DEMAND Project: Bottom-Up Aggregation of Prosumers in Distribution Networks

Author(s):  
Diego Arnone ◽  
Marzia Mammina ◽  
Salvatore Favuzza ◽  
Mariano G. Ippolito ◽  
Eleonora Riva Sanseverino ◽  
...  
Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 867
Author(s):  
Jie Yu ◽  
Li Zhang ◽  
Jinyu Chen ◽  
Yao Xiao ◽  
Dibo Hou ◽  
...  

Loss of water due to leakage is a common phenomenon observed practically in all water distribution networks (WDNs). However, the leakage volume can be reduced significantly if the occurrence of leakage is detected within minimal time after its occurrence. Based on the discriminative behavior of different consumption in water balance, an integrated bottom-up water balance model is presented for leak detection in WDNs. The adaptive moment estimation (Adam) algorithm is employed to assess the parameters in the model. By analyzing the current value and the rising rate of the assessed parameters, abnormal events (e.g., leak, illegal use, or metering inaccuracy) could be detected. Furthermore, a one-step-slower strategy is proposed to estimate the weighted coefficient of pressure sensors to provide approximate location information of leak. The method was applied in a benchmark WDN and an experimental WDN to evaluate its performance. The results showed that relatively small leak could be detected in near-real-time. In addition, the method was able to identify the pressure sensors near to the leak.


Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1287 ◽  
Author(s):  
Alireza Vahabzadeh ◽  
Alibakhsh Kasaeian ◽  
Hasan Monsef ◽  
Alireza Aslani

This study proposes a fuzzy self-organized neural networks (SOM) model for detecting fraud by domestic customers, the major cause of non-technical losses in power distribution networks. Using a bottom-up approach, normal behavior patterns of household loads with and without photovoltaic (PV) sources are determined as normal behavior. Customers suspected of energy theft are distinguished by calculating the anomaly index of each subscriber. The bottom-up method used is validated using measurement data of a real network. The performance of the algorithm in detecting fraud in old electromagnetic meters is evaluated and verified. Types of energy theft methods are introduced in smart meters. The proposed algorithm is tested and evaluated to detect fraud in smart meters also.


PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  

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