Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

• The optimal power flow problem is formulated as a multi-objective optimization problem.
• A teaching learning based optimization (TLBO) algorithm is presented for solving the OPF problem.
• Modifying the TLBO algorithm by applying opposition based learning (OBL) to enhance its search ability.
• The proposed approaches are implemented on three standard test systems.
• The effectiveness of the proposed TLBO and QOTLBO approaches are compared with NSGA-II, MOHS, EP, EPM and SFLA.

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.

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