B. Venkateswara Rao

Implementation of Static VAR Compensator for Improvement of Power System Stability

Static VAR compensator (SVC) is incorporated in Newton Raphson method in which Power Flow Solution is a solution of the network under steady state conditions subjected to certain constraints under which the system operates. The power flow solution gives the nodal voltages and phase angles given a set of power injections at buses and specified voltages at a few, both the models of SVC i.e.SVC Susceptance and Firing Angle Models are discussed. It is also shown that the power system losses are decreased after incorporating the SVC in this N-R method. The results are generated for 24-Bus system. The reactors are thyristor-controlled and the capacitors can be either fixed or controlled. Advanced load flow models for the SVC are presented in this paper. The models are incorporated into existing load flow (LF) Newton Raphson algorithm. The new models depart from the generator representation of the SVC and are based instead on the variable susceptance concept. The SVC state variables are combined with the nodal voltage magnitudes and angles of the network in a single frame of reference for a unified, iterative solution through Newton methods. The algorithm for Load Flow exhibit very strong convergence characteristics, regardless of the network size and the number of controllable devices. Results are presented which demonstrate the process of the new SVC models.

A Comparative Study of BAT and Firefly Algorithms for Optimal Placement and Sizing of Static VAR Compensator for Enhancement of Voltage Stability

Modern electric power utilities are facing many challenges due to increasing power demand but the growth of power generation and transmission has been limited due to limited resources, environmental restrictions and right-of-way problems. These problems can be minimized by installing Flexible Alternating Current Transmission System (FACTS) devices in modern electric utilities to optimize the existing transmission system. Most effective use of the FACTS devices depend on the fact, how these devices are placed in the power system, i.e. the location and size. An optimal location and size of FACTS devices allows controlling its power flows and thus enhances the stability and reliability of the power systems. In this paper, Firefly Algorithm (FA) and BAT Algorithm (BAT) have been applied and compared to determine the optimal location and size of Static VAR Compensator (SVC) in a power system to improve voltage stability subjected to minimize the active power losses, fuel cost, branching loading and voltage deviation. The effectiveness of the proposed algorithms and improvement of power system stability has been demonstrated on IEEE 57 bus system using fast voltage stability index. The results obtained with variation of parameters of Firefly and BAT Algorithms has been studied and compared with Genetic Algorithm. The results are presented and analyzed.

Optimal Power Flow by Newton Method for Reduction of Operating Cost with SVC Models

The optimal power flow is a power flow problem in which certain variables are adjusted to minimize an objective function such as cost of the active power generation or the losses,while satisfying physical operating limits on various controls, dependent variables and function of control variables. Current interest in OPF covers around its ability to solve for the optimal solution that takes account of security of the system. Practical solutions for OPF problems with separable objective functions have been obtained with special linear programming methods,but the classical OPF has defined practical solutions, the Newton approach is a flexible formulation that can be used to develop different OPF algorithms suited to the requirements of different applications. In other words, the optimal power problem seeks to find an optimal profile of active and reactive power generations along with voltage magnitudes in such a manner as to minimize the total operating costs of a thermal electric power system, while satisfying network security constraints. The OPF method is based on load flow solution by the Newton’s method, a first order gradient adjustment algorithm for minimizing the objective function and use of penalty functions to account for inequality constraints on dependent variables.

Multi-Objective Optimal Power Flow using BAT Search Algorithm with Unified Power Flow Controller for Minimization of Real Power Losses

In this paper a multi objective optimal power flow OPF is obtained by using BAT search algorithm BAT with Unified power flow controller UPFC. UPFC is a voltage source converter type Flexible Alternating Current Transmission System FACTS device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. UPFC along with BAT algorithm is used to minimize the total real power generation cost, real power losses in OPF control. The BAT algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm are compared with Differential Evaluation DE.

A Computational Comparison of Swarm Optimization Techniques for Optimal Load Shedding under the presence of Unified Power Flow Controller to Avoid Voltage Instability

Voltage instability has become a serious threat to the operation of modern power systems. Load shedding is one of the effective countermeasures for avoiding instability. Improper load shedding may result in huge technical and economic losses. So, an optimal load shedding is to be carried out for supplying more demand. This paper implements BAT and Firefly algorithms for solving the optimal load shedding problem to identify the optimal amount of load to be shed. This is applied for a multi objective function which conatins minimization of amount of load to be sheded, active power loss minimization and voltage profile improvement. The presence of with and with out Unified Power Flow Controller UPFC on load shedding for IEEE 57 bus system has been presented and analyzed. The results obtained with BAT and Firefly Algorithms were compared with Genetic Algorithm GA.

Voltage Collapse Proximity Indicator based Placement and Sizing of Static VAR Compensator using BAT Algorithm to Improve Power System Performance

Power systems are becoming increasingly more complex due to the interconnection of regional system and deregulation of the overall electricity market. At present owing to the increase in power demand, power system has become more complex and heavily loaded, and is subjected to unstable or insecure operations. For secure operation, it is required to enhance the level of a security margin of the power system. In this paper sensitivity analysis based Voltage collapse proximity Indicator (VCPI) is proposed to select the optimal location of Static VAR Compensator (SVC). An Optimal Power Flow using BAT algorithm is carried out in order to find the optimal size of SVC which minimizes the total real power generation cost. Power System security is assessed using Line security index and voltage security index for IEEE 14 and IEEE 30-bus system using MATLAB Simulation. The results are presented and analyzed for BAT Algorithm based Optimal Power Flow without and with SVC and compared with Genetic Algorithm (GA).

Optimization of a power system with Interior Point method

Power flow or load flow solution is essential for continuous evaluation of the performance of the power systems so that suitable control measures can be taken in case of necessity. Load flow studies are made to plan the best operation and control of the existing system as well as plan the future expansion to keep pace with the load growth. Generally in any power system operation, our main aim is to operate a power system optimally. Optimality can be achieved by minimizing the cost, losses and maintaining voltage profile. So in order to achieve the above conditions that is to operate a system in an optimal way we choose various optimization techniques namely, optimal power flow by Newton method (OPF) and Interior Point (IP) method. In this paper a 5-BUS test system is taken and analyzed using the above mentioned two methods. It is shown that how the system power losses are decreased and voltage profiles are increased after using Interior Point (IP) method in this model. The results are generated for 5-Bus system.

Effect of Advanced Static VAR Compensator on Control of Power System Load Shedding

This paper investigates the effect of Static VAR Compensator (SVC) on power system load shedding. SVC is mainly used in power system stability improvement. This paper proposes a new use of SVC to reduce load shedding. An algorithm of Newton Raphson method (NR) to reduce the load shedding for installing SVC in the system is proposed in this paper. 5 bus test system example is used to demonstrate the effect on load shedding. The test results show that the effect of SVC is significant, in this Static VAR compensator (SVC) is incorporated in Newton Raphson method in which Power Flow Solution is a solution of the network under steady state conditions subjected to certain constraints under which the system operates. The power flow solution gives the nodal voltages and phase angles given a set of power injections at buses and specified voltages at a few, the model of SVC i.e. SVC Susceptance model is discussed. It is also shown that the power system losses are decreased after incorporating the SVC in this N-R method. The results are generated for 5-Bus system. By incorporating the SVC the amount of load shedding is reduced to get the voltages in their limits.

Look Up Table Based Fuzzy Logic Controller for Unmanned Autonomous Underwater Vehicle

The underwater vehicle is six degrees of freedom model. The execution of spatial maneuvers are determined mainly by the dynamic properties of underwater vehicle particularly controllability and stability. The control surfaces are situated at the rear end of the underwater vehicle which moves either vertically or horizontally (Pitch, Yaw, Roll, Pitch-rate,Yaw-rate etc.) used to steer the vehicle to run according to preprogrammed course as per logic till such a time the target is acquired. The underwater vehicle response is slow compared to air scenario due to constraints like higher density of water; the resistance motion is many hundred times greater than air. In this paper a rule-based fuzzy logic controller is designed for Yaw control, which is used for the rudder movement of an underwater vehicle. A Plant model is extracted using the input and output behavior and is assumed to be a linear time invariant second order. For on line implementation a decision table is stored in underwater vehicle computer memory in the form of Lookup table. For each combination of Inputs the required search will be done in the table and the appropriate value will be picked up. Using this technique the control algorithm becomes shorter and runs faster than those that reinterpret the rules at each control cycle of the system. This Lookup Table is used in the simulation of Yaw control of a Six Degrees of Freedom Model. The plant responses are compared for both conventional controller and fuzzy logic controller with regard to time of response, overshoot and steady state error.

Voltage stability index based optimal placement of static VAR compensator and sizing using Cuckoo search algorithm

This paper furnish the new Metaheuristic algorithm called Cuckoo Search Algorithm (CSA) for solving optimal power flow (OPF) problem with minimization of real power generation cost. The CSA is found to be the most efficient algorithm for solving single objective optimal power flow problems. The CSA performance is tested on IEEE 57 bus test system with real power generation cost minimization as objective function. Static VAR Compensator (SVC) is one of the best shunt connected device in the Flexible Alternating Current Transmission System (FACTS) family. It has capable of controlling the voltage magnitudes of buses by injecting the reactive power to system. In this paper SVC is integrated in CSA based Optimal Power Flow to optimize the real power generation cost. SVC is used to improve the voltage profile of the system. CSA gives better results as compared to genetic algorithm (GA) in both without and with SVC conditions.