MVC architecture based neuro-fuzzy approach for distribution feeder reconfiguration for loss reduction and load balancing

Srinivasan, T.; Nirmalkumar, A.; Sakthivel, A.

doi:10.1109/tdc.2008.4517097

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…There are four fuzzy set models produced for advancement as depicted in Thiruvenkadam et al 22 They are in charge of limiting any arrangement from bus voltage deviation, branch current deviation and increment in real power loss:…”

Section: Proposed Algorithm and Its Implementation To Reconfigurationmentioning

confidence: 99%

“…The workstation model has a base voltage of 110 V and base power of 100 kVA, which relates to a base impedance of 0.121 Ω ( Table 5). The z-bus matrix for one pi-model is derived as shown in equation (22)…”

Section: Test System IIImentioning

confidence: 99%

“…For instance, a fuzzy set with a level of truth or false may comprise of genuine, genuine, pretty much evident, false, very false, pretty much false, false and so forth. There are four fuzzy set models produced for advancement as depicted in Thiruvenkadam et al 22 They are in charge of limiting any arrangement from bus voltage deviation, branch current deviation and increment in real power loss:…”

Section: Heuristic Fuzzymentioning

confidence: 99%

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RETRACTED: An expert system for distribution system reconfiguration through fuzzy logic and flower pollination algorithm

Mariaraja¹,

Manigandan²,

Srinivasan³

2018

Measurement and Control

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Distribution system optimization is one of the essential undertaking should be tended to for productive power system operation. A large number of the advancement methodologies were completed at distribution level, for example, ideal reconfiguration, ideal situation of capacitors and Distributed Generators, and additionally the blend of all. This research work proposes distribution system reconfiguration algorithm as it sidestep the utilization of outer types of equipments for Radial Distribution System (RDS) performance enhancement. This paper addresses optimal reconfiguration of distribution system by hybrid Fuzzy-Flower Pollination Algorithm (FFPA). This strategy takes the advantage of FPA and fuzzy for execution improvement. FPA handles the compelling optimization process. The combination of heuristic fuzzy guarantees the synchronous treatment of constraints alongside the primary goal of the optimization issue. The main objective of this work is to actualize an algorithm for the use of distribution system reconfiguration under normal and abnormal working condition. The adequacy of the calculation is approved through actualizing into IEEE 33 bus RDS and 83 bus Taiwan Power Distribution Company (TPDC) system under typical working conditions. Further, the proposed algorithm is actualized to distribution system workstation which is a scale down prototype of power distribution system.

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Section: Proposed Algorithm and Its Implementation To Reconfigurationmentioning

confidence: 99%

Section: Test System IIImentioning

confidence: 99%

Section: Heuristic Fuzzymentioning

confidence: 99%

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RETRACTED: An expert system for distribution system reconfiguration through fuzzy logic and flower pollination algorithm

Mariaraja¹,

Manigandan²,

Srinivasan³

2018

Measurement and Control

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“…The initial contribution for distribution system loss reduction was attempted by the authors [1] using conventional approaches. In order to minimize the complexity involved in the optimal reconfiguration process, recent optimization techniques and heuristic approaches are practiced for reconfiguration [2][3][4][5][6]. Further, the analyses on the distribution system under abnormal operating conditions were initiated by the researchers, where the performance of the system was studied at prefault and postfault conditions.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Outage Management in Smart Grid under Single, Multiple, and Critical Fault Conditions through Teaching–Learning Algorithm

Srinivasan

Kim

2020

Energies

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A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System.

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Distribution Network Optimisation Through Fusion Technology

Srinivasan¹,

Nirmalkumar²,

Sathiskumar³

2010

Australian Journal of Electrical and Electronics Engineering

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MVC architecture based neuro-fuzzy approach for distribution feeder reconfiguration for loss reduction and load balancing

Cited by 8 publications

References 13 publications

RETRACTED: An expert system for distribution system reconfiguration through fuzzy logic and flower pollination algorithm

RETRACTED: An expert system for distribution system reconfiguration through fuzzy logic and flower pollination algorithm

Cost-Effective Outage Management in Smart Grid under Single, Multiple, and Critical Fault Conditions through Teaching–Learning Algorithm

Distribution Network Optimisation Through Fusion Technology

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