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The data loss and corresponding false fault features of single-phase or multiphase detection signals caused by sensors is a relatively troublesome problem, which can increase the difficulty to the fault diagnosis of switch fault and open-phase fault of inverter, and even can lead to false alarm. For these problems, a complementary virtual mirror fault diagnosis method for microgrid inverter is proposed to improve them. First, the virtual mirrors are constructed, which contain fault information in different angle. Second, a cross comparison processing method is used to gain the cross variables and corresponding mirror cross variables by the obtained virtual mirrors and detected signals, which can extract and normalize fault components from different angle in the same way, respectively. Thus, it can reduce the impact of data loss. Third, the self-correction fault degree (scf) and averages fault degree (af) are calculated. Specially, scf can further reduce the influence of data loss and false fault features through the complementarity of cross variables and mirror cross variables. For af, it is a steady-state expression of fault degree, which can reduce the fluctuation of fault degree variable and the possibility of false alarm. Then, fault detection is implemented through the joint decision of scf and af. Finally, the fault is located by the fault detection results and the extracted mirror complementary location information. Compared with the existing fault diagnosis methods, it not only can diagnose switch fault of inverter, but also can have certain robustness for single-phase or multiphase data loss. Meanwhile, it also has relatively fewer parameters and the lower difficulty of debugging, which are conducive to practical application. The effectiveness of the proposed method is validated by the experiment results.

In this article, considering the plasticity of reservoir, a deep echo state network with multiple adaptive reservoirs in series configuration, called MAR-DESN, is proposed for time series prediction. First, according to the characteristics of input signals and reservoir states, the number of reservoirs of MAR-DESN and each reservoir size can be automatically determined by using the principal component analysis. Second, a parameter optimization method based on Broyden–Fletcher–Goldfarb–Shanno quasi-Newton algorithm is given to optimize the reservoir parameters of MAR-DESN. Third, a sufficient condition for the uniform echo state property of MAR-DESN is given, such that the MAR-DESN can be stably applied in different applications. Finally, three examples are used to verify the effectiveness of MAR-DESN.

In this article, a robust inverter fault diagnosis algorithm is proposed under microgrid environment considering unbalanced state and overcurrent component interference. First, the detected signals are transformed into different data triangles by the proposed slidable triangularization processing which can conveniently and effectively extract the multiscale trend features and data jitter components. Further, the signed features are obtained by the proposed jitter signing processing method, which can reduce the influence of amplitude, and facilitate the logical operation and mixed operation. Then, a quantitative trend fault degree is calculated by the signed information to reflect the fault degree and location information, which has the characteristics of quantitative change. It can reduce the fluctuation and quantity of fault data features. Finally, echo state network is used to implement the intelligent classification. Because the design of hidden layer and the network training speed is simple and fast, which makes the design, implementation, and debugging of fault diagnosis more convenient. Compared with the existing fault diagnosis algorithms, it is robust to unbalanced state and overcurrent component interference of microgrid. These features make it more suitable for inverter fault diagnosis of microgrid environment. The effectiveness of fault diagnosis algorithm is verified by the experiments and comparisons.

Background: Aldosterone is an important hormone in the renin-angiotensin-aldosterone system (RAAS), and playing a pivotal role in the development of hypertension, heart failure, and other cardiovascular diseases. Material and method: In this study, the role of the aldosterone in vascular calcification was underwent in rat model compared with other drugs. Vascular calcification, calcium concentration, activity of alkaline phosphatase (ALP), aldosterone, Urotensin II, mineralocorticoid receptor (MR) and Osteopontin (OPN) were detected or confirmed by the von Kossa staining, colorimetric assays, immunohistochemistry and radioimmunoassay, separately. Result: Results revealed that the aldosterone was significantly increased compared calcification + aldosterone group with calcification group, whereas it was notably decreased in calcification + Spironolactone group in the aortic wall. Compared with control group and aldosterone group, calcium content in vascular tissues was increased in calcification group and calcification + aldosterone group. As the immunoreactivity of the MR, OPN, Urotensin II, IL-6, monocyte chemoattractant protein-1, and deposition of collagen in calcification group and aldosterone group, they all were increased slightly, but were significantly increased in calcification + aldosterone group. Conclusion: It is implied that aldosterone may be involved in the development of vascular calcification, however, the mechanism needs to be further studied.

For most existing aperiodic fault diagnosis algorithms of microgrid inverter, because of the common aperiodic processing features, they have relatively higher amount of algorithm startup, calculation, and complexity. These features increase the hardware requirements and realization difficulty, greatly affect the practicability. In order to improve above-mentioned problems, a practical fault diagnosis algorithm is investigated. In this paper, first, aperiodic corrected-second low-frequency processing method is proposed to get aperiodic small low-frequency data (ASLFD) by a simple way in the real time, which greatly reduces the amount of algorithm startup and corresponding calculation. Second, these ASLFD are processed by the real-time normalization method. Next, the degree of asymmetry and distortion degree of root mean square are extracted, respectively. Furthermore, the feature variables and results are realized through the logical operations. Compared with the existing fault diagnosis algorithms, the proposed algorithm has lower amount of startup and calculation, smaller complexity, and easy realization, which are conducive to practical applications. The detailed experimental results and comparisons are shown to validate the proposed algorithm.

For the inverter fault diagnosis of permanent magnet synchronous machine drives, it is difficult to simply and conveniently use the small low-frequency data without needing additional hardware and affecting the accuracy of the diagnosis results. This problem often leads to unnecessary computation and wastes a lot of corresponding system memory, increases the hardware requirements and realization difficulty, and affects practicability. In order to improve this problem, to make the design, debugging, and implementation of fault diagnosis more convenient and at low cost, and further to improve the practicability of the algorithm, a novel multi-switches fault diagnosis algorithm is proposed. First, a second low frequency processing method is used to obtain the small low-frequency data simply from the feedback signals of the controller. These small low-frequency data contain the main feature information of different switch states. Second, these small low-frequency data are effectively normalized by the single extremum normalization method, which is based on the symmetry features of different state data. These processed data can keep the asymmetry feature of the fault state of an inverter very well. Third, the main fault components and features are extracted from the distortion part and envelope change of the processed small low-frequency data. Fourth, echo state network is used by combining it with the extracted features to implement the intelligent classification. Different from the traditional neural network, the design of structure of hidden layer network is simplified, and the network training speed is very fast. Therefore, the debugging of this network is very convenient. Compared with the existing fault diagnosis algorithms, the proposed algorithm is convenient and at low cost to realize the multi-switches fault diagnosis. The effectiveness of fault diagnosis algorithm is verified by the experiment.

This paper presents a new open-circuit fault diagnosis algorithm for multiple switch of microgrid inverter in different load change condition. From the analysis of the waveform features and frequency composition for the main fault component under the different switch states, a low frequency sampling principle of the main fault component is developed. For the proposed fault diagnosis algorithm, first, based on above principle, small sampling data are obtained by the second low frequency mean sample processing, which can be used to reflect the main component features of different switch states and reduce the amount of the processed data. Because the data of the second low frequency mean sample processing comes from the obtained system data, it does not add any additional hardware and has the lower cost. Moreover, the obtained low frequency sampling data are divided into different feature data to adopt corresponding data processing scheme. Thus, the information of the main fault component is retained and the impact of load change is removed for any feature data. Furthermore, the feature values of the processed low frequency sampling data are extracted from the viewpoint of data attenuation. Finally, neural network is used to implement intelligent classification. The dependency and the number of threshold can be reduced, and low frequency sampling data are fully utilized to reflect the overall features. Compared with the existing fault diagnosis algorithms, the actual computational quantity is effectively reduced without affecting the accuracy and stability of the diagnosis results for microgrid inverter under load change condition. Finally, the effectiveness of fault diagnosis algorithm is verified through the detailed simulation and experiment results.

In this paper, a multiscale adaptive fault diagnosis (MAFD) method based on signal symmetry reconstitution preprocessing (SSRP) is proposed to realize fault diagnosis for any switch of the microgrid inverter under changing load condition. The MAFD method is composed of SSRP, multiscale features extraction, and artificial neural network (ANN). First, the SSRP method is used to generate the input signals of multiscale features extraction, which can reduce the impact of the changing load. Then, the multiscale features extraction is realized by the means of multilevel signal decomposition and coefficients reconstruction to extract energy content of different frequency groups signal. It can represent the detailed signal change laws at different levels for three-phase current. Finally, in order to achieve data-based adaptive fault diagnosis, ANN is used to detect the type and the location of the inverter switch fault. Compared to conventional fault diagnosis methods, the proposed fault diagnosis method can accurately detect and locate fault for any switch of the microgrid inverter under changing load condition. The effectiveness of the proposed fault diagnosis method is verified through detailed simulation and experimental results.

In this article, through the preparation of artificial contaminated soil in laboratory and simulated acid rain pollution to zinc magnesium phosphate cement (MPC for short) solidified soil erosion, the unconfined compressive strength characteristics of a single heavy metal (Zn) pollution under the above conditions was studied. In addition, water glass was added to improve the acid resistance of magnesium phosphate cement solidified soil. The effects of the mixing ratio of curing agent (MgO/KH2PO4, M/P for short), initial pH value by simulating acid rain, and the water glass content on the physical and mechanical properties of solidified zinc contaminated soil were analyzed. Studies have shown that when M/P=4, solidified soil contaminated by zinc has better solidification strength and solidification effect than M/P=6; adding water glass can increase the unconfined compressive strength of solidification of zinc contaminated soil by cement. 4% water glass content has a better effect to improve the strength of solidified soil; When the cement with M/P=4 has better engineering characteristics and acid resistance than that with M/P=6. Therefore, water glass can improve the engineering properties of solidified soil contaminated by zinc under sour environment effectively.

The bearing capacity of the pile foundation determines the safety and stability of the upper structure. However, during the thermal cycling of the energy pile, the stress of the pile will be changed due to temperature changes., which will affect the bearing capacity of the energy pile foundation. In order to analyze the mechanical properties of the energy pile under multi-field coupling, the finite element software COMSOL Multiphysics is used to establish the thermal-mechanical coupling analysis model of the energy pile. We apply thermal load on the pile body and vertical load on the top of the pile. The results show that: Under the multi-field coupling of temperature and mechanical load, the energy pile body expands when heated, and the displacement of the pile top decreases; this will cause uneven settlement between the energy pile foundation laying the heat exchange tube and other pile foundations, which will lead to the tilt and damage of the superstructure; the stress of the pile during the heating of the energy pile will increase.

This paper investigates the delay-dependent stability of the LFC scheme considering two time-varying delays, a new less conservative delay-dependent stability criterion is proposed to study the delay margin of LFC system with PI controller based on Lyapunov-Krasovskii (LK) functional method and linear matrix inequality (LMI) technique. In the presented stability analysis procedure, the time derivative of the LK functional is bounded tightly using the Jesen inequality and the extended reciprocally convex matrix inequality. The proposed stability criterion is tested on the one-area and multi-area LFC systems, and the delay margins with respect to different value of gains of PI controllers are computed to investigate the quantitative effect of the controller gains on the delay margin, and are obtained for different load disturbance to discuss the effect of the exogenous load disturbance.

An optimal control of boost converter based on model-free adaptive controller (MFAC) and fuzzy particle swarm optimization algorithm (FPSOA) is proposed to realize a better response performance and robustness under model mismatch and the parameter time variation. The data-based MFAC is investigated to design the boost converter, which avoids the precise modeling and greatly reduces the influence of model mismatch. Additionally, the FPSOA is used for online optimization of the controller parameters to improve the response performance and robustness. Comparing with the traditional optimal control methods of boost converter, the proposed method avoids the contradiction of establishing accurate model and complex systems design. It inhibits the influences of the parameter time variation and model mismatch, and has a better dynamic and static performance. Finally, the test is implemented on a semi-physical simulation platform for power electronic control systems. The comparison of MFAC and classic state feedback exact linearization control in various model mismatch situations are used to show the effectiveness of the obtained results.