Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.     

Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

Optimal resilient power grid operation during the course of a progressing wildfire

We study a two-stage stochastic and nonlinear optimization model for operating a power grid exposed to a natural disaster. Although this approach can be generalized to any natural hazard of continuous (and not instantaneous) nature, our focus is on wildfires. We assume that an approaching wildfire impacts the power grid by reducing the transmission capacity of its overhead lines. At the time when proactive decisions have to be taken, the severity of the wildfire is not known. This introduces uncertainty. In this paper, we extend previous work by more realistically capturing this uncertainty and by strengthening the mathematical programming formulation through standard reformulation techniques. With these reformulation techniques, the resulting two-stage, convex mixed-integer quadratically constrained programming formulation can be efficiently solved using commercial quadratic programming solvers as demonstrated on a case study on a modified version of the IEEE 123-bus test system with 100 scenarios. We also quantify the uncertainties through a second case study using the following three standard metrics of two-stage stochastic optimization: the expected value of perfect information, the expected result of using the expected value solution and the value of the stochastic solution.     

Microstructure and mechanical properties of laser beam welded Al–Li alloy 2060 with Al–Mg filler wire

Alloy 2060-T8 is a newly developed high-strength Al–Li alloy for applications in aircraft industry. Crack-free welds were obtained in laser beam welding with 5087 filler wire under optimized welding conditions. In this paper, fusion zone microstructure and joint mechanical properties were investigated. Microstructure typical for the weld metal consists of α-Al matrix with a few nanoscale precipitates inside and a coarse icosahedral quasicrystalline T₂ phase at the dendritic and grain boundaries. The quasicrystalline occurred normally in Al–Li–Cu alloys with higher Li contents. Our investigations show that the icosahedral quasicrystalline phase T₂ phase forms in the laser-welded Al–Li alloy 2060 with lower Li content as a result of segregation and replacement of Mg element. The joint tensile strength in as-welded condition is around 317 MPa, about 63% of that of the base metal, and fracture occurs within the fusion zone.     

Improved synergetic excitation control for transient stability enhancement and voltage regulation of power systems

This paper proposes decentralized improved synergetic excitation controllers (ISEC) for synchronous generators to enhance transient stability and obtain satisfactory voltage regulation performance of power systems. Each generator is considered as a subsystem, for which an ISEC is designed. According to the control objectives, a manifold, which is a linear combination of the deviation of generator terminal voltage, rotor speed and active power, is chosen for the design of ISEC. Compared with the conventional synergetic excitation controller (CSEC), a parameter adaptation scheme is proposed for updating the controller parameter online in order to improve the transient stability and voltage regulation performance simultaneously under various operating conditions. Case studies are undertaken on a single-machine infinite-bus power system and a two-area four-machine power system, respectively. Simulation results show the ISEC can provide better damping and voltage regulation performance, compared with the CSEC without parameter adaptation scheme and the conventional power system stabilizer.     

Exergoeconomic performances of the desiccant-evaporative air-conditioning system at different regeneration and reference temperatures

This paper presented the exergoeconomic evaluation of the developed desiccant-evaporative air-conditioning system. The developed system was evaluated based on the steady-state conditions at different regeneration and reference temperatures. The exergoeconomic evaluation method was implemented to the system components and the whole system to evaluate the exergy efficiency, exergy destruction ratios, cost rates, relative cost differences and exergoeconomic factors. The regeneration and reference temperatures affected the exergy efficiencies, exergy destruction ratios, cost rates, relative cost differences and exergoeconomic factors. The desiccant wheel, heating coil and evaporative cooler had a high cost rate (investment cost, operation and maintenance cost, and exergy destruction cost). The exit air fan, outdoor air fan and evaporative cooler had a high relative cost difference. The exit air fan, outdoor air fan and secondary heat exchanger had a high exergoeconomic factor. Replacement of the desiccant wheel with a higher dehumidification performance could decrease the high cost rate. A higher efficiency evaporative cooler and heating coil were needed. Cheaper air fans (outdoor air fans and exit air fans) were needed.     

Optimized dual-function varistor-capacitor ceramics of core-shell structured xBi2/3Cu3Ti4O12/(1-x)CaCu3Ti4O12 composites

xBi_2/3Cu₃Ti₄O₁₂/(1-x)CaCu₃Ti₄O₁₂ composites were prepared by traditional solid-state reaction method. Extremely high nonlinear coefficient of 25 and breakdown field of 18.92 kV·cm⁻¹ were obtained in small current range of 0.1−1 mA·cm^-2. In addition, reduced dielectric loss of 0.055 was achieved with high dielectric constant of 1369. Optimized nonlinear and dielectric properties were integrated to make the composites a promising dual-function varistor-capacitor candidate. Microstructure analysis discovered two areas with various Bi/Ca ratio, designated as Bi-H and Bi-L respectively. It was found that the maximum ratio of Bi-H/Bi-L heterogeneous interface corresponded to optimized nonlinear and dielectric performance, which was associated with elevated potential barrier height and huge grain boundary resistance. Combined with relaxation analysis, a core-shell structure was proposed to elaborate microstructure evolution in xBi_2/3Cu₃Ti₄O₁₂/(1-x)CaCu₃Ti₄O₁₂ composite. According to the core-shell model, variation of heterogeneous interface was illustrated on how to influence nonlinear properties, which was well fitted to experimental results.