A Brief Discussion on Wide Area Security and Stability Control of Power System Based on Response

At present, with the continuous development of China's social economy, the scale of domestic power system has been further expanded, which also makes the structure of China's power grid system gradually become more complex[1]. Therefore, it is necessary to continuously increase the single unit capacity of power equipment. The purpose is to make the single unit capacity match the operation of power system, so as to improve the operation performance of power system. Besides, it can also increase economic benefits. Based on this, this paper expounds the concept and control mode of power system stability. Then the key technology of wide area security and stability control of power system based on response is analyzed from four aspects. They are wide area dynamic feature information extraction, disturbed trajectory prediction, system stability discrimination and stability control. Finally, the practical application is discussed in detail. It hopes that the power sectors can improve the stability control level of power system wide area security

Online lithium-ion battery intelligent perception for thermal fault detection and localization

—Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The improved LBIP-V2 performs better than LBIP-V1 in most cases. We tested the performance of LBIP on the single-cell battery dataset, the 1P3S battery pack dataset, and the flattened 1P3S battery pack dataset. The results show that the recognition accuracy of LBIP exceeded 95 %. At the same time, we simulated the failure of the 1P3S battery pack within 0–15 min and tested the effectiveness of LBIP in real-time battery fault diagnosis. The results indicate that LBIP can effectively respond to online faults with a confidence level of over 98 %

CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input into a Mask Region-based Convolutional Neural Network for training. The results demonstrate that the proposed model surpasses both traditional Generative Adversarial Network and Wasserstein Generative Adversarial Network in terms of the quality of generated thermal images of lithium-ion batteries. Moreover, the augmentation of the dataset leads to an improvement in the fault diagnosis accuracy of the Mask Region-based Convolutional Neural Network

Figure 21 in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 21. Arctosa vaginalis Yu & Song, 1988, male and female from Hainan. A. Male habitus, dorsal view; B. Female habitus, dorsal view; C. Left male palp, bulbus, ventral view; D. Same, retrolateral view; E. Left male palp, ventral view; F. Same, retrolateral view; G. Epigyne, ventral view; H. Vulva, dorsal view. Scale bars: A–B = 1.0 mm; C–D, G–H = 0.2 mm; E–F = 0.3 mm.Published as part of Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1) on page 33, DOI: 10.11865/zs.2021102, http://zenodo.org/record/536634

Figure 6. Allotrochosina limu Wang, Li in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 6. Allotrochosina limu Wang, Li & Zhang, sp. nov., holotype male. A. Male habitus, dorsal view; B. Left male palp, ventral view; C. Same, retrolateral view. Scale bars: A = 0.5 mm; B–C = 0.1 mm.Published as part of Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1) on page 23, DOI: 10.11865/zs.2021102, http://zenodo.org/record/536634

Figure 8 in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 8. Arctosa depectinata (Bösenberg & Strand, 1906), male and female from Hainan. A. Male habitus, dorsal view; B. Female habitus, dorsal view; C. Left male palp, bulbus, ventral view; D. Same, retrolateral view; E. Left male palp, ventral view; F. Same, retrolateral view; G. Epigyne, ventral view; H. Vulva, dorsal view. Scale bars: A–B = 1.0 mm; C–H = 0.2 mm.Published as part of <i>Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1)</i> on page 24, DOI: 10.11865/zs.2021102, <a href="http://zenodo.org/record/5366340">http://zenodo.org/record/5366340</a&gt

Figure 63. Trochosa ruricoloides Schenkel, 1963 in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 63. Trochosa ruricoloides Schenkel, 1963, male and female from Hainan. A. Male habitus, dorsal view; B. Female habitus, dorsal view; C. Left male palp, bulbus, ventral view; D. Same, retrolateral view; E. Left male palp, ventral view; F. Same, retrolateral view; G. Epigyne, ventral view; H. Vulva, dorsal view. Scale bars: A = 1.0 mm; B = 2.0 mm; C–H = 0.2 mm.Published as part of Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1) on page 64, DOI: 10.11865/zs.2021102, http://zenodo.org/record/536634

Figure 61 in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 61. Trochosa honggiana Barrion, Barrion-Dupo & Heong, 2012, holotype female. A. Female habitus, dorsal view; B. Epigyne, ventral view; C. Vulva, dorsal view. Scale bars: A = 4.0 mm; B–C = 0.3 mm.Published as part of Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1) on page 63, DOI: 10.11865/zs.2021102, http://zenodo.org/record/536634

Figure 3. Allotrochosina huangi Wang, Li in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 3. Allotrochosina huangi Wang, Li & Zhang, sp. nov., holotype male (A, E–F), paratype male (C–D, G), paratype female (B, H–I). A. Male habitus, dorsal view; B. Female habitus, dorsal view; C. Left male palp, bulbus, ventral view; D. Same, retrolateral view; E. Left male palp, ventral view; F. Same, retrolateral view; G. Embolus and terminal apophysis, ventral view; H. Epigyne, ventral view; I. Vulva, dorsal view. Scale bars: A = 0.5 mm; B = 1.0 mm; C–F, H–I = 0.2 mm; G = 0.1 mm.Published as part of Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1) on page 20, DOI: 10.11865/zs.2021102, http://zenodo.org/record/536634

Figure 10. Arctosa hainan Wang, Li in Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae)

Figure 10. Arctosa hainan Wang, Li & Zhang, sp. nov., holotype male (A, E–F), paratype male (C–D), paratype female (B, G–H). A. Male habitus, dorsal view; B. Female habitus, dorsal view; C. Left male palp, bulbus, ventral view; D. Same, retrolateral view; E. Left male palp, ventral view; F. Same, retrolateral view; G. Epigyne, ventral view; H. Vulva, dorsal view. Scale bars: A–B = 2.0 mm; C–D, G–H = 0.2 mm; E–F= 0.3 mm.Published as part of Wang, Luyu, Lu, Tian, Cai, Ducheng, Barrion, Alberto Tomas, Heong, Kong-Luen, Li, Shuqiang & Zhang, Zhisheng, 2021, Review of the wolf spiders from Hainan Island, China (Araneae: Lycosidae), pp. 16-74 in Zoological Systematics 46 (1) on page 26, DOI: 10.11865/zs.2021102, http://zenodo.org/record/536634