Search

• The spatiotemporal distribution of the future fault events in power transmission systems is predicted in a longer timescale. • The rarely occurred environmental elements and fault causes are incorporated by the proposed RARM model. • The relative weights of environmental elements and fault causes are established. • Specific assessment of the prediction according to each fault cause is conducted by an extended CIM. • The accuracy of predictions is improved and the scope of real applications is enlarged. The spatiotemporal distribution of future fault events in a power transmission system assists in operational planning and maintenance scheduling. To this end, this paper proposes an environmental attributes-based framework for the spatiotemporal distribution prediction of potential fault events in the system. In this framework, the distribution of future fault events is predicted via the forecasted information of the environmental attributes rather than the electrical attributes. An extensive investigation covering all environmental attributes including the fault causes is presented, and the underlying fault-attribute relationships are explored. Notably, the rare association rule mining is employed to cope with the rare occurred elements in each environmental attribute through five new significance measurements. Next, to distinguish the diverse influence of each environmental element on the reliability of the whole system, the relative weights are developed. Also, the impact of the latent erroneous predictions of the events caused by one fault cause on the overall prediction performance is assessed via an extended definition of the component importance measurement. Ultimately, the efficiency of the modified significance measurements, the prediction performance in the two test cases, and the impact of each single fault cause are validated by an empirical study. The flexibility and the robustness of this framework in real applications are therefore demonstrated. [ABSTRACT FROM AUTHOR]

Summary Objectives To investigate natural-resistance-associated macrophage protein 1 (NRAMP1), mannose-binding lectin (MBL), vitamin D receptor (VDR) gene polymorphisms and their interaction with susceptibility to pulmonary tuberculosis (PTB) in a Chinese population. Methods A case-control study was conducted in PTB (n=151), age- and sex- matched healthy controls (HCs) (n=453). Genetic polymorphisms of NRAMP1 (INT4, D543NA and 3′UTR), MBL (HL, PQ, XY and AB) and VDR (FokI and Taq) were analyzed by using PCR-restriction fragment length polymorphism (RFLP) and PCR- single- strand conformation polymorphism (SSCP) techniques. Multifactor dimensionality reduction (MDR) analysis was carried out to assess the effects of the interaction between SNPs. Results The distribution of NRAMP1- 3′UTR (TGTG/del), MBL- HL (H/L) and FokI (F/f) were significantly different between PTB patients and HCs (p<0.05). HPYA (OR: 1.88; 95% CI: 1.22-2.91), LPXA (OR: 3.17; 95% CI: 1.69- 5.96), LQYA (OR: 3.52; 95%CI: 1.50-8.23) and LPYB (OR: 12.37; 95%CI: 3.75- 40.85) of MBL were risk haplotypes for PTB. The TGTG- H- f (OR: 1.70; 95%CI: 1.10-2.62) and del- H-f (OR: 3.48; 95% CI: 1.45-8.37) of 3′UTR- HL- FokI were also high-risk haplotypes associated with tuberculosis. Conclusions Our study suggests that genotypes of many polymorphic genes are associated with TB, it is necessary to further explore the mechanism of genotypes and gene-gene interaction in susceptibility to tuberculosis. [ABSTRACT FROM AUTHOR]

Microgrid (MG) is an effective way to integrate renewable energy into power system at the consumer side. In the MG, the energy management system (EMS) is necessary to be deployed to realize efficient utilization and stable operation. To help the EMS make optimal schedule decisions, we proposed a real-time dynamic optimal energy management (OEM) based on deep reinforcement learning (DRL) algorithm. Traditionally, the OEM problem is solved by mathematical programming (MP) or heuristic algorithms, which may lead to low computation accuracy or efficiency. While for the proposed DRL algorithm, the MG-OEM is formulated as a Markov decision process (MDP) considering environment uncertainties, and then solved by the PPO algorithm. The PPO is a novel policy-based DRL algorithm with continuous state and action spaces, which includes two phases: offline training and online operation. In the training process, the PPO can learn from historical data to capture the uncertainty characteristic of renewable energy generation and load consumption. Finally, the case study demonstrates the effectiveness and the computation efficiency of the proposed method. • A real-time MG OEM model with uncertainty is built and mapped into the MDP. • The proximal policy optimization (PPO) is proposed to find the OEM strategy. • The offline training and online dispatch are combined to deal with uncertainty. • The computational efficiency and accuracy are verified in the test case. [ABSTRACT FROM AUTHOR]

• A bilevel optimization framework is formulated for energy management of DN with MGs. • A Stackelberg game is designed to describe the interactive OEM mechanism. • A data-driven Dueling-DQN and DDPG algorithm are proposed to find the OEM strategy. • The off-line learning and online operation are combined to make decision for OEM. • The computational efficiency and accuracy outperform the MPEC and GA. As an effective way to integrate renewable energy, more and more microgrids (MGs) are connected to distribution system. However, the model-based energy management approach is confronted with challenges as the MGs data scale increases rapidly. The data-driven analysis and decision approach is widely utilized to maintain the secure and stable operation of MG. Hence, this paper firstly proposes a bi-level coordinated optimal energy management (OEM) framework for the distribution system with Multi-MGs. In this framework, the distribution system operator (DSO) makes decisions at the upper level, and the MGs make their own decision at the lower level. Secondly, an interactive mechanism based on a-leader-multi-followers Stackelberg game is provided to improve the utility of both sides by dynamic game, where the DSO is the leader, and the MGs are followers. Furthermore, a data-driven multi-agent deep reinforcement learning (DRL) approach is investigated to calculate the Stackelberg equilibrium for the OEM problem. Finally, the case study in modified IEEE-33 test systems with multi-MGs demonstrates the performance of the proposed approach. The computation efficiency and accuracy are verified by the dispatch result. [ABSTRACT FROM AUTHOR]

• The spatiotemporal distribution of power transmission fault events is predicted. • The periods with fewer fault events and rarely occurred elements are incorporated. • Twofold relative weight is formed to probe the impact of elements and period. • An automatic self-adaption process is designed to dynamically modify parameters. • This framework is validated based on an empirical case. Power transmission systems are principal for energy supplies, and their reliability is remarkably threatened by fault events. The spatiotemporal distribution of such reliability weaknesses can provide crucial information for maintenance arrangement and operational scheduling in systems. It is also salutary for system operators to get sufficient preparation time. With such motivations, this paper presents original insights on the prediction of the spatiotemporal distribution of power transmission fault events. A framework based on the dynamic association rule mining with rare environmental elements and time series model is proposed. In this model, the rarely occurred environmental elements and fault causes, as well as the periods with fewer fault events, are incorporated and assessed explicitly. The twofold relative weights are developed to measure the influence of the different elements within the dissimilar periods on the reliability of the whole system. To further improve the prediction performance, an automatic self-adaption process is established to dynamically calibrate the current criteria and parameters in light of the consequences from the previous period. Finally, this framework is applied and testified via a practical instance, and the results of this empirical case demonstrate the flexibility and robustness of it during real applications. [ABSTRACT FROM AUTHOR]

• The longer-term prediction of energy security weakness distribution is assessed. • An ensemble system is formed to assess continuous and discrete features separately. • The rarely appeared environmental elements and fault causes are incorporated. • The probabilistic fuzzy risks are utilized to reduce uncertainties. • An empirical case validates that the predictions can be improved by this system. The security of energy supplies requires the depletion of potential fault events in power transmission networks. To achieve this, sufficient lead time before the happening of a fault event is indispensable for preparing countermeasures. With this inspiration, this paper establishes the fuzzy inference with rare association rule learning system. This ensemble system is designed for the long-term prediction of the spatiotemporal distribution of such energy security weaknesses, that is, to predict when and where these events are more expected to appear. In this system, merely the environmental features rather than the electrical features are needed as inputs. All the selected input features are divided into discrete and continuous features, and are evaluated separately. For the discrete features, the rare association rule learning model is implemented so that the rarely distributed environmental elements are extracted and diagnosed specifically. The risk indices of each element on the overall reliability are worked out as well. For the continuous features, a hierarchical fuzzy inference system along with the rare association rule learning model is deployed to calculate the corresponding risk indices of all the elements. In the hierarchical fuzzy inference system, the probabilistic fuzzy risks are employed instead of the direct fuzzy risks. Then the relative weights of these two sides are optimized. At last, an empirical case based on a practical transmission network is conducted, and the flexibility and the robustness of the proposed system during real applications can be validated consequently. [ABSTRACT FROM AUTHOR]