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In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. [ABSTRACT FROM AUTHOR]

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system's normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset's optimization to demonstrate the expanded dataset's dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks. [ABSTRACT FROM AUTHOR]

This document provides an overview of the advancements in energy harvesting technologies and their integration into the Internet of Things (IoT) and wireless sensor networks. It includes 31 papers covering various energy harvesting devices, optimization techniques, and potential applications. The research aims to improve the efficiency and sustainability of wireless sensor networks while minimizing energy consumption. The articles explore different methods of energy harvesting, such as solar, thermal, vibrational, and radio frequency energy, and discuss their application in various fields, including wearable devices, solar energy harvesting, and IoT-enabled smart meters. Overall, this collection offers a comprehensive overview of the current research and advancements in energy harvesting for sensor networks. [Extracted from the article]

This paper reports on the testing and evaluation of a passive autoranging (AR) method designed to dynamically extend the measurement range of a photonic current transducer (PCT) to pave the way toward a realization of a combined metering- and protection-class current sensor. The PCT utilizes a current transformer (CT), a piezoelectric transducer (PZT), and a fiber Bragg grating (FBG) to enable current measurement at multiple points in an electrical power network whereby multiple sensors are deployed and interrogated serially using a single optical fiber. The autoranging technique relies on incorporating static MOSFET switches to instantaneously short individual serially connected CT burdens in response to a measured current magnitude exceeding pre-set thresholds. The AR circuit switching events produce distinctive signal features that are used by the proposed switching algorithm to apply appropriate scaling factors to reconstruct the measured current from the optical signal. It is shown through laboratory experiments that the AR circuit correctly reacts to pre-set burden current thresholds of 130% of the nominal value and 22 times the nominal value, signifying its "metering" and "protection" range boundaries. The circuit reaction time is below 4 ms, rendering it suitable for standard power system protection purposes. Moreover, the operation of the AR circuit is demonstrated for burden currents of up to 100 A for over 1 s, satisfying a test procedure for the secondary CT circuit, as required by some power system operators. It is demonstrated that the proposed switching algorithm allows for a correct reconstruction of the burden currents from the optical signal acquired by the FBG interrogator, offering the potential to realize a dual-class optical current sensor. [ABSTRACT FROM AUTHOR]

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs. [ABSTRACT FROM AUTHOR]

This paper presents the design and the non-linearity optimization of a new vertical non-contact angle sensor based on the electromagnetic induction principle. The proposed sensor consists of a stator part (with one solenoidal excitation coil and three sinusoidal receiver coils) and a rotor part (with six rectangular metal sheets). The receiver coil was designed based on the differential principle, which eliminates the effect of the excitation coil on the induced voltage of the receiver coil, and essentially decouples the excitation field from the eddy current field. Moreover, the induced voltages in the three receiver coils are three-phase sinusoidal signals with a phase difference of 10°, which are linearized by CLARK transformation. To minimize the sensor non-linearity, the Plackett–Burman technique was used, which identified the stator radius and the rotor blade thickness as the key factors affecting the sensor linearity. Then, the particle swarm algorithm with decreasing inertia weights was utilized to optimize the sensor linearity. A sensor prototype was made and tested in the laboratory, where the experimental results showed that the sensor non-linearity was only 0.648% and 0.645% in the clockwise and counterclockwise directions, respectively. Notably, the non-linearity of the sensor was less than −0.696% at different speeds. [ABSTRACT FROM AUTHOR]

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44 × 10 − 12 , outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply. [ABSTRACT FROM AUTHOR]

This paper presents a simple engineering method for evaluating the optical power emitted by light-emitting diodes (LEDs) using infrared thermography. The method is based on the simultaneous measurement of the electrical power and temperature of an LED and a heat source (resistor) that are enclosed in the same plastic packaging under the same cooling conditions. This ensures the calculation of the optical power emitted by the LED regardless of the value of the heat transfer coefficient. The obtained result was confirmed by comparing it with the standard direct measurement method using an integrated sphere. The values of the estimated optical power using the proposed method and the integrated sphere equipped with a spectrometer were consistent with each other. The tested LED exhibited a high optical energy efficiency, reaching approximately η ≈ 30%. In addition, an uncertainty analysis of the obtained results was performed. Compact modelling based on a thermal resistor network (Rth) and a 3D-FEM analysis were performed to confirm the experimental results. [ABSTRACT FROM AUTHOR]

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes. [ABSTRACT FROM AUTHOR]

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary. [ABSTRACT FROM AUTHOR]

Instrument transformers (ITs) play a key role in electrical power systems, facilitating the accurate monitoring and measurement of electrical quantities. They are essential for measurement, protection, and metering in transmission and distribution grids and accurately reducing the grid voltage and current for low-voltage input instrumentation. With the increase in renewable energy sources, electronic converters, and electric vehicles connected to power grids, ITs now face challenging distorted conditions that differ from the nominal ones. The study presented in this paper is a collaborative work between national metrology institutes and universities that analyzes IT performance in measuring distorted voltages and currents in medium-voltage grids under realistic conditions. Both current and voltage measuring transformers are examined, considering influence quantities like the temperature, mechanical vibration, burden, adjacent phases, and proximity effects. The study provides detailed insights into measurement setups and procedures, and it quantifies potential errors arising from IT behavior in measuring distorted signals in the presence of the various considered influence quantities and their combinations. The main findings reveal that the temperature has the most evident impact on the inductive voltage transformer performance, as well as the burden, causing significant changes in ratio error and phase displacement at the lower temperatures. As for low-power ITs, establishing a priori the effects of adjacent phases and proximity on the frequency responses of low-power ITs is a complex matter, because of their different characteristics and construction solutions. [ABSTRACT FROM AUTHOR]

This paper introduces an analog notch filtering-based coupling circuit for receivers in ultra-narrowband and narrowband power line communication systems, which are connected to low-voltage electric power grids. It is composed of a twin-T notch analog filter, which is responsible for imposing a significant attenuation on the main frequency (i.e., f 0 ∈ { 50 , 60 } Hz) in cascade with an elliptic low-pass analog filter, designed with a 3 dB cut-off frequency of f c ≫ f 0 . For f 0 = 60 Hz and f c = 2 MHz, the prototype of the analog notch filtering-based coupling circuit attains attenuation values of 22 dB and less than 2 dB at the main frequency and in the rest of the frequency bandwidth, respectively, when practical scenarios are considered. Lastly, it shows that the analog notch filtering-based coupling circuit is more effective than a typical capacitive coupling circuit when frequencies lower than 3 kHz are considered for data communication and sensing purposes. [ABSTRACT FROM AUTHOR]

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed. [ABSTRACT FROM AUTHOR]

This work discusses the proposition of the identification of electrical waveforms resulting from the executive systems' operation, controlled by the vehicle electronic modules. This proposition results from the fact that the electric current powering the actuator has two functions: to supply and to control. Observation of such waveforms enables the ongoing control as well as the diagnostics of the state of the executive elements. This work focused on the fundamentals of the method implementation in the vehicle controller. The algorithm for detecting the model values of the waveform has been developed and described, allowing for an efficient control of the system. The algorithm, after being used in the memory of the control module and having the measuring subassemblies (gauging the voltage and electric current) added, will enable the automatic detection of the essential values. The developed code, after the optimization, can support the control performed by the ECU, which is damage-orientated. The paper presents examples of the operation of a computational program developed on the basis of the adopted algorithm. Tests were performed on an electromagnetic valve—fuel injector—of a spark-ignition engine for different cases of its operation. The effectiveness of the program was demonstrated when detecting changes occurring in the current signals of fuel injectors corresponding to different engine speeds (time of 4 and 8 ms) and different loads (pressure of 0.2 and 0.4 MPa). [ABSTRACT FROM AUTHOR]

The traditional approach to calculate the active and reactive power in AC power systems requires the measurement of the phase shift between the voltage and current for the evaluation of the power factor. To do this, power analyzers can implement several methods. In principle, it is always necessary to identify specific points of waveforms (e.g., using a zero-crossing detection technique) and get their time shift. In a similar way, the frequency value must be evaluated in order to calculate the angular frequency. Unfortunately, this kind of common method exhibits some issues, such as the large sensitivity to noise. Moreover, inaccuracies in the evaluation of the power factor have a big impact on the final estimation of the electric power. This paper presents a simple but effective way to calculate the electric power, overcoming the need for a direct measurement of the phase shift and frequency. In particular, it is shown that the active power can be easily calculated as the difference between the peak value of the instantaneous power and apparent power. The reactive power and power factor are evaluated by exploiting the same quantities. The practical implementation of the proposed formulation in power analyzers guarantees several benefits without reducing accuracy. [ABSTRACT FROM AUTHOR]

As a power source for autonomous underwater vehicles (AUVs), lithium-ion batteries play an important role in ensuring AUVs' electric power propulsion performance. An accurate state of charge (SOC) estimation method is the key to achieving energy optimization for lithium-ion batteries. Due to the complicated ocean environments, traditional filtering methods cannot effectively estimate the SOC of lithium-ion batteries in an AUV. Based on the standard extended Kalman filter (EKF), an adaptive iterative extended Kalman filter (AIEKF) method for the SOC in an AUV is proposed to address the traditional filter's problems, such as low accuracy and large errors. In this method, the adaptive update is introduced to deal with the uncertain noise from the lithium-ion battery. The iteration is used to improve the convergence speed and to reduce the computational burden. Compared with the EKF, iterative extended Kalman filter (IEKF) and adaptive extended Kalman filter (AEKF), the proposed AIEKF has a higher estimation accuracy and anti-interference capability, which is suitable for the AUV's SOC estimation. In addition, based on the second-order equivalent circuit model of the lithium-ion battery, a forgetting factor recursive least squares (FFRLS) method is proposed to deal with the multi-variability problem. In the end, four different methods, including EKF, IEKF, AEKF, and the proposed AIEKF, are compared in computational time. The experiment results show that the proposed method has high accuracy and fast estimation speed, meaning that it has good application potential in AUVs. [ABSTRACT FROM AUTHOR]

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions. [ABSTRACT FROM AUTHOR]

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption. [ABSTRACT FROM AUTHOR]

A commercial thermo-optic phase shifter (TOPS) is an efficient solution to the imbalance problem in the fabrication process of Mach–Zehnder modulator (MZM) arms. The TOPS consumes electrical power and transforms it into thermal energy, which changes the real part of the effective refractive index at the waveguide and adjusts the MZM transfer function to work in the linear region. The common model being used today is constructed with only one heater; however, this solution requires more electrical power, which can increase the transmitter system cost. To reduce the system energy cost, we propose a pioneering optimal double titanium nitride heater model under forward biasing at 1550 nm wavelength using the standard silicon-on-insulator technology. Numerical investigations were carried out on the key relative geometrical parameters, heat distribution at the silicon layer, thermal crosstalk, and laser wavelength drift. Results show that the optimal TOPS design can function with a low electrical power of 19.1 mW to achieve a π-phase shift, with a low thermal crosstalk of 0.404 and very low optical losses over 1 mm length. Thus, the proposed device can be used for improving the imbalance problem in MZMs with low electrical power consumption and low losses. This functionality can be utilized to obtain better performances in transmitter systems for data centers and long-range optical communication system applications. [ABSTRACT FROM AUTHOR]

Due to the modern power system's rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies' inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system's attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart. [ABSTRACT FROM AUTHOR]

Electric train system is a very large load for the power network. This load consumes a large amount of reactive power. In addition, it causes a massive unbalance to the network, which results in many problems such as voltage drops, high transmission losses, reduction in the transformer output ability, negative sequence current, mal-operation of protective relays, etc. In this paper, a novel real-time optimization approach is presented to adjust the static VAR compensator (SVC) for the traction system to realize two objectives; current unbalance reduction and reactive power compensation. A multi-objective optimization technique entitled non-dominated sorting genetic algorithm (NSGA-II) is used to fulfill the regarded objectives simultaneously. A comprehensive simulator has been designed for electric train network modeling that is able to adjust the parameters of SVC in an optimum manner at any time and under any circumstances. The results illustrate that the provided method can efficiently reduce the unbalancing in current as well as supply the demanded reactive power with acceptable precision. [ABSTRACT FROM AUTHOR]

Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil. [ABSTRACT FROM AUTHOR]

There are increasing demands for the Internet of Things (IoT), wearable electronics, and medical implants. Wearable devices provide various important daily applications by monitoring real-life human activities. They demand low-cost autonomous operation in a miniaturized form factor, which is challenging to realize using a rechargeable battery. One promising energy source is thermoelectric generators (TEGs), considered the only way to generate a small amount of electric power for the autonomous operation of wearable devices. In this work, we propose a compact and efficient converter system for energy harvesting from TEGs. The system consists of an 83.7% efficient boost converter and a 90 mV self-startup, sharing a single inductor. Innovated techniques are applied to adaptive maximum power point tracking (A-MPPT) and indirect zero current switching (I-ZCS) controllers for efficient operation. The startup circuit is realized using a gain-boosted tri-state buffer, which achieves 69.8% improved gain at the input VIN = 200 mV compared to the conventional approach. To extract the maximum power, we use an A-MPPT controller based on a simple capacitive divider, achieving 95.2% tracking efficiency. To address the challenge of realizing accurate voltage or current sensors, we propose an I-ZCS controller based on a new concept of maximum output voltage tracking (MOVT). The integrated circuit (IC) is fabricated using a 28 nm CMOS in a compact chip area of 0.03 mm2. The compact size, which has not been obtained with previous designs, is suitable for wearable device applications. Measured results show successful startup operation at an ultralow input, VIN = 90 mV. A peak conversion efficiency of 85.9% is achieved for the output of 1.07 mW. [ABSTRACT FROM AUTHOR]

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments. [ABSTRACT FROM AUTHOR]

Variation in the ambient temperature deteriorates the accuracy of a resolver. In this paper, a temperature-compensation technique is introduced to improve resolver accuracy. The ambient temperature causes deviations in the resolver signal; therefore, the disturbed signal is investigated through the change in current in the primary winding of the resolver. For the proposed technique, the primary winding of the resolver is driven by a class-AB output stage of an operational amplifier (opamp), where the primary winding current forms part of the supply current of the opamp. The opamp supply-current sensing technique is used to extract the primary winding current. The error of the resolver signal due to temperature variations is directly evaluated from the supply current of the opamp. Therefore, the proposed technique does not require a temperature-sensitive device. Using the proposed technique, the error of the resolver signal when the ambient temperature increases to 70 °C can be minimized from 1.463% without temperature compensation to 0.017% with temperature compensation. The performance of the proposed technique is discussed in detail and is confirmed by experimental implementation using commercial devices. The results show that the proposed circuit can compensate for wide variations in ambient temperature. [ABSTRACT FROM AUTHOR]

The quick and accurate characterization of commercial electrochemical double-layer capacitor (EDLC) cells, especially their capacitance and direct-current equivalent series internal resistance (DCESR), is of great significance for the design, maintenance, and monitoring of EDLCs used in areas of energy, sensors, electric power, construction machinery, rail transit, automobile transportation, and military. In this study, the capacitance and DCESR of three commercial EDLC cells with similar performance were determined and compared by following the three commonly-used standards of IEC 62391, Maxwell, and QC/T741-2014, which are significantly different in test procedures and calculation methods. The analysis of the test procedures and results demonstrated that the IEC 62391 standard has the disadvantages of a large testing current, long testing time, and a complex and inaccurate DCESR calculation, whereas the Maxwell standard has the disadvantages of a large testing current, a small capacitance, and large DCESR testing results, and furthermore the QC/T 741 standard has the disadvantages of a high resolution requirement for the equipment and small DCESR results. Therefore, an improved method was proposed to determine the capacitance and DCESR of EDLC cells by short-time constant voltage charging and discharging interruption methods, respectively, with the advantages of high accuracy, low equipment requirements, short testing time, and the easy calculation of DCESR over the original three standards. [ABSTRACT FROM AUTHOR]

An active electrode (AE) and back-end (BE) integrated system for enhanced electrocardiogram (ECG)/electrode-tissue impedance (ETI) measurement is proposed. The AE consists of a balanced current driver and a preamplifier. To increase the output impedance, the current driver uses a matched current source and sink, which operates under negative feedback. To increase the linear input range, a new source degeneration method is proposed. The preamplifier is realized using a capacitively-coupled instrumentation amplifier (CCIA) with a ripple-reduction loop (RRL). Compared to the traditional Miller compensation, active frequency feedback compensation (AFFC) achieves bandwidth extension using the reduced size of the compensation capacitor. The BE performs three types of signal sensing: ECG, band power (BP), and impedance (IMP) data. The BP channel is used to detect the Q-, R-, and S-wave (QRS) complex in the ECG signal. The IMP channel measures the resistance and reactance of the electrode-tissue. The integrated circuits for the ECG/ETI system are realized in the 180 nm CMOS process and occupy a 1.26 mm2 area. The measured results show that the current driver supplies a relatively high current (>600 μApp) and achieves a high output impedance (1 MΩ at 500 kHz). The ETI system can detect resistance and capacitance in the ranges of 10 mΩ–3 kΩ and 100 nF–100 μF, respectively. The ECG/ETI system consumes 3.6 mW using a single 1.8 V supply. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles. [ABSTRACT FROM AUTHOR]

As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene "overlapping" with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value. [ABSTRACT FROM AUTHOR]

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values. [ABSTRACT FROM AUTHOR]

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current practical requirements. Monitoring, testing, and diagnosis altogether represent key activities in the development process of electric power elements. This work presents the detailed structure and implementation of a complex, configurable system which can assure efficient monitoring, testing, and diagnosis for various electric power infrastructures, with proven efficiency through a comprehensive set of experimental results obtained in real running conditions. The developed hardware and software implementation is a robust structure, optimized for acquiring a large variety of electrical signals, also providing easy and fast connection within the monitored environment. Its high level of configurability and very good price–performance ratio makes it an original and handy solution for electric power infrastructures. [ABSTRACT FROM AUTHOR]

The relative positioning precisions of coordinate points is an important indicator that affects the final accuracy in the visual measurement system of space cooperative targets. Many factors, such as measurement methods, environmental conditions, data processing principles and equipment parameters, are supposed to influence the cooperative target's acquisition and determine the precision of the cooperative target's position in a ground simulation experiment with laser projected spots on parallel screens. To overcome the precision insufficiencies of cooperative target measurement, the factors of the laser diode supply current and charge couple device (CCD) camera exposure time are studied in this article. On the hypothesis of the optimal experimental conditions, the state equations under the image coordinates' system that describe the laser spot position's variation are established. The novel optimizing method is proposed by taking laser spot position as state variables, diode supply current and exposure time as controllable variables, calculating the optimal controllable variables through intersecting the focal spot centroid line and the 3-D surface, and avoiding the inconvenience of solving nonlinear equations. The experiment based on the new algorithm shows that the optimal solution can guarantee the focal spot's variation range in 5–10 pixels under image coordinates' system equivalent to the space with a 3 m distance and 0.6–1.2 mm positioning accuracy. [ABSTRACT FROM AUTHOR]

Electric power infrastructure has revolutionized our world and our way of living has completely changed. The necessary amount of energy is increasing faster than we realize. In these conditions, the grid is forced to run against its limitations, resulting in more frequent blackouts. Thus, urgent solutions need to be found to meet this greater and greater energy demand. By using the internet of things infrastructure, we can remotely manage distribution points, receiving data that can predict any future failure points on the grid. In this work, we present the design of a fully reconfigurable wireless sensor node that can sense the smart grid environment. The proposed prototype uses a modular developed hardware platform that can be easily integrated into the smart grid concept in a scalable manner and collects data using the LoRaWAN communication protocol. The designed architecture was tested for a period of 6 months, revealing the feasibility and scalability of the system, and opening new directions in the remote failure prediction of low voltage/medium voltage switchgears on the electric grid. [ABSTRACT FROM AUTHOR]