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Owing to the advantages of scientific computation and the data feature support provided by artificial intelligence technology, the theoretical exploration and application research of new computing methods for large generators, one of the most expensive energy equipment in a power system, has become a research hotspot toward solving the bearing limit and operation capacity under abnormal working conditions. Because of many factors affecting the distribution of negative sequence loss and temperature rise that have an extremely complex nonlinear relationship, the traditional calculation and prediction methods of negative sequence conditions cannot suitably reflect the time accumulation effect. Therefore, a prediction method of rotor steady-state negative sequence heating based on radial basis function process neural network is proposed in this paper, and a negative sequence working condition prediction model is established. Accordingly, this study focuses on the study of the relationship among negative sequence heating characteristics, negative sequence component proportion, and transverse slot; additionally, their influence degree, variation relationship, and main principles are further explored that provide a theoretical basis for the design and operation of large generators. As observed from the test results, the steady-state negative sequence condition prediction method based on the improved genetic algorithm radial basis function process neural network features high accuracy; it is a feasible prediction method, specifically for negative sequence conditions of large generators. [ABSTRACT FROM AUTHOR]

The increasing number of satellites for specific space missions is hindering efficient control from the ground stations. In addition, the risk of failing or deteriorating elements in a multi-satellite system (MSS) leads to the uncertainty in its operating environment which challenges traditional centralized planning algorithms. Centralized algorithms depend on one coordinator to overview entire system, which is a single poitn of failure, so if it malfunctional, the MSS may go down. To overcome this problem, a distributed onboard mission planning algorithm based on the Hybrid Dynamic Mutation Genetic Algorithm (HDMGA) is proposed for such MSS. The satellites are considered as autonomous agents which can communicate and negotiate with other satellites. The mission objective is decomposed through the team negotiation procedures, and the local goals are dispatched to the participant satellite. The local planning procedure on each satellite is modeled as local search problems, while the planning problem for entire team is formulated as strongly-coupled distributed optimization problem. The verification shows that the proposed distributed approach can achieve the same results as the centralized approach. The simulation results illustrate that the proposed approach shows superior performance on both computation time and success rate, as compared with other two state-of-the-art distributed mission planning approaches. [ABSTRACT FROM AUTHOR]

This paper presents an onboard autonomous mission re-planning system for Multi-Satellites System (MSS) to perform onboard re-planing in disruptive situations. The proposed re-planning system can deal with different potential emergency situations. This paper uses Multi-Objective Hybrid Dynamic Mutation Genetic Algorithm (MO-HDM GA) combined with re-planning techniques as the core algorithm. The Cyclically Re-planning Method (CRM) and the Near Real-time Re-planning Method (NRRM) are developed to meet different mission requirements. Simulations results show that both methods can provide feasible re-planning sequences under unforeseen situations. The comparisons illustrate that using the CRM is average 20 % faster than the NRRM on computation time. However, by using the NRRM more raw data can be observed and transmitted than using the CRM within the same period. The usability of this onboard re-planning system is not limited to multi-satellite system. Other mission planning and re-planning problems related to autonomous multiple vehicles with similar demands are also applicable. [ABSTRACT FROM AUTHOR]

Space missions have traditionally been controlled by operators from a mission control center. Given the increasing number of satellites for some space missions, generating a command list for multiple satellites can be time-consuming and inefficient. Developing multi-satellite, onboard mission scheduling & planning techniques is, therefore, a key research field for future space mission operations. In this paper, an improved Genetic Algorithm (GA) using a new mutation strategy is proposed as a mission scheduling algorithm. This new mutation strategy, called Hybrid Dynamic Mutation (HDM), combines the advantages of both dynamic mutation strategy and adaptive mutation strategy, overcoming weaknesses such as early convergence and long computing time, which helps standard GA to be more efficient and accurate in dealing with complex missions. HDM-GA shows excellent performance in solving both unconstrained and constrained test functions. The experiments of using HDM-GA to simulate a multi-satellite, mission scheduling problem demonstrates that both the computation time and success rate mission requirements can be met. The results of a comparative test between HDM-GA and three other mutation strategies also show that HDM has outstanding performance in terms of speed and reliability. [ABSTRACT FROM AUTHOR]

• A possible mechanism for the formation of vacancy dislocation loops in α-Fe is proposed. • Vacancy-NH clusters prefer to shape into 2D clusters, especially on (211) planes. • The presence of H can greatly promote the growth of vacancy dislocation loops. Genetic algorithm was applied to search the energy minimization configuration of vacancy clusters in α-Fe. Molecular statics calculations and dynamics annealing relaxation were employed to calculate the formation and binding energies of vacancies and 3D vacancy or vacancy-hydrogen (H) clusters, as well as 2D vacancy or vacancy-H clusters on (111), (011) and (211) planes. Our calculations show that vacancies prefer to form 3D clusters and vacancy dislocation loops are difficult to form, while vacancy-H clusters prefer to shape into 2D clusters, especially on (211) planes. Since H prefers the directional bonding, a vacancy cluster with H atoms tend to form a vacancy dislocation loop with its slip direction along the 〈100〉 direction and on the (211) habit plane. Our results are consistent with the experimental observations, and provide a possible mechanism for the formation of vacancy dislocation loops in α-Fe. Furthermore, we have also explored how dislocation loops trap self-interstitial atoms, vacancies and H atoms. It is of interest to note that H atoms strongly bound to a 〈100〉 vacancy dislocation loop, and are able to enhance the ability for the dislocation loop to further trap vacancies and reduce its ability to absorb self-interstitial atoms, thus promoting the growth of the 〈100〉 vacancy dislocation loops. [ABSTRACT FROM AUTHOR]