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In order to investigate the diffusion bonding between Ni-based GH4099 alloy and Mo-based MHC alloy, the wettability of AgCuTi, TiZrCuTi and NiCrSiB filler metals on the surface of GH4099 alloy and MHC alloy was firstly performed. The results show that three kinds of filler metals have excellent wettability on the surface of two kinds of substrates, and are the suitable interlayers for joining GH4099 and MHC alloys. Secondly, diffusion bonding tests of GH4099 and MHC alloys were carried out by Gleeble thermally simulated experiments. The microstructures of three kinds of welding joints with different interlayers were compared and analyzed. It was found that Ti element had significant effects on improving the bonding strength of MHC side. A large number of Ni-Mo intermetallic compounds can easily be formed, which led to high brittleness of the joints. The experimental results showed that NiCrSiB is the best interlayer material for joining GH4099 and MHC alloys, and the shear strength of the joints can reach 116 MPa.

In distribution networks with arc suppression coil grounding, the fault line can be set as the energy leakage channel of the capacitive current of the entire system. During the period from fault occurrence to the whole action process of arc suppression coil, there is abundant fault information in the transient waves. However, the data of this period are usually ignored in previous methods. In this study, a novel fault section location method based on the leakage energy function is proposed. Only the fault phase voltage and fault phase current are used in the proposed method, to avoid the difficulty of the zero-sequence current measurement. Then, the action characteristics of two types of arc suppression devices are analysed. The influence of the capacitor current on the fault phase voltage and current during the entire process of arc suppression coil operation is deduced. The difference between the leakage energy on both sides of the fault section is used as the criterion for fault section location. Finally, the effectiveness of the method is verified by a case study. The simulation results indicate that the proposed method has high sensitivity and high impedance adaptability, that will have more advantages in the actual situations.

In this work, laminated Al/Ti composites were fabricated by hot rolling at three different temperatures (400 °C, 450 °C and 500 °C). The deformation behavior and fracture mechanism of composites were studied by the in-situ tensile test. The results indicated that the ductile shear fracture (for 400 °C and 450 °C rolled specimens) and ductile delamination fracture (for 500 °C rolled specimen) were the main fracture failure modes of Al/Ti multilayers. Results shown that the tensile load and displacement of the 500 °C prepared Al/Ti were better than other samples due to the higher rolling temperature. The changes of slip lines and plastic bands were the main plastic deformation characteristics for the Al layer and Ti layer metals, respectively. The investigation found that the major crack nucleation type in Ti layer was fold belt cracking. The slip bands cracking, cross-slip cracking, particle interface cracking and grain boundary slide cracking were the primary crack nucleation patterns for the Al layer.

The 2Cr13/316L multilayered steel (MLS) composites were prepared by accumulative hot roll bonding and heat treatment. The element diffusion, interface bonding mechanism and interface bondin...

The 2Cr13/316L multilayered composite plates were fabricated by hot rolling with recycle heating step. The effect of rolling reductions on microstructure and properties was investigated. The 2Cr13 layer consists of martensite and lath ferrite, but the middle layer has less ferrite than both sides. The content and grains of ferrite increase with the increase of the reduction and number of reheating, which leads to a decrease in the hardness of the 2Cr13 layer. The hardness of the 2Cr13 layer is determined by the volume ratio of martensite and ferrite. Tensile strength of the specimens with the rolling reduction of 72% and 82% reached 815.8 MPa and 763.4 MPa, while elongations were 20% and 20.8%, respectively. With the increase of the rolling reduction, the fracture mode also changed from cleavage fracture to dimple fracture. There were no cracks and delamination when the 2Cr13/316L composite plate bent to 130° and 180°, which indicated better interfacial bonding.

The high tensile strength and irradiation resistance of oxide dispersion strengthened (ODS) ferritic steels is attributed to the ultrafine and dispersed oxides within the matrix. The high content of oxygen and yttrium is critical for the formation of dense Y-rich oxides. However, only few studies have reported the effect of oxygen content on the microstructure and mechanical properties of ODS steels. Herein, we employed gas atomization reactive synthesis to prepare pre-alloy powders and then hot isostatic pressing (HIP) to consolidate two 22Cr-5Al ODS steels with different oxygen content. Our results showed Y-rich precipitates at and near grain boundaries of the as-HIPed alloys. Moreover, with the oxygen content increasing from 0.04 to 0.16 wt%, more precipitates precipitated in the as-HIPed alloy, and the ultimate tensile strength of the alloy was improved. However, increasing the oxygen content to 0.16 wt% led to formation of stripe and chain precipitates at and near grain boundaries, which caused a partial intergranular fracture of the as-HIPed alloy.

With the conversion of the global power economy and energy structure, access to a large amount of renewable energy has led to a decrease in power system inertia. The slight abnormal disturbance in the distribution network may have a significant impact on social and economic development. Aim at enhancing power stability and system resiliency; this study focuses on the detection and location of multiple abnormal sources in the distribution network. Most traditional methods use models relying on precise line parameters, subject to poor adaptability to the distribution network with a large number of nodes, and rapidly changing topology. Therefore, this study proposes a novel random matrix model, driven by monitoring data from phasor measurement units distributed on the overhead transmission lines. In this model, linear shrinkage (LS) theory, and Marchenko-Pastur law are combined for noise reduction to ensure the dynamic character and anti-noise ability. Moreover, data dimensions and sample points may be at the same level in an extensive scale network. The LS and standard condition number rule (SCN) are used for estimating the number of abnormal sources. Finally, the effectiveness of this paper’s model is verified in PSCAD. The results indicate that the method has specific dynamic performance and anti-noise ability.

Under natural disasters, confirming the failure location of distribution systems is the basis for post-disaster repair work. We can estimate the state of distribution systems by collecting and analyzing real-time data from sensing devices installed in the distribution systems. However, due to the destructive nature of the natural disaster, the collected data often in a missing mode. The reason for the missing data is not only the damage of monitored object but also the damage of the monitoring devices and the failure of communication systems. As a result, the collected data are in the form of limited credibility, leading to the difficulty of failure location for distribution systems under nature disaster. In this paper, a denoising auto-encoder based state estimation method is proposed to solve the problem. By adding a process of missing data randomly in the training phase, the scenario of the monitored object damage can be identified, and then the state of distribution systems can be estimated effectively. The superior performance of the proposed method is verified through simulation and experiments.

10kV overhead distribution lines are usually used for power supply in urban, rural and other areas. If the trees planted along the line are too close to the overhead line, the air between the tree and the conductor will break down, causing an arc. The high temperature of the arc will burn the wires and branches, and in some cases will cause the conductor wires to break and the trees to burn, thereby posing a serious safety threat. To analyze the characteristics of the arc caused by the tree-line contact, a magnetohydrodynamic model of the arc was established, and the effects of the tree-conductor separation speed and natural wind speed on the development of the arc were studied. The research results show that the separation of the branch and the blowing of wind will lead to an increase in the length of the arc and a decrease in temperature, which will affect the voltage and current waveform of the arc and the moment of flameout. The higher the separation speed and the wind speed, the faster the temperature decreases, and the shorter the duration of the arc.

Detecting abnormal operating states in the power grid is a fundamental method used in processing abnormal events in the power grid, while the massive applications of WAMS (wide area measurement system) provide conditions for detecting weak anomalies in the power grid. According to the random matrix theory, acquisition signals of a variety of PMUs (phase measurement units) were constructed into a large-dimensional random matrix. Based on the characteristics of spectral distribution of the large dimensional random matrix, the principle and criterion for detecting abnormal states in the power grid were proposed on the basis of the spectral distribution of the large-dimensional random matrix. What’s more, the detection method proposed was proven to be effective by analyzing PMU frequency monitoring data in the case. The method proposed in this paper can be used for sensitively detecting abnormal events in the power grid and making a quick judgment, which offers a new way of analyzing and processing abnormal events in the power grid.

Nano B2 FeCu ordered clusters and multiphase microstructure consisting of intercritical ferrite, tempered martensite and nano-γ phase (reverted austenite) were obtained by two-step heat treatment involving intercritical annealing and intercritical tempering. The experimental steel with nano Cu precipitates and nano-γ phase exhibited high strength and high ductility combination. The yield strength and total elongation of the experimental steel increased from 758 MPa and 16.8% to 984 MPa and 29.5% after the second step intercritical tempering for 5 min. High resolution transmission electron microscopy (HRTEM) and three-dimensional atom probe (3DAP) studies provided evidence to support that high density of nano B2 FeCu ordered clusters contributed to high strength. First principle calculations suggested that the feasibility of B2 FeCu nano-ordered clusters, and the stability of B2 structure is related to the coherent stress field at the interface between clusters and the BCC-Fe matrix. The average size of B2 FeCu nano-ordered clusters was 4 nm with a lattice constant of 0.2893 nm and an orientation relationship of (1 1 0)B2//(1 1 0)α and [0 0 1]B2//[0 0 1]α. 9R Cu without twinned structure was discovered at different tempering times. The proportion of Cu in Cu precipitates varied from 24.4 at% to 61.2 at% with change in crystal structure and increase in the size of precipitates. The significant increase in yield strength is discussed in terms of ordered domain strengthening, modulus strengthening and lattice misfit strengthening mechanisms. Bright field TEM image and selected area electron diffraction (SAED) pattern discovered and proved that spindle nano-γ phase was present at phase boundary between tempered martensite and intercritical ferrite. The size of them were among 242–375 nm in length and 52–80 nm in width. The two needle tip ends of nano-γ phase were just at phase boundaries, indicating the preferred growth direction. (1 −1 1)γ plane of nano-γ phase was parallel to (1 −1 0)α plane of the adjacent tempered martensite. The effect of nano-γ phase on enhancing the plasticity was revealed by instantaneous work hardening index curve.

The microstructure and mechanical properties of 2Cr13/316L composite plates by hot-rolling at different rolling temperatures of 1130 °C, 1180 °C, 1250 °C, 1300 °C are investigated in details. With the increase of the rolling temperature, the tensile strength and bending strength of 2Cr13/316Lwere reduced first and then increased slightly due to the coarsening of grains and the increasing of ferrite content in 2Cr13 layers. The highest tensile elongation is obtained at the composite plate with rolling temperature of 1180 °C. 2Cr13/316L composite plates obtained by hot rolling processes were able to withstand larger bending deformation, and no cracks or interfacial delamination can be found in the bending process because of a strong combined interface.

This paper analyzes the micro-mechanism of decrease of impact toughness with increasing Silicon and increasing Copper in weld metal of a low-alloy high-strength X120 steel. By comparing the microstructures, measuring the extending length of the fibrous crack, and calculating the local cleavage fracture stress σf, and then using the basic principles of the micro-mechanism of cleavage fracture, the essential causes of change of toughness in the weld metal of high-strength steel welded joints are revealed in this work.

The behaviors and effects of Zn-coating on the welding-brazing processes of steel with aluminum and magnesium were investigated by Cold Metal Transfer (CMT) arc. The behaviors of Zn-coating at the very beginning of burning and during the spreading of the liquid aluminum and liquid magnesium droplet over the steel plate were observed respectively. The effects of Zn-coating on the wetting process of liquid droplets and on the fracture of welding-brazing joints were analyzed. The effects of Zn-coating on joining mechanisms and tensile load of Al-steel joints and Mg-steel joint were compared and analyzed. For Al-steel, Mg-steel, some common behaviors can be found, the main effect of Zn is its evaporation which decreases the temperature at the interface to prevent steel from melting and makes the steel to expose the fresh metallic surface which is much easier to be spread by a liquid droplet than an oxidized surface. Zn-coating improves the wettability of liquid Al or liquid Mg over the steel surface. However, some effects of zinc coating on wetting behavior, joining mechanisms and tensile load of Al-steel joints and Mg-steel joint are still different. For Al-steel arc, Zn-rich triangle zone is not formed by driving the liquid Zn from inside and accumulated at the toe, rather it is formed by the diffusion of the liquid Zn from outside Zn-enriched periphery into the droplet. In the case of Mg-steel arc, the Zn-rich zone forms partly due to the accumulation of Zn driven from the center during the Zn evaporation. For Al-steel joint, Zinc coating has not reacted to form the interface layer between Al weld metal and steel, however, zinc coating reacted to form the interface layer between Mg weld metal and steel. Zinc coating improved the strength of Al-steel joints, but, it is not beneficial to the strength of Mg-steel joint.

3,5-Dinitrobenzamide has been widely used as a feed additive to control coccidiosis in poultry, and part of the added 3,5-dinitrobenzamide is excreted into wastewater and surface water. The removal of 3,5-dinitrobenzamide from wastewater and surface water has not been reported in previous studies. Highly reactive hydroxyl radicals from UV/hydrogen peroxide (H2O2) and UV/titanium dioxide (TiO2) advanced oxidation processes (AOPs) can decompose organic contaminants efficiently. In this study, the decomposition of 3,5-dinitrobenzamide in aqueous solution during UV/H2O2 and UV/TiO2 oxidation processes was investigated. The decomposition of 3,5-dinitrobenzamide fits well with a fluence-based pseudo-first-order kinetics model. The decomposition in both two oxidation processes was affected by solution pH, and was inhibited under alkaline conditions. Inorganic anions such as NO3 −, Cl−, SO4 2−, HCO3 −, and CO3 2− inhibited the degradation of 3,5-dinitrobenzamide during the UV/H2O2 and UV/TiO2 oxidation processes. After complete decomposition in both oxidation processes, approximately 50% of 3,5-dinitrobenzamide was decomposed into organic intermediates, and the rest was mineralized to CO2, H2O, and other inorganic anions. Ions such as NH4 +, NO3 −, and NO2 − were released into aqueous solution during the degradation. The primary decomposition products of 3,5-dinitrobenzamide were identified using time-of-flight mass spectrometry (LCMS-IT-TOF). Based on these products and ions release, a possible decomposition pathway of 3,5-dinitrobenzamide in both UV/H2O2 and UV/TiO2 processes was proposed.

For the heat resistance steel weld metal, special post-weld heat treatments need to be performed to maintain the creep resistance, improve the impact toughness, and relieve the welding residual stress. In this paper, firstly, the three factors affecting variability and lower values of toughness were revealed. Secondly, two different post-weld heat treatment (PWHT) methods have been used to improve the impact toughness of a heat resistance steel weld metal. Two PWHT methods include post-weld direct tempering (PWDT) process at 690 °C/26 h/air cooling and post-weld normalized tempering (PWNT) process with re-austenitization at 960 °C/1.5 h/air cooling + tempering at 690 °C/26 h/air cooling. For three type specimens with as-welded (AW), PWDT, PWNT processes, the impact toughness and the hardness were measured, and the fracture surface, microstructures were observed and analyzed by scanning electron microscope (SEM). Results indicated that PWDT heat treatment can improve the impact toughness of the weld metals from the original 79 J - 169 J to 82 J–290 J because the microstructure is changed from granular bainite to block ferrite and granular bainite. PWNT heat treatment can remarkably improve the impact toughness of the weld metals from the original 79 J- 169 J to 148 J–325 J. It is attributed to that PWNT heat treatment process can remove the boundaries of the columnar grain zone and reheated zone, make the difference of the microstructure between the columnar grain zone and reheated zone decrease, and improve the homogeneity of the microstructure.

Oxidation and cracking behavior during thermal-shock experiments of a new Co-based alloy were analyzed in the present work. Comparing to the static conditions, the oxidation behavior during thermal shocks is stress-assisted and crack-accelerated. Cracks during the thermal shocks were initiated from the sample surfaces and their growth are driven by various stresses, including those induced by volume change, mismatch of coefficients of thermal expansion (CTEs), and temperature gradient between the matrix and the internal oxides. It is found that the crack propagation correlates to these stresses in the classic Paris’ law.

The demand for heat-resistant steel has increased owing to its utility in numerous devices that must withstand high steam pressures and high temperatures, such as turbine rotors and blades in ultra-supercritical power plants. It is inevitable to join heat-resistance steel part by welding method, so it is important to maintain the toughness of the weld metals. In this study, the microstructure, low-temperature impact toughness, and fracture surface of as-welded and post-weld heat treatment (PWHT) of 2.25Cr-1Mo-0.25V weld metal were investigated. The microstructures of the as-welded and PWHT specimens are granular bainite and ferrite, respectively. This work revealed the relationship between effective microstructure nearby crack initiation origin and low temperature impact toughness for both the as-welded and PWHT specimens. The evolution of the microstructure and prior austenite was then investigated using confocal laser scanning microscopy (CLSM) to observe the formation of coarse ferrite grain structures. A suggestion for enhancing the low-temperature toughness was provided based on the effect of adjusting Mn content and forming acicular ferrite.

The martensite/austenite MLS composite plates were fabricated by hot roll bonding of the alternately stacked 2Cr13 and 316L stainless steel plates. The effects of heat treatment on the microstructure transformation, interface behavior, tensile properties, fracture modes and failure mechanisms have been studied in depth. The microstructure of the composite plates is composed of austenite in 316L layers, martensite and a small amount of ferrite in 2Cr13 layers. After heat treatment, the microstructure of the composite plates changes from rolling texture to equiaxed grains. Most importantly, the recrystallization and thermal diffusion formed after heat treatment can effectively increase the overall plasticity of composite plates at the expense of small strength and hardness, improve the interface deformation compatibility and interfacial bonding strength, change the fracture mode from the quasi-cleavage fracture into the full dimple fracture, and fracture mechanism from the interface failure into the intralayer failure.

The growth of individual trees in a forest is affected by many factors, a crucial one being the intensity of competition among trees, because it affects the spatial structure of the forest and is in turn influenced by silvicultural practices. In a mixed forest in particular, the growth of trees is affected by multiple interactions. To analyse the competition between moso bamboo (Phyllostachys pubescens (Pradelle) Mazel ex J.Houz.) and broad-leaved trees in a mixed forest, data were extracted by sampling six spots within such a forest using terrestrial laser scanning (TLS). The convex hull algorithm was used for calculating the overlap volume between the crowns of the broad-leaved trees and the bamboo canopy. Bamboos growing at least 3 m away from any of the broad-leaved trees were the most numerous and the diameter at breast height (DBH) is larger than those growing closer than that, which suggests that broad-leaved trees suppressed the growth of bamboo if they are closer but promote it beyond 3 m up to a point at which the distance is too great for any such effect. The modified Hegyi&rsquo, s competition index was constructed based on the canopy factor, which may better describe the competitive interaction among the trees and bamboos. Using TLS can enhance our understanding of the competition among trees in mixed forests and help in planning the spatial structure of such forests in general and provide a benchmark for choosing planting distances in particular.

The accurate prediction and evaluation of the operating status of transmission line can provide technical support for the safe, economical and efficient operation of the power system. Most of the traditional methods just use a single parameter for the forecasting and analysis of transmission line. The running state of transmission line is also affected by weather conditions, operating conditions and many other factors. Due to the low quality of the monitoring data and the large randomness of the environmental conditions, the traditional methods have great limitations in the accuracy and timeliness of the prediction. In this paper, a load prediction method based on Principal Component Analysis (PCA) and online sequential extreme learning machine (OSELM) is proposed. Firstly, the principal component analysis is used to reduce the dimension of the original data, and the parameters with less influencing factors are eliminated. Main parameters are extracted to train the online sequential extreme learning machine. The practical examples show that the proposed method has better data fitting ability and higher prediction accuracy.

Analysis of monitoring data of transformer can provide important theoretical basis for transformer fault diagnosis. In this paper, a transformer fault diagnosis method based on kernel principal component analysis (KPCA) and improved Elman neural network is proposed. Firstly, gas concentration, several commonly used ratios and seven new parameters are introduced as reference vectors. Then the main characteristic parameters extracted by KPCA algorithm are used as the input of Elman neural network to train the neural network. Finally, the test set is experimented with the trained network. The results indicate that the method in this paper compared with SVM and BP neural network has a higher diagnosis accuracy.

The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.

the traditional anomaly detecting methods for power equipment do not consider the spatial information of the state data. This paper proposed a method for anomaly detection of state data of power equipment based on spatiotemporal clustering method, which considers historical big data and visualizes the structures revealed within data. Using a sliding window, the time series are divided into a number of subsequences. The available spatiotemporal structure within each time window is discovered using the FCM method. In the sequel, an anomaly score is assigned to each cluster. By using a fuzzy relation formed between revealed structures, a propagation of anomalies occurring in consecutive time intervals is visualized. At last, the effectiveness of the method is verified by an icing example.

It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.