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Research Of Three-phase Balance Loss Reduction Of Low Voltage Istribution Networks Based On Users Classified

Posted on:2014-08-20Degree:MasterType:Thesis
Country:ChinaCandidate:H ZhangFull Text:PDF
GTID:2252330401952434Subject:Power System Operation and Control
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Saving energy and reducing consumption is our country economy and social developmentof a long-term concept of strategy, reduce the distribution network line loss of energyconservation and emissions reduction and electric power system economic operation hasfar-reaching significance. Low-voltage power grid in China’s electricity consumptionaccounted for50%of the whole power supply grid loss-60%, therefore, effective lowvoltage distribution network loss reduction measures and management method has become ahot spot in the power supply enterprise. Three-phase unbalance is one of the main reasons forlow voltage distribution network line loss, the three-phase imbalance degree is higher, the lossof distribution transformer and the power grid loss will increase greatly, and not conducive tothe safe operation of the electrical equipment. Delve into the three-phase imbalance problemhas profound significance to safe and economic operation of distribution network.Thesis introduces the background of research, the harm of three-phase imbalance and statusresearch domestic and overseas. Single phase in the low voltage distribution network user typecomplex, electricity usage gap is bigger, electricity strong randomicity, three-phase load onthe gap is bigger, cause serious unbalanced three phase low voltage distribution network,large line loss. Paper low-voltage distribution network loss are introduced in detail, and thecalculation method of three-phase imbalance degree, and derived the meter and thethree-phase imbalance degree of low voltage distribution network line loss calculation method.At present is mainly according to the traditional way of managing a three-phase unbalancedload classification will be the same user uniform distribution on the three phase. Classificationand traditional load mainly according to the user on the user input classification, ignoring theusers within the same industry, there are considerable differences in their electricity usage.For this defect, the user classification of fuzzy c-means clustering is proposed, the user isdivided in detail, according to the daily load curves divided into the same category users ofelectricity characteristics has high similarity, and then points to each type of user evenly to theA, B, C three-phase, three-phase imbalance problem from the source. In view of past researchand analysis of the unbalanced three-phase, does not take into account the user’s access pointto the influence of three-phase unbalanced, namely think roughly the same load in different access to distribution network, line loss of distribution network and the influence ofthree-phase imbalance degree is the same. To this end, proposed the distance meter andelectrical users daily load curve classification, will split line loss by the users of electricity toeach user, the user is the user’s actual load and total load the users from the line loss of thesum. Users daily load curve of the gauge and electrical distance compared with the originaldaily load curve is an obvious difference, especially in peak season.In xingsha MuYun town west lake village stone mountain pond area as an example for thesimulation calculation. Results show that the fuzzy c-means clustering algorithm is able togauge and electrical distance of residents daily load curves are divided in detail, according tothe classification results for users that don’t adjusted phase, three-phase imbalance degree andline loss decreases obviously, powerfully proved the feasibility of the proposed method andmodel. This study provide a basis for low voltage distribution network planning, three-phaseimbalance from the source to solve the problem, effectively reduce the low voltagedistribution network line loss.
Keywords/Search Tags:Low voltage distribution network, Three-phase imbalance, Daily load curve, Fuzzy c-means clustering, Electric distance
PDF Full Text Request
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