Oppositional Brain Storm Optimization for Fault Section Location in Distribution Networks

Xiong, Guojiang; Zhang, Jing; Shi, Dongyuan; He, Yu

doi:10.1007/978-3-030-15070-9_3

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…To verify the effectiveness of LOBGSK, a total number of seven stateof-the-art algorithms and the original GSK are considered. These seven algorithms are binary artificial bee colony algorithm (BABC), 38 binary biogeography based optimization (BBBO), 39 binary particle swarm optimization (BPSO), 40 (BTLBO), 41 binary whale optimization algorithm (bWOA), 42 oppositional brain storm optimization (OBSO), 43 and binary GSK optimization algorithm (BGSK). 44 It is worth noting that when applying the original GSK to solve the concerned problem, the individuals are converted from floating encoding to binary encoding with the sigmoid transfer function as that done in [40,42,43].…”

Section: Parameter Settings For Simulationmentioning

confidence: 99%

“…These seven algorithms are binary artificial bee colony algorithm (BABC), 38 binary biogeography based optimization (BBBO), 39 binary particle swarm optimization (BPSO), 40 (BTLBO), 41 binary whale optimization algorithm (bWOA), 42 oppositional brain storm optimization (OBSO), 43 and binary GSK optimization algorithm (BGSK). 44 It is worth noting that when applying the original GSK to solve the concerned problem, the individuals are converted from floating encoding to binary encoding with the sigmoid transfer function as that done in [40,42,43]. For the original GSK and the proposed LOBGSK, the level division parameter p, knowledge rate K and knowledge ratio k r are set to 0.1, 10, and 0.9, respectively.…”

Section: Parameter Settings For Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm

Xiong

Yuan

Mohamed

et al. 2021

Int J of Intelligent Sys

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Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining-sharing knowledge-based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0-1 integer programming FSD problem. In LOBGSK, individuals are encoded by binary numbers and logical operations instead of real arithmetic operations are designed to update the individuals. By this, LOBGSK can avoid transcoding in solving the FSD problem. To validate the effectiveness of LOBGSK, it is first applied to a 4-substation test system considering various fault scenarios. Then it is further implemented to the IEEE 118-bus system and an actual fault event occurred in a practical power grid in Jilin province of China. In addition, the influence of three key parameters of LOBGSK is also investigated. Simulation results show that LOBGSK is robust against its key parameters and can offer a 100% successful rate to diagnose different faults quickly, which is demonstrated by the reported results of some published FSD methods. Furthermore, it outperforms seven state-of-the-art metaheuristic algorithms and the original GSK in solving the FSD problem of power systems.

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Section: Parameter Settings For Simulationmentioning

confidence: 99%

Section: Parameter Settings For Simulationmentioning

confidence: 99%

Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm

Xiong

Yuan

Mohamed

et al. 2021

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…An alternative to the aforementioned methods are the analytic optimization models [19]- [31]. In general, the analytic model-based methods establish criteria for identifying the true and false fault hypothesis (FH).…”

Section: Introductionmentioning

confidence: 99%

“…The GA was used in [28] for fault location by using alarms from feeder terminal units (FTU), nevertheless, each network node must be equipped with an FTU, i.e., the method needs a large number of FTU to ensure the fault location. In [31], a method using an oppositional BSO and data from FTU was proposed for fault location in distribution networks, however, each bus of the network must be monitored by an FTU, which is unusual in distribution systems, making the fault location difficult in networks with a low monitoring level.…”

Section: Introductionmentioning

confidence: 99%

Optimization Based Method for Fault Section Estimation on Distribution Systems

et al. 2022

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Faults are inevitable in power systems and their location represents a primary role in power recovery. An alternative for quick and accurate fault location is to monitor the protective devices alarms and status to identify the faulted section. This paper presents a novel approach for fault section estimation in electrical distribution systems based on genetic algorithm. The fault section location is treated as a 0-1 integer programming problem by using a fault hypothesis based on actual and expected status for protective relays, circuit breakers, reclosers and fault indicators. To solve the programming problem, a new objective function is proposed and the genetic algorithm was used. The proposed method is applied to a typical distribution system containing 33 buses. The performance and effectiveness of the proposed method are assessed in challenging scenarios, including spurious data and operation failure of protective devices all over the studied distribution system. The results evince the feasibility of the proposed method to locate faults in distribution systems with a low monitoring level.

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An improved bacterial colony optimization using opposition-based learning for data clustering

Prakash¹,

Vinothina²,

Kalaiselvi³

et al. 2022

Cluster Comput

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Oppositional Brain Storm Optimization for Fault Section Location in Distribution Networks

Cited by 4 publications

References 28 publications

Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm

Fault section diagnosis of power systems with logical operation binary gaining‐sharing knowledge‐based algorithm

Optimization Based Method for Fault Section Estimation on Distribution Systems

An improved bacterial colony optimization using opposition-based learning for data clustering

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