Global Energy Interconnection最新文献

Amidst the swift advancement of new power systems and electric vehicles, inverter-fed machines have progressively materialized as a pivotal apparatus for efficient energy conversion. Stator winding turn insulation failure is the root cause of inverter-fed machine breakdown. The online monitoring of turn insulation health can detect potential safety risks promptly, but faces the challenge of weak characteristics of turn insulation degradation. This study proposes an innovative method to evaluate the turn insulation state of inverter-fed machines by utilizing the fractional Fourier transform with a Mel filter (FrFT-Mel). First, the sensitivity of the high-frequency (HF) switching oscillation current to variations in turn insulation was analyzed within the fractional domain. Subsequently, an improved Mel filter is introduced, and its structure and parameters are specifically designed based on the features intrinsic to the common-mode impedance resonance point of the electrical machine. Finally, an evaluation index was proposed for the turn insulation state of inverter-fed machines. Experimental results on a 3kW permanent magnet synchronous machine (PMSM) demonstrate that the proposed FrFT-Mel method significantly enhances the sensitivity of turn insulation state perception by approximately five times, compared to the traditional Fourier transform method.

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform (EWT) for a through- type cophase traction direct power supply system, where both sides of a traction network line exhibit a distinctive boundary structure. This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals, resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line. During internal line faults, the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines. Conversely, for faults external to the line, the energy is lower compared to adjacent lines. EWT is employed to decompose the collected fault current signals, allowing access to the high-frequency transient energy. The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary. Moreover, simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles, distances to faults, and fault transition resistances.

The rapid growth of the Chinese economy has fueled the expansion of power grids. Power transformers are key equipment in power grid projects, and their price changes have a significant impact on cost control. However, the prices of power transformer materials manifest as nonsmooth and nonlinear sequences. Hence, estimating the acquisition costs of power grid projects is difficult, hindering the normal operation of power engineering construction. To more accurately predict the price of power transformer materials, this study proposes a method based on complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) network. First, the CEEMD decomposed the price series into multiple intrinsic mode functions (IMFs). Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF. Then, an empirical wavelet transform (EWT) was applied to the aggregation sequence with a large sample entropy, and the multiple subsequences obtained from the decomposition were predicted by the GRU model. The GRU model was used to directly predict the aggregation sequences with a small sample entropy. In this study, we used authentic historical pricing data for power transformer materials to validate the proposed approach. The empirical findings demonstrated the efficacy of our method across both datasets, with mean absolute percentage errors (MAPEs) of less than 1% and 3%. This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Integrated energy systems (IESs) can improve energy efficiency and reduce carbon emissions, essential for achieving peak carbon emissions and carbon neutrality. This study investigated the characteristics of the CHP model considering P2G and carbon capture systems, and a two-stage robust optimization model of the electricity-heat-gas- cold integrated energy system was developed. First, a CHP model considering the P2G and carbon capture system was established, and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived, which proved that the model could weaken the electric-thermal coupling characteristics, increase the electric power regulation range, and reduce carbon emissions. Subsequently, a two-stage robust optimal scheduling model of an IES was constructed, in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs. The objective function in the real-time scheduling stage was to minimize the equipment operating costs, carbon emission costs, wind curtailment, and solar curtailment costs, considering multiple uncertainties. Finally, after the objective function is linearized with a ψ-piecewise method, the model is solved based on the C&CG algorithm. Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

With the development of green data centers, a large number of Uninterruptible Power Supply (UPS) resources in Internet Data Center (IDC) are becoming idle assets owing to their low utilization rate. The revitalization of these idle UPS resources is an urgent problem that must be addressed. Based on the energy storage type of the UPS (EUPS) and using renewable sources, a solution for IDCs is proposed in this study. Subsequently, an EUPS cluster classification method based on the concept of shared mechanism niche (CSMN) was proposed to effectively solve the EUPS control problem. Accordingly, the classified EUPS aggregation unit was used to determine the optimal operation of the IDC. An IDC cost minimization optimization model was established, and the Quantum Particle Swarm Optimization (QPSO) algorithm was adopted. Finally, the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

The penetration of new energy sources such as wind power is increasing, which consequently increases the occurrence rate of subsynchronous oscillation events. However, existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features, such as frequency drift, caused by the random volatility of wind farms when oscillations occur. This paper proposes a subsynchronous oscillation source-localization method that involves an enhanced short-time Fourier transform and a convolutional neural network (CNN). First, an enhanced STFT is performed to secure high-resolution time-frequency distribution (TFD) images from the measured data of the generation unit ports. Next, these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model. Ultimately, the trained CNN model realizes the online localization of subsynchronous oscillation sources. The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform. Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms, thus providing a foundation for oscillation suppression in practical engineering scenarios.

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system. Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency. This study was conducted along two main axes. The first axis focuses on designing a digital twin for a residential community microgrid platform. This phase involves data collection, cleaning, exploration, and interpretation. Moreover, it includes replicating the functionality of the real platform and validating the results. The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid, while maintaining the concept of peer-to-peer energy trading. Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid. Meanwhile, a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit. This is achieved by optimizing battery usage using a cost optimization solution. The proposed solution is validated using the developed digital twin.