Iet Generation Transmission & Distribution最新文献

The linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to efficiently perform operations and scheduling. In addition, it is difficult to realize the online update of parameters in the existing LPF models. The model retraining brings serious data burden and inefficiency. To serve these applications and comply with requirements, a brand new LPF model is proposed in this paper. A quadratic power flow model is trained by regression learning first, and then the proposed LPF model is derived by Taylor expansion. After only one initial regression learning, the proposed LPF model no longer needs retraining when updated. The refreshed parameter is simply updated online according to the real-time measurement data, which improves the generalization ability. In conclusion, the proposed LPF model is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies these superiorities.

The increased penetration of renewable energy sources and the intensification of peak-valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation-side resources proves to be challenging. Large-scale fixed frequency air-conditioning (FFAC) and inverter air-conditioning (IAC) are high-quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air-conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power-constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power-constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs.

Due to the increasing share of inverter-based renewable energy sources and the increased retrofitting of conventional hydropower plants with power electronics, novel possibilities arise for providing damping power in case of occurring inter-area oscillations in power systems. This paper presents a complete model of a variable-speed hydropower plant (VSHP) in a single-machine infinite bus system. The demonstrated analytical model is applied for small-signal stability analysis that contains all relevant converter dynamics and is verified by an additional non-linear time-domain simulation. The damping controller approach located in the full-size converter (FSC) consists of the classical power system stabilizer with additional phase compensation, to which an appropriate wide-area input signal is fed via a phasor measurement unit device. Subsequently, the FSC-VSHP with additional damping algorithm is examined with respect to the significantly higher damping performance with regard to inter-area modes in a three-machine model.

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high-fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current-based method.

To better detect targets that may cause external damage to transmission lines, the authors present You Only Look Once-Asymptotic Feature Pyramid Network (YOLO-AFPN), a lightweight but efficient model. Firstly, the authors adopt a feature comparison strategy based on the knowledge of transmission line scenes, which facilitates increased attention to target features during the training. Secondly, the YOLOv8 detection network is built, and the backbone adds three layers of simple parameter-free attention module, which extracts features while maintaining lightness, and improves the detection capability in complex scenarios. Then, in the feature fusion stage, an AFPN is constructed, which improves the multi-scale target detection capability while reducing the number of model parameters by asymptotically fusing features that have small semantic gaps between neighbouring layers. When during the training process, the improved Mosaic data augmentation method is used to enhance the number of distributions of small targets, improve the robustness of the model. Finally, the improved model is validated, and the experimental results show that the improved model can achieve mean average precision of 86.1% at 6.6 MB, which is better than the original network for detection and meets the requirements for deployment on edge devices.

Contemporary distribution networks can be seen with diverse dispatchable and non-dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non-dispatchable resources can provide several techno-economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a-priori thus guides the artificial intelligence-based solution technique for sequential optimization. This may also reduce the computational burden of complex real-life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33-bus test distribution system highlights the importance of the proposed methodology.

A machine learning-based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication-less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.

This paper proposes a new multistage AC model for transmission expansion planning that finds an optimal combination of transmission lines, fault current-limiting high-temperature superconducting cables, and multiple distributed generations (DGs). On this basis, the proposed model, from a new perspective, allows for simultaneous improvement of the short-circuit level and grid-scale flexibility (GFLX) under both normal and fault conditions. The objective function to be minimized includes not only the net present worth of the total investment and operation costs but also the congestion-induced GFLX degradation measure. This model also takes the AC power balance and flow relationships, equipment capacity limits, nodal voltage bounds, DG penetration level limit, as well as discrete logical and financial restrictions together into account with the short-circuit level constraint. To overcome the complexity of solving the resultant non-convex mixed-integer non-linear optimization problem, a multi-objective integer-coded melody search algorithm is employed, followed by a fuzzy satisfying decision-making mechanism to obtain the final optimal solution. The exhaustive case studies conducted on the IEEE 24- and 118-bus test systems verify the efficacy of the newly developed model in terms of cost-effectiveness, flexibility, and short-circuit level suppression when facing different normal and fault conditions.

The global warming crisis, together with environmental concerns, has already led governments to replace traditional fossil-fuel cars with low-emission Electric Vehicles (EVs). This replacement has resulted in the addition of a large number of EVs capable of connecting to the grid. A coordinated charging method is needed to promote EVs on a large scale while preventing grid overloading. In this work, EVs are categorized based on their battery capacity, as cars, trucks, and buses. A charging/discharging mechanism based on Real Time Pricing for each hour is developed for a 20-min block that has been formed. The locations of EV charging stations have been identified using Voltage Stability Index.

In the present work, the power loss when Distributed Generator (DG) units are deployed after the EVs are located in the system for charging and discharging is analyzed. The DGs are considered as solar-based or wind-based generators for studying the economic benefit of such an investment under two different weather scenarios and topologies on standard test systems. The results thus obtained give an insight into the various technical and economic benefits.

This paper introduces a virtual consensus-based wide area differential protection method through cooperative control concepts and graph theory. Using multi-agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide-area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide-area supervisor protection for distance relays. Performance evaluations on an IEEE 39-bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates.