Global Energy Interconnection最新文献

Insulation failure significantly contributes to the unpredictable shutdown of power equipment. Compared to the partial discharge and high-frequency (HF) injection methods, the HF common-mode (CM) leakage current method offers a non-intrusive and highly sensitive alternative. However, the detection of HF CM currents is susceptible to interference from differential-mode (DM) currents, which exhibit high-amplitude and multifrequency components during normal operation. To address this challenge, this paper proposes a double-ring current sensor based on the principle of magnetic shielding for inverter-fed machine winding insulation monitoring. The inner ring harnesses the magnetic aggregation effect to isolate the DM current magnetic field, whereas the outer ring serves as the magnetic core of the Rogowski current sensor, enabling HF CM current monitoring. First, the magnetic field distributions of the CM and DM currents were analyzed. Then, a correlation between the sensor parameters and signal-to-noise ratio of the target HF CM current was established. Finally, an experimental study was conducted on a 3-kW PMSM for verification. The results indicate that the proposed double-ring HF CM sensor can effectively mitigate DM current interference. Compared to a single-ring sensor, a reduction of approximately 40% in the DM component was achieved, which significantly enhanced the precision of online insulation monitoring.

With the expansion and implementation of rural revitalization strategies, there is a constant need for new energy sources for the construction of new townships. Consequently, integrated energy systems with the interconnection and interaction of multiple energy sources are developing rapidly. Biomass energy, a renewable green energy source with low pollution and wide distribution, has significant application potential in integrated energy systems. Considering the application of biomass energy in townships, this study established an integrated biomass energy system and proposed a model to optimize its operation. Lowest economic cost and highest clean energy utilization rate were considered as the objective functions. In addition, a plan was suggested to adjust the heat-electricity ratio based on the characteristics of the combined heat and power of the biomass. Finally, a simulation analysis conducted for a town in China was discussed, demonstrating that the construction of a township integrated-energy system and the use of biomass can significantly reduce operating costs and improve the energy utilization rate. Moreover, by adjusting the heat-electricity ratio, the economic cost was further reduced by 6.70%, whereas the clean energy utilization rate was increased by 5.14%.

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

To address the impact of wind-power fluctuations on the stability of power systems, we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system. First, we employ a strategy that restricts long- and short-term power output deviations to smoothen wind power fluctuations in real time. Second, we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise (SW-ICEEMDAN) strategy to achieve real-time decomposition of the energy storage power, facilitating internal power distribution within the hybrid energy storage system. Finally, we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components. Through simulation validation, we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power. Compared with traditional empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition strategies, the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal. Additionally, the state-of-charge of energy storage components fluctuates within a reasonable range, enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Renewable energy has garnered attention due to the need for sustainable energy sources. Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy. As the importance of wind energy grows, it can be crucial to provide forecasts that optimize its performance potential. Artificial intelligence (AI) methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction. This study explored the area of AI to predict wind-energy production at a wind farm in Yalova, Turkey, using four different AI approaches: support vector machines (SVMs), decision trees, adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANNs). Wind speed and direction were considered as essential input parameters, with wind energy as the target parameter, and models are thoroughly evaluated using metrics such as the mean absolute percentage error (MAPE), coefficient of determination (R²), and mean absolute error (MAE). The findings accentuate the superior performance of the SVM, which delivered the lowest MAPE (2.42%), the highest R² (0.95), and the lowest MAE (71.21%) compared with actual values, while ANFIS was less effective in this context. The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms, such as metaheuristic algorithms.

In light of the growing integration of renewable energy sources in power systems, the adoption of DC microgrids has become increasingly popular, due to its simple structure, having no frequency, power factor concerns. However, the dependence of DC microgrids on cyber-networks also makes them susceptible to cyber-attacks. Potential cyber- attacks can disrupt power system facilities and result in significant economic and loss of life. To address this concern, this paper presents an attack-resilient control strategy for microgrids to ensure voltage regulation and power sharing with stable operation under cyber-attack on the actuators. This paper first formulates the cyber-security problem considering a distributed generation based microgrid using the converter model, after which an attack-resilient control is proposed to eliminate the actuator attack impact on the system. Steady state analysis and root locus validation illustrate the feasibility of the proposed method. The effectiveness of the proposed control scheme is demonstrated through simulation results.

In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China, this study proposes an optimal dispatch approach for a rural multi-energy supply system (RMESS) considering virtual energy storage (VES). First, to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes, this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint. Subsequently, an RMESS model encompassing these two types of VES was formulated. This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations. Based on the above models, a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation. Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.

The increasing proportion of distributed photovoltaics (DPVs) and electric vehicle charging stations in low-voltage distribution networks (LVDNs) has resulted in challenges such as distribution transformer overloads and voltage violations. To address these problems, we propose a coordinated planning method for flexible interconnections and energy storage systems (ESSs) to improve the accommodation capacity of DPVs. First, the power-transfer characteristics of flexible interconnection and ESSs are analyzed. The equipment costs of the voltage source converters (VSCs) and ESSs are also analyzed comprehensively, considering the differences in installation and maintenance costs for different installation locations. Second, a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity. Within this framework, the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs, whereas the lower-level model optimizes the operating power of the VSCs and ESSs. The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II). The effectiveness of the proposed planning method is validated through an actual LVDN scenario, which demonstrates its advantages in enhancing PV accommodation capacity. In addition, the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed, demonstrating the adaptability of the proposed coordinated planning method.

The reliability analysis of vertically integrated protection devices is crucial for designing International Electrotechnical Commission (IEC) 61850-based substations. This paper presents the hardware architecture of a four-in- one vertically integrated device and the information transmission path of each function based on the functional information transmission chain of protection devices, measurement and control devices, merging units, and intelligent terminals. Additionally, a reliability analysis model of the protection device and its protection system is constructed using the fault tree analysis method while considering the characteristics of each module of the vertically integrated device. The stability probability of the protection system in each state is analyzed by combining the state-transfer equations of line and busbar protection with a Markov chain. Finally, the failure rate and availability of the protection device and its protection system are calculated under different ambient temperatures using a 110 kV intelligent substation as an example. The sensitivity of each device module is analyzed.

As the number of power terminals continues to increase and their usage becomes more widespread, the security of power systems is under great threat. In response to the lack of effective trust evaluation methods for terminals, we propose a trust evaluation model based on equipment portraits for power terminals. First, we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic. Second, we propose an exception evaluation method based on syntax and semantics. The key fields of each message are extracted, and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal. Thus, by combining the network flow order, syntax, and semantic analysis, an equipment portrait can be constructed to guarantee security of the power network terminals. We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time. Finally, the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate, as well as a higher real-time performance, which is more suitable for power terminals.