Xinhui Zhang

When faults occur in microgrid lines, the large-scale integration of distributed energy resources and the switching between grid-connected and off-grid modes can lead to prolonged operation times of inverse-time overcurrent protection and the failure of coordinated protection at different levels. To address these issues, this paper proposes an improved protection scheme for inverse-time overcurrent protection based on the enhanced manta ray foraging optimization algorithm. The proposed scheme incorporates reverse learning and fractional-order calculus to enhance the performance of the MRFO algorithm. The optimized algorithm is applied to the coordinated setting of ITOCR parameters with the objectives of accelerating protection response and improving selectivity. The effectiveness of the proposed scheme is validated through extensive fault simulations in PSCAD/EMTDC under different operating modes, fault locations, and fault types. Simulation results show that the proposed scheme reduces the primary protection operating time by approximately 50% on average compared with the conventional ITOCR, while maintaining the coordination time interval within the required range of 0.2–0.5 s, demonstrating superior speed and coordination performance.

The mid-long-term forecasting of load in existing building clusters has given relatively little consideration to the prediction of fixed power loads that do not actively participate in renewable energy consumption, which may lead to certain errors in the forecasting results of active renewable energy-consuming loads. Based on power supply dependency, this paper categorizes building electrical loads into fixed loads and those capable of actively consuming renewable energy. Following this categorization, a Modified Northern Goshawk Optimization algorithm (MNGO) is utilized to optimize the XGBoost model, ultimately establishing a mid-long-term load forecasting algorithm tailored for building groups. Initially, a Random Forest (RF) algorithm is deployed to filter the key feature factors influencing the accuracy of load forecasting. Secondly, the Northern Goshawk Optimization (NGO) algorithm is modified to optimize the XGBoost model for the electric load forecasting of building groups. A comparative analysis of the forecasting outcomes reveals that the XGBoost model, refined by the NGO algorithm, significantly diminishes the Mean Absolute Percentage Error (MAPE) and markedly escalates the coefficient of determination (R²), thereby validating the efficacy of the proposed methodology. This approach not only furnishes data support for energy storage planning and ameliorates the capacity for new energy assimilation, but also ensures a stable power supply for buildings reliant on fixed electrical loads.

Addressing the issue of misoperation in traditional three-zone current protection caused by the assisted, drawdown, or reverse current due to the large-scale integration of distributed generation (DG) into distribution networks (DNs), an adaptive current short-circuit protection scheme based on local information is proposed for active DNs (ADNs). Firstly, the adaptive current protection reliability coefficients are improved for the setting of protection zones I and II of the lines in the ANDs. On the DG side, an improved reliability coefficient is constructed using an e-exponential function. Subsequently, the distance between the fault point and the busbar is calculated by analyzing the relation between the positive-sequence voltage and positive-sequence current at the measuring point when faults occur at various locations. Based on this distance parameter, the reliability coefficient is adjusted in real time. Finally, the modified adjustable reliability coefficient, along with the online-calculated equivalent impedance and equivalent electromotive force, is used to coordinate the setting of short-circuit protection zones I and II of the lines in the ADNs. Simulation results demonstrate that, compared with traditional adaptive current protection schemes, this proposed scheme is independent of the location of short-circuit fault points, the grid-connected capacity, or the number of DGs, exhibiting better selectivity and reliability.

When a high impedance fault (HIF) occurs in a resonant grounding system, the electrical signal is weak and the current distinction between the faulty feeder and non-faulty feeder is inconspicuous. To address this issue, this paper proposes a faulty feeder selection method based on discrete Fréchet distance (DFD). Firstly, the instantaneous value of the zero-sequence current flowing through each sampling point, along with the instantaneous values at its previous sampling moment, is accumulated to enhance the fault signal, and furthermore to magnify the distinction in the amplitude of the accumulated waveforms of zero-sequence current between the faulty feeder and non-faulty feeder. Then, the DFD of the accumulated waveforms of the zero-sequence current for each feeder is calculated, and the Fréchet distance matrix is established to acquire the average DFD (ADFD) value of the accumulated waveform for each feeder. Finally, the relative DFD (RDFD) value is defined as a criterion to distinguish the bus fault from the feeder fault according to the values of the two feeders with the maximum ADFD value and the minimum ADFD value. If the RDFD value is negative, it is identified as a bus fault, otherwise, the feeder corresponding to the maximum ADFD value is identified as a faulty feeder. MATLAB/Simulink simulation results and field measurement data show that the proposed method is not affected by various fault conditions, and the accuracy and reliability in faulty feeder selection are high when detecting an HIF, with good applicability.

The power distribution network business gradually extends from the grid domain to the social service domain, and the new business keeps expanding. The edge device uses microservice architecture and container technology to realize the processing of different services by one physical device. Although the power distribution network IoT with cloud-edge architecture has good scalability, scenarios with insufficient resources for edge devices may occur. In order to support the scheduling and collaborative processing of tasks under the resource-constrained scenario from the edge device, this paper proposes a cloud-edge collaborative online scheduling method for the distribution of station area tasks under the microservice architecture. The article models the characteristics of power tasks and their constraints in the cloud-edge containerized scenario, designs the priority policy and task assignment policy based on the cloud-edge scheduling mechanism of containerized power tasks, and schedules the tasks in real time by an improved online algorithm. Simulation results show that the algorithm proposed in this paper has high task execution efficiency, can improve the completion rate of important tasks with limited resources of edge devices, and improves system security through resource replacement.

This article proposes a state‐of‐charge (SOC) estimation method to eliminate the influence of the hysteresis effect and the ambient temperature. First, an improved dual‐polarization (DP) model considering the hysteresis effect and the ambient temperature is established. A hysteresis voltage source is connected in series with a couple of resistance–capacitance pairs in the improved DP model, all the parameters of which are related to the ambient temperature to depict the temperature characteristics of the battery. Second, the forgetting factor recursive least squares method is utilized to identify the parameters under the battery dynamic test data at different temperatures. The proposed model and parameterization scheme integrate the effects of hysteresis and temperature, greatly enhancing the performance of the proposed method at different temperatures. Finally, an extended Kalman filter algorithm for SOC estimation is adopted to verify the improved DP model and the simulation indicates that the error of SOC estimation is within 1.5% at different ambient temperatures. The proposed method can improve the precision of the SOC estimation even if the temperature is below −10 or above 50 °C.

Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled estimators utilizing electrochemical impedance spectroscopy (EIS) measurements, rather than utilizing direct battery surface measurements, are adopted to estimate the battery temperature and SOC, respectively. The information being exchanged between the temperature estimator and the SOC estimator effectively improves the estimation accuracy. Extensive experiments show that, in contrast with the EKF-based separate estimation method, the DEKF-based coestimation method is more favorable in reducing errors for estimating both the temperature and SOC even if the battery core temperature has increased by 17°C or more during the process of test.

Direct current (DC) bus voltage stability is essential for the stable and reliable operation of a DC system. If an oscillation source can be quickly and accurately localized, the oscillation can be adequately eliminated. We propose a method based on the power spectral density for identifying the voltage oscillation source. Specifically, a DC distribution network model combined with the component connection method is developed, and the network is separated into multiple power modules. Compared with a conventional method, the proposed method does not require determining the model parameters of the entire power grid, which is typically challenging. Furthermore, combined with a novel judgment index, the oscillation source can be identified more intuitively and clearly to enhance the applicability to real power grids. The performance of the proposed method has been evaluated using the MATLAB/Simulink software and PLECS RT Box experimental platform. The simulation and experimental results verify that the proposed method can accurately identify oscillation sources in a DC distribution network.

When a single-phase grounding fault occurs in a resonant grounding system, the determination of the fault location remains a significant challenge due to the small fault current and the instability of the grounding arc. In order to solve the problem of low protection sensitivity when a high-resistance grounding fault occurs in a resonant grounding system, this paper proposes a fault location method based on the combination of dynamic time warping (DTW) distance and fuzzy C-means (FCM) clustering. By analyzing the characteristics of the zero-sequence current upstream and downstream of the fault point when a single-phase grounding fault occurs in the resonant grounding system, it is concluded that the waveform similarity on both sides of the fault point is low. DTW distance can be used to measure the similarity of two time series, and has the characteristics of good fault tolerance and synchronization error tolerance. According to the rule that the DTW value of faulty section is much larger than that of nonfaulty sections, FCM clustering is used to classify the DTW value of each section. The membership degree matrix and cluster centers are obtained. In the membership degree matrix, the section corresponding to the data in a class of their own is the faulty section, and all other data correspond to the nonfaulty section; otherwise, it is a fault occurring at the end of the line. The simulation results of MATLAB/Simulink and the field data test show that the method can accurately locate the faulty section.

A flexible grounding system is a system in which the neutral point of the power supply is grounded via the arc suppression coil in parallel with a low-resistance resistor. When operating normally or a temporary ground fault occurs, the arc suppression coil is used for grounding, whereas the small resistance is switched on when a permanent ground fault occurs. At present, the problem of low protection sensitivity when a high-resistance ground fault occurs in a flexible grounding system has not been solved yet. According to the characteristics of low waveform similarity between the faulty line and the non-faulty line when a single-phase grounding fault occurred, a new faulty line selection method based on a combination of Dynamic Time Warping (DTW) distance and the transient projection method is proposed in this paper. Firstly, the fault transient signal is extracted by a digital filter as a basis for faulty line selection. Secondly, the transient zero-sequence current of each line is projected onto the busbar transient zero-sequence voltage, and the projected DTW distance of each line is calculated. Finally, according to the calculation formula of waveform comprehensive similarity coefficient, the Comprehensive DTW (CDTW) distance is obtained, and the top three CDTW distance values are selected to determine the faulty line. If the maximum value is greater than the sum of the other two CDTW distance values, the line corresponding to the maximum value is judged as the faulty line; otherwise, it is judged as a busbar fault. The simulation results based on MATLAB/Simulink and field data test show that the method can accurately determine the faulty line under diverse fault conditions.