Protective Device and Switch Allocation for Reliability Optimization With Distributed Generators

Lwin, Min; Jia, Ge; Dimitrov, Nedialko B.; Santoso, Surya

doi:10.1109/tste.2018.2850805

Cited by 27 publications

(37 citation statements)

References 21 publications

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“…lateral fuses) clear the fault. The allocation of the recloser is considered in the following reviewed works [27,29,31,35,39,41,43,45,46,[48][49][50]54,64,69,74,77,78,83,84,87,89,90,94,100,104,105,113].…”

Section: Reclosermentioning

confidence: 99%

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Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

et al. 2021

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The fundamental goal of the distribution system operator (DSO) is to serve its customers with reliable and low-cost electricity. Failures in power distribution systems are responsible for 80% of customer service interruptions. The emergence of smart distribution system (SDS) with advanced distribution automation (DA) and communication infrastructure offers a great opportunity to improve reliability, through the automation of fault location, isolation, and service restoration (FLISR) process. DA includes the installation of protection and control devices (PCD). The use of PCD makes fault management more efficient, reduces average outage duration per customer in case of faults, reduces costs due to unsupplied energy, and improves distribution system reliability. Although the use of PCD remarkably enhances distribution system reliability, it is neither economical nor affordable to install them in all potential locations. To obtain the optimal allocation of PCD (OAPCD), an optimisation problem has to be formulated and solved. Several models and methods have been suggested for the OAPCD in SDSs. Herein, an overview of the state-of-the-art models and methods applied to the OAPCD in SDSs are introduced, identifying the contributions of reviewed works, identifying advantages and disadvantages, classifying and analysing current and future research directions in this area.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Reclosermentioning

confidence: 99%

“…In case an overcurrent passes through the fuse, it is heated and, depending on time, it may melt. The allocation of fuse is considered in the following reviewed works [27,29,31,35,41,45,[48][49][50]63,64,77,85,89,94,[98][99][100]104,108,110,113], and [117].…”

Section: Fusementioning

confidence: 99%

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

et al. 2021

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“…Finally, methods [22,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] consider optimal placement of switching devices using a single, weighted objective function. Approaches [22,[33][34][35][36][37][38][39][40][41][42][43][44][45][46] combine switch investments and customer outage costs, whilst minimization of investments subject to maximized restored load is done in [47,48]; the disadvantages are similar as before. It is worth noting that the sophisticated optimization model in [49] offers a CI-CML improvement method that is close to reality; however it is limited to solving the network reconfiguration on a specific, non-general network due to model complexity.…”

Section: Setsmentioning

confidence: 99%

Operational Planning of Distribution Networks Based on Utility Planning Concepts

Mansor

Leví

2019

IEEE Trans. Power Syst.

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The paper presents the operational planning of medium voltage (11 kV; 6.6 kV) distribution networks that is an integral part of the previously developed integrated planning approach based on utility planning concepts. The operational planning problem is decoupled from the investment planning stage and it is further divided into two phases, the quality of supply (QoS) planning and the minimization of operational costs. The QoS planning maximizes the benefits of the regulatory incentive regime by installing and automating switchgear, as well as finding optimal normally open points (NOPs). It is solved in two ways, by applying genetic algorithm (GA) optimization and then the approach based on engineering rules. Operational cost minimization considers costs of O&M, switching, losses and reliability. It includes security constraints for radially operated networks and allows that the locations of new switchgear from the QoS stage are changed. The simplified version of the model is also intended to be used as a real-time network reconfiguration tool. The entire methodology is tested on the 69-bus test system.

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“…A widely used strategy is to utilize surrogate models and replace the expensive simulation model during the process. Various types of optimization designs (multi-objective optimization, reliability-based design optimization, and multi-disciplinary design optimization) can be carried out quickly and conveniently, based on surrogate models and optimization algorithms [6]. Currently, mainstream surrogate models such as Polynomial Response Surface (PRS) models, Kriging (KRG) models, Radial Basis Function (RBF) models, and Elliptic Base Function (EBF) models have been widely applied to engineering optimization and achieved greater successes [7]- [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Weighted Adaptive Sampling Approach Based on Hybrid Surrogate Model

2021

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In order to improve the fitting accuracy and optimization efficiency of the surrogate model, a multi-response weighted adaptive sampling (MWAS) approach based on the hybrid surrogate model was proposed and implemented to a multi-objective lightweight design of car seats. In this approach, the sample discreteness index in the input design space was calculated by the maximum and minimum distance approach (MDA), the fitting uncertainty index of output response was calculated by a strategy based on the weighted prediction variance (WPV), and the two indices are combined by the weight coefficients. In the iterative process, the weight coefficients of the two indices were determined according to the accuracy of the hybrid surrogate model. The balance of global and local accuracy was realized by considering the sample dispersion and the fitting uncertainty of the surrogate model comprehensively. Numerical examples of single-response and multi-response systems showed that the proposed approach has excellent sampling efficiency and robustness. Moreover, the results of actual engineering application showed that the hybrid surrogate model constructed through MWAS could significantly improve the efficiency of model optimization. Hence, a high-precision optimization solution to the multi-objective lightweight design of passenger car rear seat was obtained.

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Protective Device and Switch Allocation for Reliability Optimization With Distributed Generators

Cited by 27 publications

References 21 publications

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

Operational Planning of Distribution Networks Based on Utility Planning Concepts

Multi-Response Weighted Adaptive Sampling Approach Based on Hybrid Surrogate Model

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