Electrical load modeling has been considered an essential task in power system studies. With the recent development of power systems, load modeling is becoming more and more challenging. The previous methods on load modeling are suffered from: i) high sensitivity to noi
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Electrical load modeling has been considered an essential task in power system studies. With the recent development of power systems, load modeling is becoming more and more challenging. The previous methods on load modeling are suffered from: i) high sensitivity to noise; ii) neglecting the load correlation in a power system, iii) high computational burden, and iv) dependency on the local measurement devices. To address these problems, this paper develops a deep neural network-based structure that can identify a large number of parameters simultaneously with fast performance as well as high accuracy. The designed network can fully understand the temporal features using a gated recurrent neural network-based structure. Furthermore, to provide the ability to estimate a large number of load parameters, a technique to assign the learning weight has been developed. Consequ­ently, to enhance the robustness of the designed network considering noisy conditions, a loss function has been developed in this paper. The numerical results on the IEEE 68-bus system demonstrate the effectiveness and superiority of the proposed network in comparison with several shallow-based and deep-based structures.
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