A Classification of Power Load Based on the Load Characteristic in Distribution Networks

2013 ◽  
Vol 427-429 ◽  
pp. 1132-1135
Author(s):  
Jian Peng Li ◽  
Song Zhang ◽  
Yuan Yuan Shan ◽  
Yi Nong Li ◽  
Feng Qing Jiang
Keyword(s):  
Low Voltage ◽  
Distribution Networks ◽  
Load Curve ◽  
Load Characteristic ◽  
Power Load ◽  
Load Characteristics ◽  
Load Types ◽  
Daily Load

Nowadays the widest used classification of power load is based on the industry they belonged to, but, as low voltage loads are varietal, even the loads in the same industry can be considerably different in load characteristics of them. In this paper, power loads are classified according to three load characteristic indicators: load averages, load changing cycle and the peak-valley ratio, which are based on the typical daily load curve of them. In this method, the typical daily load curves of loads in the same type are relatively more similar, which is in favor of further study using daily load curve. In this paper, thousands of 10kV loads in shanghai have been classified into 8 types in the method, and the analysis of the load types have been presented.

scholarly journals Analysis of Load Characteristics of Typical Large Industrial Users in Hunan Province Based on K-means Clustering

2019 ◽  
Vol 118 ◽  
pp. 01035
Author(s):  
Ming Wen ◽  
Yong Chen ◽  
Fenkai Chen ◽  
Lili An ◽  
Bo Chen ◽  
...  
Keyword(s):  
Electricity Consumption ◽  
Optimal Number ◽  
Hunan Province ◽  
Load Curve ◽  
Number Of Clusters ◽  
Consumption Behavior ◽  
User Classification ◽  
Power Load ◽  
Load Characteristics ◽  
Optimal Number Of Clusters

The analysis of load characteristics of large industrial users is the basis for understanding the way of using electricity and analyzing its electricity consumption behavior. The power load of 13 typical large industrial users in Hunan Province was selected, and based on K-means clustering method and distance equalization function, the optimal number of clusters for large industrial users was determined, and the user classification was finally realized to analyze the load characteristics of each type of users. The results show that the load characteristics of each type of users have a certain degree of difference, and the trend of power consumption trends is different, but the daily average load curve fluctuations of each type of users are basically the same, which are consistent with the law of electricity consumption.

Classification of low voltage distribution networks by means of Voltage Distortion

2005 ◽  
Author(s):  
P. Schegner ◽  
J. Meyer ◽  
T. Lobos ◽  
Z. Waclawek ◽  
M. Muehlwitz ◽  
...  
Keyword(s):  
Low Voltage ◽  
Distribution Networks ◽  
Voltage Distribution ◽  
Voltage Distortion

scholarly journals Linear Power Flow Method Improved With Numerical Analysis Techniques Applied to a Very Large Network

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4078 ◽  
Author(s):  
Baljinnyam Sereeter ◽  
Werner van Westering ◽  
Cornelis Vuik ◽  
Cees Witteveen
Keyword(s):  
Numerical Analysis ◽  
Power Flow ◽  
Low Voltage ◽  
Distribution Networks ◽  
Design Tool ◽  
Large Network ◽  
Load Model ◽  
Flow Method ◽  
Power Load ◽  
Power Flow Method

In this paper, we propose a fast linear power flow method using a constant impedance load model to simulate both the entire Low Voltage (LV) and Medium Voltage (MV) networks in a single simulation. Accuracy and efficiency of this linear approach are validated by comparing it with the Newton power flow algorithm and a commercial network design tool Vision on various distribution networks including real network data. Results show that our method can be as accurate as classical Nonlinear Power Flow (NPF) methods using a constant power load model and additionally, it is much faster than NPF computations. In our research, it is shown that voltage problems can be identified more efficiently when MV and LV are integrally evaluated. Moreover, Numerical Analysis (NA) techniques are applied to the Large Linear Power Flow (LLPF) problem with 27 million nonzeros in order to improve the computation time by studying the properties of the linear system. Finally, the original computation times of LLPF problems with real and complex components are reduced by 2.8 times and 5.7 times, respectively.

The Study on Acceptable Capacity of Wind Power for Gansu Power Grid Based on Power Balance

2012 ◽  
Vol 236-237 ◽  
pp. 720-724
Author(s):  
Xi Min Cao ◽  
Xiang Dong Xu ◽  
Fu Jun Wang ◽  
Tian Qi Liu ◽  
Zhen Huan Chen ◽  
...  
Keyword(s):  
Wind Power ◽  
Power Output ◽  
Power Grid ◽  
Operating Mode ◽  
Power Balance ◽  
Power Load ◽  
Load Characteristics ◽  
Installed Capacity ◽  
Daily Load ◽  
Grid Based

Randomness and volatility of wind power, the accepted capacity of wind power (ACWP) will become the wind power planning early first thing to consider with the increasement of the scale of wind power in grid. In this paper, we studied the correlation between wind power output and the change of daily load. The ACWP were analysis based on the power load characteristics, peak-valley difference of the Gansu Power Grid and power balance. The maximum installed capacity of wind power integrated of the five typical operating mode in 2011 is given. It is pointed out that ACWP is less than the planned capacity. At last, the specific measures to increase the ACWP are proposed.

scholarly journals Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2852
Author(s):  
Egnonnumi Lorraine Codjo ◽  
Bashir Bakhshideh Zad ◽  
Jean-François Toubeau ◽  
Bruno François ◽  
François Vallée
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Low Voltage ◽  
Distribution Networks ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Cable Insulation ◽  
Insulation Thickness ◽  
Nodal Voltage

Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.

scholarly journals D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2754
Author(s):  
Mengmeng Xiao ◽  
Shaorong Wang ◽  
Zia Ullah
Keyword(s):  
Mobile Networks ◽  
Control Method ◽  
Low Voltage ◽  
Control Unit ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Positive Sequence ◽  
Measurement Unit ◽  
Control Units ◽  
Three Phase

Three-phase imbalance is a long-term issue existing in low-voltage distribution networks (LVDNs), which consequently has an inverse impact on the safe and optimal operation of LVDNs. Recently, the increasing integration of single-phase distributed generations (DGs) and flexible loads has increased the probability of imbalance occurrence in LVDNs. To overcome the above challenges, this paper proposes a novel methodology based on the concept of "Active Asymmetry Energy-Absorbing (AAEA)" utilizing loads with a back-to-back converter, denoted as “AAEA Unit” in this paper. AAEA Units are deployed and coordinated to actively absorb asymmetry power among three phases for imbalance mitigation in LVDNs based on the high-precision, high-accuracy, and real-time distribution-level phasor measurement unit (D-PMU) data acquisition system and the 5th generation mobile networks (5G) communication channels. Furthermore, the control scheme of the proposed method includes three control units. Specifically, the positive-sequence control unit is designed to maintain the voltage of the DC-capacitor of the back-to-back converter. Likewise, the negative-sequence and zero-sequence control units are expected to mitigate the imbalanced current components. A simple imbalanced LVDN is modeled and tested in Simulink/Matlab (MathWorks, US). The obtained results demonstrate the effectiveness of the proposed methodology.

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