Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

Rao, B. Srinivasa; Vaisakh, K.

doi:10.1016/j.asoc.2014.06.043

Cited by 25 publications

(12 citation statements)

References 26 publications

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“…FCSA Input: N (the size of the population), n (the number of antibodies selected for cloning), n c (the number of clones), m (the degree of variation), c (Rac1 protein activity threshold) Output: the best antibody (1) Begin (2) Randomly generate N antibodies to form the initial candidate set (3) while not meet algorithm termination conditions do (4) Calculate the affinity Aff ab i of each antibody for antigen in the candidate set and record antibody survival time T ab i (5) Sort the antibodies in the candidate set according to their affinity, and put the best n antibodies into the antibody set Ab s (6) forab i inAb s (7) Update the value of the appropriate memory of antibody ab i : S ab i + � 1. See CLONING METHOD, clone antibody ab i according to n c and Aff ab i , and put all antibodies obtained by cloning into antibody set Ab c (8) end for (9) forab i inAb c (10) See VARIATION METHOD, according to the degree of variation m and the affinity of the antibody for the antigen Aff ab i to mutate ab i (11) if antibody ab i is a variant antibody (12) e ab i survival time T ab i � 0, e appropriate memory intensity S ab i � 1 (13) end if (14) end for (15) Select the N antibodies with the highest antigen affinity in Ab c and Ab to replace the N antibodies in Ab (16) See FORGETTING METHOD, calculate the Rac1 protein activity of each antibody in Ab according to the ratio of T ab i to S ab i (17) if antibody ab i Rac1 protein activity > threshold (18) forget the antibody ab i (19)…”

Section: Resultsmentioning

confidence: 99%

“…Gong et al [14] proposed an improved cloning selection algorithm based on the Baldwin effect, which makes the algorithm more effective and robust by promoting the evolutionary exploration of good genotypes. Rao and Vaisakh proposed [15] a multiobjective adaptive cloning selection algorithm to solve the optimal power flow problem. Pareto optimization was found by using the crowded distance, and the best strategy was selected based on the fuzzy mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

et al. 2020

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The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

et al. 2020

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“…An Adaptive Clonal Selection Algorithm (ACSA) was presented in [143] for solving the MOOPF problem considering multi-type FACTS devices-UPFC, IPFC, and GUPFC-and load uncertainty. Different objective functions, total generation cost, transmission losses, and voltage stability index (L-index), were considered as well.…”

Section: • Clonal Selection Algorithmmentioning

confidence: 99%

State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints

et al. 2021

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Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.

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“…λ is represents the permanent failure rate as defined in Eq. (2). c is depends on the circuit topology and location of switches, which is determined by using the following equation.…”

Section: System Average Interruption Duration Index (Saidi)mentioning

confidence: 99%

“…All distribution of electrical energy and power is done by using a constant voltage system. A static synchronous compensator (STATCOM) is a type of flexible alternating current transmission system (FACTS) [2] device, which is mainly used for voltage stability maintenance and reactive power compensation in power systems. In this paper, the STATCOM [3] is mainly used for reactive power compensation and voltage stability enhancement.…”

Section: Introductionmentioning

confidence: 99%

A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

Sankaramoorthy¹,

Veeraraghavan²

2017

Turk J Elec Eng & Comp Sci

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This paper presents a multiobjective optimization methodology to optimally place a STATCOM in electric power distribution networks. The combination of multiobjective ant colony optimization (MACO) and the bacterial foraging optimization algorithm (BFOA) is proposed to minimize the power loss and total cost. The main intention of this analysis is to optimally place the STATCOM at multiple locations such as the transmission side, middle, and load side. Identifying the type and location of the STATCOM is a combinatorial optimization problem in power systems.In order to overcome this problem, the combination of hybrid MACO and BFOA algorithms is applied in this analysis to minimize the total cost and power loss. The total cost of the overall network is calculated by using system average interruption duration index and[ system average interruption frequency index metrics. Moreover, the BFOA is used in this paper to minimize the power loss during distribution, which is adequate in searching for the optimal solution.The problem of reducing power losses in distribution systems through the BFOA approach is described here for a 5-bus system. Test results of a 5-bus network show that the proposed MACO with BFOA method can efficiently ensure the power loss and total cost minimization.

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Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

Cited by 25 publications

References 26 publications

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints

A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

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