Using Phase Swapping to Solve Load Phase Balancing by ADSCHNN in LV Distribution Network

Fei, Chunguo; Wang, Rui

doi:10.14257/ijca.2014.7.7.01

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The significant merit for the first one on genetic was there needing to fewer swaps to obtaining the load balancing. In [25], the authors applied the adding decaying self-feedback continuous neural network (ADSCHNN) algorithm to reach the load balancing. In the first, the loads have been represented by the current or the power they are consumed at the connection point.…”

Section: Fig 2 Distribution Feedermentioning

confidence: 99%

Phase Swapping Load Balancing Algorithms, Comprehensive Survey

Al‐Kharsan¹,

Marhoon²,

Mahmood³

2019

IJEEE

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The power quality nowadays of the low voltage distribution system is vital for the utility and the consumer at the same time. One disturbing issue affected the quality conditions in the radial distribution system is load balancing. This survey paper is looking most the articles that deal with the phase nodal and lateral phase swapping because it is the efficient and direct method to maintain the current and voltage in balance situation, lead to a suitable reduction in the losses and preventing the wrong tripping of the protective relays.

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Section: Fig 2 Distribution Feedermentioning

confidence: 99%

Phase Swapping Load Balancing Algorithms, Comprehensive Survey

Al‐Kharsan¹,

Marhoon²,

Mahmood³

2019

IJEEE

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“…Additional methodologies applied to the phase-balancing problem include immune algorithms [12], artificial neural networks [17], fuzzy logic [18], a differential evolution algorithm [19], particle swarm optimization [20], and a bacterial foraging algorithm [21]. A different method to solve the phase-balancing problem was recently proposed in [22].…”

Section: Introductionmentioning

confidence: 99%

Operating Cost Reduction in Distribution Networks Based on the Optimal Phase-Swapping including the Costs of the Working Groups and Energy Losses

2021

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The problem of optimal phase-balancing in three-phase asymmetric distribution networks is addressed in this research from the point of view of combinatorial optimization using a master–slave optimization approach. The master stage employs an improved sine cosine algorithm (ISCA), which is entrusted with determining the load reconfiguration at each node. The slave stage evaluates the energy losses for each set of load connections provided by the master stage by implementing the triangular-based power flow method. The mathematical model that was solved using the ISCA is designed to minimize the annual operating costs of the three-phase network. These costs include the annual costs of the energy losses, considering daily active and reactive power curves, as well as the costs of the working groups tasked with the implementation of the phase-balancing plan at each node. The peak load scenario was evaluated for a 15-bus test system to demonstrate the effectiveness of the proposed ISCA in reducing the power loss (18.66%) compared with optimization methods such as genetic algorithm (18.64%), the classical sine cosine algorithm (18.42%), black-hole optimizer (18.38%), and vortex search algorithm (18.59%). The IEEE 37-bus system was employed to determine the annual total costs of the network before and after implementing the phase-balancing plan provided by the proposed ISCA. The annual operative costs were reduced by about 13% with respect to the benchmark case, with investments between USD 2100 and USD 2200 in phase-balancing activities developed by the working groups. In addition, the positive effects of implementing the phase-balancing plan were evidenced in the voltage performance of the IEEE 37-bus system by improving the voltage regulation with a maximum of 4% in the whole network from an initial regulation of 6.30%. All numerical validations were performed in the MATLAB programming environment.

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“…In [14] phase swapping is addressed as a load-to-line assignment problem and tackled under a mixed-integer programming formulation. In [15] the optimal load phase balance is obtained by solving the load redistribution problem by using a decaying self-feedback continuous Hopfield neural network (ADSCHNN). Likewise the work in [16] proposes a new approach for phase balancing planning using a specialized Genetic Algorithm which considers discretized load duration curve.…”

Section: Introductionmentioning

confidence: 99%

Optimal Phase Swapping in Low Voltage Distribution Networks Based on Smart Meter Data and Optimization Heuristics

Mendia

Gil-López

Ser

et al. 2017

Advances in Intelligent Systems and Computing

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In this paper a modified version of the Harmony Search algorithm is proposed as a novel tool for phase swapping in Low Voltage Distribution Networks where the objective is to determine to which phase each load should be connected in order to reduce the unbalance when all phases are added into the neutral conductor. Unbalanced loads deteriorate power quality and increase costs of investment and operation. A correct assignment is a direct, effective alternative to prevent voltage peaks and network outages. The main contribution of this paper is the proposal of an optimization model for allocating phases consumers according to their individual consumption in the network of low-voltage distribution considering mono and bi-phase connections using real hourly load patterns, which implies that the computational complexity of the defined combinatorial optimization problem is heavily increased. For this purpose a novel metric function is defined in the proposed scheme. The performance of the HS algorithm has been compared with classical Genetic Algorithm. Presented results show that HS outperforms GA not only on terms of quality but on the convergence rate, reducing the computational complexity of the proposed scheme while provide mono and bi phase connections.

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Using Phase Swapping to Solve Load Phase Balancing by ADSCHNN in LV Distribution Network

Abstract: Abstract

Cited by 8 publications

References 20 publications

Phase Swapping Load Balancing Algorithms, Comprehensive Survey

Phase Swapping Load Balancing Algorithms, Comprehensive Survey

Operating Cost Reduction in Distribution Networks Based on the Optimal Phase-Swapping including the Costs of the Working Groups and Energy Losses

Optimal Phase Swapping in Low Voltage Distribution Networks Based on Smart Meter Data and Optimization Heuristics

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