scholarly journals A data-driven approach for online aggregated load modeling through intelligent terminals

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771982599 ◽  
Author(s):  
Yi Tang ◽  
Liangliang Zhu ◽  
Jia Ning ◽  
Qi Wang
Keyword(s):  
Power System ◽  
Induction Motor ◽  
Power Distribution ◽  
Distribution Networks ◽  
Data Driven ◽  
Load Modeling ◽  
Load Model ◽  
Power Models ◽  
Data Driven Approach ◽  
Aggregated Load

Load model has significant impact on power system simulation. Current load modeling approaches are inadequate on revealing the accuracy and time-variation of load compositions. The application of wireless sensors dispersed in power distribution networks provides further opportunities for load modeling. In this article, a data-driven online aggregated load modeling approach is proposed systematically. First, all the electricity consumers are clustered according to big data of power consumption behaviors. In each cluster, typical users are designated to stand for the characteristics of the cluster, and intrusive measurement is adapted to capture these typical users’ time-varying information by employing wireless intelligent terminals, which can identify the composition of static load and induction motor load online. Second, the load models of other users in each cluster are assumed identical to typical users, including static impedance–current–power models and induction motor models. Finally, the composite load model is achieved by hierarchical aggregation and bottom-to-up stepwise equivalence. Simulations demonstrate that the load model built by proposed approach reflects higher accuracy and adaptability in power system.

scholarly journals Dynamic load modeling of some low voltage devices

2009 ◽  
Vol 22 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Lidija Korunovic ◽  
Dobrivoje Stojanovic
Keyword(s):  
Induction Motor ◽  
Dynamic Load ◽  
Low Voltage ◽  
Supply Voltage ◽  
Incandescent Lamp ◽  
Mercury Lamp ◽  
Load Modeling ◽  
Load Model ◽  
Fluorescent Lamps

This paper presents the results of dynamic load modeling for some frequently used low voltage devices. The modeling of long-term dynamics is performed on the basis of step changes of supply voltage of the heater, incandescent lamp, mercury lamp, fluorescent lamps, refrigerator, TV set and induction motor. Parameters of dynamic exponential load model of these load devices are identified, analyzed and mutually compared.

scholarly journals Accurate Load Modeling Based on Analytic Hierarchy Process

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenshu Wang ◽  
Xiaohui Jiang ◽  
Shaorun Bian ◽  
Yangyang Ma ◽  
Bowen Fan
Keyword(s):  
Power System ◽  
Analytic Hierarchy Process ◽  
Load Modeling ◽  
Analytic Hierarchy ◽  
Load Model ◽  
Load Models ◽  
Power Load ◽  
Hierarchy Process ◽  
Weight Coefficients ◽  
Traction Power

Establishing an accurate load model is a critical problem in power system modeling. That has significant meaning in power system digital simulation and dynamic security analysis. The synthesis load model (SLM) considers the impact of power distribution network and compensation capacitor, while randomness of power load is more precisely described by traction power system load model (TPSLM). On the basis of these two load models, a load modeling method that combines synthesis load with traction power load is proposed in this paper. This method uses analytic hierarchy process (AHP) to interact with two load models. Weight coefficients of two models can be calculated after formulating criteria and judgment matrixes and then establishing a synthesis model by weight coefficients. The effectiveness of the proposed method was examined through simulation. The results show that accurate load modeling based on AHP can effectively improve the accuracy of load model and prove the validity of this method.

A Robust Data-Driven Approach for Adaptive Dynamic Load Modeling

2021 ◽  
pp. 1-1
Author(s):  
Arindam Mitra ◽  
Rajarshi Dutta ◽  
Akhilesh Prakash Gupta ◽  
Abheejeet Mohapatra ◽  
Saikat Chakrabarti
Keyword(s):  
Dynamic Load ◽  
Data Driven ◽  
Load Modeling ◽  
Adaptive Dynamic ◽  
Data Driven Approach

Dynamic Load Modeling Based on Extreme Learning Machine

2012 ◽  
Vol 195-196 ◽  
pp. 1043-1048
Author(s):  
Zhong Hui Liu ◽  
Zhen Shu Wang ◽  
Mei Hua Su
Keyword(s):  
Power System ◽  
Extreme Learning Machine ◽  
Dynamic Simulation ◽  
Dynamic Load ◽  
Reactive Power ◽  
Power Model ◽  
Load Modeling ◽  
Load Model ◽  
Load Characteristics ◽  
Learning Machine

The dynamic load characteristics have significant impact on the power flow, transient stability computation, voltage stability calculation of the power system, and so on. Noticing that traditional mechanism loads model has difficulty in precisely describing the dynamic characteristics of synthetic load, this paper presents a non-mechanism dynamic load model based on Extreme Learning Machine (ELM). The Power Fault Recorder and Measurement System (PFRMS) is used to obtain data for load modeling. Take voltage and real/reactive power with different time delay as inputs, and take real/reactive power as output, train the ELM using the samples formed by fault data, the real power model and reactive power model are established respectively. The number of hidden layer nodes which has impact on the ELM model is also discussed. Dynamic simulation experiment is conducted at power system dynamic simulation laboratory. The simulation result shows that the ELM load model is simple and flexible, its parameters are easy to be identified. The ELM load model can describe the dynamic load characteristics accurately.

A PMU-Based Data-Driven Approach for Classifying Power System Events Considering Cyberattacks

2020 ◽  
Vol 14 (3) ◽  
pp. 3558-3569 ◽  
Author(s):  
Rui Ma ◽  
Sagnik Basumallik ◽  
Sara Eftekharnejad
Keyword(s):  
Power System ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Leak Localization in Water Distribution Networks Using Pressure and Data-Driven Classifier Approach

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 54 ◽  
Author(s):  
Congcong Sun ◽  
Benjamí Parellada ◽  
Vicenç Puig ◽  
Gabriela Cembrano
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Data Driven ◽  
Time Step ◽  
Water Losses ◽  
Localization Accuracy ◽  
Linear Discriminant ◽  
Data Driven Approach ◽  
Leak Localization

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.

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