Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

Lee, Kyung-Yong; Park, Jung-Sung; Kim, Yun-Su

doi:10.3390/en14227767

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…3. Three PMUs are assumed as the available measurement devices in the network w are located at buses 9, 16, and 31 [63]. Voltage and branch current magnitudes each PMU are used for FDIA detection and for performing DSSE using the prop method.…”

Section: Case Study Imentioning

confidence: 99%

“…1. Three PMUs-located at buses 20, 60, and 67 [63]-are assumed as the available meas urement devices in the network. The bus voltage and branch current magnitude The hourly data of power generation of a photovoltaic system are computed and adopted based on actual data for Bozeman, MT, USA [66,67].…”

Section: Case Study IImentioning

confidence: 99%

“…1. Three PMUs-located at buses 20, 60, and 67 [63]-are assumed as the available measurement devices in the network. The bus voltage and branch current magnitudes from each PMU are used for detection of FDIAs and for performing DSSE using the proposed method.…”

Section: Case Study IImentioning

confidence: 99%

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Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network

et al. 2023

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Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.

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Section: Case Study Imentioning

confidence: 99%

Section: Case Study IImentioning

confidence: 99%

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Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network

et al. 2023

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Multi-Objective Optimal D-PMU Placement for Fast, Reliable and High-Precision Observations of Active Distribution Networks

Sun

Hu²,

Kong

et al. 2022

Applied Sciences

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The distribution-level phasor measurement unit(D-PMU), as a new type of measurement equipment, can support the fast and high-precision observations of active distribution networks. This paper presents a new D-PMU optimal placement method that can be used to reconcile investments with the reliability of high-precision observation systems. The multi-objective optimization model primarily considers the influence of topology changes and N-1 contingencies on observation reliability. Its objectives include minimizing the number of D-PMUs, maximizing network measurement redundancy (NMR) and the average number of observable buses under N-1 contingencies (ANOBC). The model is extended to a form of multi-topology weighting and is combined with zero-injection buses and other measurements. We take the observability of high-weight topology set as the constraint. The model can be solved using the non-dominated sorting genetic algorithm-III (NSGA-III), which gives the set of Pareto optimal solutions. Then, the D-PMU placement order determination method based on the spatial electrical distance is proposed to improve the effect of state estimation faster. IEEE standard systems are taken to verify the effectiveness of the proposed method. With the same number of D-PMUs, this method derives more ANOBC and NMR than other methods. When considering topology changes, the proposed method can use fewer additional D-PMUs to substantially increase the ANOBC and NMR.

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Advanced Electrical Measurements Technologies

Manousakis

2022

Energies

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Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

Cited by 3 publications

References 30 publications

Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network

Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network

Multi-Objective Optimal D-PMU Placement for Fast, Reliable and High-Precision Observations of Active Distribution Networks

Advanced Electrical Measurements Technologies

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