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Series Arc Faults (SAFs) are the most common faults in photovoltaic systems. If a SAF is not detected in time, it may lead to some catastrophic damages to the system. This paper presents a new method for detecting SAFs in photovoltaic systems based on determinism in the voltage signal. The determinism of the signal is derived using the recurrence plots method. The recurrence plots provide a two-dimensional plot of the signal, from which the degree of determinism in the signal is derived as a criterion for detection of SAFs. The performance of the method is evaluated using plenty of experiments carried out in different SAF and normal conditions. The results confirm the high accuracy, high speed, and low computational burden of the proposed method.

Line-Line (LL) and Line-Ground (LG) faults may not be detected by common protection devices in PV arrays due to these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. This paper proposes a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the DC side of PV systems. For this purpose, the main features of Current-Voltage (I-V) curves under different fault events and normal conditions are extracted. The faults are categorized using the Hierarchical Classification (HC) platform. Later, the LL and LG faults are detected and classified by Machine Learning (ML) methods. The proposed method aims to reduce the amount of dataset which is required for the learning process and also obtain a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared to other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under the different conditions and severity with the accuracy of 96.66% and 91.66%, respectively.

Control based on vision data is a growing field of research and it is widespread in industry. The amount of data in each image and the processing needed to obtain control-relevant information from this data lead to significant delays with a large variability in the control loops. This often causes performance deterioration since in many cases the delay variability is not explicitly addressed in the control design. In this paper, we approach this problem by applying the ideas of recently developed model-based control design methods, which are tailored to address stochastic delays directly, to the motion control of an omnidirectional robot with a vision-based self-localization algorithm. The completion time or delay of the Random Sample Consensus (RANSAC) based localization algorithm is identified as a stochastic random variable with significant variability, illustrating the practical difficulties with data processing. Our main aim is to show that the novel deadline-driven and event-driven control designs significantly outperform a traditional periodic control implementation for a stochastic optimal control performance index.

In this paper, we study the adaptive fuzzy formation control of multiple autonomous agents with prioritized missions. For a platoon of autonomous agents in an unknown environment containing multiple obstacles, formation control is investigated, where each agent is modeled by a second-order nonlinear system under unknown external disturbance in the Brunovsky form. We introduce the systematic procedure of null-space-based projection to convert the prioritized multimission control problem into a behavioral control problem. Then, we further develop a class of nonlinear-fast-terminal-sliding-mode-based adaptive control strategies that combine the fuzzy logic systems by jointly considering both kinematic and dynamic levels of the agents. The proposed controllers can guarantee each individual agent to achieve the predesigned desired pattern and drive the entire systems to achieve the prescribed missions. A simulation example with five agents demonstrates the effectiveness of the algorithm. Finally, the strategies are experimentally validated using a platoon of Pioneer 3AT and 3DX mobile robots.

The operation of more electric aircraft is dependent on the embedded power grid. Therefore, the onboard power-distribution system must be reliable, having a high level of survivability, and promptly respond to any change in aircraft’s operation. Recent studies have presented a number of frequency-response-based tools with which to analyze both single- and multi-converter systems. The methods can be efficiently applied for on-board system analysis, stability assessment and adaptive control design. Most often, wideband measurement techniques have been applied to obtain the frequency response from a specific converter or a subsystem required for the analysis. In the methods, a broadband excitation such as a pseudo-random binary sequence (PRBS) is used as an external injection, and Fourier techniques are applied to extract the spectral information. This paper presents implementation techniques of the wideband methods using power-hardware-in-the-loop measurements based on OPAL-RT real-time simulator. The presented methods make it possible to modify the system characteristics, such as impedance behavior, in real time, thereby providing means for various stability and control design tools for on-board power distribution systems. Experimental measurements are shown from a high-power energy distribution system recently developed at DNV GL, Arnhem, Netherlands. acceptedVersion

In this paper, a novel direct load control (DLC) planning based on providing free energy credits to residential end-users for their heating, ventilation, and air conditioning load during demand response (DR) events is proposed. The obtained credit can then be used by the end-users during relatively higher price periods free-of-cost to enable them lowering their energy procurement costs. Furthermore, the resulting reduction in the total household energy consumption considerably decreases the critical load demands in power systems, which is of vital importance for load-serving entities in maintaining the balance between supply and demand during peak load periods. In this regard, the aforementioned energy credits-based incentive mechanism is proposed for end-users enrolled in the DLC-based DR program, as a new contribution to the existing literature, testing it in a stochastic day-ahead planning context.

This paper addresses robust design of the active-power and dc-link control loops of power-synchronization control. Robustness is obtained by analytic gain selections which give large enough stability margins. The proposed design allows robust stability irrespective of the grid strength and of the operating point, the latter with one exception. The proposed design is compared to design based on the principle virtual synchronous machine. Experiments show that the time-domain results correlate well with the frequency-domain results.

Bearing load estimation would form a valuable addition to the fields of condition monitoring and system control. Despite effort spend on its development by all major bearing manufacturers, no product solution has come to market yet. This can be attributed to both the complexity in conditioning of the strain measurement as well as its nonlinearity with respect to the bearing loading. To address these issues, this paper proposes a novel approach based on modeling of the physical behavior of the bearing. An extended Kalman filter including a novel strain model is applied for signal conditioning, whereas an unscented Kalman filter including a semianalytical bearing model is proposed for reconstruction of the bearing load. An experimental study in both laboratory and field conditions shows that the proposed cascaded Kalman filtering approach leads to accurate estimates for all four considered bearings loads in various loading conditions. Besides an improvement on accuracy, the novel approach leads to a reduction in calibration effort.

In this paper, a novel design of a one degree of freedom actively positioned bearingless motor, hereafter referred to as a single-drive bearingless motor, has been proposed. The concept is based on an axial flux motor where field weakening and strengthening with d-axis current modulation produce the suspension force for axial motion control. The single-drive bearingless motor requires neither additional windings, nor inverters for noncontact magnetic suspension. It has only one three-phase winding set and one three-phase inverter. To verify this novel concept, theoretical calculations, finite-element method analysis, and test machine development are carried out. The test results successfully demonstrate the magnetic suspension and noncontact rotation of the single-drive bearingless motor.

In this paper, a modular three-phase multilevel inverter specially suited for electrical drive applications is proposed. Unlike the cascaded H-bridge inverter, this topology is based on power cells connected in cascade using two inverter legs in series. A detailed analysis of the structure and the development of design equations for the load voltage with $n$ levels are carried out using pulsewidth-modulation phase-shifted multicarrier modulation. Simulations and experimental results for a 15-kW three-phase system, with nine voltage levels, validate the study presented.

A robust servo-system, based on a combination of linear robust control and sliding mode control have been proposed. This new control system can be said to be a non-linear system (sliding mode control system) which has a inner loop of linear control (two-degree-of-freedom control). Due to this inner loop of linear control, a disturbance is strongly suppressed but not completely. Then, the outer loop of sliding mode control eliminates this disturbance suppression error. First in this paper, a linear robust-servo design of two-degree-of-freedom control system is shown. Next, a sliding mode control is applied to this system and the disturbance suppression characteristics are discussed. Then, through the simulations and experiments, it is proved that the introduction of non-linear control (sliding mode) drastically improves the disturbance suppression characteristics of a linear system (two-degree-of-freedom control).

Offshore ac fault protection of wind turbines (WTs) connecting with diode rectifier unit-based high-voltage dc (DRU-HVdc) system is investigated in this paper. A voltage-error-dependent fault current injection is proposed to regulate the WT current during offshore ac fault transients and quickly provide fault current for fault detection. Considering different fault locations, the fault characteristics during symmetrical and asymmetrical faults are presented and the requirements for fault detection are addressed. A simple and effective offshore ac fault protection solution, combining both overcurrent protection and differential protection, is proposed by utilizing the developed fast fault current providing control. To improve system availability, reduced dc voltage of the DRU-HVdc system is investigated, where one of the series-connected DRUs is disconnected and the onshore modular multilevel converter actively reduces dc voltage to resume wind power transmission. The proposed scheme is robust to various offshore ac faults and can automatically restore normal operation. Simulation results confirm the proposed fault protection strategy.

In direct current (DC) microgrids, communication between converters is necessary for better power distribution and finer energy scheduling. This paper proposes an orthogonal frequency division multiplexing (OFDM)-based carrier communication method for DC microgrid applications. The method integrates the communication function in the process of power conversion by modulating data into power control loop. To maximize the communication rate in a narrow band limited by PWM control, OFDM technology is applied in the data modulation process. The data is modulated to multiple low-frequency signal carriers, then the modulated wave is added to the output of the power control loop. Since no additional component is needed for sending data, this method has the advantages of low cost and simple implementation. In order to provide theoretical guidance for system design, the DC microgrid applying the proposed method is modeled. Furthermore, important issues of the communication system are discussed, including data frame format and inter-symbol interference (ISI) problem. At last, a 2 kW DC microgrid experimental platform is established and 9.6 kbps communication rate with a transmission distance of 200 m is achieved, which verifies the feasibility of the proposed method.

This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach. Accepted version

Induction motors are widely used in industrial plants for critical operations. Stator faults, bearing faults, or rotor faults can lead to unplanned downtime with associated cost and safety implications. Different sensors may be used to monitor the health state of induction motors with each sensor typically being better suited for diagnosing different faults. Condition monitoring approaches that fuse data from multiple sensors have the potential to diagnose a greater number of faults. In this paper, a sensor fusion approach based on the combination of a two-stage Bayesian method and principal component analysis (PCA) is proposed for diagnosing both electrical and mechanical faults in induction motors. Acoustic, electric, and vibration signals are gathered from motors operating under different loading conditions and health states. The inclusion of the PCA step ensures robustness to varying loading conditions. The obtained results highlight that the proposed method performs better than the equivalent single-stage or feature-based Bayesian methods.