This paper presents the use of a recent developed algorithm inspired by the hunting mechanism of antlions in nature, called ant lion optimizer (ALO) algorithm for solving optimal reactive power dispatch (ORPD) problem considering a large-scale power system. The ORPD is formulated as a complex combinatorial optimization problem with nonlinear characteristic. The ALO algorithm is inspired from the hunting mechanism of antlions. One of the most interesting things in antlions is that they have a unique hunting behaviour and exhibit high capability of escaping the local optima stagnation. The ALO is used to find the set of optimal control variables of ORPD problem, such as generators terminal voltage, position of tap changers of transformers, and number of switchable capacitor banks. The performance and feasibility of the proposed algorithm are demonstrated through several simulation cases on IEEE 30-bus, IEEE 118-bus power systems and large-scale power system IEEE 300-bus power system. Comparison of obtained results with those reported in the literature shows clearly the superiority of ALO algorithm over other recently published algorithms in regards to real power losses and computational time, and hence confirmation of the efficiency of ALO algorithm in providing near-optimal solution

Purpose: In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach: A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings: Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value: The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

In this study, the authors developed a novel universal approximator by the integration of Petri networks into type 2 fuzzy neural networks (T2FNN). T2FNN involve large number of rules, which result in heavy computational burden and great computation time. By incorporating Petri layers to optimise the number of rules; these two drawbacks could be very well overcome. Moreover, a new inference type 2 fuzzy system was developed to reduce the time consumed in the iterative K–M inference procedure, and to increase the approximation accuracy. The proposed inference engine is based on the use of an adaptive modulation of the upper and the lower outputs. The Petri type 2 fuzzy neural networks (PT2FNN) approximator was used to approximate the adaptive control for uncertain single‐input single‐output non‐linear system. The stability of the closed‐loop system was proven and demonstrated using the Lyaponov approach. Comparative studies of the proposed PT2FNN approximator with type 1 fuzzy neural network and T2FNN were performed. The performances of PT2FNN over the two types of the fuzzy system were shown on the inverted pendulum system.