Transient stability constrained optimal power flow using teaching learning based optimization

2016 ◽  
Author(s):  
Youcef Oubbati ◽  
Salem Arif
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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.

A Novel Optimization Algorithm for Transient Stability Constrained Optimal Power Flow

2016 ◽  
pp. 147-176
Author(s):  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
New England ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Linear Optimization ◽  
Standard Test ◽  
Optimal Power ◽  
Teaching Learning ◽  
Bus System

Optimal power flow with transient stability constraints (TSCOPF) becomes an effective tool of many problems in power systems since it simultaneously considers economy and dynamic stability of power system. TSC-OPF is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. This paper presents a novel and efficient optimisation approach named the teaching learning based optimisation (TLBO) for solving the TSCOPF problem. The quality and usefulness of the proposed algorithm is demonstrated through its application to four standard test systems namely, IEEE 30-bus system, IEEE 118-bus system, WSCC 3-generator 9-bus system and New England 10-generator 39-bus system. To demonstrate the applicability and validity of the proposed method, the results obtained from the proposed algorithm are compared with those obtained from other algorithms available in the literature. The experimental results show that the proposed TLBO approach is comparatively capable of obtaining higher quality solution and faster computational time.

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.

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