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The high-frequency isolation (HFI) charging DC port can serve as the interface between unipolar/bipolar DC buses and electric vehicles (EVs) through the two-power-stage system structure that combines the front-end three-level converter with the back-end logical link control (LLC) resonant converter. The DC output voltage can be maintained within the desired voltage range by the front-end converter. The electrical isolation can be realized by the back-end LLC converter, which has the bus converter function. According to the three-level topology, the low-voltage rating power devices can be adapted for half-voltage stress of the total DC grid, and the PWM phase-shift control can double the equivalent switching frequency to greatly reduce the filter volume. LLC resonant converters have advance characteristics of inverter-side zero-voltage-switching (ZVS) and rectifier-side zero-current switching (ZCS). In particular, it can achieve better performance under quasi-resonant frequency mode. Additionally, the magnetizing current can be modified following different DC output voltages, which have the self-adaptation ZVS condition for decreasing the circulating current. Here, the principles of the proposed topology are analyzed in detail, and the design conditions of the three-level output filter and high-frequency isolation transformer are explored. Finally, a 20 kW prototype with the 760 V input and 200-500 V output are designed and tested. The experimental results are demonstrated to verify the validity and performance of this charging DC port system structure. [ABSTRACT FROM AUTHOR]

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel nonlinear robust damping controller is proposed to suppress power oscillation in interconnected power systems. The proposed power oscillation damping controller exhibits good nonlinearity and robustness. It can consider the strong nonlinearity of power oscillation and uncertainty of its model. First, through differential homeomorphic mapping, a mathematical model of the system can be transformed into the Brunovsky standard. Next, an extended state observer (ESO) estimated and compensated for model errors and external disturbances as well as uncertain factors to achieve dynamic linearization of the nonlinear model. A power oscillation damping controller for interconnected power systems was designed on a backstepping-fractional order sliding mode variable structure control theory (BFSMC). Compared with traditional methods, the controller exhibits good dynamic performance and strong robustness. Simulations involving a four-generator two-area and partial test system of Northeast China were conducted under various disturbances to prove the effectiveness and robustness of the proposed damping control method. [ABSTRACT FROM AUTHOR]

Large frequency deviations after islanding are exceedingly critical in small receiving-end power systems. The under-frequency load shedding (UFLS) scheme is an efficient protection step for preventing system black outs. It is very important to get an exact model to design the UFLS schemes. In this paper, an optimization model to achieve the system frequency response (SFR) model either from the full-scale power system or from test records was proposed. The optimized SFR model took into account the response of governors-prime movers and the dynamic characteristics of loads developed in the modern power system. Then the UFLS schemes were designed via the optimized SFR model and particle swarm optimization (PSO) method. The time-domain simulation with the actual small receiving-end power system was presented to investigate the validity of the presented model and the developed technique. [ABSTRACT FROM AUTHOR]

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach. [ABSTRACT FROM AUTHOR]

This study presents the experimental study and analysis of double-LCL resonant compensation network based wireless power transfer (WPT) system for electric vehicles. The WPT system could accomplish both constant current and the unity-power-factor for the transmitting terminal and the receiving terminal. In addition, the transmitting terminal inverter can achieve zero-voltage-switching. The multi-physical characteristics of the transmitting and receiving terminals based on LCL resonant compensation network are analysed in detail. Finally, three-dimensional finite element analysis and an experimental system with circular pads is setup to verify the theoretical analysis under different loads and horizontal/vertical misalignment conditions. At the condition of 200 mm gap and no horizontal misalignment, the system can supply the maximum output power of about 7.36 kW having the maximum efficiency of 95.87%. [ABSTRACT FROM AUTHOR]

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods. [ABSTRACT FROM AUTHOR]

In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can be constructed automatically according to the ability of feature classification. In different noise environments, the classification accuracy of the new method is higher than the method based on probabilistic neural network, extreme learning machine, and support vector machine. [ABSTRACT FROM AUTHOR]

A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as well as the redundancy among the original features. Second, a weighting factor selected by statistical experiments is used to balance the relevance and redundancy of features when using the G-mRMR. Third, each feature is ranked in a descending order according to its relevance and redundancy as computed by G-mRMR. A sequential forward selection method is utilized for choosing the optimal subset. Finally, a STLF predictor is constructed based on random forest with the obtained optimal subset. The effectiveness and improvement of the proposed method was tested with actual load data. [ABSTRACT FROM AUTHOR]

In order to improve the identification accuracy of the high voltage circuit breakers' (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples. [ABSTRACT FROM AUTHOR]

In this paper, a robust online approach based on wavelet transform and matrix pencil (WTMP) is proposed to extract the dominant oscillation mode and parameters (frequency, damping, and mode shape) of a power system from wide-area measurements. For accurate and robust extraction of parameters, WTMP is verified as an effective identification algorithm for output-only modal analysis. First, singular value decomposition (SVD) is used to reduce the covariance signals obtained by natural excitation technique. Second, the orders and range of the corresponding frequency are determined by SVD from positive power spectrum matrix. Finally, the modal parameters are extracted from each mode of reduced signals using the matrix pencil algorithm in different frequency ranges. Compared with the original algorithm, the advantage of the proposed method is that it reduces computation data size and can extract mode shape. The effectiveness of the scheme, which is used for accurate extraction of the dominant oscillation mode and its parameters, is thoroughly studied and verified using the response signal data generated from 4-generator 2-area and 16-generator 5-area test systems. [ABSTRACT FROM AUTHOR]

Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI.With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%. [ABSTRACT FROM AUTHOR]

This study presents H∞ control synthesis of a miniature unmanned aerial vehicle to improve its hovering capability in the presence of wind disturbances. By time-scale techniques, the six-degree-freedom flight system can be decoupled into subsystems in different time-scales: one including slow-varying translational dynamics and Euler angles, and another composed of fast-varying angular velocities. To avoid contradictions between subsystems, control specifications for subsystems are classified, respectively, in disjoint frequency ranges. Robust H∞ controllers, applied directly to the original flight system, are formulated by composing the sub-controllers. To achieve desired handling and flying qualities, wind characteristics are considered, and a novel flexible strategy is then developed to ensure an adequate response in all flight modes under different weather conditions. The effectiveness and merits of the proposed methods are illustrated with a simulation example. [ABSTRACT FROM AUTHOR]

Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs' vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

In this study, the cascade dual-boost/buck half-bridge and full-bridge bidirectional ac-dc converters are proposed for grid-tie transformerless battery energy storage systems (BESSs). The proposed converter contains the advantages of the traditional cascade H-bridge (CHB) converter. However, compared with CHB converter, there is no shoot-through issue in the proposed converter, which is more reliable for BESS. In addition, a unified control scheme is proposed for active-power control and individual state-of-charge-balancing control of BESS, which is very modular and easy to implement. At the end, a laboratory BESS is constructed and tested combining a cascade twounit dual-boost/buck half-bridge converter with nine lead-acid series-connected battery units. Experimental results obtained from the laboratory system verified the feasibility and effectiveness of the proposed BESS and unified control scheme. [ABSTRACT FROM AUTHOR]

In a microgrid, the distributed generators (DG) can power the user loads directly. As a result, power quality (PQ) events are more likely to affect the users. This paper proposes a Multiresolution Generalized S-transform (MGST) approach to improve the ability of analyzing and monitoring the power quality in a microgrid. Firstly, the time-frequency distribution characteristics of different types of disturbances are analyzed. Based on the characteristics, the frequency domain is segmented into three frequency areas. After that, the width factor of the window function in the S-transform is set in different frequency areas. MGST has different time-frequency resolution in each frequency area to satisfy the recognition requirements of different disturbances in each frequency area. Then, a rule-based decision tree classifier is designed. In addition, particle swarm optimization (PSO) is applied to extract the applicable features. Finally, the proposed method is compared with some others. The simulation experiments show that the new approach has better accuracy and noise immunity. [ABSTRACT FROM AUTHOR]

As to strong nonlinearity of doubly fed induction generators (DFIG) and uncertainty of its model, a novel rotor current controller with nonlinearity and robustness is proposed to enhance fault ride-though (FRT) capacities of grid-connected DFIG. Firstly, the model error, external disturbances, and the uncertain factors were estimated by constructing extended state observer (ESO) so as to achieve linearization model, which is compensated dynamically from nonlinear model. And then rotor current controller of DFIG is designed by using terminal sliding mode variable structure control theory (TSMC). The controller has superior dynamic performance and strong robustness. The simulation results show that the proposed control approach is effective. [ABSTRACT FROM AUTHOR]

In this study, a novel individual voltage balancing control scheme for multilevel cascade active-front-end rectifier is proposed to allow a different voltage setting capability among different dc outputs in series. First, the proportional resonant controller and admittance compensation controller are introduced for the active inner-current control loop of cascade activefront- end rectifier to achieve better steady-state and dynamic performance. Second, an assumption based on conservation of energy is proposed to decouple a series connected ac-dc system into several individual subsystems to facilitate the individual dc-bus controller design. Both simulation and experimental results based on the cascade two-unit cascade dual-buck/boosttype active-front-end rectifier proved the effectiveness of the proposed individual balancing control scheme, which also verified the proposed assumption. [ABSTRACT FROM AUTHOR]

As an important part of the smart grid, a wide-area measurement system (WAMS) provides the key technical support for power system monitoring, protection and control. But 20 uncertainties in system parameters and signal transmission time delay could worsen the damping effect and deteriorate the system stability. In the presented study, the subspace system identification technique (SIT) is used to firstly derive a low-order linear model of a power system from the measurements. Then, a novel adaptive wide-area damping control scheme for online tuning of the wide-area damping controller (WADC) parameters using the residue method is proposed. In order to eliminate the effects of the time delay to the signal transmission, a simple and practical time delay compensation algorithm is proposed to compensate the time delay in each wide-area control signal. Detailed examples, inspired by the IEEE test system under various disturbance scenarios, have been used to verify the effectiveness of the proposed adaptive wide-area damping control scheme. [ABSTRACT FROM AUTHOR]

We present in this paper an integration scheme of a low-cost inertial attitude and position reference system for a mini unmanned helicopter by utilizing the robust and H∞ filtering technique. The result has been successfully implemented and tested on our mini-scale unmanned helicopter. Simulation and flight experiment results show that the proposed technique is very effective in real-time and suitable for control, stabilization and navigation for mini-scale unmanned air vehicles. [ABSTRACT FROM PUBLISHER]

We present in this paper the modeling and flight- control-system design for the yaw channel of an unmanned- aerial-vehicle (UAV) helicopter using a newly developed composite nonlinear feedback (CNF)-control technique. The CNF-control method has been proven to be capable of yielding a fast transient response with no or very minimal overshoot in tracking a specific target. From the actual flight tests on our UAV helicopter, it has been found that the commonly used yaw dynamical model for the UAV helicopter proposed in the literature is very rough and inaccurate, which might cause the helicopter to shake severely in certain flight conditions. This motivates us to first obtain a more accurate model for the yaw channel of our UAV helicopter. The CNF-control method is then utilized to design an efficient control law, which gives excellent overall performance. In particular, our design has achieved a Level 1 performance according to the standards set for military rotorcraft. The results are verified through actual flight tests. [ABSTRACT FROM AUTHOR]

• Monte Carlo sampling method is proposed to solve the correlation between renewable energy sources. • The sampling accuracy of Monte Carlo method is higher than that of empirical formula. • A simplified Ls-index is proposed. Ls-index can maintain enough calculation accuracy and reduce calculation time. • This article gives a practical calculation formula for GSA. The practical calculation formula solves the problem that the conditional expectation cannot be solved directly. The high penetration of renewable energy in power systems significantly increases the difficulty of system modelling. Adequate attention should be paid to the impact of the correlation between renewable energy sources and voltage stability. In this paper, a global sensitivity analysis method is proposed to analyse the sensitivity of correlated renewable energy generation to voltage stability. The established model fully considers the uncertainty of wind speed and solar irradiance. Moreover, the simplified local voltage stability index is used to evaluate voltage stability. The global sensitivity analysis method is used to identify the sensitivity of random factors. In addition, the Monte Carlo method is proposed to calculate Pearson correlation coefficients in different distribution spaces. The Nataf inverse transform is used to sample correlated renewable energy. Finally, the global sensitivity index of renewable energy generations is obtained. Numerical results on the modified IEEE 30-bus system and modified IEEE 118-bus system show that with the enhancement of correlation between renewable energy sources, the sensitivity of renewable energy generations to voltage stability is also increasing. [ABSTRACT FROM AUTHOR]

Background: Knee osteoarthritis (KOA) is a common disabling condition and a heavy financial burden to the society. Platelet-rich plasma (PRP) is considered to be an effective method in the repair and regeneration of cartilage and alleviate pain in KOA. But the utilising of PRP to treat KOA in clinical has shown variable results from many studies. The objective of this protocol is to determine the efficacy of PRP in pain control and cartilage repair in KOA animal models., Method: We will search the following three electronic databases: MEDLINE, EMBASE and Web of Science. The primary outcome will include the histological score of cartilage and pain score. The secondary outcomes will be the behavioural assessments and cartilage thickness. SYRCLE's risk of bias tool will be used to assessment the risk of bias of including studies. The standardized mean difference and 95% confidence interval will be used to calculate the effect of PRP treatment. The I2 inconsistency values will be used to calculated the heterogeneity between studies., Results: The results of this paper will be submitted to a peer-reviewed journal for publication., Conclusion: This research will determine the efficacy of PRP of the treatment of knee osteoarthritis model., Prospero Registration Number: CRD42020181589., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Acupuncture is an emerging alternative therapy that has been beneficial for the pain of osteoarthritis (OA). However, the underlying mechanism of protective effect remains unclear. MCP1/CCR2 axis can be stimulated in various periods of OA, and we hypothesize that acupuncture may treat OA by regulating the MCP1/CCR2 axis. This study aimed to explore the effect of acupuncture at points ST35 and ST36 on the effects of hyperalgesia and cartilage in OA rats including the expression of chemokines, nerve growth factor (NGF), and inflammatory-related proteins. OA was induced in male Sprague-Dawley rats by anterior cruciate ligament transection at the right knee. The first acupuncture intervention was performed on the seventh day after surgery and once a day for seven weeks. The knee-pain-related behaviors, histology, and related protein were examined in this study. We have found that electroacupuncture at ST35 and ST36 can significantly alleviate the hyperalgesia and cartilage degeneration as well as reducing nerve sprouting in OA knee joint. Moreover, acupuncture treatment may inhibit the MCP1/CCR2 axis as well as down-regulate inflaming factor and NGF in cartilage and synovial tissue. The data presented here indicate that acupuncture exerts a protective effect against hyperalgesia and cartilage degeneration, and the mechanism might involve in chemokines and NGF pathway.