An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow

Ghasemi, Maryam; Ghavidel, Sahand; Gitizadeh, Mohsen; Akbari, Ebrahim

doi:10.1016/j.ijepes.2014.10.027

Cited by 153 publications

(94 citation statements)

References 41 publications

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“…The necessary line and bus data of the system are taken from [13]. The dimension of this problem is 12 including 6 generator voltages, 4 transformers tap settings and 2 static var compensators.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Application of Fruit Fly Algorithm for Security Constrained Optimal Power Flow Problem

Sakthivel¹,

Kavipriya²,

Poovarasi³

et al. 2017

IJCA

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Optimal power flow (OPF) is a major task in power system economics and operation. In OPF power real power outputs from the generators of a power system are so adjusted that the total production cost is minimum. Security constraint OPF (SC-OPF) is minimizing the cost keeping line flows within their respective limits for security reasons. Real power output from generators, generator bus voltage magnitudes, var outputs from shunt compensators and transformer tap settings are controlled for optimizing the total fuel cost in this OPF problem. This proposed work considers the bio inspired fruit fly algorithm (FFA) for optimally selecting the values for control variables. The proposed algorithm is simple, with less number of parameters and easy to implement. The performance of this algorithm in OPF task is tested on IEEE 30 bus test system. Numerical results are compared to literature results and found to be improved. KeywordsOptimal power flow, security constraint optimal power flow, bio inspired algorithm, line flow limit.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Application of Fruit Fly Algorithm for Security Constrained Optimal Power Flow Problem

Sakthivel¹,

Kavipriya²,

Poovarasi³

et al. 2017

IJCA

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show abstract

“…compared with Case 1.1. Furthermore, the comparison of system voltage profiles between Case 1.1 and Case 1.3 is presented in Figure 5, which [3] 0.093269 0.093952 0.094171 LTLBO [24] 0.0974 0.0983 0.1006 DE-PS [25] 0.0978 0.0997 0.1022 GABC [26] 0.1007 0.1052 0.1097 BBO [12] 0.1020 0.1105 0.1207 clearly shows the improvement of bus voltage profile. The simulation results of FCGCS and other methods summarized in Table 5 indicate the FCGCS method has powerful searching ability.…”

Section: Case 12 Active Power Lossesmentioning

confidence: 99%

Optimal Power Flow Using Gbest‐Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

Chen

Qiu

Zhang

et al. 2017

Mathematical Problems in Engineering

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The optimal power flow (OPF) is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbestguided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.

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“…The numerical results of the ITLBO were compared with those of the basic TLBO and other state-of-the-art optimization algorithms, such as the TLBO algorithm with Lévy mutation in the learner phase (LTLBO) [33], the genetic algorithm with multi-parent crossover (GAMPC) [34] and the self-regulation particle swarm optimization algorithm (SRPSO) [35]. The "D" refers to the dimension of the optimization problems.…”

Section: Experiments On Benchmark Functionsmentioning

confidence: 99%

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

Zhang

Yang

2015

Symmetry

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Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuckoo search phase are incorporated to improve the diversity of the population and enhance the exploration. In the worst recombination phase, the worst solution is updated by a crossover recombination operation to prevent the premature convergence. The cuckoo search phase is adopted to escape the local optima. Experimental results indicate that the recognition accuracy is up to 98.9% which strongly demonstrates the effectiveness and reliability of the proposed detection method.

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An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow

Cited by 153 publications

References 41 publications

Application of Fruit Fly Algorithm for Security Constrained Optimal Power Flow Problem

Application of Fruit Fly Algorithm for Security Constrained Optimal Power Flow Problem

Optimal Power Flow Using Gbest‐Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

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