Energy Loss Estimation in Distribution Feeders

Rao, P. V. Subba; Deekshit, Ravishankar

doi:10.1109/tpwrd.2005.861240

Cited by 51 publications

(24 citation statements)

References 14 publications

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“…These estimations are generally based on some rules of thumb. P S Nagendra Rao et al [17] presented the results of a joint investigation undertaken in collaboration with a local utility to study this issue. Based on data collected from feeders specially instrumented for this purpose, true losses in some primary and secondary feeders are obtained.…”

Section: Distributed Generation Planning Strategy Withmentioning

confidence: 99%

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

Singh¹,

Singh²,

Verma³

2009

IJCEE

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Abstract-This paper proposes a new approach for distributed generation (DG) planning from the perspective of a distribution company (disco). Optimal sizing and siting decisions for distributed generator is obtained through different approaches: Power loss minimization, System MVA minimization, System cost minimization and System Energy loss minimization. The methodology adopted permits the planner to decide optimal location and size of DG with compromise between Power loss, System MVA, System cost and System energy loss. Normally a constant power (real and reactive) load model is assumed in most of the studies. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. A comparative study of optimal location and size of distributed generator provided by installing DG resources with different type of loads models has been performed.

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Section: Distributed Generation Planning Strategy Withmentioning

confidence: 99%

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

Singh¹,

Singh²,

Verma³

2009

IJCEE

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“…Fitted polynomials have been shown to be useful in estimating the energy loss on a system as a function of the amount of demand and other key system parameters [14], [15]. This work investigates the use of fitted parametric equations to accurately estimate both local and remote distribution system conditions.…”

Section: Introductionmentioning

confidence: 99%

Local and Remote Estimations Using Fitted Polynomials in Distribution Systems

Murphy

Keane

2017

IEEE Trans. Power Syst.

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“…Loss formulas for clustered losses using fuzzy cnumber (FCN) are presented based on weekday's data of January 2000 [14]. Low voltage energy loss estimation is done considering IP (irrigation pump-sets) non-heterogeneous loads [15]. There is need to know the energy losses in distribution system of different regions at different intervals in a year or more, without considering the aggregate information.…”

Section: Introductionmentioning

confidence: 99%

Electrical load profile analysis and peak load assessment using clustering technique

Sharma

Singh

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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Load profile analysis in different regions is very useful to power utilities for managing the load requirements in economic and efficient manner. For the demand side management and grid operation, the variation in demand is to be known. In this paper, classical k-means clustering approach is used for finding similar types of profiles of a practical system for demand variation analysis and energy loss estimation. For different zones, typical load profiles based on similar consumption are obtained. Primarily, the load factor represents feeder demand variation, and loss factor helps average energy loss estimation in distribution power system without load flow studies. In this paper, a concept is proposed for analysis of electricity consumption pattern on different days in particular zones based on cluster load factor and cluster loss factor. Normal and abnormal peak load requirements in cluster of similar types of profile of days of different zones are identified. Cluster loss factor helps in identifying the energy loss variation due to different load patterns.Index Terms-k-means clustering algorithm, load shape, electricity consumption variation, load factor, peak load I.

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Energy Loss Estimation in Distribution Feeders

Cited by 51 publications

References 14 publications

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

Local and Remote Estimations Using Fitted Polynomials in Distribution Systems

Electrical load profile analysis and peak load assessment using clustering technique

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