Optimal Allocation Of Static VAR Compensator Research Articles

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems. Pareto Envelope-based Selection Algorithm II (PESA-II) with fuzzy logic decision maker is developed to determine the optimal location and size of SVC based on the minimum total power losses and Voltage Deviation (VD). Combined cumulants and gram-chalier expansion are used for solving the probabilistic load flow problem. The proposed algorithm is tested on 33- bus and 69-bus distribution systems. The developed algorithm gives an acceptable solution with low number of iterations and less computation cost compared with the Monte Carlo method.

Optimal allocation of static VAR compensator by a hybrid algorithm

Flexible AC transmission system (FACTS) devices are gaining popularity in electrical transmission networks for the last few years due to their ability to improve a system’s performance without restructuring or expanding the existing network. But, placing the FACTS device at any arbitrary location in the network may not be optimally beneficial. Also, judging the optimally beneficial location of these devices becomes necessary prior to investment owing to their high expense of installation and maintenance. In this paper, a novel hybrid algorithm combining Cuckoo Search Algorithm (CSA) and Chemical Reaction Optimization (CRO) is proposed to solve the problem of optimally allocating a static VAR compensator (SVC) for the standard IEEE 14-bus, 30-bus and 57-bus transmission systems under heavily loaded condition. The optimal allocation of SVC is done by considering technical aspects like voltage stability, power generation minimization and line loss reduction and also by considering commercial facets like savings on annual cost of power generation and return-on-investment (ROI) time period for the expenditure incurred on the SVC. The ROI calculation is a novel addition to the problem formulation which adds a practical outlook to the feasibility of investment. Thus, a holistic approach is followed for determining the optimal location of the SVC in the power network and the results validate the superior performance of the proposed algorithm.

An approach to optimal allocation of SVC in power systems connected to DFIG wind farms based on maximization of voltage stability and system loadability

This paper proposed an approach to determine optimal location of SVC (Static Var Compensator) to improve the voltage profile and maximize system loadability in power networks connected to a doubly-fed induction generator (DFIG)-based wind farm. A variable reactance model for SVC is presented in steady state studies and implemented in the load flow program with embedded Flexible AC Transmission Systems (FACTS) devices. The continuation power flow method is used to determine optimal location of SVC and steady state stability margin, based on closing to point of voltage collapse. As an important result in this paper, we obtained an optimal location of SVC devices on wind farm by calculating the new indices such as maximum loading point (MLP) and mega watt margin (MWM). A case study and simulation are done on modified IEEE14 bus test system.

Sensitivity Analysis based Optimal Location and Tuning of Static VAR Compensator using Firefly Algorithm

This paper presents a new Meta heuristic optimization algorithm called firefly algorithm (FA) used to solve the multi objective optimal power flow to identify the optimal setting of Static VAR Compensator (SVC) and optimal location is identified by using sensitivity analysis when the system is operating under normal and overloaded conditions. Sensitivity analysis based Voltage collapse proximity Index is proposed for placing the SVC at appropriate location under normal and over loaded conditions. Once the location to install SVC is identified, the optimal allocation of SVC is determined through firefly algorithm based A multi-criterion objective function comprising of four objectives minimize total power loss, minimize total voltage magnitude deviations, minimize the fuel cost of total generation and minimize the branch loading to obtain the optimal power flow. Simulations have been implemented in MATLAB and the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems have been used as a case study. The results we have obtained indicate that installing SVC can significantly enhance the voltage stability of power system. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Genetic Algorithm (GA). The results we have obtained indicate that FA is an easy to use, robust, and powerful optimization technique compared with GA.

Sensitivity Analysis Based Optimal Location and Tuning of Static Var Compensator Using Firefly Algorithm

This paper presents a new Meta heuristic optimization algorithm called firefly algorithm (FA) used to solve the multi objective optimal power flow to identify the optimal setting of Static VAR Compensator (SVC) and optimal location is identified by using sensitivity analysis when the system is operating under normal and overloaded conditions. Sensitivity analysis based Voltage collapse proximity Index is proposed for placing the SVC at appropriate location under normal and over loaded conditions. Once the location to install SVC is identified, the optimal allocation of SVC is determined through firefly algorithm based A multi-criterion objective function comprising of four objectives minimize total power loss, minimize total voltage magnitude deviations, minimize the fuel cost of total generation and minimize the branch loading to obtain the optimal power flow. Simulations have been implemented in MATLAB and the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems have been used as a case study. The results we have obtained indicate that installing SVC can significantly enhance the voltage stability of power system. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Genetic Algorithm (GA). The results we have obtained indicate that FA is an easy to use, robust, and powerful optimization technique compared with GA.