Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations

Tran, Tung The; Truong, Khoa Hoang; Vo, Dieu Ngoc

doi:10.1016/j.asej.2019.08.015

Cited by 91 publications

(79 citation statements)

References 41 publications

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“…The result of IBSA is identical to that of PSO and CGA [10]. Compared to FA, HTELA [13], SFS [11] and SSA [15], power loss reduction of IBSA is respectively 2.58%, 0.49%, 0.01% and 0.02% higher and the minimum voltage amplitude of IBSA is 0.0067 and 0.0003 higher than that of the HTELA [13] and SSA [15] methods. The current of branches of the system before and after reconfiguration is shown in Figure 6 (a).…”

mentioning

confidence: 64%

“…The result of IBSA is identical to that of PSO, CGA [10] and better than that of FA. Compared with HTELA [13], GWO-PSO [12], SSA [15] and SFS [11] power loss reduction of IBSA is 0.21% lower but the minimum voltage amplitude is 0.0034 higher than that of the aforementioned methods. The current of branches of the system before and after reconfiguration is shown in Figure 5 (a).…”

mentioning

confidence: 87%

“…The pseudo-code of IBSA for solving the network reconfiguration problem is shown in Figure 1. Assign the starting solution to the current population by using 7Validate the adaptive function of each solution is validated by (4) Determine the best so-far solution ( ) with the best adaptive function value ( ) Generate the historical population by using (5) and (6) Set the current generation equal to zero While the current generation < do // Step 3: Redefine the historical population Redefine the historical population by using (8) Permute the historical population by using (9) // Step 4: Generate new population Generate new population by using (11) Check and correct the bound of new population by using (13) // Step 5: update the population for next generation Modify the generated population by using (6) Validate the adaptive function of each solution is validated by (4) Update the current population by using (14) and (15) // Step 6: update the best so-far solution Update the best so-far solution by using (16) and (17) Increase the current generation End while…”

Section: Improved Backtracking Search Algorithm For Electric Distribumentioning

confidence: 99%

“…However, network reconfiguration is a nonlinear and discrete problem that needs to have effective solving methods. For finding optimal network configuration, there are a lot of solving methods consisting of methods based on mathematical approaches [1][2][3][4] methods based on experience of operating the power system [5][6][7] and methods developed from metaheuristic algorithms [8][9][10][11][12][13][14][15][16]. By using the methods based on mathematical approaches, description of network reconfiguration problem and solving process are complicated because the network reconfiguration problem has to be linearized.…”

Section: Introductionmentioning

confidence: 99%

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Optimal distribution network configuration using improved backtracking search algorithm

Nguyen

2020

TELKOMNIKA

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Optimal network configuration is one of the effective approaches for power loss reduction of the distribution network. This paper shows a network reconfiguration method using improved backtracking search algorithm (IBSA). Wherein, IBSA is improved in the process of generating randomly the initialization population. The network reconfiguration method based on IBSA is used to find the optimal network configuration for the 33-node and 69-node systems. The results are compared to the original backtracking search algorithm (BSA), particle swarm optimization (PSO), firefly algorithm (FA) and previous approaches. From the compared results, IBSA can determine the optimal network configuration with higher success rate than BSA, PSO, FA and lower power loss than other previous approaches. As a result, IBSA is an effective approach for finding the optimal network configuration.

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mentioning

confidence: 64%

mentioning

confidence: 87%

Section: Improved Backtracking Search Algorithm For Electric Distribumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal distribution network configuration using improved backtracking search algorithm

Nguyen

2020

TELKOMNIKA

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“…In fact, as current is smaller, the voltage drop is decreased accordingly. In addition to the two basic methods, other methods can be applied such as (1) placement of distributed generators [6,7], (2) the combination of reconfiguration and capacitor placement [8,9], (3) the combination of reconfiguration and distributed generator placement [10,11], and (4) the combination of capacitor placement and distributed generator placement [12,13]. In this paper, we focus on the second basic method of optimally installing capacitors with the task of determining the best location and the best rated reactive power.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reactive Power Generation for Radial Distribution Systems Using a Highly Effective Proposed Algorithm

et al. 2021

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In this paper, a proposed modified stochastic fractal search algorithm (MSFS) is applied to find the most appropriate site and size of capacitor banks for distribution systems with 33, 69, and 85 buses. Two single-objective functions are considered to be reduction of power loss and reduction of total cost of energy loss and capacitor investment while satisfying limit of capacitors, limit of conductor, and power balance of the systems. MSFS was developed by performing three new mechanisms including new diffusion mechanism and two new update mechanisms on the conventional stochastic fractal search algorithm (SFS). As a result, MSFS can reduce 0.002%, 0.003%, and 0.18% of the total power loss from SFS for the three study systems. As compared to other methods, MSFS can reduce power loss from 0.07% to 3.98% for the first system, from 3.7% to 7.3% for the second system, and from 0.92% to 6.98% for the third system. For the reduction of total cost, the improvement level of the proposed method over SFS and two other methods is more significant. It is 0.03%, 1.22%, and 5.76% for the second system and 2.31%, 0.87%, and 3.77% for the third system. It is emphasized that the proposed method can find the global optimal solutions for all study cases while SFS was still implementing search process nearby or far away from the solutions. Furthermore, MSFS can converge to the best solutions much faster than these compared methods. Consequently, it can be concluded that the proposed method is very effective for finding the best location and size of added capacitors in distribution power systems.

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Optimum estimation of series capacitors for enhancing distribution system performance via an improved hybrid optimization algorithm

Mohamed

Abdelaziz

Darwish

et al. 2023

Energy Science & Engineering

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As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy-saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I-GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I-GWO has a movement strategy called dimension learning-based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I-GWO-TS) algorithm can solve mixed-integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high-power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors' location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system.

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Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations

Cited by 91 publications

References 41 publications

Optimal distribution network configuration using improved backtracking search algorithm

Optimal distribution network configuration using improved backtracking search algorithm

Optimal Reactive Power Generation for Radial Distribution Systems Using a Highly Effective Proposed Algorithm

Optimum estimation of series capacitors for enhancing distribution system performance via an improved hybrid optimization algorithm

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