Adaptive Data Recovery Model for PMU Data Based on SDAE in Transient Stability Assessment

Wang, Huaiyuan; Ouyang, Yucheng

doi:10.1109/tim.2022.3212551

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…The single-layer DAE has limited feature extraction capability, so to overcome this limitation, the hidden layer Z from the previous DAE is utilized as the input for the subsequent DAE. This stacking of single-layer DAEs is referred to as SDAE [23]. The diagram in FIGURE 1 illustrates the structure of SDAE.…”

Section: Stacked Denoising Auto-encodermentioning

confidence: 99%

Refined Deep Transfer Learning with CNN-LSTM and SDAE for Adaptive Assessment of Power System Transient Stability with Time Series Data

Song

Yang

Rashed

et al. 2023

Preprint

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Transient stability assessment (TSA) plays a critical role in ensuring the reliable operation of power systems. However, existing approaches for TSA often encounter challenges such as data imbalances, limited sample sizes, and the need for adaptability in the face of system changes, necessitating the exploration of more advanced techniques. This paper proposes a novel deep transfer learning (DTL) framework to address these limitations that incorporates CNN-LSTM and stacked denoising auto-encoder (SDAE) techniques, aiming to significantly improve the speed and accuracy of power system TSA, especially in online applications and adaptability to system changes. First, the utilization of SDAE enables effective feature extraction, while the implementation of class weight balancing and cross-entropy loss function techniques effectively addresses data imbalances. Second, a CNN-LSTM classifier is constructed using transfer progressive learning. This approach allows for the effective analysis of spatial and temporal dynamic measurements by leveraging unsupervised pre-training (auto-encoder) and additional CNN-LSTM layers. Third, we propose the DTL, which leverages knowledge transfer from the CNN-LSTM model and incorporates fine-tuning techniques. This innovative approach ensures adaptability under in four scenarios, which is a prevalent challenge in power systems for continuous prediction. As compared with other techniques, the results demonstrate that our proposed approach achieves TSA accuracy of up to 99.68% on the IEEE 39-bus system and 99.80% on the South Carolina 500-bus system. Furthermore, to compare the performance of continuous prediction with other methods, our proposed method exhibits a significant improvement of 2% even with a limited sample size.

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Section: Stacked Denoising Auto-encodermentioning

confidence: 99%

Refined Deep Transfer Learning with CNN-LSTM and SDAE for Adaptive Assessment of Power System Transient Stability with Time Series Data

Song

Yang

Rashed

et al. 2023

Preprint

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“…Artificial intelligence can reduce the dependence on physical modeling and efficiently process multi-dimensional complex information (Wang and Ouyang, 2022;Chen et al, 2023a). Therefore, data-driven methods based on artificial intelligence have gradually demonstrated superior control advantages, especially the RL-based dispatch method in datadriven models has been researched recently due to its advantages of fast decision-making, balancing long-term and short-term benefits, and solving non-convex and non-linear problems (Tang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Li,

Huang,

Liang

et al. 2023

Front. Energy Res.

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Reinforcement learning (RL) is recently studied for realizing fast and adaptive power system dispatch under the increasing penetration of renewable energy. RL has the limitation of relying on samples for agent training, and the application in power systems often faces the difficulty of insufficient scenario samples. So, scenario generation is of great importance for the application of RL. However, most of the existing scenario generation methods cannot handle time-series correlation, especially the correlation over long time scales, when generating the scenario. To address this issue, this paper proposes an RL-based dispatch method which can generate power system operational scenarios with time-series correlation for the agent’s training. First, a time-generative adversarial network (GAN)-based scenario generation model is constructed, which generates system operational scenarios with long- and short-time scale time-series correlations. Next, the “N-1” security is ensured by simulating “N-1” branch contingencies in the agent’s training. Finally, the model is trained in parallel in an actual power system environment, and its effectiveness is verified by comparisons against benchmark methods.

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“…In recent years, with the widespread use of Wide Area Measurement Systems (WAMS) in power systems, the ability to capture dynamic processes in power systems has been greatly enhanced [5]. Compared to the measurement information of conventional supervisory control and data acquisition (SCADA) systems, phasor measurement unit (PMU) measurements provided by WAMS have the advantage of a high sampling frequency and can measure phase angle [6][7][8], which provides a  new perspective for data-driven short-term voltage stability assessment (STVSA). Our paper focuses on STVSA in interconnected power systems that are involved in the generation, transmission, and distribution of electric power, emphasizing the use of phasor measurement units (PMUs) and considering the influence of dynamic load components, and various operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

Zhang

et al. 2021

IEEE Access

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To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.

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Adaptive Data Recovery Model for PMU Data Based on SDAE in Transient Stability Assessment

Cited by 7 publications

References 41 publications

Refined Deep Transfer Learning with CNN-LSTM and SDAE for Adaptive Assessment of Power System Transient Stability with Time Series Data

Refined Deep Transfer Learning with CNN-LSTM and SDAE for Adaptive Assessment of Power System Transient Stability with Time Series Data

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

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