American V-King Scientific Publishing Optimal Power Flows with security constraints using Cubic Lattice structured multi agent based PSO algorithm by optimal placement of Multiple TCSCs (original) (raw)
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International Journal of Computer Applications, 2012
This paper puts forward the implementation of multiagent based PSO algorithms (TDLSMADSO & CLSMAPSO) to obtain the optimal power flows by optimally placing SVC devices. The static var compensator (SVC) is modeled using susceptance model with modifications in the Y bus of the Newton Raphson Algorithm. The constraints related to violation limits, minimization of voltage stability index, and line loss are dealt using penalty factor approach. The new multi agent based cubic lattice and two dimensional lattice structured based PSO algorithms were considered for optimizing power flows while satisfying all the constraints mentioned above. These algorithms were tested on IEEE 30 and IEEE 14 bus systems to identify the suitable location, its susceptance value and firing angle. The results obtained were quite encouraging and will be useful in electrical restructuring.
Particle swarm optimisation for security constrained optimal power flow including TCSC
International Journal of Power and Energy Conversion, 2014
The planning, operation and control of power system are having great significance governed by security considerations. In an interconnected power system network obtaining maximum performance, maintaining system stability limits and facilitating efficient system operation are the challenging tasks. The estimation of effect of contingencies and planning suitable measures are desirable issues for maintaining security of complex power system. This paper describes the application of particle swarm optimisation (PSO) technique including thyristor controlled series capacitors (TCSCs) in order to eliminate or minimise line overloads under single contingency. A fuzzy logic composite criterion-based severity index is used as an objective to be minimised in order to improve the security of the power system. The proposed TCSC included PSO-OPF approach enhances the static security. A case study example on IEEE 30-bus test system demonstrates the applicability of the proposed approach.
International Journal of Computer Applications, 2012
This paper puts forward two new evolutionary multi agent based particle swarm optimization algorithms for solving security constrained (line flows and bus voltages) optimal power flows. These two methods combine the multi agents in two dimensional and cubic lattice structures with particle swarm optimization (PSO). All agents occupy in a cubic and square lattice like environments, with agents fixed on a lattice point in the ascending order of their fitness values. To obtain the optimal solution, each agent in cubic and square lattice competes and cooperates with its neighbor. Making use of these agent-agent interactions, CLSMAPSO and TDLSMAPSO accomplish the purpose of minimizing the Fuel cost value while maintaining all the constraints. In this paper, a Variable constriction factor has been considered for TDLSMAPSO and CLSMAPSO. Both the smooth and non-smooth cost functions were considered to take the effect of multiple fuels and multiple valves effects in to consideration. The outcomes are compared with many other methods like Genetic Algorithms, Differential Evolution, Normal PSO and Ant Colony optimization etc. , The OPF problem has been considered with three different cost functions to realize Optimal Power Flow using CLSMAPSO and TDLSMAPSO applied to IEEE 30 bus system. This unique method has the advantage of
2012 International Conference on Innovation, Management and Technology Research, ICIMTR 2012, 2012
One of the disturbances experienced by the power system is increase in loading condition, which often led the system to no longer remains in secure operating region. When the power system is exposed to any kind of time delay and inaccessibility of control scheme, system may become inconsistent leading to uncontrolled condition. Under this condition, the main purpose of the operator is to execute control actions to get the system back into the secure operating regions. Flexible AC transmission system (FACTS) device is one of the devices, which can be inserting to control power system stability improvement. This paper describes the optimal placement and sizing of TCSC using on Particle Swarm Optimization (PSO) method. The objective function for this study is to minimize the transmission loss, increase the voltage profile, while considering the cost of installation. Effect of weight coefficient and effect of population size during the optimization process towards obtaining the solution is also explored. To validate the proposed techniques, simulations are performed on an IEEE 30-bus system.
Multi Objective For Optimal Reactive Power Flow Using Modified PSO Considering TCSC
International Journal of Energy Engineering, 2012
Multi objective optimal reactive power flow considering FACTS technology is becoming one of the most important issue in power system planning and control. This paper presents a new variant of particle swarm algorithm with time varying acceleration coefficients (TVAC) to solve multi objective optimal reactive power flow (MOORPF) (power loss minimization and voltage deviation). The proposed algorithm is used to adjust dynamically the parameters setting of Thyristor controlled series capacitor (TCSC) in coordination with voltages of generating units. This study is implemented on the standard IEEE 30-Bus system and the results are compared with other evolutionary programs such as simple genetic algorithm (SGA) and the simple particle swarm algorithm (SPSO). Simulation results confirm robustness of this new variant based PSO in term of solution quality and convergence time.
Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC's in power systems
2015
Allocation of flexible AC transmission systems (FACTS) devices is a challenging power system problem. This paper proposes a new particle swarm optimisation (PSO) variant, called enhanced leader PSO (ELPSO), for solving this problem. This algorithm is capable of solving FACTS allocation problem in a way leading to lower amounts of power flow violations, voltage deviations and power losses with respect to other optimisation algorithms. Distributed thyristor controlled series compensators (D-TCSC's) are used. D-TCSC's are installed at all branches except those with regulating transformers. The reactances of D-TCSC's are found in optimisation process. ELPSO features a five-staged successive mutation strategy which mitigates premature convergence problem of conventional PSO. ELPSO and other optimisation algorithms are applied to IEEE 14 bus and 118 bus power systems for N-1 contingencies and also for simultaneous outage of four branches. The results show that it leads to lower amounts of power flow violations, voltage deviations and power losses with respect to conventional PSO (CPSO) and eight other optimisation algorithms including genetic algorithm (GA), gravitational search algorithm (GSA), galaxy based search algorithm (GBSA), invasive weed optimisation (IWO), asexual reproduction optimisation (ARO), threshold acceptance (TA), pattern search and nonlinear programming (NLP).
FACTS Devices Allocation Using a Novel Dedicated Improved PSO for Optimal Operation of Power System
DOAJ (DOAJ: Directory of Open Access Journals), 2007
Flexible AC Transmission Systems (FACTS) controllers with its ability to directly control the power flow can offer great opportunities in modern power system, allowing better and safer operation of transmission network. In this paper, in order to find type, size and location of FACTS devices in a power system a Dedicated Improved Particle Swarm Optimization (DIPSO) algorithm is developed for decreasing the overall costs of power generation and maximizing of profit. Thyristor-Controlled Series Capacitor (TCSC) and Static VAr compensator (SVC) are two types of FACTS devices that are considered to be installed in power network. The purpose of this study is reducing the power generation costs and the costs of FACTS devices with considering different load levels. The main bases of this paper are using of Optimal Power Flow (OPF) and DIPSO algorithm to technoeconomical analysis of the system for finding optimal operation. The Net Present Value (NPV) method is used to economic analysis of the system and power losses and maximum possibility load demand are considered for technical analysis. The proposed method is implemented on IEEE 57-bus test system and the achieved results are compared with genetic algorithm and particle swarm optimization methods to illustrate its effectiveness.
Procedia Engineering, 2011
Flexible Alternating Current Transmission Systems(FACTS) devices represents a recent technological development in electrical power systems, which makes utilities able to control power flow, increase transmission line stability limits, and improve security of transmission system. In a multi machine network, the influence of TCSCs on the network flows is complex since the control of any one device influences all others. In a competitive (deregulated) power market, the location of these devices and their control can significantly affect the operation of the system. This project investigates the use of TCSC to maximize total transfer capability generally defined as the maximum power transfer transaction between a specific power-seller and a power-buyer in a network. For this purpose, propose one of the Evolutionary Optimization Techniques, namely Differential Evolution (DE) to select the optimal location and the optimal parameter setting of TCSC which minimize the active power loss in the power network, and compare it's performances with Genetic Algorithm (GA). To show the validity of the proposed techniques and for comparison purposes, simulations will be carried out on an IEEE-14 bus power system. The results will expect that DE is quantitatively an easy to use, fast, robust and powerful optimization technique compared with genetic algorithm (GA).
Transmission Loss and TCSC Cost Minimization in Power System using Particle Swarm Optimization
The economical perspective of Flexible AC Transmission System (FACTS) installation and performance has been a crucial issue since inception and increasing every day due to strategic, technical and market constraints. The optimization of these issues plays an important role in the success of any utility and ultimately low cost availability of power at the user end. This paper presents the optimal location of Thyristor Controlled Series Capacitor (TCSC) in power system to minimize the transmission loss using Particle Swarm Optimization technique. The simulations are performed on the IEEE-14 bus system, IEEE 30 bus system and Indian 75 bus system with Newton Raphson load flow algorithm including TCSC.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/comparative-evaluation-of-optimum-power-flow-with-and-without-facts-devices-by-using-particle-swarm-optimization https://www.ijert.org/research/comparative-evaluation-of-optimum-power-flow-with-and-without-facts-devices-by-using-particle-swarm-optimization-IJERTV3IS110288.pdf In this paper, a Particle Swarm Optimization (PSO) approach is proposed to minimize the losses and generator fuel cost in optimal power flow (OPF) control with& without flexible AC transmission systems (FACTS) devices. The optimal settings of FACTS parameters are searched by the PSO approach. Particle Swarm Optimization (PSO) based OPF algorithm is developed in MATLAB 7.0.The optimum power flow using PSO with FACTS devices such as TCSC and UPFC in IEEE-26 bus system is done. The simulation results have tabulated with& without FACTS devices. The comparison of with &with FACTS devices, The optimum with UPFC by using PSO serves better results. Keywords-Thyrisyor controlled series capacitor (TCSC) Unified power flow controller (UPFC).