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With the continuously increasing integration of renewable energy sources into power grids, the dynamic response of a power system is becoming more complex. For example, the interaction between the dynamic loads and low-voltage ride-through of renewable energy generators makes the voltage response more rapid and unpredictable. Ensuring the accuracy and speed of traditional voltage stability assessment methods is difficult. This study developed a novel hybrid model and data-driven voltage stability assessment approach. First, the equivalent parameters of a power system were calculated based on the measured data, and the parameters were constantly modified based on the response data. To further improve the accuracy of the approach, a data-driven method was introduced to correct the assessment results using a Thevenin equivalent-based assessment. The difference between the Thevenin and system impedances, which better reflects the system stability, was included in the data-driven input data. Finally, by combining the clear physical mechanism of the model-driven method and high accuracy of the data-driven method, the final the assessment process was a serial combination of the model- and data-driven methods. The effectiveness of the method was verified using an IEEE New England 10-generator 39-bus test system and a 100-bus actual system in China. The results showed that the method developed was more accurate and had higher robustness under data loss and noise conditions than other methods.

Modern power systems are gradually evolving into low-inertia systems due to the integration of high penetration of renewable energy and power electronic equipment. For the natural inertia on the rotor, doubly-fed induction generator (DFIG) based wind farms are expected to provide inertia support for systems. The paper proposed an analytical model of DFIG with df/dt inertia control for studying frequency dynamics, and investigates the optimal design of virtual inertia from wind farms, as well as its equivalent inertia contribution to system. The inertia response differences between DFIG and synchronous generator (SG) are discussed and analyzed for the first time, and then to accurately describe the frequency response with low cost, an analytical model of DFIG with inertia control is developed considering wind turbine aerodynamic characteristics. The explicit expression of virtual inertia provided by DFIG is derived according to the kinetic energy equilibrium, and the key factors that dominate the magnitude of virtual inertia are illustrated. Based on the system frequency response model including wind farms, taking the system frequency nadir (SFN) and the time of reaching nadir (ToN) as targets, a multi-objective optimal configuration model of virtual inertia is constructed and solved through MOPSO. The time domain simulations of a generic two machine four bus system and modified IEEE 9-bus test system are both performed in MATLAB/Simulink, the accuracy and efficiency of our proposed DFIG model on system frequency dynamics research are demonstrated, the pareto frontiers of virtual inertia configuration under different working conditions are optimized and analyzed as well as the impacts of different parameters on inertia evaluation.

Abstract Severe disturbances in a power network can cause the system frequency to exceed the safe operating range. As the last defensive line for system emergency control, under frequency load shedding (UFLS) is an important method for preventing a wide range of frequency excursions. This paper proposes a hierarchical UFLS scheme of “centralized real-time decision-making and decentralized real-time control” for inter-connected systems. The centralized decision-layer of the scheme takes into account the importance of the load based on the equivalent transformation of kinetic energy (KE) and potential energy (PE) in the transient energy function (TEF), while the load PE is used to determine the load shedding amount (LSA) allocation in different loads after faults in real-time. At the same time, the influence of inertia loss is considered in the calculation of unbalanced power, and the decentralized control center is used to implement the one-stage UFLS process to compensate for the unbalanced power. Simulations are carried out on the modified New England 10-generator 39-bus system and 197-bus system in China to verify the performance of the proposed scheme. The results show that, compared with other LSA allocation indicators, the proposed allocation indicators can achieve better f nadir and t d . At the same time, compared with other multi-stage UFLS schemes, the proposed scheme can obtain the maximum f nadir with a smaller LSA in scenarios with high renewable energy sources (RES) penetration.

BackgroundChemotherapy is an important treatment for children with cancer, and chemotherapy-induced pain is an important role in affecting patients' quality of life. In our study, bibliometric analysis was used to identify current research hotspots and future research trends of chemotherapy and pain in children over the last decade. Our findings can provide a reference for the research in the field of chemotherapy and pain in children.MethodPublications of chemotherapy and pain in children were collected from the Web of Science Core Collection database. CiteSpace was used to analyze publication characteristics from 2013 to 2022.ResultsWe identified 1,130 eligible publications in the field of chemotherapy and pain in children, with an increasing trend of publications over the last decade. In the field of chemotherapy and pain in children, the United States had the most publication with 346, followed by China with 135. The author with the most published papers was Pamela S Hinds (n = 8) from the United States. The journals that published the most papers were the Journal of pediatric hematology oncology (n = 44) and Medicine (n = 44). The Journal of Clinical Oncology was cited the most frequency (n = 422). St. Jude Children's Research Hospital had the most publication (n = 23). The specific keywords related to the field of chemotherapy and pain in children were “children”, “chemotherapy”, “management”, “childhood cancer”, “randomized controlled trial” and “efficacy”. Emerging research focuses predominantly on symptomatic and supportive interventions for chemotherapy and pain in children.ConclusionAttention to chemotherapy and pain in children with cancer was insufficient. This bibliometric analysis showed the upward trend of chemotherapy and pain in children over the last decade. More studies are needed to improve the quality of life in children with chemotherapy-induced pain. This study may provide useful information to guide future research on chemotherapy and pain in children.

Oxalicine B (1) is an α-pyrone meroterpenoid with a unique bispirocyclic ring system derived from Penicillium oxalicum. The biosynthetic pathway of 15-deoxyoxalicine B (4) was preliminarily reported in Penicillium canescens, however, the genetic base and biochemical characterization of tailoring reactions for oxalicine B (1) has remained enigmatic. In this study, we characterized three oxygenases from the metabolic pathway of oxalicine B (1), including a cytochrome P450 hydroxylase OxaL, a hydroxylating Fe(II)/α-KG-dependent dioxygenase OxaK, and a multifunctional cytochrome P450 OxaB. Intriguingly, OxaK can catalyze various multicyclic intermediates or shunt products of oxalicines with impressive substrate promiscuity. OxaB was further proven via biochemical assays to have the ability to convert 15-hydroxdecaturin A (3) to 1 with a spiro-lactone core skeleton through oxidative rearrangement. We also solved the mystery of OxaL that controls C-15 hydroxylation. Chemical investigation of the wild-type strain and deletants enabled us to identify 10 metabolites including three new compounds, and the isolated compounds displayed potent anti-influenza A virus bioactivities exhibiting IC50 values in the range of 4.0−19.9 μmol/L. Our studies have allowed us to propose a late-stage biosynthetic pathway for oxalicine B (1) and create downstream derivatizations of oxalicines by employing enzymatic strategies.

Abstract The integration of high proportion renewable energy not only brings many benefits, but also increases the difficulty of power system stability analysis. One of the reasons for this problem is the unclear internal characteristics of new energy equipment. Therefore, there is an urgent need for a more detailed analysis of the characteristics of new energy equipment. Taking a double‐fed induction generator (DFIG), which has complex operation characteristics and is widely used, as an example, a detailed energy function (DEF) of DFIG for small signal stability analysis is proposed in this paper. This method performs DFIG's internal energy analysis, making the traditional energy structure more comprehensive. On this basis, the internal parameters of DFIG with high sensitivity to stability are obtained, and the damping evaluation index is constructed to evaluate the damping of DFIG. Finally, the proposed model's correctness and method are verified in the 4‐generator 2‐area system.

Abstract Background Genomic analysis indicated that the genomes of ascomycetes might carry dozens of biosynthetic gene clusters (BGCs), yet many clusters have remained enigmatic. The ascomycete genus Epicoccum, belonging to the family Didymellaceae, is ubiquitous that colonizes different types of substrates and is associated with phyllosphere or decaying vegetation. Species of this genus are prolific producers of bioactive substances. The epicoccamides, as biosynthetically distinct mannosylated tetramate, were first isolated in 2003 from Epicoccum sp. In this study, using a combination of genome mining, chemical identification, genetic deletion, and bioinformatic analysis, we identified the required BGC epi responsible for epicoccamide A biosynthesis in Epicoccum sp. CPCC 400996. Results The unconventional biosynthetic gene cluster epi was obtained from an endophyte Epicoccum sp. CPCC 400996 through AntiSMASH-based genome mining. The cluster epi includes six putative open reading frames (epiA-epiF) altogether, in which the epiA encodes a tetramate-forming polyketide synthase and nonribosomal peptide synthetases (PKS−NRPS hybrid). Sequence alignments and bioinformatic analysis to other metabolic pathways of fungal tetramates, we proposed that the gene cluster epi could be involved in generating epicoccamides. Genetic knockout of epiA completely abolished the biosynthesis of epicoccamide A (1), thereby establishing the correlation between the BGC epi and biosynthesis of epicoccamide A. Bioinformatic adenylation domain signature analysis of EpiA and other fungal PKS-NRPSs (NRPs) indicated that the EpiA is l-alanine incorporating tetramates megasynthase. Furthermore, based on the molecular structures of epicoccamide A and deduced gene functions of the cluster epi, a hypothetic metabolic pathway for biosynthesizing compound 1 was proposed. The corresponding tetramates releasing during epicoccamide A biosynthesis was catalyzed through Dieckmann-type cyclization, in which the reductive (R) domain residing in terminal module of EpiA accomplished the conversion. These results unveiled the underlying mechanism of epicoccamides biosynthesis and these findings might provide opportunities for derivatization of epicoccamides or generation of new chemical entities. Conclusion Genome mining and genetic inactivation experiments unveiled a previously uncharacterized PKS − NRPS hybrid-based BGC epi responsible for the generation of epicoccamide A (1) in endophyte Epicoccum sp. CPCC 400996. In addition, based on the gene cluster data, a hypothetical biosynthetic pathway of epicoccamide A was proposed.

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.

The optimal allocation of energy storage capacity is an important issue for integrated energy systems (IES). To reduce the impact of volatility and intermittency of renewable energy sources, the impact of volatility needs to be smoothed out by rational allocation of energy storage. To address the above issues, this paper proposes a method for the optimal allocation of source storage capacity considering integrated demand response(IDR). Firstly, the basic mechanism of IES based on energy hub(EH) is constructed, and the model and coupling relationship of equipment components in the system are introduced. The IDR model is built to optimize the load curve according to the real-time market tariff scheme, and then the confidence interval method is used to analyze the scenery uncertainty in IES and determine the scenery output curve under different confidence levels. On this basis, a bi-level optimization model is built that takes into account both source and storage capacity allocation and operational optimization. The upper-level planning model takes into account the uncertainty of wind power and photovoltaic output, and solves the allocation scheme of energy storage intending to minimize the total planning cost; the lower-level operation optimization model takes into account the output constraints of each unit and optimizes the output of the equipment to minimize the operation cost. Finally, it is verified that considering IDR can reasonably optimize the allocation of the system’s source storage capacity and reduce the system’s investment and operating costs.

To solve the problem of the large gap in the regulation resources of the power system due to the large number of wind power connected to the grid and to help achieve the strategic energy goal of “carbon neutrality and carbon peaking,” the demand-side adjustable energy-intensive load is taken as the research object in this paper, and a two-stage low-carbon economic scheduling method of the wind power system with multi-form energy-intensive load (MEL) is proposed. First, the operating characteristics of the system with MEL are analyzed. At the same time, a refined mathematical model that conforms to the actual operation characteristics is established. Then, the complementary low-carbon characteristics of the source-charge side and the difference in the response characteristics of the demand-side MEL are considered. Meanwhile, combining with the energy storage system, a day-ahead and intra-day two-stage low-carbon economic scheduling model of the wind power system with MEL is constructed. Finally, an example of the improved IEEE-39 bus system is analyzed to verify the effectiveness of the proposed scheduling method in the low-carbon economic scheduling of wind power systems and to provide a reference for power grid scheduling.

Abstract In the microgrid, virtual synchronous generator technology can significantly enhance the anti‐interference characteristics of the system frequency and bus voltage, as well as solve the problems of insufficient damping and low inertia. However, the system frequency and active power oscillation caused by power fluctuations and grid faults threaten the stable operation of the grid seriously. Therefore, for an alternating current (AC) microgrid multi‐virtual synchronous generator (VSG) parallel system, an improved virtual synchronous generator control algorithm based on a fuzzy inference system is proposed, which adjusts the values of virtual inertia and damping coefficient dynamically through fuzzy logic rules to realize the coordinated control of the two. The enhanced VSG algorithm described in this research has a substantial influence on power‐frequency oscillation suppression, decreases active power and frequency overshoot, shortens the adjustment time, and improves system frequency stability active power, according to simulation and experimental findings.

In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types.

Abstract This paper proposes a power system multimode generator energy loss factor (GELF) based on energy flow method under environmental excitation. This index can evaluate generator damping changes in different oscillation modes in power systems, timely discover the damping deterioration of the generator, and track the source of low‐frequency oscillation. A signal expression method for electrical frequency domain under environmental excitation is derived. This method is combined with dissipated energy flow method to obtain a new type of GELF. In terms of signal processing, dynamic mode decomposition and Morlet wavelet filter extraction system mode components are used to improve computing accuracy. The proposed index enables online calculation with the support of phasor measurement unit and wide area measurement system. Simulation analysis is conducted in a standard 39 node system and wind‐integrated power system. Test results show the effectiveness and generality of the proposed index and method.

Abstract Inertial support for hybrid AC/DC microgrid systems is provided by the virtual synchronous generator (VSG). However, the fast performance of the system is neglected while enhancing the stability of the system. To address this problem, an adaptive virtual inertia control strategy based on VSG technology is proposed. This control approach adaptively adjusts the system virtual inertia when the system operation deviates from the nominal value, by slowing down the rate of change of AC frequency and DC voltage and improving the deviation of AC frequency and DC voltage. Meanwhile, when the AC frequency and DC voltage restore back to the rated value, the rate of change of AC frequency is accelerated and the DC voltage fluctuation is reduced by dynamically adjusting the virtual inertia of the system. The proposed adaptive virtual inertia control method combines the advantages of large and small inertia to effectively improve the dynamic response of the system voltage and frequency in both rectifier and inverter modes. Finally, the simulation and experimental results verify the effectiveness of the proposed control algorithm.

To solve the problem that the fractional-order system is difficult to implement on FPGA, the chaotic behavior of a single fractional-order Chen system is realized on the FPGA platform in this article by using Laplace transform and Bode-domain approximation to transform the fractional-order operator. Then the System Generator model of two different fractional-order systems is studied, and FTS/PTS (fixed/predefined time synchronization) algorithms are designed to realize the FTS/PTS of the FOTD (fractional-order time-delay) chaotic systems on FPGA. By changing the initial conditions and parameters of the FOTD chaotic systems, several groups of experiments are carried out on FPGA. The experimental results show that the FTS/PTS controllers of two different FOTD chaotic systems are feasible on FPGA.

An ambient modal framework for inertia estimation using synchrophasor data is proposed in this letter. Specifically, an analytical formulation is developed for the estimation of inertia based on the frequency and damping ratio modes extracted from ambient data. An advantage of the proposed framework is that it can rely on synchronized ambient data under non-disturbed conditions for online estimation and tracking of inertia. Ultimately, numerical simulation studies and physical experiments demonstrate the feasibility of the proposed approach.

Abstract Virtual synchronous generator technology can effectively improve the anti‐interference characteristics of the system frequency and bus voltage in the microgrid, and solve the problems of insufficient damping and low inertia of the system. However, in an islanded microgrid with multiple distributed generation, the difference in line impedance will cause local voltage deviations, which in turn leads to a series of problems, such as reducing power distribution accuracy and increasing bus voltage drop. Therefore, for the island‐type microgrid multi‐inverter distributed power generation parallel system, in order to solve the problem of low power distribution accuracy and large frequency oscillation caused by system parameters in virtual synchronous generator control, an improved virtual synchronous generator control algorithm based on adaptive droop coefficient is proposed in this paper, which not only eliminates the resistance component of the line impedance, makes the system impedance characteristic present a purely inductive nature, but also realises real‐time adjustment of active and reactive power. While maintaining the stability of the bus voltage and system frequency, it maintains high power distribution accuracy and improves the dynamic performance and operational stability of the power grid system.

With the increase in the power system scale, the identification of electromechanical oscillation mode parameters by traditional numerical methods can no longer meet the requirements of complete real-time analysis. Therefore, a method based on machine learning (multilayer artificial neural networks) is proposed to identify the electromechanical oscillation mode parameters of the power system. This method can take the monitorable variables of the WAMS as the input of the model and the key characteristic information such as frequency and damping ratio as the output. After processing the input and output data with randomized dynamic mode decomposition (randomized-DMD), their mapping relationship can be analyzed by using the multilayer neuron architecture. The simulation results of the 4-generator 2-area system and the IEEE 16-generator 5-area system show that this method can accurately calculate the key characteristic parameters of the system without considering the change in the control parameters and after the offline training of historical data, which shows higher accuracy, stronger robustness, and sensitive online tracking ability.

With the rapid development of industrialization, the proportion of electric arc furnaces (EAF) in distribution networks is getting higher and higher. Aiming at the problem that grid voltage is distorted by a large number of arc furnace nonlinear loads accessing to distribution networks, it is crucial to make dynamic analysis of distribution network voltage and adopt a control strategy of grid-connected converter based on the new phase-locked loop (PLL) technology. As a result, the harmonic distortion rate is reduced and the quality of grid-connected current and voltage is improved. In this paper, photovoltaic (PV) system model and the typical dynamic model of the EAF are established. By analyzing the influence of the EAF model on the PV grid-connected converter with the traditional phase-locked loop while connected to distribution network, a control strategy of the PV grid-connected converter with the self-adjusting double SOGI (MAF-SASOGI) phase-locked loop with the ideal low-pass filter is proposed. Through the simulation analysis, it is verified that precise phase locked can be realized and harmonic content of the system can be reduced by the PLL strategy proposed in this paper. Therefore, the quality of PV grid-connected voltage and current is improved.

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

Synchrophasor-based electromechanical modes extraction offers essential information for monitoring the small-signal stability of power systems without models. This article proposes an optimal mode decomposition (OMD) method for extracting electromechanical modes from noise-contaminated measurement data. In the proposed method, an iterative optimal process based on the Grassman manifold is employed to enhance robustness to measurement noise, such that the fixed low-dimensional subspace using proper orthogonal decomposition (POD) is not required in the proposed method. The proposed method gives a reliable extraction of frequency and damping ratio as well as the mode shape using multichannel wide-area measurement data. The performance of the proposed method is tested using simulated data from the IEEE 4-machine 2-area test system, 16-generator 5-area test system and measured data from a real power system.

The battery energy storage system (BESS) integrated with a wind farm is an efficient way to smooth wind power fluctuations and improve wind farm dispatchability. The presented study proposed a model predictive control (MPC)-based power dispatch strategy for a wind farm incorporated with dual-battery energy storage system (DBESS). The state space model of the active power balance of the wind farm incorporated with DBESS was established to analyze the operation mode of the DBESS. The MPC was then applied to acquire the control inputs of the two batteries, thereby enabling both batteries to respectively be in a state of charging and discharging to track the alternating dispatch order. Actual wind power data was applied in the simulation, wherein the results indicate that the proposed MPC-based dispatching strategy of the DBESS-incorporated wind farm effectively tracks the dispatching order using the output of the wind/battery storage hybrid system, thereby improve the dispatchability of the wind farm while simultaneously reducing battery switching times and extending the lifetime of battery.

A novel approach for estimating the inertia of an interconnected power system is presented using the identification of interarea oscillation modes (frequency, damping and mode shape) extracted from ambient data. The proposed method concentrates on estimating the values of the effective inertia of each area rather than the equivalent inertia of the entire system. Based on an equivalent two-machine system (ETmS) obtained by combining small signal stability analysis (SSSA) with the structure of the power grid, we derive a mathematical relationship between the effective inertia of each area and the interarea oscillation modes. Furthermore, the interarea oscillation modes can be extracted from ambient data, and the developed scheme enables an online estimation of the inertia only by using the outputs measured by PMUs. The performance of the proposed methodology is tested via numerical simulation cases and real data.

Objectives: The purpose of this study was to explore the current status and trends of acupuncture for depression in the last decade and provide new insights for researchers in future studies.Methods: The articles regarding acupuncture treatment for depression published between 2011 and 2020 were extracted from the Web of Science Core Collection. We used CiteSpace to analyze data on publications, countries, institutions, cited journals, cited authors, cited references, keywords, and citation bursts about acupuncture and depression.Results: A total of 1,032 publications were obtained from 2011 to 2020. We identified the most prolific journals, countries, institutions, and authors in the field of acupuncture for depression in the last decade. The most prolific country and institutions were the People's Republic of China and KyungHee University, respectively. Evidence-based Complementary and Alternative Medicine was the most prolific and cited journal. The author with the highest centrality was Zhangjin Zhang, and the author with the most publications was Park Hi-Joon. The keyword “cognitive behavioral therapy” was first for research developments with the highest citation burst. The five hot topics in acupuncture on depression were “acupuncture,” “depression,” “electro-acupuncture,” “quality of life,” and “anxiety.”Conclusions: The results from this bibliometric study provide insight into the research trends in acupuncture therapy for depression, and the current status and trends of the past decade, which may help researchers determine the current status, hotspots, and frontier trends in this field.

The ideal wind turbine power curve provided by the manufacturer cannot monitor the practical performance of wind turbines accurately in the engineering stage; in this paper, a modified approach of the wind turbine power curve is proposed based on improved Bins and K-means++ clustering. By analyzing the wind speed-power data collected by the supervisory control and data acquisition system (SCADA), the relationship between wind speed and output is compared and elaborated on. On the basis of data preprocessing, an improved Bins method for equal frequency division of data is proposed, and the results are clustered through K-means++. Then, the wind turbine power curve correction is realized by data weighting and regression analysis. Finally, an example is given to show that the power curve of the same type of wind turbines, which, installed in different locations, are discrepant and different from the MPC, and the wind turbine power curve obtained by using this method can reflect the output characteristics of the wind turbine operating more effectively in a complex environment.

This study contrives a discrete-time adaptive decentralized control algorithm with input quantization for interconnected multi-machine power systems with SVC. First, a dynamic surface scheme is applied to the excitation controller design, in which first-order digital low-pass filters are used to predict the next virtual control law, which overcomes the model conversion problem in backstepping. Therefore, the controller design and structure are simplified. Further, an improved hysteresis quantizer is utilized for amplitude quantization of control input signals; along with the discretization of time, this achieves digital decentralized control. Finally, semi-global uniformly ultimately boundedness (SGUUB) of the whole control system is demonstrated based on the Lyapunov stability theory, and the effectiveness of the proposed control algorithm is verified on the ModelingTech real-time simulation experimental platform for power electronics.

The increase in the scale of distribution networks significantly reduces the efficiency of intelligent planning for distributed generation (DG). To improve the efficiency of intelligent DG planning and avoid the impact of uncertainty concerning renewable energy on it, this paper proposes a sensitivity index for the bus-embedded multi-objective estimation of distribution algorithm (MEDA) based on the semi-invariant probabilistic power flow approach to achieve an optimal solution. The sensitivity indices of the buses are comprehensively enabled to obtain a new index and determine their sensitivity sequences based on the area grey incidence decision-making method. Subsequently, according to the uncertainty of wind turbine generators and photovoltaics, a probability model is established, and the semi-invariant method is used to solve for the probabilistic power flow according to a correlation model. Finally, the sensitivity of the proposed bus-embedded MEDA to enhancing the efficiency of the solution is examined. The optimal DG allocation scheme is obtained with the goal of achieving the lowest total cost in the planning year. Finally, the feasibility and effectiveness of the proposed model and method are verified using simulations of the IEEE 33-bus, IEEE 69-bus, and IEEE 118-bus test systems.

It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances.

Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of the wind power output and effectively improve a system's wind power accommodation. However, the high-dimensional historical wind speed information should be taken into account in the wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Furthermore, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. The experiments show that it reduces the information loss. Thus, the model has a higher forecast accuracy than the traditional model.

Abstract In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters (frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified, first using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.

With the development of new energy technology, more and more plug-in electric vehicles (PEVs) have appeared in our lives. In vehicle to grid (V2G) information exchange, the method that PEVs use to access the grid for information and vehicular data collection is the key factor that affects the reliability and real-time nature of V2G communication. Vehicular power line communication (VPLC) reuses vehicular power cables as a communication medium, which avoids the need for an additional vehicular communication line and optimizes the in-vehicle space. VPLC is being considered as a more competitive solution to reduce the complexity of design, weight, and cost associated with the rapid growth of electronic devices installed in vehicles, especially for PEVs. Nevertheless, there are factors that impact VPLC in such a way that disrupts reliability and introduces problems. One of these challenges is VPLC channel noise, which can reduce communication reliability. Hence, in this paper, we introduce a V2G information interaction system structure, based on power line communication, to establish a VPLC channel model for the vehicular data collection system. The model is developed in Simulink and leverages various vehicular noise models and binary frequency shift keying (BFSK) modulation; it also analyzes the channel characteristics by examining different types of noise based on BFSK. Our simulation results demonstrate the feasibility of PLC under BFSK modulation and demodulation, often used in PEVs, which could provide guidance to the implementation of VPLC in support of practical engineering.

In this paper, new tethered aerial robots including roaming tethered aerial robots (RTARs) for radioactive material sampling and stationary tethered aerial robots (STARs) for environment monitoring are proposed to meet extremely-long-endurance missions of nuclear power plants. The flight of the proposed tethered aerial robots may last for a few days or even a few months as long as the tethered cable provides continuous power. A high voltage AC or DC power system was newly adopted to reduce the mass of the tethered cable. The RTAR uses a tethered cable spooled from the aerial robot and an aerial tension control system. The aerial tension control system provides the appropriate tension to the tethered cable, which is accordingly laid down on the ground as the RTAR roams. The STAR includes a tethered cable spooled from the ground and a ground tension control system, which enables the STAR to reach high altitudes. Prototypes of the RTAR and STAR were designed and successfully demonstrated in outdoor environments, where the load power, power type, operating frequency, and flight attitude of the RTAR and STAR were: 180 W, AC 100 kHz, and 20 m; and 300 W, AC or DC 100 kHz, and 80 m, respectively.

This study focuses on the dynamics of a grid-tied voltage source converter (GVSC) during electromechanical oscillations. A small-signal model with GVSC port variables (DC voltage and AC power) as the outputs and a terminal voltage vector as the input is derived to reveal the passive response of the GVSC on the basis of the power equation in the d−q coordinate system. An input−output transfer function matrix is constructed according to the proposed model. The frequency response of this matrix in the electromechanical bandwidth is described to reflect the dynamic behavior of the GVSC. The effects of the operation parameters, i.e., the grid strength, reference value of the control system, and grid voltage, on the dynamic behavior of the GVSC in the electromechanical bandwidth, are investigated using frequency domain sensitivity. Analysis results show that the GVSC generates responses with respect to the electromechanical mode. These responses have different sensitivities to the operation parameters. The IEEE 10-machine power system simulation is performed, and the power hardware-in-the-loop platform with the GVSC was applied to validate the analysis.

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.

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage

Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

In the direct current (DC) microgrid composed of multiple distributed generations, due to the different distances between various converters and the DC bus in the system, the difference of the line resistance will reduce the current sharing accuracy of the system. The droop control was widely used in the operation control of the DC microgrid. It was necessary to select a large droop coefficient to improve the current sharing accuracy, but a too large droop coefficient will lead to a serious bus voltage drop and affect the power quality. In view of the contradiction between the voltage regulation and load current sharing in the traditional droop control, a hierarchical control algorithm based on the improved droop control of the fuzzy logic was proposed in this paper. By improving the droop curve, the problems of voltage regulation and current sharing were solved simultaneously. The effectiveness of the algorithm was verified by simulation.

Using a doubly-fed induction generator (DFIG), with an additional active or reactive damping controller, is a new method of suppressing the inter-area oscillation of a power system. However, using active power modulation (APM) may decrease the damping of the shaft oscillation mode of a DFIG and the system damping target cannot be achieved through reactive power modulation (RPM) in some cases. Either single APM or RPM does not consider system damping and torsional damping simultaneously. In this paper, an active-reactive coordinated dual-channel power modulation (DCPM) damping controller is proposed for DFIGs. First, considering the electromechanical parts and control structure of the wind turbine, an electromechanical transient model and an additional damping controller model of DFIGs are established. Then, the dynamic objective function for coordinating the parameters of the additional damping controller is proposed. The ratio between the active power channel and reactive power channel modulation is derived from the parameters optimized by the particle swarm optimization algorithm. Finally, the effectiveness and practicability of the designed strategy is verified by comparing it with a traditional, simple damping controller design strategy. Standard simulation system examples are used in the comparison. Results show that the DCPM is better at maximizing the damping control capability of the rotor-side controller of a DFIG and simultaneously minimizing adverse effects on torsional damping than the traditional strategy.

Low-voltage direct current (DC) microgrid based on distributed generation (DG), the problems of load mutation affecting the DC bus under island mode, and the security problems that may arise when the DC microgrid is switched from island mode to grid-connected mode are considered. Firstly, a DC bus control algorithm based on disturbance observer (DOB) was proposed to suppress the impact of system load mutation on DC bus in island mode. Then, in a grid-connected mode, a pre-synchronization control algorithm based on a neural network adaptive control was proposed, and the droop controller was improved to ensure better control accuracy. Through this pre-synchronization control, the microgrid inverters output voltage could quickly track the power grid’s voltage and achieve an accurate grid-connected operation. The effectiveness of the algorithms was verified by simulation.

The harmonic pollution problem in power grids has become increasingly prominent with the large-scale application of power electronic equipment, nonlinear loads, and renewable energy. This study proposes a method based on adaptive variational mode decomposition (AVMD) and Hilbert transform (HT) that is applicable to harmonic detection in power system. The AVMD method constructs and solves the constrained variational model. Then, a single-frequency harmonic component with stable features can be obtained. The proposed method can effectively avoid the recursive process in empirical mode decomposition (EMD). In this study, the variational mode decomposition algorithm is used to obtain the periodic harmonic components concurrently. Subsequently, the characteristic parameters of each harmonic component are extracted via HT. Simulation analysis and measured data verify the validity and feasibility of the proposed algorithm. Compared with the detection results obtained using the EMD algorithm, the proposed method is proven to exhibit stronger applicability to harmonic detection in power system.

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.

Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments.

The application of power to gas (P2G) provides a new way to absorb intermittent renewable energy generation, which improves the efficiency of renewable energy utilization and provides the necessary flexibility for operating the integrated energy system. The electro-thermal integrated energy system with P2G is a new form of using energy efficiently. In this paper, we first introduce the technology and application of P2G. On the basis of considering the characteristics of P2G facilities, power systems, natural gas systems and heating systems, an optimal dispatching model of electro-thermal multi-energy system with P2G facilities is proposed. Particle swarm optimization is used to solve the optimal scheduling model. The simulation results are discussed for the six-bus and six-node integration system and show that when the volume fraction of hydrogen does not exceed 20% in the gas network, for the same operating mode, an integrated energy grid with P2G function will save about 20 tons of standard coal per day and the abandoned wind rate can be regarded as 0.

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.

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.

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.

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.

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.