Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System State Estimation  

作　　者：Behrouz Azimian Shiva Moshtagh Anamitra Pal Shanshan Ma 

机构地区：[1]School of Electrical,Computer and Energy Engineering,Arizona State University,Tempe,AZ 85281,USA [2]Quanta Technology,Raleigh,NC 27607,USA

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第4期1126-1134,共9页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the Department of Energy(No.DE-AR-0001001,No.DE-EE0009355);the National Science Foundation(NSF)(No.ECCS-2145063)。

摘　　要：Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.

关 键 词：Deep neural network(DNN) distribution system state estimation(DSSE) mixed-integer linear programming(MILP) ROBUSTNESS trustworthiness 

分 类 号：TM73[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]