Iet Generation Transmission & Distribution最新文献

This paper presents an analytical examination of the small-single stability (SSS) criterion of the permanent magnet synchronous generator (PMSG)-based wind power delivery system via voltage source converter-based high voltage direct current (VSC-HVDC). First, a small-signal model of the PMSG-based WPDS is developed. Then, the SSS criterion, driven by the phase-locked loop of the PMSG in the sub-synchronous timescale, is derived. The derived SSS criterion provides analytical insights into why and how the loading condition, the grid connection, and the control parameters affect the system’s SSS. It is unambiguously revealed that increasing loading conditions of the PMSG or/and the grid connection of the WPDS to VSC-HVDC shall bring about a higher risk of oscillatory instability. Hence, analytical derivation of the SSS criterion helps better understand the instability mechanism in the PMSG-based WPDS via VSC-HVDC. In addition, while the derivation of the SSS criterion presupposes identical dynamics among PMSGs, this derived criterion can still be approximately utilized to assess the SSS of the PMSG-based WPDS via VSC-HVDC, irrespective of whether the dynamics of the PMSGs are similar or different. Finally, the SSS criterion is demonstrated and evaluated through three examples of the PMSG-based WPDS via VSC-HVDC.

After large-scale distributed power sources are connected to the distribution network, the fault current undergoes noticeable changes, affecting the accuracy of traditional single-phase grounding fault algorithms. Therefore, the objective of this paper is to address the impact of distributed power integration on the grounding algorithms of distribution networks. The main contribution is: by establishing an electrical system structure and model for distribution network grounding faults that include distributed generation (DG), theoretically deriving and calculating the transient zero-sequence current frequency changes at the moment of fault, analysing the directional characteristics of zero-sequence currents under DG connection conditions, designing a local grounding fault judgment algorithm based on energy extremum direction, and providing a fault judgment and isolation process for grounding fault monitoring devices. The results show: through simulation and experimentation, the algorithm was tested, and the method can reliably judge grounding faults under various transition resistances, different numbers and capacities of connected distributed power sources, and different grounding switch-on angles. The applicability of the algorithm covers both methods of neutral grounding through an arc suppression coil and ungrounded neutrals, adapting to scenarios with or without DG connections.

The increasing penetration of power-electronics interfaced resources brings new challenges regarding the small-signal stability of power systems. To address this issue, grid-forming (GFM) controlled converters have emerged as an alternative to their conventional grid-following counterparts. This paper investigates the mechanisms behind converters driven stability and quantifies the stabilizing effect of GFM controls. The linearized state-space model of different combinations of control strategies is analysed in a multi-infeed system considering various operating points. Through a parametric sensitivity study and an examination of the participation factors of key eigenvalues of the linearized models, it is confirmed that GFM controls contribute to system stabilization. Moreover, this paper demonstrates that this stabilizing effect varies significantly depending on the specific GFM control implemented: whether a current control loop is used or not notably impacts stability.

Increasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive-based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost-saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive-based demand response programming.

The introduction of cloud-native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud-native PDECT are studied, and the modelling and simulation of cloud-native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self-defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

The recognition of the transient dominant instability mode is of great significance for rapidly and accurately formulating transient emergency decisions in power systems. In response to the challenge of accurately distinguishing between angle instability and voltage instability, which are coupled in actual power grids, this paper explores the mapping relationship between simulation data and the stable state of the system, as well as the dominant instability mode. The method enables real-time identification of the dominant instability mode, which bypasses complex physical mechanisms. Firstly, spatio-temporal feature mining is conducted, where convolutional neural networks are employed to learn crucial local features of transient curves, and bidirectional gated recurrent unit s utilized to learn transient features over time sequences. Next, a multihead attention mechanism is introduced to enhance sensitivity to important time steps in the sequence data. Finally, the transit search optimization algorithm optimizes the global model parameters, further increasing the accuracy of the model. Using the IEEE 10-machine and 39-node system as an example for simulation, the results validate that the proposed method exhibits significant advantages in terms of accuracy and applicability compared with other machine learning methods.

The escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta-heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio-dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Maintenance of power transmission lines is essential for the safe and reliable operation of the power grid. The use of deep learning-based networks to improve the performance of power line defect detection faces significant challenges, such as small target sizes, shape similarities, and occlusion issues. In response to these challenges, a transformer-based end-to-end power line detection network called Power-DETR is introduced. Initially, building upon Deformable DETR, a large pre-trained model (Swin-large) is utilized to increase the number of multi-scale features, and activation checkpoint technology is applied to ensure effective training within limited memory capacity. Subsequently, a contrastive denoising training strategy is integrated to combat ambiguity and instability of the Hungarian matching algorithm during training, aiming to expedite model convergence. Additionally, a hybrid label assignment strategy combining OHEM and cost-based ATSS is proposed to provide the model with high-quality queries, ensuring adequate training for the decoder and enhancing encoder supervision. Experimental results substantiate the efficacy of the proposed Power-DETR model as a novel end-to-end detection paradigm, surpassing both one-stage and two-stage detection models. Furthermore, the model demonstrates a significant 15.7% enhancement in mAP0.5 compared to the baseline.

Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber-attackers. In this study, a cyber-resilient hybrid deep learning-based model is developed that accurately forecasts electric load in short-term and long-term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE-GAN. In the proposed framework, SMDAE layer is used to pre-process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE-GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real-world noise and damaged data under false data injection attacks.

In an era characterized by the rapid proliferation of distributed flexible resources (DFRs), the development of customized energy management and regulation strategies has attracted significant interest from the field. The inherent geographical dispersion and unpredictability of these resources, however, pose substantial barriers to their effective and computationally tractable regulation. To address these impediments, this paper proposes a deep reinforcement learning-based distributed resource energy management strategy, taking into account the inherent physical and structural constraints of the distribution network. This proposed strategy is modelled as a sequential decision-making framework with a Markov decision process, informed by physical states and external information. In particular, targeting the community energy management system for critical public infrastructure and community holistic benefits maximization, the proposed approach proficiently adapts to fluctuations in resource variability and fluctuating market prices, ensuring intelligent regulation of distributed flexible resources. Simulation and empirical analysis demonstrate that the proposed deep reinforcement learning-based strategy can improve the economic benefits and decision-making efficiency of distributed flexible resource regulation while ensuring the security of distribution network power flow.