The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately.

This paper presents an integrated fuzzy controller design approach to synchronize a dissimilar redundant actuation system of a hydraulic actuator (HA) and an electro-hydrostatic actuator (EHA) with system uncertainties and disturbances. The motion synchronous control system consists of a trajectory generator, an individual position controller for each actuator, and a fuzzy force tracking controller (FFTC) for both actuators. The trajectory generator provides the desired motion dynamics and designing parameters of the trajectory which are taken according to the dynamic characteristics of the EHA. The position controller consists of a feed-forward controller and a fuzzy position tracking controller (FPTC) and acts as a decoupled controller, improving position tracking performance with the help of the feed-forward controller and the FPTC. The FFTC acts as a coupled controller and takes into account the inherent coupling effect. The simulation results show that the proposed controller not only eliminates initial force fighting by synchronizing the two actuators, but also improves disturbance rejection performance

Active hydrostatic journal bearings represent a mechatronic answer to the fast-growing industrial needs to high-performance rotary machineries. The aim of this research is to study and improve the dynamic characteristics of hydrostatic bearing, with four pockets or recess. In order to improve dynamic characteristics for hydrostatic journal bearing, this research presents an active hydrostatic journal bearing with servo control. The results of active hydrostatic journal bearing with servo control were compared with those of traditional hydrostatic journal bearing with capillary throttling. The dynamic characteristics of the newly proposed bearing and traditional bearing are studied and compared under different dynamic conditions such as variable speed, viscosity, load, pressure, and bearing clearance. The numerical result shows that the proposed active hydrostatic journal due to active lubrication has good performance including stability and controllability, high stiffness, faster response, and strong resistance. Furthermore, two strategies such as proportional–integral–derivative and integrator backstepping control are also presented to improve the performance and efficiency of active hydrostatic journal bearing with servo control. The result shows that integrator backstepping control is more effective than proportional–integral–derivative control in terms of faster response and greater amplitude reduction, quickly regaining equilibrium position, and good stiffness against load. Hence, it is observed that active lubrication in hydrostatic journal bearing provides us better dynamic characteristics which will have potential applications in heavy and high-speed machinery.

DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%.

Most of the researchers are focusing to bring automation in mechanical systems. So, current research work presents a mechatronic system called active hydrostatic journal bearing that is a higher demand of fast-growing industrial needs. The objective is to improve the dynamics and static characteristics of hydrostatic journal bearing. To achieve the objective, a feedback servo controlled system is presented. A mathematical model is proposed for newly proposed active hydrostatic journal bearing with servo control. A comparison has been performed between the active hydrostatic journal bearing and traditional/conventional hydrostatic journal bearing under the influence of different dynamic conditions of load, viscosity, speed, and pressure. The results demonstrate that proposed active hydrostatic journal bearing has better performance, including controllability, stability, faster response, higher stiffness as well as better disturbance rejection. To further enhance the performance of proposed active hydrostatic journal bearing, two different control strategies are proposed such as proportional–integral–derivative and intelligent control. The results show that fuzzy logic–based intelligent control has faster response, greater amplitude reduction, and good stiffness against load. So, it shows that role of active lubrication in hydrostatic journal bearing will have potential applications in high load and high speed.