Distribution feeder energy conservation by using heuristics fuzzy approach

Huang, Kun-Yuan; Chin, Hong-Chan

doi:10.1016/s0142-0615(01)00056-4

Cited by 28 publications

(13 citation statements)

References 11 publications

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“…This artifact renders the joint DSR and OPF problem infeasible in various practical cases. Consider for example a network with 5 nodes, lines E = {(1, 2), (2, 3), (2,4), (2,5), (3,5), (4, 5)}, and switches in E R = {(2, 3), (2, 4), (2, 5)}. Suppose that only one switch must be closed in order to obtain a radial network, and this switch is the one on line (2,3).…”

Section: Accounting For Voltage Constraintsmentioning

confidence: 99%

Sparsity-Leveraging Reconfiguration of Smart Distribution Systems

Dall’Anese

Giannakis

2014

IEEE Trans. Power Delivery

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A system reconfiguration problem is considered for three-phase power distribution networks featuring distributed generation. In lieu of binary line selection variables, the notion of group sparsity is advocated to re-formulate the nonconvex distribution system reconfiguration (DSR) problem into a convex one. Using the duality theory, it is shown that the line selection task boils down to a shrinkage and thresholding operation on the line currents. Further, numerical tests illustrate the ability of the proposed scheme to identify meshed, weakly-meshed, or even radial configurations by adjusting a sparsity-tuning parameter in the DSR cost. Constraints on the voltages are investigated, and incorporated in the novel DSR problem to effect voltage regulation.

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Section: Accounting For Voltage Constraintsmentioning

confidence: 99%

Sparsity-Leveraging Reconfiguration of Smart Distribution Systems

Dall’Anese

Giannakis

2014

IEEE Trans. Power Delivery

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“…A 69-node distribution system after reconfiguration using the fuzzy multiobjective approach. ematically, the membership function can be expressed as for for for (26) In this work, the values of and considered are 0.80 and 1.0, respectively. Hence, if the absolute values of branch currents of the system are less than or equal to 80% of their respective line capacities, unity membership value is assigned .…”

Section: ) Membership Function For Absolute Value Of Branch Current mentioning

confidence: 99%

“…Lin et al [20], Jeon and Kim [21], Shin et al [22], Hsiao [23], Huang [25], and Huang and Chin [26] have proposed artificial-intelligence-based applications in a minimum loss configuration. Das [27] has presented an algorithm for network reconfiguration based on heuristic rule and fuzzy multiobjective approach.…”

Section: Introductionmentioning

confidence: 99%

Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems

Savier

Das

2007

IEEE Trans. Power Delivery

476

185

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This paper presents allocation of power losses to consumers connected to radial distribution networks before and after network reconfiguration in a deregulated environment. Loss allocation is made in a quadratic way and it is based on identifying the real and imaginary parts of current in each branch, and losses are allocated to consumers. The network reconfiguration algorithm is based on the fuzzy multiobjective approach and the max-min principle is adopted for the multiobjective optimization in a fuzzy framework. Multiple objectives are considered for real-power loss reduction in which nodes voltage deviation is kept within a range, and an absolute value of branch currents is not allowed to exceed their rated capacities. At the same time, a radial network structure is maintained with all loads energized. The three objectives considered are modeled with fuzzy sets to evaluate their imprecise nature and one can provide his or her anticipated value of each objective. A 69-node example is considered to demonstrate the effectiveness of the proposed method.

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“…Other methods, such as mathematical programming techniques [5][6][7], expert systems [8][9][10] and optimization algorithm [11] have been proposed in recent years. In [12], the solution procedure employing heuristic rules and fuzzy multiobjective approach are developed to solve the network reconfiguration problem. Wang and Cheng [13] present an approach to minimize the total system losses by using the PGSA to optimize the network configuration of the distribution system while satisfying some constraints.…”

Section: Introductionmentioning

confidence: 99%

Distribution System Loss Reduction through Hybrid Heuristic Technique

Wiselin¹,

S²

2012

IJCA

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This paper proposes a hybrid technology to solve the distribution system reconfiguration problem. The technology with the mixture of Plant Growth Simulation Algorithm (PGSA), Greedy and heuristic based fuzzy operation has been proposed. The optimization approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy with PGSA improves the efficiency of optimization by identifying the best loop sequence. Furthermore, the heuristic fuzzy has been introduced with PGSA and Greedy for handling constraints amid optimization. With the use of proposed algorithm, the system loss has been reduced convincingly without compromising the power flow constraints. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to IEEE 33 bus distribution system and 83 bus Distribution system of Taiwan Power Company.

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Distribution feeder energy conservation by using heuristics fuzzy approach

Cited by 28 publications

References 11 publications

Sparsity-Leveraging Reconfiguration of Smart Distribution Systems

Sparsity-Leveraging Reconfiguration of Smart Distribution Systems

Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems

Distribution System Loss Reduction through Hybrid Heuristic Technique

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