Iet Generation Transmission & Distribution最新文献

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.

To devise more scientifically rational interruptible load contracts, this paper introduces a novel model for interruptible load contracts within modern electric power systems, grounded in data mining techniques. Initially, user characteristics are clustered using data mining technology to determine the optimal number of clusters. Building on this, the potential for different users to participate in interruptible load programs is analysed based on daily load ratios, yielding various user-type parameters. Furthermore, the paper develops an interruptible load contract model that incorporates load response capabilities, enhancing the traditional interruptible load contract model based on principal-agent theory through considerations of user type parameters and maximum interruptible load limits. The objective function, aimed at maximizing the profits of the electric company, is solved, and lastly, through the use of real data, a case study analysis focusing on commercial users with the strongest load response capabilities is conducted. The results affirm the efficacy of the proposed model.

This research introduces a novel damped alternating current (DAC) testing methodology, which integrates an enhanced DAC generator with a pulse-based distributed partial discharge (PD) detection technique. The advanced DAC generator is designed without the need for high voltage (HV) solid-state switches, thereby offering a cost-effective solution for field-testing voltage generation through a direct current–alternating current conversion approach. To meet the demand for high instantaneous power, a capacitor bank is employed as the power supply. Furthermore, the implementation of a distributed PD detection technique enhances sensitivity and eliminates limitations associated with cable length. To minimize the construction costs of the distributed PD-detection system, a pulse synchronization technique has been employed. Efforts to reduce the system's weight were informed by simulations, resulting in the design and development of a prototype weighing 850 kg for a 64/110 kV power cable. The reduction in the number of HV solid-state switches contributes to significant cost savings, amounting to tens of thousands of dollars, when compared to conventional DAC generators. Laboratory and field tests validated the effectiveness of the cost-efficient DAC testing methodology.

The high adoption of electric vehicles (EVs) and the rising need for charging power in recent years calls for advancing charging service infrastructures and assessing the readiness of the power system to cope with such infrastructures. This paper proposes a novel model for the integrated operation of dynamic wireless charging (DWC) and power distribution systems offering charging service to in-motion EVs. The proposed model benefits from a hierarchical design, where DWC controllers capture the traffic flows of in-motion EVs on different routes and translate them into estimations of charging power requests on power distribution system nodes. The charging power requests are then communicated with a central controller that monitors the distribution system operation by enforcing an optimal power flow model. This controller coordinates the operation of distributed energy resources to leverage charging power delivery to in-motion EVs and mitigate stress on the distribution system operation. The proposed model is tested on a test distribution system connected to multiple DWC systems in Salt Lake City, and the findings demonstrate its efficiency in quantifying the traffic flow of in-motion EVs and its translation to charging power requests while highlighting the role of distributed energy resources in alleviating stress on the distribution system operation.

To address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short-term power prediction for distributed photovoltaic (PV) farms, a TPE-CBiGRU-SCA model based on multiscale feature fusion is proposed. First, multiscale feature fusion of meteorological features, temporal features, and hidden periodic features is performed in PV power to construct the model input features. Second, the relationships between PV power and its influencing factors are modelled from spatial and temporal scales using CNN and Bi-GRU, respectively. The spatiotemporal features are then weighted and fused using the SCA attention mechanism. Finally, TPE-based hyperparameter optimization is used to refine network parameters, achieving PV power prediction for a single field station. Validation with data from a PV field station shows that this method significantly enhances feature extraction comprehensiveness through multiscale fusion at both data and model layers. This improvement leads to a reduction in MAE and RMSE by 26.03% and 38.15%, respectively, and an increase in R2 to 96.22%, representing a 3.26% improvement over other models.

The cover image is based on the Article Disturbance observer-based finite-time control of a photovoltaic-battery hybrid power system by Fatemeh Esmaeili and Hamid Reza Koofigar, https://doi.org/10.1049/gtd2.13248.

The variability of renewable energy within microgrids (MGs) necessitates the smoothing of power fluctuations through the effective scheduling of internal power equipment. Otherwise, significant power variations on the tie-line connecting the MG to the main power grid could occur. This study introduces an innovative scheduling strategy that utilizes a data-driven approach, employing a deep reinforcement learning algorithm to achieve this smoothing effect. The strategy prioritizes the scheduling of MG's internal power devices, taking into account the stochastic charging patterns of electric vehicles. The scheduling optimization model is initially described as a Markov decision process with the goal of minimizing power fluctuations on the interconnection lines and operational costs of the MG. Subsequently, after preprocessing the historical operational data of the MG, an enhanced scheduling strategy is developed through a neural network learning process. Finally, the results from four scheduling scenarios demonstrate the significant impact of the proposed strategy. Comparisons of reward curves before and after data preprocessing underscore its importance. In contrast to optimization results from deep deterministic policy gradient, soft actor-critic, and particle swarm optimization algorithms, the superiority of the deep deterministic policy gradient algorithm with the addition of a priority experience replay mechanism is highlighted.

In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi-level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power-to-gas (P2G) and gas-to-power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy-sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14-bus power system and a 10-node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model.

To solve the problem of negative-sequence and reactive power in electrified railway, this paper proposes a comprehensive compensation method based on V/v transformer and electromagnetic single-phase var compensator (ESVC) for co-phase power supply. First, based on the principles of static var generator and single-phase rotary phase shifting transformer, the topology and model of ESVC have been established, and the compensation mechanism has been analysed. Second, the topological structure and mathematical model of co-phase power comprehensive compensation device (CPCD) are put forward based on the principles of V/v transformer and ESVC, and the operation mode of CPCD in multiple scenarios is designed. Then the strategy of CPCD with double closed loop control is analysed: corresponding compensation mode is selected according to negative-sequence unbalance degree and expected power factor value. In this strategy, the compensated current output by ESVC is taken as the quantity of external loop control, and the rotation angle of ESVC is taken as the quantity of internal loop control to realize double-closed loop control of CPCD. Finally, the comprehensive CPCD compensation model is built on the simulation platform and validated the accuracy of the mathematical model and the effectiveness of the control strategy.