The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources and flexible loads pose challenges to the power system voltage stability. To address this issue, this paper proposes a probabilistic transferable deep kernel emulator (DKE) to extract the hidden relationship between uncertain sources, i.e., wind generations and loads, and load margin for probabilistic load margin assessment (PLMA). This emulator extends the Gaussian process kernel to the deep neural network (DNN) structure and thus gains the advantages of DNN in dealing with high-dimension uncertain inputs and the uncertainty quantification capability of the Gaussian process. A transfer learning framework is also developed to reduce the invariant representation space distance between the old topology and new one. It allows the DKE to be quickly fine tuned with only a few samples under the new topology. Numerical results carried out on the modified IEEE 39-bus and 118-bus power systems demonstrate the strong robustness of the proposed transferable DKE to uncertain wind and load power as well as topology changes while maintaining high accuracy.
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Developments in Robust Topology Detection under Load Uncertainty
This paper reports two extensions to the authors’ recent work on the design of an optimally robust topology detector for a power transmission circuit with uncertain loads. Such a detector was implemented as a linear discriminator for the IEEE 9-bus system to identify, with a sub-millisecond latency, the intact circuit, or any single open-circuited line, using only the phasor measurements at the generators’ terminals. The first extension aims to replace the previously required bounded uncertain load set by a load distribution that permits rarer measurement outliers. This problem is formulated and solved as a support vector classifier. The second extension explores the solvability of a linear discriminator for topology identification for larger power systems under a bounded uncertain load set. A measure of adequacy of the involved measurement network is introduced, under which a sensor placement problem is formulated for the addition of a minimum number of phasor measurement units to meet a prescribed level of topology identifiability. In this case, sensor placement, detector design, and detector performance and robustness are demonstrated on the IEEE 68-bus system.
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- Award ID(s):
- 1845523
- NSF-PAR ID:
- 10397679
- Date Published:
- Journal Name:
- 2022 American Control Conference (ACC)
- Page Range / eLocation ID:
- 4574 to 4579
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC53348.2022.9867509
