Multi-point synchronous temporary grounding wire detection device based on convolutional neural networks

To prevent grounding accidents caused by forgetting to remove the temporary grounding wires and the contact of trees with the distribution network, a multi-point synchronous temporary grounding wire detection device based on convolutional neural networks is developed. The developed device can judge the type of groundings on the test line quickly and locate the ground points accurately during the cut-out. Three-phase alternating current (AC) signals with a specific frequency are injected at multiple points on the line by multiple groups of the device controlled by Raspberry Pi 3B + and embedded Android. The device can achieve the approximate location through the impedance ranging method and the accurate location by integrating the data from multiple points. The trained EfficientNet-B0 algorithm is utilized to predict the grounding positions. The tested results of the prototype verify the accuracy and rapidity of grounding diagnosis and positioning

Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep forest is applied to reinforcement learning. Therefore, deep forest reinforcement learning (DFRL) as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of deep forest and multiple subsidiary reinforcement learning. The deep forest component of the DFRL is applied to predict the next systemic state of a power system, including emergency states and normal states. The multiple subsidiary reinforcement learning component, which includes reinforcement learning for emergency states and reinforcement learning for normal states, is applied to learn the features of the power system. The performance of the DFRL algorithm was compared to that of 10 other conventional AGC algorithms on a two-area load frequency control power system, a three-area power system, and the China Southern Power Grid. The DFRL method achieved the highest control performance. With this new method, both the occurrences of emergency situations and the curse of dimensionality can be simultaneously reduced

Some Remarks on the Electrical Conductivity of Hydrous Silicate Minerals in the Earth Crust, Upper Mantle and Subduction Zone at High Temperatures and High Pressures

As a dominant water carrier, hydrous silicate minerals and rocks are widespread throughout the representative regions of the mid-lower crust, upper mantle, and subduction zone of the deep Earth interior. Owing to the high sensitivity of electrical conductivity on the variation of water content, high-pressure laboratory-based electrical characterizations for hydrous silicate minerals and rocks have been paid more attention to by many researchers. With the improvement and development of experimental technique and measurement method for electrical conductivity, there are many related results to be reported on the electrical conductivity of hydrous silicate minerals and rocks at high-temperature and high-pressure conditions in the last several years. In this review paper, we concentrated on some recently reported electrical conductivity results for four typical hydrous silicate minerals (e.g., hydrous Ti-bearing olivine, epidote, amphibole, and kaolinite) investigated by the multi-anvil press and diamond anvil cell under conditions of high temperatures and pressures. Particularly, four potential influence factors including titanium-bearing content, dehydration effect, oxidation−dehydrogenation effect, and structural phase transition on the high-pressure electrical conductivity of these hydrous silicate minerals are deeply explored. Finally, some comprehensive remarks on the possible future research aspects are discussed in detail

Some Remarks on the Electrical Conductivity of Hydrous Silicate Minerals in the Earth Crust, Upper Mantle and Subduction Zone at High Temperatures and High Pressures

As a dominant water carrier, hydrous silicate minerals and rocks are widespread throughout the representative regions of the mid-lower crust, upper mantle, and subduction zone of the deep Earth interior. Owing to the high sensitivity of electrical conductivity on the variation of water content, high-pressure laboratory-based electrical characterizations for hydrous silicate minerals and rocks have been paid more attention to by many researchers. With the improvement and development of experimental technique and measurement method for electrical conductivity, there are many related results to be reported on the electrical conductivity of hydrous silicate minerals and rocks at high-temperature and high-pressure conditions in the last several years. In this review paper, we concentrated on some recently reported electrical conductivity results for four typical hydrous silicate minerals (e.g., hydrous Ti-bearing olivine, epidote, amphibole, and kaolinite) investigated by the multi-anvil press and diamond anvil cell under conditions of high temperatures and pressures. Particularly, four potential influence factors including titanium-bearing content, dehydration effect, oxidation−dehydrogenation effect, and structural phase transition on the high-pressure electrical conductivity of these hydrous silicate minerals are deeply explored. Finally, some comprehensive remarks on the possible future research aspects are discussed in detail