Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Journal Article · 17 March 2024 · Electric Power Systems Research

DOI: https://doi.org/10.1016/j.epsr.2024.110327 · OSTI ID:2449701

Tran, Quan ^[1]; Mitra, Joydeep  ^[2]; Nguyen, Nga  ^[3]

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1. Danang Power Company, Da Nang (Vietnam); University of Wyoming
2. Michigan State University, East Lansing, MI (United States)
3. University of Wyoming, Laramie, WY (United States)

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

• Research Organization: University of Wyoming, Laramie, WY (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE); USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: SC0023422
• OSTI ID: 2449701
• Journal Information: Electric Power Systems Research, Journal Name: Electric Power Systems Research Vol. 231; ISSN 0378-7796
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English