Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

2016 ◽  
Vol 5 (2) ◽  
pp. 64-84 ◽  
Author(s):  
Susanta Dutta ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Parameter ◽  
Electric Power Supply ◽  
Facts Devices ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Simulation Results ◽  
Teaching Learning

In this paper, quasi-oppositional teaching-learning based optimization (QOTLBO) is introduced and successfully applied for solving an optimal power flow (OPF) problem in power system incorporating flexible AC transmission systems (FACTS). The main drawback of the original teaching-learning based optimization (TLBO) is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning (OBL) concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization (HDE-BBO). This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.

Optimal Location of TCSC Using Opposition Teaching Learning Based Optimization

2015 ◽  
Vol 4 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Pranabesh Mukhopadhyay ◽  
Susanta Dutta ◽  
Provas Kumar Roy
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Population Based ◽  
Flow Solution ◽  
Opposition Based Learning ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Thyristor Controlled Series Compensator

This paper focuses on the optimal power flow solution and the enhancement of the performance of a power system network. The paper presents a secured optimal power flow solution by integrating Thyristor controlled series compensator (TCSC) with the optimization model developed under overload condition. The Teaching Learning Based Optimization (TLBO) has been implemented here. Recently, the opposition-based learning (OBL) technique has been applied in various conventional population based techniques to improve the convergence performance and get better simulation results. In this paper, opposition-based learning (OBL) has been integrated with teaching learning based optimization (TLBO) to form the opposition teaching learning based optimization (OTLBO). Flexible AC Transmission System (FACTS) devices such as Thyristor controlled series compensator (TCSC) can be very effective for power system security. Numerical results on test systems IEEE 30-Bus with valve point effect is presented and compared with results of other competitive global approaches. The results show that the proposed approach can converge to the optimum solution and obtains the solution with high accuracy.

scholarly journals A Fuzzy Based Evolutionary Algorithm for Solving Multiobjective Optimal Power Flow with FACTS Devices

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
R. Vanitha ◽  
J. Baskaran
Keyword(s):  
Power System ◽  
Differential Evolution ◽  
Goal Programming ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Technique ◽  
Fuzzy Goal Programming ◽  
Facts Devices ◽  
Optimal Power ◽  
Fuzzy Goal

A new Fuzzy Differential Evolution (FDE) algorithm is proposed for solving multiobjective optimal power flow with FACTS devices. This new optimization technique combines the advantages of Weighted Additive Fuzzy Goal Programming (WAFGP) and Differential Evolution (DE) in enhancing the capacity, stability, and security of the power system. As the weights used in WAFGP would have a significant impact on the operational and economical enhancements achieved in the optimization, they are optimized using evolutionary DE algorithm. This provides a way for exploring a balanced solution for a multiobjective problem without sacrificing any individual objective’s uniqueness and priority. The multiple objectives considered are maximizing the loadability condition of the power system with minimum system real power loss and minimum installation cost of the FACTS devices. Indian utility Neyveli Thermal Power Station (NTPS) 23 bus system is used to test the proposed algorithm using multiple FACTS devices. The results compared with that of DE based fuzzy goal programming (FGP) demonstrates that DE based WAFGP algorithm not only provides a balanced optimal solution for all objectives but also provides the best economical solution.

scholarly journals OPTIMAL POWER FLOW WITH TCSC DEVICE USING CUCKOO OPTIMIZATION ALGORITHM

2018 ◽  
Vol 54 (3A) ◽  
pp. 52
Author(s):  
Duong Thanh Long
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Power Transmission ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Cuckoo Optimization Algorithm ◽  
Facts Devices ◽  
Optimal Power

Optimal Power Flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Series FACTS devices such as Thyristor controlled series compensators (TCSC), with its ability to directly control the power flow can be very effective to power system security. Thus, integration TCSC in the OPF is one of important current problems and is a suitable method for better utilization of the existing system. This paper is applied Cuckoo Optimization Algorithm (COA) for the solution of the OPF problem of power system equipped with TCSC. The proposed approach has been examined and tested on the IEEE 30-bus system. The results presented in this paper demonstrate the potential of COA algorithm and show its effectiveness for solving the OPF problem with TCSC devices over the other evolutionary optimization techniques.

Transient Stability Constrained Optimal Power Flow Using Teaching Learning-Based Optimization

2014 ◽  
Vol 3 (4) ◽  
pp. 55-71 ◽  
Author(s):  
Aparajita Mukherjee ◽  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Nonlinear Problems ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Bus System

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

Transient Stability Constrained Optimal Power Flow Using Teaching Learning Based Optimization

2015 ◽  
Vol 4 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Aparajita Mukherjee ◽  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Nonlinear Problems ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Bus System

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

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