Through the analysis of the recovery inrush current generated by the external fault removal of the converter transformer, it is pointed out that the zero-sequence current caused by the recovery inrush may result in the saturation of the neutral current transformer (CT), whose measurement distortion contributes to the mis-operation of zero-sequence differential current protection. In this paper, a new scheme of zero-sequence differential current protection based on waveform correlation is proposed. By analyzing the characteristics of zero-sequence current under internal fault, external fault and external fault removal, the waveform correlation of the zero-sequence current measured at the terminal of the transformer and the zero-sequence current measured at the neutral point of the transformer is used for identification. The polarity of the CT is selected to guarantee the zero-sequence currents at the terminal and neutral point of the transformer exhibit a "ride through" characteristic u... [more]

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and ro... [more]

The new generation of fast charging systems faces a formidable technological challenge, aiming to drastically reduce the time needed to recharge an electric vehicle as a way to tackle the range anxiety issue. To achieve this, high power (up to 350 kW) is transferred from the grid to the vehicle, leading to potentially high values of low frequency magnetic fields. This study presents the results of measurements of magnetic flux density (B-field) emitted by two different high power charging systems. The electric vehicle used for the recharge was able to digest up to 83 kW of delivered power. The test procedure was designed to identify the locations where the maximum B-field levels were recorded and to measure the exposure indices according to reference levels for general public exposure defined in the Council Recommendation 1999/519/EC. Measurements in close proximity to the power cabinets during the recharge revealed that, at some points, exposure indices were higher than 100%, leading... [more]

Due to the complex structure of the T-connection transmission lines, it is extremely difficult to identify the reflected travelling wave from the fault point and that from the connection point by the measurement from only one terminal. According to the characteristics of the structure of the T-connection transmission line, the reflection of the travelling wave within the line after the failure of different sections in T-connection transmission line are analyzed. Based on the lattice diagram of the travelling wave, the sequence of travelling waves detected at the measuring terminal varies with the fault distance and the faulty section. Moreover, the sequence of travelling waves detected in one terminal is unique at each faulty section. This article calculates the arrival time of travelling waves of fault points at different locations in different sections to form the collection of the travelling wave arrival time sequence. Then the sequence of travelling waves of the new added fault wav... [more]

The article provides an analysis of the existing methods of identifying the consumer’s contribution to voltage distortion at a point of common coupling. The considered methods do not allow correctly and fairly determining the source of harmonic distortions, or they have limited application and difficulties in implementation. The paper proposes new methods for determining the source of high harmonics. The developed methods and techniques are based on the analysis of the grid operation modes with two connected consumers using compensating devices, such as reactive power compensation devices and passive harmonic filters. It is shown that the most promising method is the application of harmonic filters, which allows determining the share of the consumer’s contribution to the voltage distortion. The present research is carried out using a computer simulation of the existing electrical grid, to which consumers with nonlinear electric load are connected. These methods can be implemented to as... [more]

The electric rudder system (ERS) is the executive mechanism of the flight control system, which can make the missile complete the route correction according to the control command. The performance and quality of the ERS directly determine the dynamic quality of the flight control system. However, the transient and static characteristic of ERS is affected by the uncertainty of physical parameters caused by nonlinear factors. Therefore, the control strategy based on genetic algorithm (GA) identification method and finite-time rudder control (FTRC) theory is studied to improve the control accuracy and speed of the system. Differently from the existing methods, in this method, the difficulty of parameter uncertainty in the controller design is solved based on the ERS mathematical model parameter identification strategy. Besides, in this way, the performance of the FTRC controller was verified by cosimulation experiments based on automatic dynamic analysis of mechanical systems (ADAMS) (MSC... [more]

Pore-fluid identification is one of the key technologies in seismic exploration. Fluid indicators play important roles in pore-fluid identification. For sandstone reservoirs, the effective pore-fluid bulk modulus is more susceptible to pore-fluid than other fluid indicators. AVO (amplitude variation with offset) inversion is an effective way to obtain fluid indicators from seismic data directly. Nevertheless, current methods lack a high-order AVO equation for a direct, effective pore-fluid bulk modulus inversion. Therefore, based on the Zoeppritz equations and Biot−Gassmann theory, we derived a high-order P-wave AVO approximation for an effective pore-fluid bulk modulus. Series reversion and Bayesian theory were introduced to establish a direct non-linear P-wave AVO inversion method. By adopting this method, the effective pore-fluid bulk modulus, porosity, and density can be inverted directly from seismic data. Numerical simulation results demonstrate the precision of our proposed meth... [more]

Rapidly increasing demand for renewable energy has created a need for the photovoltaic and wind farms to be placed in various locations that have diverse and possibly time-variant grid conditions. A mismatch between the grid impedance and output admittance of an inverter causes impedance-based stability issues, which appear as power quality problems and poor transient performance. Grid synchronization with phase-locked loop (PLL) introduces a negative-resistance-like behavior to inverter output admittance. High control bandwidth of the PLL makes the system sensitive to impedance-based stability issues when the inverter is connected to a weak grid that has high impedance. However, very conservative tunings lead to overly damped dynamic responses in strong grids, where the control performance and power quality can be improved by applying higher PLL control bandwidths. Continuous evaluation of grid conditions makes it possible to avoid the risk of instability and poor dynamic responses, a... [more]

Parametric identification of the single diode model of a photovoltaic generator is a key element in simulation and diagnosis. Parameters’ values are often determined by using experimental data the modules manufacturers provide in the data sheets. In outdoor applications, the parametric identification is instead performed by starting from the current vs. voltage curve acquired in non-standard operating conditions. This paper refers to this latter case and introduces an approach based on the use of interval arithmetic. Photovoltaic generators based on crystalline silicon cells are considered: they are modeled by using the single diode model, and a divide-and-conquer algorithm is used to contract the initial search space up to a small hyper-rectangle including the identified set of parameters. The proposed approach is validated by using experimental data measured in outdoor conditions. The information provided by the approach, in terms of parametric sensitivity and of correlation between... [more]

Formation lithology identification is of great importance for reservoir characterization and petroleum exploration. Previous methods are based on cutting logging and well-logging data and have a significant time lag. In recent years, many machine learning methods have been applied to lithology identification by utilizing well-logging data, which may be affected by drilling fluid. Drilling string vibration data is a high-density ancillary data, and it has the advantages of low-latency, which can be acquired in real-time. Drilling string vibration data is more accessible and available compared to well-logging data in ultra-deep well drilling. Machine learning algorithms enable us to develop new lithology identification models based on these vibration data. In this study, a vibration dataset is used as the signal source, and the original vibration signal is filtered by Butterworth (BHPF). Vibration time−frequency characteristics were extracted into time−frequency images with the applicati... [more]

Non-intrusive load monitoring, a convenient way to discern the energy consumption of a house, has been studied extensively. However, most research works have been carried out based on a hypothetical condition that each electric appliance has only one running state. This leads to low identification accuracy for multi-state electric appliances. To deal with this problem, a method for identifying the type and state of electric appliances based on a power time series is proposed in this paper. First, to identify the type of appliance, a convolutional neural network model was constructed that incorporated residual modules. Then, a k-means clustering algorithm was applied to calculate the number of states of the appliance. Finally, in order to identify the states of the appliances, different k-means clustering models were established for different multi-state electric appliances. Experimental results show effectiveness of the proposed method in identifying both the type and the running state... [more]

In this review, we present a comprehensive revisit of past research and advances developed on the stay-green (SG) paradigm. The study aims to provide an application-focused review of the SG phenotypes as crop residuals for bioenergy. Little is known about the SG trait as a germplasm enhancer resource for energy storage as a system for alternative energy. Initially described as a single locus recessive trait, SG was shortly after reported as a quantitative trait governed by complex physiological and metabolic networks including chlorophyll efficiency, nitrogen contents, nutrient remobilization and source-sink balance. Together with the fact that phenotyping efforts have improved rapidly in the last decade, new approaches based on sensing technologies have had an impact in SG identification. Since SG is linked to delayed senescence, we present a review of the term senescence applied to crop residuals and bioenergy. Firstly, we discuss the idiosyncrasy of senescence. Secondly, we present... [more]

The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimen... [more]

In this paper, photovoltaic (PV) string failure analysis and health monitoring of PV modules based on a low-cost self-powered wireless sensor network (WSN) are presented. Simple and effective fault detection and diagnosis method based on the real-time operating voltage of PV modules is proposed. The proposed method is verified using the developed health monitoring system which is installed in a grid-connected PV system. Each of the PV modules is monitored via WSN to detect any individual faulty module. The analysis of PV string failure includes several electrical fault scenarios and their impact on the PV string characteristics. The results show that a degraded or faulty module exhibits low operating voltage as compared to the normal module. The developed health monitoring system also includes a graphical user interface (GUI) program which graphically displays the real-time operating voltage of each module with colors and thus helping users to identify the faulty modules easily. The fa... [more]

Accurate temperature estimation inside an electrical motor is key for condition monitoring, fault detection, and enhanced end-of-life duration. Additionally, thermal information can benefit motor control to improve operational performance. Lumped-parameter thermal networks (LPTNs) for electrical machines are both flexible and cost-effective in computation time, which makes them attractive for use in real-time condition monitoring and integration in motor control. However, the accuracy of these thermal networks heavily depends on the accuracy of its system parameters, some of which are difficult to calculate analytically or even empirically and need to be determined experimentally. In this paper, a methodology for the thermal condition monitoring of long-duration transient and steady-state temperatures in an induction motor is presented. To achieve this goal, a computationally efficient second-order LPTN for a 5.5 kW squirrel-cage induction motor is proposed to apprehend the dominant he... [more]

Electronic load (e-load) is essential equipment for power converter performance test, where a designated load profile is executed. Electronic load is usually implemented with the analog controller for fast tracking of the load profile reference. In this paper, a low-power low-cost electronic load is proposed. MOSFETs (metal-oxide-semiconductor field-effect transistors) are used as the power consumption devices, which are regulated to the active region as controlled current-sink. In order to achieve fast transient response using the low-cost digital signal controller (DSC) PWM peripherals, the interleaving PWM method is proposed to achieve active current ripple mitigation. To obtain the system open-loop gain for current-sink operation, an offline digital system identification method, followed by model reduction, is proposed by applying Pseudo-Random Binary Sequence (PRBS) excitation. Pole-zero cancelation method is used in the control system design and later implemented in a DSC. The pr... [more]

During the last decade the dynamic properties of power systems have been altered drastically, due to the emerge of new non-conventional types of loads as well as to the increasing penetration of distributed generation. To analyze the power system dynamics and develop accurate models, measurement-based techniques are usually employed by academia and power system operators. In this regard, in this paper an identification toolbox is developed for the derivation of measurement-based equivalent models and the analysis of dynamic responses. The toolbox incorporates eight of the most widely used mode identification techniques as well as several static and dynamic network equivalencing models. First, the theoretical background of the mode identification techniques as well as the mathematical formulation of the examined equivalent models is presented and analyzed. Additionally, multi-signal analysis methods are incorporated in the toolbox to facilitate the development of robust equivalent model... [more]

A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults corre... [more]

With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured termina... [more]

A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance have received significant attention. Complex network theory transfers the power grid to a network with nodes and links, which helps evaluate the system conveniently with a global view. In this paper, we employ the complex network method to address the cascade failure process and grid structure performance assessment simultaneously. Firstly, a grid cascade failure model based on network topology and power system characteristics is constructed. Then, a set of performance evaluation indicators, including invulnerability, reliability, and vulnerability, is proposed based on the actual functional properties of the grid by renewing the power-weighted degree, medium, and clustering coefficients according t... [more]

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage−current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures... [more]

Accurate identification of overflow fluid types facilitates timely and effective handling of onsite overflow accidents. Research into identifying the type of overflow fluid is limited, and there are only simple calculation models that do not consider enough effects; additionally, accuracy needs to be improved and the identification method is not perfect. If there is no drilling data, it is impossible to identify the overflow fluid. Therefore, this paper modifies the density calculation model of overflow fluid by considering the influence of the temperature, pressure field, and two-phase flow model, making the calculation result more accurate and universal, and puts forward a comprehensive method for auxiliary identification based on gas logging interpretation. This paper uses the gas state equation to verify the accuracy of the overflow density model; after verification using data from more than 20 overflowing wells, the method was found to be practical and had an accuracy rate of more... [more]

Due to the complex and changing drilling conditions and the large scale of logging data, it is extremely difficult to process the data in real time and identify dangerous working conditions. Based on the multi-classification intelligent algorithm of Stacking model fusion, the 24 h actual working conditions of an XX well are classified and identified. The drilling conditions are divided into standpipe connection, tripping out, tripping in, Reaming, back Reaming, circulation, drilling, and other conditions. In the Stacking fusion model, the accuracy of the integrated model and the base learner is compared, and the confusion matrix of the drilling multi-condition recognition results is output, which verifies the effectiveness of the Stacking model fusion. Based on the variation in the parameter characteristics of different working conditions, a real-time working condition recognition diagram of the classification results is drawn, and the adaptation rules of the Stacking fusion model unde... [more]

Since carbonate reservoirs develop pores and fractures and have a complex formation pressure system, overflow and even blowout seriously threaten the exploration and development of these kinds of reservoirs. According to the overflow characteristics of fractured-vuggy carbonate reservoirs, a field monitoring and identification method for overflow has been developed. This method is based on the top-down logic framework for early overflow identification, combined with optimized monitoring parameters. The DBSCAN clustering algorithm is used to identify abnormal logging parameters, and thus, a probability weight coefficient of overflow (K) indicated by the abnormal engineering parameters of a gas well can be calculated. K is divided into four early warning response levels of overflow, and the overflow control operation can be made according to the different levels of early warning response. Based on this method, an integrated software system for field monitoring and identification of early... [more]

Hydrogen is an attractive, clean, sustainable energy source primarily produced via industry. At present, most reviews on hydrogen mainly focus on the preparation and storage of hydrogen, while the development and utilization of natural hydrogen will greatly reduce its cost. Natural hydrogen has been discovered in many geological environments. Therefore, based on extensive literature research, in this study, the distribution and sources of natural hydrogen were systematically sorted, and the identification method and occurrence state of natural hydrogen were examined and summarized. The results of this research show that hydrogen has been discovered in oceanic spreading centers, transform faults, passive margins, convergent margins, and intraplate settings. The primary sources of the hydrogen include alterations in Fe(II)-containing rocks, the radiolysis of water, degassed magma, and the reaction of water- and silica-containing rocks during the mechanical fracturing. Hydrogen can appear... [more]