Abstract:Renewable energy such as wind power, photovoltaics require power inversion to generate stable AC voltage for connection to loads or the grid. As a core device in renewable energy power conversion systems, inverters are required to meet the application demands of high efficiency, high power density, and wide-range adjustable voltage gain. Traditional H-bridge inverter suffers from a high total harmonic distortion (THD) of the output AC voltage and no boosting capability. Recently the switched-capacitor multi-level inverter (SCMLI) excels in applications like renewable energy due to its high gain, self-balancing capabilities, low device voltage stress, and minimal output voltage harmonics. However existing SCMLIs are limited by their weak voltage modulation and high peak input pulse current. A novel quasi-Z-source single-phase SCMLI is proposed in this paper, offering a wide adjustable input voltage range, continuous and pulse-free input current, and scalability. Compared to similar topologies, the proposed topology requires fewer components and lower voltage stress, enabling direct conduction of the power switches in the output bridge arm of the quasi-Z-source circuit and supporting a wider input voltage range. Firstly, the working principle and parameter design method of the proposed topology are introduced. Based on this, the prototype system parameters are determined, and the correctness of the theoretical analysis is verified through simulation and experiments. The results indicate that the proposed topology can output high-quality seven-level AC with a wide input voltage range. The THD is as low as 23.8%, significantly lower than that of traditional two-level inverters, effectively improving the output power quality and reducing the size of the output filter.

Abstract:Problems such as the difficulty of characterizing the dynamic properties of gas and heat networks in the scheduling process of integrated energy systems, and the difficulty of users to intuitively obtain carbon emission information and participate in carbon reduction hinder the development of integrated energy systems. A low-carbon economic dispatch method for integrated energy based on carbon intensity and generalized electrothermal dual response is proposed in this paper. On the one hand, a waste heat recovery device is introduced at the source side to regulate the electrical and thermal loads, and a generalized electrical and thermal demand response is constructed in collaboration with the integrated electrical and thermal demand response. On the other hand, based on the theory of carbon emission flow, the calculation method of carbon emission flow of gas and heat system with dynamic characteristics is derived, and the carbon potential demand response is constructed based on the carbon potential signal. Finally, a low-carbon economic dispatch model is constructed with the objective of optimizing the total system cost in the upper layer and minimizing the system's total carbon emission cost in the lower layer. After the arithmetic verification, the combination of carbon potential-generalized electric-thermal dual-response scheduling reduces the system operating cost by 2.42% and the carbon emission by 7.32% compared with the traditional operation method. The proposed scheduling method realizes the low-carbon economic operation of the system.

Abstract:A two-stage robust optimization configuration strategy for the new energy town is proposed to address the issue of insufficient power supply reliability in integrated energy systems due to the fluctuation and intermittency characteristics during the integration of a high proportion of renewable energy. In the first stage, historical source-load data is utilized to make preliminary decisions on unit capacity configuration, with the objective of minimizing system configuration costs. In the second stage, polyhedral uncertainty sets are employed to describe the uncertainties of source-load, aiming to minimize system operation costs, and power data predictions for the worst-case scenarios are obtained based on the decision outcomes of the first stage. An uncertainty parameter is then introduced to control the conservativeness of the robust optimization configuration scheme. The model is solved using the column and constraint generation (C&CG) algorithm, which iteratively determines unit capacity configuration and converges to the optimal configuration scheme. A case study of the new energy town in Northern China is conducted, and the results verify the effectiveness and feasibility of the proposed strategy and optimization method, demonstrating their capability to enhance the power supply reliability and economy of the new energy town.

Abstract:Given the problem of large output voltage ripple of switched reluctance generator (SRG) system and the demand for safe and reliable data communication in DC microgrid, an improved power converter and power synchronous transmission strategy suitable for SRG is proposed in this article. By appropriately modulating an introduced additional circuit cascaded to the conventional asymmetric half bridge (AHB) converter, the voltage ripple caused by SRG winding commutation is effectively suppressed. The power switch in the additional circuit is modulated using frequency shift keying to generate voltage ripples on the output voltage bus for carry data. Welch method-based power spectrum is adopted to demodulate the transmitted signals. Experimental results show that by rationally modulating the improved SRG converter, the voltage ripples can be effectively suppressed while achieving synchronous transmission of power and data. The proposed data transmission method can also serve as a backup or emergency communication strategy for SRG systems operated in scenarios that require low data communication rates, such as remote switching, status monitoring, and coordinated control.

Abstract:In response to the challenges posed by grid-connected node voltage exceeding the limit and output consumption during photovoltaic (PV) consumption systems into green substations, the operational strategy for renewable energy consumption in green substations guided by reactive power monitoring is proposed in this research. The primary objective of this strategy is to maximize PV consumption by analyzing the relationship between reactive power-voltage sensitivity and the load coverage rate. Through the decoupling of reactive power monitoring from active power consumption and the precise prediction of crucial variables such as active power surplus and reactive power compensation control correction quantities, a reactive power-voltage relationship model is constructed. Furthermore, through considering their current charge status and capacity constraints, a green substation consumption model with energy storage systems is established. Operational strategy for renewable energy consumption in green substations guided is formulated based on a reactive power-voltage relationship model. The model is solved using a improved particle swarm algorithm and validated via simulations on the IEEE 33-node system. Results demonstrate that the proposed strategy ensures stable grid-connected voltage while significantly enhancing PV consumption capacity.

Abstract:Aiming at the problems that the prediction accuracy of lithium battery health status is not high and the model cannot be updated online, a lithium-ion battery health prediction method based on the online sequential extreme learning machine (OSELM) model is proposed. The health factors with high correlation with battery capacity are obtained from the historical charge and discharge data of lithiumion batteries, and the OSELM model is optimized by goose algorithm (GOOSE-OSELM) to improve the prediction accuracy of the model. At the same time, the Cauchy inverse cumulative distribution operator and tangent flight operator are introduced to improve the goose algorithm to improve the global optimization ability and convergence speed of the model, and form an algorithm model with fast calculation speed and online update. The prediction results of the improved goose algorithm-optimized OSELM model (IGOOSE-OSELM) are compared with those of GOOSE-OSELM, OSELM, back propagation (BP) neural networks, and whale optimization algorithm-least squares support vector machine (WOA-LSSVM). The results show that the goodness of fit values of IGOOSE-OSELM in the three battery datasets are above 0.997, and the root mean square error is less than 0.004 5. Finally, the generalization ability of the model is verified by using the Oxford battery dataset and the NASA battery dataset. The results show that the IGOOSE-OSELM model can accurately predict the health status of the battery, and the model has high robustness and adaptability.

Abstract:The zero-sequence circulating current (ZSCC) becomes prominent in the single-phase open-circuit fault of a pentacle-wired five-phase motor, and this phenomenon can lead to phase current distortion and torque fluctuation. To address this issue, a linear approximation control method is proposed, and proportional-integral (PI) control is used to suppress the alternating ZSCC. Firstly, a decoupling fault-tolerant control scheme for a pentacle-wired five-phase motor is introduced. This scheme extends the existing fault-tolerant control for star-wired five-phase motors by developing additional line-to-phase transformation matrices for the phase current and voltage. Then, a nonlinear mathematical relationship between ZSCC and zero-sequence electromagnetic torque is derived, and the nonlinear zero-sequence model is linearized using a subdivision method based on the rotor position. Combined with a multi-dimensional PI controller, ZSCC suppression is achieved. Finally, simulation and experimental results suggest that ZSCC exhibits an alternating characteristic with a significant amplitude oscillating at the fundamental frequency before suppression. ZSCC is effectively eliminated after the suppression method is applied. Also, the phase currents return to normal and can recover from the prior condition of severe imbalance and over-large amplitude, and the torque fluctuation issue is mitigated. In conclusion, when a single-phase open-circuit fault occurs in a pentacle-wired five-phase motor, the fault-tolerant control method with active suppression of ZSCC can be used to mitigate the phase current distortion and torque fluctuation.

Abstract:To address the challenge faced by power grid companies in accurately detecting changes in user industry information, which has been complicated by the increasing variability of industry characteristics in recent years, a data-driven approach for identifying anomalies in load characteristics is proposed. Initially, a two-stage methodology for developing typical load patterns for various industries is presented. The hierarchical density-based spatial clustering of applications with noise (HDBSCAN) technique is utilized to extract typical daily load curves for users under different scenarios. Subsequently, these extracted daily load curves are clustered using an improved K-means algorithm to establish typical load patterns for the respective industries. In the second phase, a multidimensional intelligent diagnostic method for load characteristic anomalies is introduced. User load characteristics are constructed, and the entropy weight method is employed to evaluate the relative significance of typical industry scenarios. The one-class support vector machine (OCSVM) algorithm is then utilized to quantify the degree of anomaly present in user load characteristics across each scenario. Comprehensive suspicion scores are calculated and ranked to accurately identify users exhibiting abnormal load characteristics. The effectiveness of the proposed method is validated through the analysis of actual user data from a specific region. The results demonstrate that the method is both feasible and practical for constructing typical industry load scenarios and for the identification of load characteristic anomalies.

Abstract:To fully leverage the characteristics of various distributed resources and the controllability of distribution grids for delivering higher-performance and dynamic ancillary services, a dynamic frequency aggregation control method for meshed distribution virtual power plants (MVPP) is proposed. Firstly, the overall architecture of MVPP is defined, and a frequency control model capable of aggregating the dynamic characteristics of its internal distributed resources for primary frequency regulation scenarios is established. Subsequently, an adaptive dynamic frequency matching control approach is employed to address the control design challenge of the dynamic frequency aggregation model. The desired characteristics are decomposed through online adaptive approach, and a local feedback controller is designed for each device using an H_∞ optimal robust control method to precisely and dynamically match the decomposed desired characteristics for primary frequency regulation. The simulation results verify the effectiveness of the proposed control method, demonstrating that it can significantly improve the primary frequency regulation characteristics of the system through the complementation of various resources under scenarios such as load disturbance and new energy output fluctuations. The proposed strategy offers a new solution for spatially distributed aggregation units to participate in dynamic frequency regulation auxiliary services.

Abstract:Ablative defects in the buffer layer of high-voltage cables are important causes of power cable failures. Buffer layer ablation releases gases, and some components of these gases and their concentrations can characterize the degree of buffer layer ablation defects. Fourier transform infrared spectroscopy for buffer layer ablation gas analysis has the unique advantages of speed, sensitivity, and non-destructiveness. To address the challenges in the detection of gases from high-voltage cable buffer layer ablation, namely noise interference, baseline drift and cross-interference, a buffer layer ablation gas Fourier transform infrared spectroscopy analysis method is proposed. The proposed method is validated by standard concentration gas analysis and ablation characteristic gas analysis experiments, using CH₄, C₂H₆ and C₂H₄ as characteristic gases. The experimental results show that the three characteristic gas concentrations are related to the buffer layer ablation defects, and the proposed method can accurately analyze the concentration of the characteristic gases from infrared spectra, with relative errors of 8.90%, 17.60% and 4.32% for CH₄, C₂H₆ and C₂H₄ in mixed gas, and the detection period is less than 15 s. This method can provide critical technical support for rapid, high-precision diagnosis of buffer layer ablation defects in high-voltage cables.

Abstract:The impact of electromagnetic interference on secondary systems in substations can be reduced by the installation of secondary equipotential grounding grid. A simplified grounding grid model based on CDEGS is established to study the effects of the connection mode between the secondary equipotential grounding grid and the main grounding grid on ground potential rise and current in the secondary equipotential grounding grid. By considering soil resistivity and current injection points, the characteristics of the grounding grid under power frequency current, lightning current, and high-frequency damped oscillation current are simulated and analyzed. It is found that when the injection current frequency is low, the ground potential difference and current inside the secondary equipotential grounding grid close to the fault point can be effectively reduced by the single-point grounding connection mode. When the injection current frequency is high, the multi-point grounding connection mode can effectively reduce ground potential rise and current. Besides, the ground potential rise and current of grounding conductors at different locations increase with increasing soil resistivity. When the current injection point is at the corner of the grounding grid, the ground potential difference and current inside the secondary equipotential grounding grid is significantly increased.

Abstract:Accurate forecasting of electric, cooling and heating loads is an important prerequisite and foundation for the operation scheduling and energy management of integrated energy systems. Leveraging the energy coupling characteristics between multivariate load, this paper constructs a joint prediction model for multivariate load based on bidirectional gated recurrent units (BiGRU) and a progressive layered extraction (PLE) network architecture. Firstly, the meteorological features with high correlation are screened as input features of the model through the maximum information coefficient. Then, the BiGRU network is used to extract the temporal features of the multivariate load time series under the integrated energy system and reconstruct the data in this way. Secondly, for the characteristics of different energy sources that are coupled with each other, the improved progressive hierarchical extraction network structure is proposed, and the coupling features are extracted from the complex and multidimensional data through the multilevel sharing of the feature extraction layer. Finally, by changing the structural parameters of the sub-task tower module, the coupled feature information is differentially fused, and the multiple load prediction results are obtained. The actual example results show that the maximum information coefficient screening method adopted in the article is more suitable for feature selection of meteorological data than the traditional Pearson coefficient screening method, and the proposed BiGRU-PLE multivariate load prediction model can reduce the prediction error by more than 5% compared with the single-task model, and by more than 3% compared with the common multitask model.

Abstract:The existing power load forecasting methods encounter significant challenges, particularly when accounting for the influence of meteorological factors on load fluctuations. Traditional methods often overlook the complex nonlinear relationship between meteorological characteristics and load, leading to reduced forecasting accuracy. A day-ahead power load forecasting model based on meteorological similar day correction (MSDC)-improved parrot optimizer (IPO)-decomposition-based linear (DLinear) is proposed. The proposed method enhances the parrot optimizer (PO) by incorporating a logistic map, adaptive mutation strategy, and spiral fluctuation search to optimize the DLinear superparameters. Periodicity and trend characteristics are extracted from the DLinear model. The load forecast value is corrected by comparing the Euclidean distance of meteorological characteristics. The resulting day-ahead power load forecasting model, IPO-DLinear-MSDC, is validated using a simulation analysis of total load data from the Zhuzhou area in Hunan from June to October 2024. The model's performance is evaluated with an average absolute percentage error (MAPE) of 4.67% and R² of 0.833, demonstrating improvements of 15.09% and 23.44%, and increases of 0.0741 and 0.1253, respectively, comparing to IPO-DLinear model and PO-DLinear model.

Abstract:The distribution network is connected to new elements such as distributed new energy and controllable resources, and the traditional state estimation model is faced with new problems such as incomplete measurement information, frequent topology changes of the distribution network and load time series fluctuations, which lead to reduced accuracy of the model estimation. Therefore, a method of distribution network state estimation by fusing improved generative adversarial network and graph attention network is proposed in this paper. Firstly, topological parameters and measurement information in different historical time sections are selected to generate data sets. The incomplete measurement information is filled by introducing the bidirectional long short-term memory (BiLSTM) network into the generative adversarial network. Secondly, the graph attention network is used to capture the spatial dynamic relationship between the nodes adaptively, and the bidirectional long short-term memory network is used to fully excavate the time-coupling relationship of the cross-sectional sequence information in different time sections. These networks are concatenated to form the spatiotemporal feature expression of the measurement to the state, and the state estimation model of the improved graph neural network is obtained. Finally, simulation experiments are carried out in IEEE 118-bus system, and compared with other neural network algorithms such as convolutional neural network and graph attention network. The results show that the proposed algorithm has better performance in the case of missing data and time-varying topology than other neural network algorithms.

Abstract:Subsynchronous resonance (SSR) caused by the interaction between thermal power generators and series-capacitor-compensated AC lines, has been a significant concern in China's power grids. An energy storage supplementary damping control (SDC) to suppress SSR is proposed in this paper. SDC enables local feedback, and utilizes the coupling between generators and power grid to enhance the overall damping level. Firstly, the target system is modelled and the mechanism and characteristics of its SSR problem are clarified through frequency and time-domain analyses. Then, the supplementary damping control strategy is established based on battery energy storage system (BESS). Its control parameters and adding positions are optimized, and the impact of SDC's control capacity on BESS's normal functions is analyzed. Finally, the effectiveness and feasibility of the proposed SDC are verified through electromagnetic transient simulations with a real-world project, i.e., the Shangdu wind-thermal-energy storage bundling system. The simulation results also show that the proposed SDC can effectively address the SSR issue with reduced control cost and equipment investment cost, serving as a more economical solution.