Nonlinear dynamic analysis and fatigue damage assessment for a deepwater test string subjected to random loads

The deepwater test string is an important but vulnerable component in offshore petroleum exploration,and its durability significantly affects the success of deepwater test operations.Considering the influence of random waves and the interaction between the test string and the riser,a time-domain nonlinear dynamic model of a deepwater test string is developed.The stress-time history of the test string is obtained to study vibration mechanisms and fatigue development in the test string.Several recommendations for reducing damage are proposed.The results indicate that the amplitude of dynamic response when the string is subjected to random loads gradually decreases along the test string,and that the von Mises stress is higher in the string sections near the top of the test string and the flex joints.In addition,the fatigue damage fluctuates with the water depth,and the maximum damage occurs in string sections adjacent to the lower flex joint and in the splash zone.Several measures are proposed to improve the operational safety of deepwater test strings：applying greater top tension,operating in a favorable marine environment,managing the order of the test string joints,and performing nondestructive testing of components at vulnerable positions.

Similarity matching method of power distribution system operating data based on neural information retrieval

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

Guest editorial:special section on energy storage systems and operational flexibility in power systems

The energy storage system(ESS) is becoming an important component in power systems to mitigate the adverse impact of intermittent renewable energy resources and improve power grid reliability and efficiency.However,storage devices driven by different technologies can have specific grid impacts.This special section is dedicated to reflecting the