Hybrid algorithm for optimisation of m-loop electric power distribution networks

Brozek, Josef

doi:10.1049/ip-gtd:20040052

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…The relaxation method is used to transform the constrained optimization problem (modeled by (1) to (6)) into the unconstrained optimization problem (see (7)).…”

Section: Min F ( X)mentioning

confidence: 99%

“…We now substitute constraints (10) to (13) into (7) to obtain the Lagrange function in (14) as total energy of the system.…”

Section: ) Expression Of the Lagrange Function As Total Energy Of Thmentioning

confidence: 99%

“…In intelligent transportation systems (ITS), for example, TSP is encountered in vehicle routing problems [2] such as pick-up and delivery problems (PDPs) [3], vehicle and crew scheduling [4], etc. Further interesting fields of application of TSP are: printed circuit boards manufacturing [5], data transmission in computer networks [6], power distribution networks [7], image processing and pattern recognition [8], robot navigation [9] and data partitioning [10], logistics and unmanned aircraft mission planning [11], just to name a few. The TSP, as a typical example of a canonical vehicle routing problem, has received tremendous attention during the past decade [12]- [18].…”

Section: Introduction a Potential Applications Of Tsp In Science mentioning

confidence: 99%

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An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks

2020

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We develop, for the first time, and validate through some illustrative examples a new neuro-processor based concept for solving (single-vehicle) traveling salesman problems (TSP) in complex and dynamically reconfigurable graph networks. Compared to existing/competing methods for solving TSP, the new concept is accurate, robust, and scalable. Also, the new concept guarantees the optimality of the TSP solution and ensures subtours avoidance and thus an always-convergence to a single-cycle TSP solution. These key characteristics of the new concept are not always satisfactorily addressed by the existing methods for solving TSP. Therefore, the main contribution of this paper is to develop a systematic analytical framework to model (from a nonlinear dynamical perspective) the TSP, avoid/eliminate subtours, and guarantee/ensure convergence to the true/exact TSP solution. Using the stability analysis (nonlinear dynamics), analytical conditions are obtained to guarantee both robustness and convergence of the neuroprocessor. Besides, a bifurcation analysis is carried out to obtain ranges (or windows) of parameters under which the neuro-processor guarantees both TSP solution's optimality and convergence to a single-cycle TSP solution. In order to validate the new neuro-processor based concept developed, two recently published application examples are considered for both benchmarking and validation as they are solved by using the developed neuro-processor. INDEX TERMS Basic differential multiplier method, bifurcation analysis, constrained optimization, convergence to a single-cycle TSP tour/solution, dynamically externally reconfigurable TSP, local stability, neuroprocessor, nonlinear optimization, subtours elimination, traveling salesman problem, validation through a benchmarking. NOMENCLATURE x Vector of decision variables λ, ϕ, γ , γ * , ω, µ l Vectors of multiplier variables x, λ, ϕ, γ , γ * , ω, µ l Dependent variables (on t) t Independent variable f (x) Objective function g 1j , g 2j , g 3i , g * 3i , g 4i , g 5k,l Constraints functions The associate editor coordinating the review of this manuscript and approving it for publication was Choon Ki Ahn .L x, λ, ϕ, γ , γ * , ω, µ l Lagrange function α Step size of decision variables β 1 , β 2 , β 3 , β * 3 , β 4 , β 5,l Step sizes of multiplier variables α, β 1 , β 2 , β 3 , β * 3 , β 4 , β 5,l Learning rate parameters U Vector of the costs of edges A Incidence matrix D Matrix of parallel edges

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“…The relaxation method is used to transform the constrained optimization problem (modeled by (1) to (6)) into the unconstrained optimization problem (see (7)).…”

Section: Min F ( X)mentioning

confidence: 99%

“…We now substitute constraints (10) to (13) into (7) to obtain the Lagrange function in (14) as total energy of the system.…”

Section: ) Expression Of the Lagrange Function As Total Energy Of Thmentioning

confidence: 99%

Section: Introduction a Potential Applications Of Tsp In Science mentioning

confidence: 99%

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An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks

2020

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“…기반의 연속 조류계산 알고리즘 [1], loop 배전계통의 설계 [2]와 최적 재구성 [3], loop 해석 simulation [4], dynamic 모델링과 parameter [5], loop 배전계통에서의 voltage sag 해석 [6], 선로 손실 및 전압 관리 [7,8], 태양광 연계를 고려한 부하균등화 [9], 정전복구 [10], 다중 loop 계통에서의 최적화 [11], loop 배전 지중 선로 지락사고에 대한 분석 [12,13] …”

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Section Voltage Calculation while a Loop Operation by Tie-Switch Close in a Distribution Management System

Seo¹,

Lim²,

Park³

et al. 2016

The Transactions of The Korean Institute of Electrical Engineer

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-Generally, an electrical distribution configuration is a radial system with one-way current in a distribution management system (DMS). All feeders in a DMS have tie-switches to make radial system. Sometimes, DMS should change a tie-switch for operation. In that case, the tie-switch has to be closed first; then a switch is opened as another tie-switch in order to prevent blackout for customers. At the moment when the tie-switch is closed, distribution system is operated in a loop state, not radial. Before the loop operation, DMS operator has to check any expected events for stable distribution system operation; and the most important event is a mis-operation of a protection relay. In addition, DMS operator should check voltage profile violation but a calculation method of section voltages had not been used. Thus, this paper proposes a calculation method of section voltages at a loop operation in a DMS. The proposed calculation algorithm is verified by Matlap Simulink.

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Hybrid algorithm for optimisation of m-loop electric power distribution networks

Cited by 2 publications

References 14 publications

An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks

An Efficient, Scalable, and Robust Neuro-Processor-Based Concept for Solving Single-Cycle Traveling Salesman Problems in Complex and Dynamically Reconfigurable Graph Networks

Section Voltage Calculation while a Loop Operation by Tie-Switch Close in a Distribution Management System

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