Distribution network reconfiguration using feasibility-preserving evolutionary optimization

Landeros, Alberto; Kozieł, Sławomir; Abdel-Fattah, Mohamed F.

doi:10.1007/s40565-018-0480-7

Cited by 37 publications

(25 citation statements)

References 18 publications

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“…The results illustrate the performance of the outage detection algorithm and the proposed sensor placement algorithm. In the following study, we will work on extending our algorithms to networks with distributed generators and microgrids since these networks pose critical challenges [29] that are not considered in the current formulation.…”

Section: Discussionmentioning

confidence: 99%

Outage Detection for Distribution Networks Using Limited Number of Power Flow Measurements

Alnajjab

Samudrala

Chen

et al. 2020

Journal of Modern Power Systems and Clean Energy

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Accurate topology estimation is crucial for effectively operating modern distribution networks. Line outages in a distribution network change the network topology by disconnecting some parts of the network from the main grid. In this paper, an outage detection (or topology estimation) algorithm for radial distribution networks is presented. The algorithm utilizes noisy power flow measurements collected from a subset of lines in the network, and statistical information characterizing errors in forecasting load demands. Additionally, a sensor placement scheme is presented. The sensor placement provides critical sensing for the outage detection algorithm so that any number of possible outages in the network can be detected. The performance of the proposed outage detection algorithm using the proposed sensor placement is demonstrated through several numerical results on the IEEE 123-node test feeder.

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Section: Discussionmentioning

confidence: 99%

Outage Detection for Distribution Networks Using Limited Number of Power Flow Measurements

Alnajjab

Samudrala

Chen

et al. 2020

Journal of Modern Power Systems and Clean Energy

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“…e singleline diagram of the system is demonstrated in Figure 1. e obtained results of the CSFSA are compared with those from other methods including FWA [29], RRA [30], HBB-BCA [32], Fuzzy-SFLA [33], MOIWO [34], IAICA [35], and CSA [31], FPEO [49], GWO-PSO [50], and ASFLA [51] as shown in Table 2.…”

Section: -Bus Systemmentioning

confidence: 99%

“…at bus 116. e 119-bus system has 15 tie-switches corresponding to 15 fundamental loops, and the tie-switches are 118-119-120-121-122-123-124-125-126-127-128-129-130-131-132 in the initial case. e results obtained by the proposed CSFSA including power loss, voltage deviation, and minimum voltage for the 119-bus system are compared to those from other methods including ITS [42], MTS [43], HAS [10], ACO-HAS [44], FWA [29], CSA [31], and FPEO [49] as given in Table 4.…”

Section: -Bus Systemmentioning

confidence: 99%

“…To verify the effectiveness of the proposed method, four distribution networks have been used for testing including the 33bus, 84-bus, 119-bus, and 136-bus systems. e obtained results from the proposed CSFSA have been compared with those from other methods reported in the literature such as HSA [10], Firework Algorithm (FWA) [29], Runner-root Algorithm (RRA) [30], CSA [31], Hybrid Big Bang-Big Crunch Algorithm (HBB-BCA) [32], Fuzzy Shuffled Frog-Leaping Algorithm (Fuzzy-SFLA) [33], Multiobjective Invasive Weed Optimization (MOIWO) [34], Improved Adaptive Imperialist Competitive Algorithm (IAICA) [35], Improved Mixed-integer hybrid Differential Evolution (IMI-DE) [36], Plant Growth Simulation Algorithm (PGSA) [37], GA [38,39], hybrid Artificial Immune Systems-Ant Colony Optimization (AIS-ACO) [40], Heuristic method [41], improved Tabu search (ITS) [42], modified Tabu search (MTS) [43], hybrid Ant Colony Optimization-Harmony Search Algorithms (ACO-HAS) [44], adaptive GA (AGA) [45], Uniform Voltage Distribution-based constructive reconfiguration algorithm (UVDA) [46], Mixed-Integer Convex Programming (MICP) [47], Non-revisiting Genetic Algorithm (NRGA) [48], Feasibility-preserving Evolutionary Optimization (FPEO) [49], a hybridization of Grey Wolf Optimizer (GWO) and PSO method (GWO-PSO) [50], and Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) [51] which are available in the literature. e remaining organization of the paper is represented in the order as follows.…”

Section: Introductionmentioning

confidence: 99%

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Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

Anh

2020

Complexity

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This paper proposes a chaotic stochastic fractal search algorithm (CSFSA) method to solve the reconfiguration problem for minimizing the power loss and improving the voltage profile in distribution systems. The proposed method is a metaheuristic method developed for overcoming the weaknesses of the conventional SFSA with two processes of diffuse and update. In the first process, new points will be created from the initial points by the Gaussian walk. For the second one, SFSA will update better positions for the particles obtained in the diffusion process. In addition, this study has also integrated the chaos theory to improve the SFSA diffusion process as well as increase the rate of convergence and the ability to find the optimal solution. The effectiveness of the proposed CSFSA has been verified on the 33-bus, 84-bus, 119-bus, and 136-bus distribution systems. The obtained results from the test cases by CSFSA have been verified to those from other natural methods in the literature. The result comparison has indicated that the proposed method is more effective than many other methods for the test systems in terms of power loss reduction and voltage profile improvement. Therefore, the proposed CSFSA can be a very promising potential method for solving the reconfiguration problem in distribution systems.

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“…The interests of the distribution system researchers and planners did not stop there, but rather numerous techniques have been introduced for optimal control of DNR. [15]- [17]. All of these researches are seek to give reliable service with satisfied power quality and minimum wasted energy with optimal allocation of the DGs, CBs, and DNR in a separate manner.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

et al. 2021

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Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This paper addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers' satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques. INDEX TERMSDistributed generators, Power losses minimization, switched capacitors, distribution reconfiguration, manta ray foraging algorithm. Nomenclature: Nbn Whole number of the branches in the system OFn Objective function CoV Control variables OT Tie branches which are opened NT Number of branches which could be opened to retain the radial structure of distribution system. Qsc Reactive output power from switching of the capacitors NC Number of the current switched capacitors. Pog Dispatchable output power of DG Ndg Number of located DGs Qsc min Minimum of reactive power resulting from switched capacitors Qsc max Maximum of reactive power resulting from switched capacitors Pog min Minimum of active power resulting from DG Pog max Maximum of active power resulting from DG V Voltage magnitude Ibn Flow of the current in the branches Ibn max Upper limit of the current flow in the branches Pd Active load demand Qd Reactive load demand PRG & PRQ Penetration level which is acceptable from the DGs and CBs Mb Buses number of the system QGsub Substation reactive power

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Distribution network reconfiguration using feasibility-preserving evolutionary optimization

Cited by 37 publications

References 18 publications

Outage Detection for Distribution Networks Using Limited Number of Power Flow Measurements

Outage Detection for Distribution Networks Using Limited Number of Power Flow Measurements

Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

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