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Electrical power connectors are critical points of electrical networks. Failure in high-voltage connectors may result in major power outages, safety risks and important economic consequences. Therefore, there is an imperious need to tackle such issue by developing suitable on-line condition monitoring strategies to minimize the aforementioned problems and to ease the application of predictive maintenance tasks. This work develops an on-line condition monitoring method to predict early failures in power connectors from data acquired on-line (electric current and voltage drop across the connector, and temperature) to determine the instantaneous value of the connector resistance, since it is used as a signature or indicator of its health condition. The proposed approach combines a parametric degradation model of the resistance of the connector, whose parameters are identified by means of the Markov chain Monte Carlo stochastic method, which also provides the confidence intervals of the electrical resistance. This fast approach allows an on-line diagnosis of the health condition of the connector, anticipating its failure and thus, easing the application of predictive maintenance plans. Laboratory results emulating the ageing conditions of the connectors prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus.

This paper presents an offline deep learning approach focused to model and identify a 270 V-to-28 V DC-DC step-down converter used in on-board distribution systems of more electric aircrafts (MEA). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black-box, and trains a wavelet convolutional neural network (WCNN) that is able of accurately reproducing the behavior of the DC-DC converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with experimental data obtained from a setup that replicates the 28 V on-board distribution system of an aircraft. The results presented in this paper show a high correlation between measured and estimated data, robustness and low computational burden. This paper also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other DC-DC converters, depending on their requirements.

This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.

Parallel conductors are found in electrical transmission and distribution systems including large ampacity feeders or loads. However, current unbalance often occurs, especially in alternating current systems. This non-regular current distribution causes overheating and premature ageing, facilitating the occurrence of failures. Therefore, a fault diagnosis system is a must, which can be performed by monitoring in real-time the individual currents flowing through the conductors. In this paper a setup including three parallel aluminum conductors of large cross section, a spacer and two terminal substation connectors is analyzed. A real-time, wireless, coreless and contactless system based on three low cost Hall effect sensors is proposed, which is also easy to install. Experimental results, which include fourteen cases, comprising thirteen fault modes and a well installed set, prove the suitability and potential of the proposed approach, since it allows a correct real-time detection of all analyzed faulty conditions as well as the detection of currents exceeding the thermal rating of the conductors.

Estimating the remaining useful life (RUL) or the state of health (SoH) of electrical components such as power connectors is still a challenging and complex task. Power connectors play a critical role in medium- and high-voltage power networks, their failure leading to important consequences such as power outages, unscheduled downtimes, safety hazards or important economic losses. Online condition monitoring strategies allow developing improved predictive maintenance plans. Due to the development of low-cost sensors and electronic communication systems compatible with Internet of Things (IoT) applications, several methods for online and offline SoH determination of diverse power devices are emerging. This paper presents, analyzes and compares the performance of three simple and effective methods for online determination of the SoH of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which defines the degradation trajectory because the electrical resistance is a reliable indicator or signature of the SoH of the connectors. The methods analyzed in this paper are validated by means of experimental ageing tests emulating real degradation conditions. Laboratory results prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus.

This paper proposes an approach to estimate the parameters of an AC-DC boost power factor corrector converter which includes an EMI filter. To this end, once the topology is known, measurements at the input and output terminals of the converter are done to identify the values of the passive elements. The proposed methodology is based on the trust-region nonlinear least squares algorithm to identify the parameters of the converter. The steady-state and the transient signals of the converter at the input/output terminals are acquired non-intrusively without any internal modification of the circuitry. The accuracy of the parameter identification carried out is determined by comparing the estimated values with the actual values provided by the manufacturer, and by contrasting the measured signals with the ones obtained with a simulation model with the estimated values of the parameters. The results presented in this paper prove the accuracy of the proposed approach, which can be extended to other power converters and filters.

This paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter used in mild hybrid electric vehicles. A black-box identification methodology is proposed to find a model based on the data acquired from the input/output terminals. Measured data include the steady state and transient response, and different operating conditions of the DC-DC converter, including the buck and boost modes. A deep learning architecture based on a long-short-term memory neural network (LSTM-NN) is applied. The trained network is tested under a set of operating points different from those used during the training stage. The proposed method is compared with three black-box modeling techniques commonly used in power converters, proving its superior performance. Results presented in this paper indicate that the proposed model is able to replicate the behavior of the bidirectional converter without a priori knowledge of the converter circuitry. This approach can also be applied to other power devices.

The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors. This research was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España, grant number RTC‐2017‐6297‐3, and by the Generalitat de Catalunya, grant number 2017 SGR 967

Insulation faults in high-voltage applications often generate partial discharges (PDs) accompanied by corona activity, optical radiation mainly in the ultraviolet (UV) and visible bands. Recent developments in low-cost, small-size, and high-resolution visible imaging sensors, which are also partially sensitive to the UV spectral region, are gaining attention due to their many industrial applications. This paper proposes a method for early PD detection by using digital imaging sensors, which allows the severity of insulation faults to be assessed. The electrical power dissipated by the PDs is correlated to the energy of the acquired visible images, and thus, the severity of insulation faults is determined from the energy of the corona effect. A criterion to quantify the severity of insulation faults based on the energy of the corona images is proposed. To this end, the point-to-plane gap configuration is analyzed in a low-pressure chamber, where digital image photographs of the PDs are taken and evaluated under different pressure conditions ranging from 10 to 100 kPa, which cover the typical pressure range of aeronautic applications. The use of digital imaging sensors also allows an early detection, location and quantification of the PD activity, and thus assessing the severity of insulation faults to perform predictive maintenance tasks, while enabling the cost and complexity of the instrumentation to be reduced. Although the approach proposed in this paper has been applied to detect PDs in aeronautic applications, it can be applied to many other high-voltage applications susceptible of PD occurrence.

This paper provides a comprehensive and critical review and evaluation of the technological state-of-the-art of high-temperature low-sag (HTLS) conductors by analyzing research articles, theses, reports, white papers and international standards. The growth of power demand requires new solutions to develop power transmission systems, while facing the issues related to power systems congestion. A possibility to solve the load growth issue is to increase the capacity of existing transmission systems by reconductoring the lines with new conductors, which are able to operate at higher temperatures, while limiting the sag and maintaining or reducing the required clearances, thus having more current carrying capacity than the existing ones. This paper also describes the limitations of such technology and identifies the research needs to fulfill the requirements of the industry and transmission system operators. This work was supported in part by the Generalitat de Catalunya under Project 2017 SGR 967 and in part by the Spanish Ministry of Economy and Competitiveness under Project RTC-2017-6297-3.

Switching mode power converters are being extensively applied in different power conversion systems. Parameter identification comprises a set of techniques focused on extracting the relevant parameters of the converters in order to generate accurate discrete simulation models or to design enhanced condition diagnosis schemes. This paper applies a non-invasive optimization approach based on the non-linear least squares algorithm to determine the model parameters of different commercially available DC-DC power converters (buck, boost and buck-boost) from experimental data, including the parameters related to passive, parasitic and control loop elements. The proposed approach is based on a non-invasive on-line acquisition of the input/output voltages and currents under both steady state and transient conditions. The proposed method can also be applied to many other applications requiring precise and efficient parameter identification, including rectifiers, filters, or power supplies among others.

The increased dependency on electricity of modern society makes reliability of power transmission systems a key point. This goal can be achieved by continuously monitoring power grid parameters, so possible failure modes can be predicted beforehand. The deployment of Internet of Things (IoT) solutions and new low-cost sensors eases this task. This paper presents a self-powered IoT solution for real-time monitoring of different parameters of a high-voltage substation connector. This new family of power connectors is called SmartConnector, which incorporates a thermal energy harvesting system powering a microcontroller that controls a transmitter, and several electronic sensors to measure the temperature, current and voltage drop across the connector to estimate the electrical contact resistance (ECR) of the connector. These measurements are sent remotely via a Bluetooth 5 wireless communication module to a local gateway, which transfers the measured data to a database server for storage and further analysis and visualization. Different experiments are presented to validate the feasibility of the SmartConnector in terms of measurement accuracy in comparison with a wired system. The proposed thermal energy harvesting is tested to calculate the optimum data transfer rate needed for extending the lifetime of the SmartConnector. A shielding solution for the electronics operating under this high voltage environment is also proposed

Next generation aircrafts will use more electrical power to reduce weight, fuel consumption, system complexity and greenhouse gas emissions. However, new failure modes and challenges arise related to the required voltage increase and consequent rise of electrical stress on wiring insulation materials, thus increasing the risk of electrical arc appearance. This work performs a critical and comprehensive review concerning arc tracking effects in wiring insulation systems, underlying mechanisms, role of materials and possible mitigation strategies, with a special focus on aircraft applications. To this end an evaluation of the scientific and technological state of the art is carried out from the analysis of theses, research articles, technical reports, international standards and white papers. This review paper also reports the limitations of existing insulation materials, standard test methods and mitigation approaches, while identifying the research needs to comply with the future demands of the aircraft industry.

Artificial neural networks allow the identification of black-box models. This paper proposes a method aimed at replicating the static and dynamic behavior of a DC-DC power converter based on a recurrent nonlinear autoregressive exogenous neural network. The method proposed in this work applies an algorithm that trains a neural network based on the inputs and outputs (currents and voltages) of a Buck converter. The approach is validated by means of simulated data of a realistic nonsynchronous Buck converter model programmed in Simulink and by means of experimental results. The predictions made by the neural network are compared to the actual outputs of the system, to determine the accuracy of the method, thus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach.

Six-pulse diode rectifiers are widely used in many power conversion systems. In this work a non-intrusive approach for parameter identification of such power device is presented and its accuracy and performance is evaluated from experimental data. This parameter identification approach is focused on generating accurate discrete simulation models of three-phase rectifiers, although it can also be applied for health condition monitoring and diagnosis purposes. The approach presented in this paper is based on applying the non-linear least squares (NLS) algorithm following an optimization strategy to identify the parameters of a white-box model of the analyzed power devices. This approach analyzes the currents and voltages measured at the input/output terminals, thus performing a non-invasive acquisition of such signals, which is compatible with sealed systems. The method proposed in this paper can also be applied to many other power devices needing accurate simulation models or applying health condition strategies, switched power supplies or power converters.

Dynamic thermal line rating (DTLR) allows us to take advantage of the maximum transmission capacity of power lines, which is an imperious need for future smart grids. This paper proposes a real-time method to determine the DTLR rating of aluminum conductor steel-reinforced (ACSR) conductors. The proposed approach requires a thermal model of the line to determine the real-time values of the solar radiation and the ambient temperature, which can be obtained from weather stations placed near the analyzed conductors as well as the temperature and the current of the conductor, which can be measured directly with a Smartconductor and can be transmitted wirelessly to a nearby gateway. Real-time weather and overhead line data monitoring and the calculation of DTLR ratings based on models of the power line is a practical smart grid application. Since it is known that the wind speed exhibits important fluctuations, even in nearby areas, and since it plays a key role in determining the DTLR, it is essential to accurately estimate this parameter at the conductor’s location. This paper presents a method to estimate the wind speed and the DTLR rating of the analyzed conductor. Experimental tests have been conducted to validate the accuracy of the proposed approach using ACSR conductors.

Visual corona tests have been broadly applied for identifying the critical corona points of diverse high-voltage devices, although other approaches based on partial discharge or radio interference voltage measurements are also widely applied to detect corona activity. Nevertheless, these two techniques must be applied in screened laboratories, which are scarce and expensive, require sophisticated instrumentation, and typically do not allow location of the discharge points. This paper describes the detection of the visual corona and location of the critical corona points of a sphere-plane gap configurations under different pressure conditions ranging from 100 to 20 kPa, covering the pressures typically found in aeronautic environments. The corona detection is made with a low-cost CMOS imaging sensor from both the visible and ultraviolet (UV) spectrum, which allows detection of the discharge points and their locations, thus significantly reducing the complexity and costs of the instrumentation required while preserving the sensitivity and accuracy of the measurements. The approach proposed in this paper can be applied in aerospace applications to prevent the arc tracking phenomenon, which can lead to catastrophic consequences since there is not a clear protection solution, due to the low levels of leakage current involved in the pre-arc phenomenon.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works With the increasing use of sensors and wireless communication systems, predictive maintenance is acquiring more and more importance to assess the condition of in-service equipment. Predictive maintenance presents promising cost savings, as it allows minimizing unscheduled power systems faults, which can have very costly and catastrophic consequences. Early stage detection of power system failure requires acquiring, monitoring, and periodically analyzing the condition of the elements involved, such as high-voltage power connectors, since they are critical devices which are often located in key points of power systems. This paper proposes a low-cost online system to determine the contact resistance of high-voltage direct current (dc) and alternating current (ac) power connectors, to determine their health condition in order to apply a predictive maintenance plan. The contact resistance is considered as a reliable indicator of the connector's health condition. However, it cannot be directly measured, and the applied strategy differs between dc and ac power systems. The experimental results show a maximum uncertainty of 4.5%, thus proving the accuracy and feasibility of the approach presented in this paper, since the proposed limit of acceptable resistance increase is 20%. This approach can also be applied to many other power systems' elements.

This paper presents a new methodology for optimal sizing of the energy storage system ( E S S ), with the aim of being used in the design process of a hybrid electric (HE) refuse collector vehicle ( R C V ). This methodology has, as the main element, to model a multi-objective optimisation problem that considers the specific energy of a basic cell of lithium polymer ( L i ⁻ P o ) battery and the cost of manufacture. Furthermore, optimal space solutions are determined from a multi-objective genetic algorithm that considers linear inequalities and limits in the decision variables. Subsequently, it is proposed to employ optimal space solutions for sizing the energy storage system, based on the energy required by the drive cycle of a conventional refuse collector vehicle. In addition, it is proposed to discard elements of optimal space solutions for sizing the energy storage system so as to achieve the highest fuel economy in the hybrid electric refuse collector vehicle design phase.

Electronic devices combining sensors, wireless communications, and data processing capability allow easing predictive maintenance tasks in many applications. This paper applies this approach in power connectors for high-voltage electrical substations, which are transformed into smart connectors. Such connectors are often linked to tubular aluminum bus bars, whose temperature increases due to the Joule losses generated by the combined effect of the electrical resistance and the electric current. Since the human intervention must be minimized, an energy harvesting system is required to supply the electronics of the smart connectors. To this end, a thermoelectric module (TEM) is used to transform heat power into electrical power. Since the voltage provided by the TEM is very low, a suitable power converter is used to supply the electronics of the smart connector. This work analyzes the effect of the various parameters that affect the power generated by the TEM when placed on a substation bus bar. Experiments have been carried out by placing a TEM with different configurations on different types of bus bars for diverse operating conditions.

This work presents a general methodology, based on standstill frequency response tests. Once the circuit topology is chosen, it allows a systematic calculation of the circuit parameters for a salient pole synchronous generator. The order of the circuit considered can be whatever, even a customized circuit topology, since the methodology does not rely on a specific formulation. The transfer function coefficients are expressed as a function of the circuit parameters by using the symbolic Matlab toolbox. From tests, the coefficients of the transfer function are fitted in both axes. Then the coefficients have to match with the symbolic transfer function previously obtained in order to calculate the circuit parameters. If the dynamic behavior obtained with the fitted parameters is not accurate enough, the circuit order is increased until attaining an accurate solution. This methodology results in a useful educational tool for machine characterization. Nowadays many methods are found in the technical literature to calculate such parameters, although most of them computationally demanding.

This paper presents the analysis and design of a new high frequency ac-ac converter applied to domestic induction heating. The proposed topology uses only four switches to control power. Converter operation is same as a conventional class D inverter. Working above the resonant frequency, a sinusoidal input current and a unit power factor are obtained. To bring higher efficiency and power density, application of emerging SiC technology in proposed converter has been evaluated. The analytical and simulation results have been verified by means of a 380-W induction heating prototype.

This paper presents a novel approach for black-box modelling of 270 V -to- 28 V DC-DC step-down converters used in more electric aircrafts (MEA). These converters normally feed constant power loads (CPL). The proposed deep learning approach, uses offline experimental data of the converter to find an accurate model that reproduces its behavior. It covers a broad range of loading conditions to build a model that replicates the whole behavior of the converter. This paper compares the performance of the proposed method, which requires a very low computational burden once the model is trained, with that of a conventional recurrent neural network (RNNs) topology. Results presented in this paper show the ability of the obtained solution to accurately emulate the behavior of the real step-down converter when the internal structure is unknown, with no knowledge of the internal parameters, thus preventing disclosure of manufacturers confidential data. The modeling strategy presented in this paper is validated with experimental data by using a step-down converter used in aircrafts. The approach is compared to existing modeling techniques to test its accuracy. This approach can also be applied to many power devices, including diverse types of power converters, power supplies, or filters among others.

To detect faults in their early stage in a substation, it is necessary to monitor, measure and analyze periodically the health condition of the electrical connectors, which are among the most critical devices in such installations. To do so, the electrical connector has to be combined with low-cost intelligent electronic devices (IED), including different sensor types and microcontrollers with wireless capabilities. Such an electrical connector is referred as SmartConnector. Using the data collected by the IED, it is possible to estimate accurately the current condition of the electrical connector in real-time, which in turn will determine the expected faults in the substation before a major failure occurrence. The electrical resistance plays a key role to determine the current health condition, and therefore to estimate the remaining lifetime of the electrical connector. The electrical resistance of a high-voltage substation connector is calculated in real-time using different methods proposed in this work, by using an analog-bipolar Hall effect sensor and an instrumentation amplifier. Experimental results, when compared with the standard 4-wire Kelvin method, show an error of less than 10%. Although the proposed methods have been validated for substation connectors, they can be applied to many other types of hardware with electrical contacts.

This paper proposes a white-box approach for identifying the parameters of DC-DC buck and boost switch mode power converters. It is based on discretizing the differential equations that describe the dynamic behavior of the converters. From the discretized equations and experimental data, the parameters of the converters are identified, thus obtaining both the values of the passive components and the transfer function coefficients of the controller. To this end, steady state and transient experimental signals are analyzed, including the input and output voltages and the inductor and output currents. To determine the accuracy of the proposed method, once the parameters are identified, a simulation with the identified parameters of the converter is run and compared with experimental signals. Such results show the accuracy and feasibility of the approach proposed in this work, which can be extended to other converters and electrical and electronic devices.