Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network - Afrasiabi, Shahabodin; Afrasiabi, Mousa; Jarrahi, Mohammad Amin; Mohammadi, Mohammad; Aghaei, Jamshid; Javadi, Mohammad Sadegh; Shafie-Khah, Miadreza; Catalão, João P. S. (IEEE, 2023)
Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance–current–power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.
Keywords: Composite load model (CLM); gated recurrent unit (GRU); pseudo-Huber loss function; residual convolutional neural network (ResCNN); deep learning; fi=Sähkötekniikka|en=Electrical Engineering|