determined. While obtaining the optimal location and sizing of SVC, single line outages are considered as contingencies and voltage limits for the load buses are considered as security constraints. The effectiveness of proposed approach has been demonstrated on IEEE 30-bus test system. The results obtained for proposed algorithm are optimistic and reveal the capability of the NSGA-II to generate well-distributed non-dominated Pareto front.

Using SVC to Economically Improve Transient Stability in Long Transmission Lines

ABSTRACTThe aim of this paper is to select the optimal size, location, and parameter setting of static var compensator (SVC) to improve power system stability in long transmission lines. Two objectives are considered in this paper to improve both the transient stability of the system and the SVC investment cost. In this study, the actual model of the long transmission line is considered to accurately determine the SVC location. By using the actual model of the line for long transmission lines, the SVC investment cost is reduced and the results have more accuracy than that found when the simplified model was used. In addition to placement of SVC for improving the system transient stability, an extra controller is used and adjusted to enhance the performance of the SVC. The problem is formulated as a form of multi-objective function and then multi-objective particle swarm optimization (MOPSO) is used to solve the problem and find the solution. In this research, results are presented as a set of responses to help the decision maker choose the appropriate response. A two-area test system with long transmission line is used to show the effectiveness and efficiency of the proposed method. The results of the non-linear simulation show that the optimum sizing, siting, and parameter setting of SVC can improve greatly the system's transient stability and reduce the SVC investment cost.

Minimization of Power Loss and Voltage Deviation by SVC Placement Using GA

Modern restructured power systems sometimes operate with heavily loaded lines resulting in power system to work under condition of higher power loss and higher voltage deviation, which may result in insecure operation of power system; even sometimes it may lead to voltage instability or system collapse. It is mainly due to continuous and uncertain growth and demand of electrical power. This paper presents a methodology to solve a multi-objective optimization problem to find optimal location and size of Static VAR Compensator (SVC); in order to minimize real power loss (RPL)& load bus voltage deviation (VD) and also enhancing voltage securityusing Continuous Genetic Algorithm (CGA). The effectiveness of the proposed method is demonstrated on a standard IEEE 30-bus system. The results obtained reveal effectiveness of CGA in handling multi objective optimization problem efficiently and successfully.

Fuzzy Genetic Algorithm Approach for the Optimal Placement of Flexible AC Transmission Systems Devices in a Power System

—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods.

Voltage Stability and Loading Margin Improvement in Power System by Optimal Placement of SVC

Proper installation of flexible ac transmission systems (FACTS) in existing transmission networks can improve transmission system loading margin (LM) to a certain degree and reduce network expansion cost. In the paper, under each contingency with high risk index (RI) value, the modal analysis (MA) technique is used to determine which buses need static var compensator (SVC) installation, and with maximum LM and minimum SVC installation cost composed into the multiobjective function the optimal LM enhancement problem is formulated as a multi-objective optimization problem (MOP) and solved by using the fitness sharing multi-objective particle swarm optimization (MOPSO) algorithm for a Pareto front set. In the Pareto front set for each considered contingency, the solution with the biggest performance index value is determined for SVC installation. Finally, an SVC installation scheme derived from the union of the SVC installations for all considered contingencies is recommended for LM enhancement. The proposed method is validated on the IEEE 30-bus reliability test system (RTS) and a practical power system.

Fuzzy based evolutionary algorithm for reactive power optimization with FACTS devices

In this paper, optimization techniques such as Genetic Algorithm (GA) and Differential Evolution (DE) along with Fuzzy Logic (FL) is used for the optimal setting of power system variables, including Flexible AC Transmission Systems (FACTS) devices. Here, two types of FACTS devices, Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are used for the optimal operation of the power system as well as in reducing congestion in transmission lines. Optimal placement of FACTS devices in the heavily loaded power system reduces transmission loss, controls reactive power flow, improves voltage profile of all nodes and also reduces operating cost. In this proposed approach fuzzy membership function is used for the selection of weak nodes in the power system for the placement of SVC’s as one of the FACTS devices while the location of TCSC’s are determined by the reactive power flow in lines. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes are detected by eigen value analysis and the amount of FACTS devices are determined by evolutionary techniques like, Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO). The superiority of the proposed fuzzy based optimization approach is established by the results and the comparative analysis with other methods.

EFFECT OF STATIC VAR COMPENSATOR ON VOLTAGE STABILITY UNDER NETWORK CONTINGENCIES

This paper focuses on enhancement of system performance under network contingencies through an optimal placement and optimal setting of static var compensator (SVC). The goal of the methodology developed is to identify the weakest bus using an index called Line Flow Index (LFI) and to maintain the voltages at all load buses within their specified limits through an optimal placement and optimal setting of SVC under contingency Condition. This premise is attested on 6- bus and 30 bus systems and the simulation results are presented to show the effectiveness of the method.

Optimal Location of SVC Considering Critical Contingency and Varying Load

The flexible Alternating Current transmission system (FACTS) may play a vital role in solving various problems of modern restructured power system networks, which have to operate under highly stressed conditions because of ever increasing demand of electric power. However, due to huge capital investment, an intensive investigation is required at planning stage to select optimal location and size of these devices to acquire maximum benefit of it. In this paper, Continuous Genetic Algorithm (CGA) based method is proposed for placement of Static Var Compensator (SVC) for minimizing real power loss, load bus voltage deviations and size of SVC. As a first step, contingency ranking is performed to determine the most severe line outages by evaluating voltage performance index (VPI) for both cases i.e. for base load condition and when load is varied randomly. Thereafter, Continuous Genetic Algorithm (CGA) has been applied to solve mixed continuous-discrete optimization problem. The optimization is carried out to find optimal location and size of SVC to minimize real power loss, load voltage deviation and rating SVC. The effectiveness of the proposed methodology is demonstrated on a standard IEEE 30-bus system.