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Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2019年第0期128卷 14-27页

作者： Kaboli, S. Hr. Aghay Alqallaf, Abdullah K. Wisma R&D Univ Malaya UM UMPEDAC Level 4JalanPantai Baharu Kuala Lumpur 59990 Malaysia Kuwait Univ Coll Engn & Petr Elect Engn Dept Safat 13060 Kuwait

According to no free lunch (NFL) theorem, a metaheuristic optimization method is superior to other meta heuristic optimization methods when it has focused on specific class of optimization problems. Thus, this paper focuses on developing artificial cooperative search (ACS) optimization algorithm to solve economic dispatch (ED) problems more precisely with less complexity than other metaheuristic optimization methods. ACS is a recently developed two population search algorithm based on coevolution process with high probability of finding optimal solution in complex and non-convex optimization problems. This merit is provided by balancing exploration of the problem's search space and exploitation of better results through use of two advanced evolutionary operators and only one control parameter. The constraint handling strategy of the proposed method for solving economic power load dispatch problems is to generate and work with feasible solutions along all the optimization iterations without any mismatch between the total amount of electric power generation and electricity demand plus network transmission loss. Unlike the penalty method, this strategy is unaffected by parameter setting of applied optimization method that complicates its applicability for solving economic power load dispatch problems. The feasibility of ACS for solving ED problem is tested on different lossy non-convex test systems of various sizes and complexities. The practical aspects such as satisfaction of power demand constraint, generation limits of generators and value-point loading effect are undertaken to solve ED problem in medium to relatively large-scale electric power systems. Obtained results confirm the ACS's capability in converging to a better solution highly robust within the reasonable computational time in all independent trials;all these as compared with other optimization algorithms reported in the literature for solving lossy non-convex ED problems. The results are analyzed stat

关键词： artificial cooperative search algorithm Economic dispatch Non-convex Optimization Transmission loss Valve-point effect

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Quadratic approximation-based hybrid artificial cooperative search algorithm for economic emission load dispatch problems

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2017年第4期27卷 1-14页

作者： Turgut, Mert Sinan Demir, Guleser Kalayci Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Mechatron Engn TR-35390 Izmir Turkey Dokuz Eylul Univ Dept Elect & Elect Engn TR-35390 Izmir Turkey

In this article, artificial cooperative search (ACS) algorithm is incorporated with the quadratic approximation (QA) operator to solve the multi-objective economic emission load dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence-based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, which is effective at global search;however, it does not perform so well at exploring promising regions. The QA operator, on the other hand, is a non-derivative-based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D-dimensional space. Solving the EELD problems with the hybridized ACS-QA algorithm, as being proposed in the present article, leads to more accurate results with fewer function evaluations. Also, multi-objectivity of the problem is handled by transforming it into a single-objective problem by using the weighted sum method. The efficiency of the proposed ACS-QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterward, the proposed ACS-QA algorithm and the ACS algorithm are implemented on multi-objective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS-QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls.

关键词： artificial cooperative search algorithm economic emission load dispatch multi-objective optimization problems quadratic approximation operator

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An optimization based resilient control strategy for voltage unbalance compensation in grid connected microgrid system

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ELECTRICAL ENGINEERING 2024年第1期107卷 205-222页

作者： Pavankumar, Yadala Debnath, Sudipta Paul, Subrata Jadavpur Univ Dept Elect Engn Kolkata India

A novel two stage multi-objective control strategy for optimal voltage unbalance compensation in low voltage microgrid systems consisting of inverter interfaced distributed generators (IIDGs) has been presented in this research. To ensure continuous and safe operation of the IIDGs during unbalanced voltage sags, the proposed control strategy supplies the optimal positive sequence voltage support and performs voltage unbalance compensation considering the current limitation of the inverters. The control strategy also ensures that the DGs deliver maximum allowable active power during voltage sag. The positive and negative sequence quantities of the IIDGs are controlled in such a way so that these objectives can be achieved simultaneously. The DGs are operated in coordination with each other to maintain the voltage profile as desired by the grid operator. Prioritisation of active power and unbalance compensation can be set depending upon the requirement of the customer. Under severe grid imbalance condition, the proposed technique can raise the positive sequence voltage to near nominal value from below 0.9 per unit maintaining all phase currents of DGs within safety *** solve the optimization problem and to generate the optimal references for the DG control unit, artificial cooperative search algorithm has been utilised. The two stage control strategy includes a local control for each DG, which coordinates with the central control to provide the optimal references for all the DGs. The multi-objective control strategy has been tested under different operating conditions and implemented in real time digital simulator to ensure the robustness and effectiveness of the proposed approach.

关键词： Microgrid Voltage unbalance compensation Multi-objective control strategy artificial cooperative search algorithm

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Impact of energy storage units on load frequency control of deregulated power systems

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ENERGY 2016年 97卷 214-228页

作者： Selvaraju, Ramesh Kumar Somaskandan, Ganapathy Annamalai Univ Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

Energy storage units are very vital for damping the oscillations due to the sudden changes in power system. The integration of small capacity energy storage unit to the power system in each area can effectively restrain the system oscillations. Hence in this paper, the energy storage devices, SMES (Superconducting Magnetic Energy Storage) units and RFB (Redox Flow Batteries) have been integrated into the interconnected deregulated LFC (Load Frequency Control) power system model and their effectiveness in improving the system performance has been realized and compared. The proposed controller design is applied to an interconnected two-area two-unit thermal deregulated power system with one reheat and one non-reheat unit in each area. This paper also proposes a new design of intelligent controller for load frequency control of interconnected deregulated power systems with energy storage devices using artificial cooperative search algorithm. To prove the scalability of the proposed framework, the design has also been implemented on a three-area interconnected deregulated power system model. The simulation results show the effective and efficient performance of RFB energy storage unit and the effectiveness of ACS (artificial cooperative search) algorithm tuned controller in improving the system performance of interconnected deregulated power system. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： artificial cooperative search algorithm Deregulated power systems Energy storage unit Load frequency control Superconducting Magnetic Energy Storage Redox Flow Batteries

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A plus Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 103卷 49-62页

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Dept Aircraft Elect & Elect Fac Aeronaut & Astronaut Kayseri Turkey Erciyes Univ Fac Engn Dept Geomat Engn Kayseri Turkey

The recently proposed artificial cooperative search (ACS) algorithm is a population-based iterative evolutionary algorithm (EA) for solving real-valued numerical optimization problems. It uses a rotation-invariant line recombination-based mutation strategy and rule-based crossover operator. However, it performs poorly for problems that include closely-related variables because, in these cases, generating uncorrelated feasible trial solution vectors using stochastic crossover methods is extremely difficult, and its mutation and crossover operators are also less effective. This paper adds a new QR-decomposition-based rotation-invariant search strategy to the ACS algorithm to improve its ability to solve such problems. This new, advanced ACS algorithm, called A+, has only one control parameter, alpha, and experimental results have shown that its performance does not strongly depend on the initial value of alpha. This paper also examines A+'s performance for noisy point cloud filtering, which is a complex real-world problem. The results of numerical experiments demonstrate that A+'s performance when solving numerical and real-world problems with closely-related variables is better than those of the comparison algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： artificial cooperative search algorithm artificial bee colony algorithm Backtracking search optimization algorithm Cuckoo search algorithm CMAES Differential evolution algorithm

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ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

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Journal of Applied Research and Technology 2017年第2期15卷 152-166页

作者： Selvaraju, Ramesh Kumar Somaskandan, Ganapathy Annamalai University Chidambaram India

In this paper, an artificial cooperative search (ACS) algorithm tuned adaptive network-based fuzzy inference system (ANFIS) controller for optimal gain tuning of load frequency control (LFC) operation in deregulated scenario has been offered. The conventional controllers for load frequency control operation are having fixed gain values intended for nominal operating conditions of the power system and they do not afford effective and efficient performance over a large range of operating scenarios in the deregulated environment. To progress the system performance to its near optimum for all probable operating circumstances of the power system, the controller gains have to be computed for the equivalent operating conditions by using the restructured parameters. For this intention, a controller based on an adaptive network-based fuzzy inference system seems to be the most excellent and valuable preference. The ANFIS is trained by off-line data obtained using a new optimization technique, artificial cooperative search optimization algorithm and the corresponding gains are updated in real-time as per the changing operating conditions. ACS is a swarm intelligence algorithm developed for solving numerical optimization problems. The swarm intelligence philosophy behind ACS algorithm is based on the migration of two artificial superorganisms as they biologically interact to achieve the global minimum value pertaining to the problem. To exhibit the competence and robustness of the projected ACS algorithm tuned ANFIS controller, the controller has been implemented on a two-area two-unit interconnected deregulated power system having one reheat unit and one non-reheat unit in each area. The simulation results exhibit the ability of the designed ACS algorithm tuned ANFIS controller for online LFC operation in deregulated environment. © 2017 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico

关键词： Adaptive network-based fuzzy inference system artificial cooperative search algorithm Deregulated power system Load frequency control

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