A Novel Probabilistic Framework with Interpretability for Generator Coherency Identification

Li, Baitong; Liu, Fengrui

doi:10.2139/ssrn.4085465

Cited by 2 publications

(2 citation statements)

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“…A major disadvantage of traditional AI algorithms is the lack of interpretability [7]. Improving the interpretability of models based on dynamics theory is one of the research hotspots [8].…”

Section: Wind Speed Prediction Model Based On Spatio-temporal Kaementioning

confidence: 99%

“…They fail to take into account the forward and backward dynamic trend of the wind power system, the physical structure inside the nonlinear system data, and the impact of time reversibility. In this paper, a short-term wind speed prediction method based on time-space Koopman AE is proposed by referring to the dynamics principle and combining it with the literature [7][8]. The main work is as follows:…”

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confidence: 99%

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Short-term Wind Speed Prediction Based on Improved Auto Encoder

Yin

Shang

et al. 2023

J. Phys.: Conf. Ser.

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In order to improve the wind power consumption level of the new power system with new energy as the main body, it is necessary to accurately predict the wind speed. The key is to refine the dynamic trend of the wind power system and the potential physical structure in the wind speed sequence. Firstly, on the basis of the library Koopman dynamics theory and the encoder structures, a physically constrained spatio-temporal neural network is built, which generates the linear evolution moment of the nonlinear variables of the wind farm. Secondly, the system trend is approximated by the linear evolution matrix, and the forward and backward dynamics are fully considered in the prediction process. Then, the bidirectional correlation prediction mechanism and the cost function adapted to different objects are set to reduce the requirements for the reversibility and stability of the prediction sequence. Meanwhile, the hidden vector of feature space is visualized to show the feature interval dependency in the system. Finally, the effectiveness of the proposed method is verified by the wind speed measurement data of Prince Mountain in Beipiao. The results show that the proposed method has high prediction accuracy, strong generalization ability, and high interpretability for strong random and strong fluctuation wind speed series.

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Section: Wind Speed Prediction Model Based On Spatio-temporal Kaementioning

confidence: 99%

mentioning

confidence: 99%

Short-term Wind Speed Prediction Based on Improved Auto Encoder

Yin

Shang

et al. 2023

J. Phys.: Conf. Ser.

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Wind Turbine Clustering and Equivalent Parameter Identification in Multitime Scales Based on the Deep Migration of Multiview Features

Shang

et al. 2022

IEEE Access

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To improve the precision of wind farm multi-machine equivalence and multi-scene generalization, this paper proposes a method for wind turbine clustering and equivalent parameter identification in multi-time scales based on the deep migration of multi-view features. The proposed technique carries out multi-machine equivalence by leveraging the multi-view information from each turbine in a wind farm. Specifically, a deep spatio-temporal Improved Auto-Encoder is designed, jointly trained with the target clustering layer. IAE is used for mining multi-view latent characteristics of wind turbines orienting to grouping turbines to improve the model's adaptability to multiple scenarios and divide turbines in an unsupervised manner. This method generates a visual heat map to represent the attended area of characteristics based on transfer learning and Class Activation Map to enable interpretability. In the next phase, this technique constructs a multi-objective optimization model by synthesizing the equivalent deviation of voltage, current, active power, and reactive power to further improve accuracy. It can identify the equivalent parameters of collector lines, the mechanical structure, and the control system at different time scales simultaneously via the black-box paralleled optimization method based on Bayes and Multi-arm Bandit. The proposed approach is evaluated on a typical double-fed wind farm with grid-side faults under various conditions of disturbing winds. Also, an ablation study is conducted to make analysis according to the two phases, i.e., turbine division and parameter identification. The results validate the accuracy and robustness of this method.

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A Novel Probabilistic Framework with Interpretability for Generator Coherency Identification

Cited by 2 publications

References 0 publications

Short-term Wind Speed Prediction Based on Improved Auto Encoder

Short-term Wind Speed Prediction Based on Improved Auto Encoder

Wind Turbine Clustering and Equivalent Parameter Identification in Multitime Scales Based on the Deep Migration of Multiview Features

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