Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems

2022 ◽  
Vol 306 ◽  
pp. 118119
Author(s):  
Manyun Huang ◽  
Zhinong Wei ◽  
Yuzhang Lin
Keyword(s):  
Deep Learning ◽  
State Estimation ◽  
Distribution Systems ◽  
Dc Distribution

scholarly journals Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning

2019 ◽  
Vol 34 (6) ◽  
pp. 4910-4920 ◽  
Author(s):  
Kursat Rasim Mestav ◽  
Jaime Luengo-Rozas ◽  
Lang Tong
Keyword(s):  
Deep Learning ◽  
State Estimation ◽  
Distribution Systems

scholarly journals Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

2022 ◽  
Vol 3 ◽  
Author(s):  
James P. Carmichael ◽  
Yuan Liao
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Power System ◽  
State Estimation ◽  
Power Distribution ◽  
Distribution Systems ◽  
System State ◽  
Power Distribution Systems ◽  
Static State ◽  
Power System State Estimation

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks (LSTMs) to mitigate the aforementioned challenges in power distribution systems. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the correlation of the dataset being predicted. The performance of MLPS, CNNs, and LSTMs to perform state estimation and state forecasting will be presented in terms of average root-mean square error (RMSE) and training execution time. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting.

Full-current-based directional pilot protection for VSC-DC distribution systems

2019 ◽  
Vol 13 (16) ◽  
pp. 3713-3724 ◽  
Author(s):  
Meng Li ◽  
Ke Jia ◽  
Tianshu Bi ◽  
Congbo Wang ◽  
Rui Zhu ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Dc Distribution ◽  
Pilot Protection

scholarly journals A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1967
Author(s):  
Gaurav Kumar Roy ◽  
Marco Pau ◽  
Ferdinanda Ponci ◽  
Antonello Monti
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Estimation Algorithm ◽  
The State ◽  
Second Step ◽  
Distribution Grid ◽  
Dc Distribution ◽  
Attractive Option ◽  
Distribution Grids

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities.

scholarly journals Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1327 ◽  
Author(s):  
Thiago Soares ◽  
Ubiratan Bezerra ◽  
Maria Tostes
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Distribution Systems ◽  
Phase State ◽  
Reactive Power ◽  
Estimation Algorithm ◽  
Load Flow ◽  
Electrical Network ◽  
The Real ◽  
Three Phase

This paper proposes the development of a three-phase state estimation algorithm, which ensures complete observability for the electric network and a low investment cost for application in typical electric power distribution systems, which usually exhibit low levels of supervision facilities and measurement redundancy. Using the customers´ energy bills to calculate average demands, a three-phase load flow algorithm is run to generate pseudo-measurements of voltage magnitudes, active and reactive power injections, as well as current injections which are used to ensure the electrical network is full-observable, even with measurements available at only one point, the substation-feeder coupling point. The estimation process begins with a load flow solution for the customers´ average demand and uses an adjustment mechanism to track the real-time operating state to calculate the pseudo-measurements successively. Besides estimating the real-time operation state the proposed methodology also generates nontechnical losses estimation for each operation state. The effectiveness of the state estimation procedure is demonstrated by simulation results obtained for the IEEE 13-bus test network and for a real urban feeder.

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