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Impedance-based algorithms commonly used for determining the fault location in transmission lines are prone to several sources of error and are specific to the line and system configuration. Furthermore, these algorithms do not utilise available valuable information about the power system surrounding the faulted line. These issues can be overcome using a model-based fault location (MBFL) approach. It uses a circuit model to simulate possible fault scenarios and compares the simulated fault currents with the measured currents recorded by the relay to identify the fault location. However, there are several difficulties and limitations while applying MBFL. There is a loss in accuracy and precision based on the number of simulated scenarios and a requirement to store voluminous simulation results. Hence, this study presents a novel application technique for implementing model-based approach efficiently to estimate the fault location and fault resistance using artificial neural networks-based approach. A key highlight of the proposed approach is the ability to identify the location of a fault present on neighbouring lines using the measured through fault current. The study also presents representative scenarios to demonstrate the capability and potential of the proposed approach.

As an innovative technology, the impedance-based technique has been extensively studied for the structural health monitoring (SHM) of various civil structures. The technique’s advantages include cost-effectiveness, ease of implementation on a complex structure, robustness to early-stage failures, and real-time damage assessment capabilities. Nonetheless, very few studies have taken those advantages for monitoring the health status and the structural condition of wind turbine structures. Thus, this paper is motivated to give the reader a general outlook of how the impedance-based SHM technology has been implemented to secure the safety and serviceability of the wind turbine structures. Firstly, possible structural failures in wind turbine systems are reviewed. Next, physical principles, hardware systems, damage quantification, and environmental compensation algorithms are outlined for the impedance-based technique. Afterwards, the current status of the application of this advanced technology for health monitoring and damage identification of wind turbine structural components such as blades, tower joints, tower segments, substructure, and the foundation are discussed. In the end, the future perspectives that can contribute to developing efficient SHM systems in the green energy field are proposed.

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

This paper addresses the challenges in Voltage Source Inverter (VSI) systems connected to weak grids, where frequent impedance changes lead to instability and power quality issues. This research studies how changing grid impedance affects current distortion and the stability of a VSI. It proposes the stability analysis of a single loop controller and optimize its settings using various techniques (ZN-method, PSO, GA) to ensure VSI stability and meet current distortion limits (THD compliance), when grid impedance varies. The primary focus revolves around addressing two key challenges: managing impedance variations at the PCC and enhancing the tracking performance of the PI controller. The VSI-based system connected to the weak grid and in standalone mode is simulated on Typhoon HIL, to validate the effectiveness of obtained optimized controller parameters by changing various conditions like, the output power regulation and sudden load change in a standalone distribution network. The MATLAB/SIMULINK with m-files is utilized for the parameters optimization and controller model simulation purposes. This research is important for developing more reliable and resilient power systems, specifically by investigating the transient behaviour of VSI frequency and voltage under sudden changes, to ensure an uninterruptible power supply to critical loads.

The electromechanical impedance-based SHM method (ISHM) aims to correlate changes in vibration signatures with physical phenomena. At the same time, monitoring of rotating systems is necessary for economic and safety reasons. Thus, the structural health monitoring of rotating machines is commonly assessed by using vibration sensors together with a SHM technique, such as the ISHM approach. As a result, a large amount of data have to measured; consequently, both machine and deep learning techniques have become relevant for fault detection purposes. It is worth mentioning that previous studies used the ISHM technique associated with CNN models for monitoring the structural condition of beams. In this sense, the main objective of this work is to contribute to the topics of SHM and artificial intelligence, demonstrating another potential application of convolutional neural networks to support the diagnosis of structural damage of rotating systems by using the ISHM approach. For this aim, structural condition of a rotor supported by two ball bearings, with two disks, and one pulley was monitored by considering four different health conditions and three different operating speeds. Then, a 6-layer 1D-CNN model was formulated individually for the three PZT sensors attached to the rotor shaft. As input data, all sample points of the measured impedance signatures were considered. The results from this implementation demonstrate the potential of the procedure conveyed as shown by a minimum accuracy of 92.22% for all evaluated PZT patches.

Offshore Direct Current (DC) collector grids are a promising technology for decreasing the installation and operation costs of offshore wind parks. Nevertheless, the stability properties and hence the design of such DC collector grids is not common or standardised. Hence, this paper describes an attempt to fill these gaps by analysing the stability of two different types of DC collector grids—star and string—by considering identical operating conditions. The approach follows a non-parametric formulation of the impedance based Nyquist Stability Criterion. The hyperbolic Π equivalent formulation of the telegraph equation is adopted for modelling the submarine cable due to high capacitance that is distributed and thus the conventional 50 Hz Π-model is not sufficient anymore. Furthermore, the paper shows how to integrate the complex dynamics of wind turbines into the overall stability assessment through an impedance building algorithm. Finally, it is shown how to stabilise the collector grids by means of active control parameter changes and it has been observed that the star configuration of wind turbines is more favourable on account of stability and controllability.

The opportunities of impedance-based condition monitoring for rolling bearings have been shown earlier by the authors: Changes in the impedance signal and the derived features enable the detection of pitting damages. Localizing and measuring the pitting length in the raceway direction is possible. Furthermore, the changes in features behavior are physically explainable. These investigations were focused on a single bearing type and only one load condition. Different bearing types and load angles were not considered yet. Thus, the impedance signals and their features of different bearing types under different load angles are investigated and compared. The signals are generated in fatigue tests on a rolling bearing test rig with conventional integrated vibration analysis based on structural borne sound. The rolling bearing impedance is gauged using an alternating current measurement bridge. Significant changes in the vibration signals mark the end of the fatigue tests. Therefore, comparing the response time of the impedance can be compared to the vibration signal response time. It can be shown that the rolling bearing impedance is an instrument for condition monitoring, independently from the bearing type. In case of pure radial loads, explicit changes in the impedance signal are detectable, which indicate a pitting damage. Under combined loads, the signal changes are detectable as well, but not as significant as under radial load. Damage-indicating signal changes occur later compared to pure radial loads, but nevertheless enable an early detection. Therefore, the rolling bearing impedance is an instrument for pitting damage detection, independently from bearing type and load angle.

The distribution network is extended throughout cities, towns, and villages. Because of the increase in loads and the decrease in power passageways and public corridor reduction, double circuit lines are increasingly being used instead of single circuit lines. Fault location in double circuit power networks is very important because it decreases the repair time and consequently the power outage time. In this paper, a new improved method for fault location in double circuit medium power distribution lines is proposed. The suggested impedance-based fault location method takes into account the mutual effect of double circuit lines on each other. To the best of our knowledge, the proposed method is the first of its kind which supports double circuit distribution networks. In the proposed method, a new quadratic equation for locating fault in power distribution networks is obtained using recorded voltage and current at the beginning of feeder. In this method, the π line model is used for improving the accuracy of the suggested method. The proposed method is supported by mathematical proofs and derivation. To evaluate the accuracy of the proposed method, the proposed method is tested on a thirteen-node network in different conditions, such as instrument error, various fault resistances, and different fault inception angles in various distances and fault types. The numerical results confirm the high accuracy and validity of the proposed method.

Modern fault location methods are robust; however, they depend strongly on the availability of the measurements given by Distributed Energy Resources (DER). If the communication or synchronism of this information is lost, the fault location is not possible. This paper proposes an adaptive impedance-based fault location algorithm for active distribution systems. The proposal combines information provided by Intelligent Electronic Devices (IEDs) located at the substation, the knowledge of the network topology and parameters, as well as the distributed power sources, to estimate the fault location. Its adaptive feature is given by the use of a Distributed Energy Resources (DER) electrical model. This model is used to estimate the DER current contribution to the fault, in case the information provided by a local IED is not available. The method takes two types of DER technologies into account: Inverter non-interfaced DER (INIDER) and Inverter-interfaced DER (IIDER). The proposed method is validated on a modified IEEE 34-node test feeder, which was simulated with ATP/EMTP. The results obtained using the IEDs information, presented a maximum error of 0.8%. When this information is not available, the method’s performance decreases slightly, obtaining a maximum error of 1.1%. The proposed method showed better performance when compared with two state of the art methods, indicating potential use for real-life applications.

The use of robotic platforms for neuro-rehabilitation may boost the neural plasticity process and improve motor recovery in patients with upper limb mobility impairment as a consequence of an acquired brain injury. A robotic platform for this aim must provide ergonomic and friendly design, human safety, intensive task-oriented therapy, and assistive forces. Its implementation is a complex process that involves new developments in the mechanical, electronics, and control fields. This article presents the end-effector rehabilitation robot, a 2-degree-of-freedom planar robotic platform for upper limb rehabilitation in patients with neuromotor disability after a stroke. We describe the ergonomic mechanical design, the system control architecture, and the rehabilitation therapies that can be performed. The impedance-based haptic controller implemented in end-effector rehabilitation robot uses the information provided by a JR3 force sensor to achieve an efficient and friendly patient–robot interaction. Two task-oriented therapy modes have been implemented based on the “assist as needed” paradigm. As a result, the amount of support provided by the robot adapts to the patient’s requirements, maintaining the therapy as intensive as possible without compromising the patient’s health and safety and promoting engagement.