Iet Generation Transmission & Distribution最新文献

This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

To address the challenges posed by frequent source and load fluctuations in existing loop closing current calculation methods, this paper proposes an online loop closing current calculation method that considers source and load uncertainties. First, a dual-stack dynamic monitoring system is utilized to obtain real-time voltage and current variations before and after disturbances. Second, Thevenin's theorem is employed to build an equivalent model of the distribution network, simplifying the complex network into a combination of an independent voltage source and a series impedance. Then, the steady-state loop closing current is calculated based on the open-circuit voltage and equivalent impedance at both sides of the loop closing point. Next, the optimal frequency method is applied to determine the equivalent impedance and attenuation time constant at a specific frequency, achieving accurate calculation of the transient loop closing current. Finally, simulations are conducted to model the fluctuations in distributed generation and load, analysing the steady-state and transient loop closing currents. The simulation results demonstrate that the proposed method accurately captures the effects of source and load fluctuations on the loop closing current in dynamic environments, with minimal calculation error, indicating its high practicality.

To mitigate subsequent commutation failure (SCF) in line-commutated converter-based high-voltage direct current transmission systems, the response and limitations of the control system are analysed. The results reveal that due to the prolonged negative deviation of the extinction angle during recovery, the integral output of the proportional–integral controller in constant extinction angle (CEA) significantly reduces the advanced firing angle order, even below normal operating angle. This directly leads to a delayed transition between control strategies and diminishes the effectiveness of CEA. Furthermore, the influence of fault severity, fault type, and AC system strength on SCF is examined. Based on these findings, an enhanced CEA control method to suppress SCF is proposed. By setting an expected firing angle and adaptively adjusting the integral parameter, the control strategy switching moment can be advanced, improving the CEA's control margin and enhancing capability. Moreover, introducing a notch filter reduces firing angle fluctuations and strengthens CEA's ability to suppress SCF. Finally, the theoretical analysis and the effectiveness of the proposed optimization method are validated.

The integration of renewable energy sources and the increasing demand for reliable power have posed significant challenges in the design and operation of distribution networks under uncertain conditions. The inherent variability in renewable energy generation and fluctuating consumer load demand requires advanced strategies for Distributed Energy Resources (DERs) allocation and sizing to enhance grid resilience and operational efficiency. This article introduces an innovative framework for optimizing distribution network design under these uncertainties. The approach integrates deep learning-assisted Distributionally Robust Optimization (DRO) with Generative Adversarial Networks (GANs) to dynamically model and manage the inherent variability in renewable sources and demand fluctuations. Employing a combination of nonlinear optimization techniques and advanced statistical methods, the framework robustly optimizes network configurations to minimize losses and improve voltage stability. The model's efficacy is rigorously tested on the IEEE 33-bus system, achieving a 15% reduction in power distribution losses and a 20% improvement in voltage stability compared to traditional models. Utilizing open-source computational tools, the method not only boosts operational reliability and efficiency but also adapts effectively to the increasing integration of volatile renewable energy sources. These results underscore the framework's potential as a scalable and robust solution for modern power network design challenges.

Grid-forming (GFM) inverters are anticipated to play an essential role in facilitating the integration of renewable energy in bulk power systems. The fault response of GFM inverters and its impact on traditional protection schemes are ongoing research topics. Distance protection is today one of the most commonly applied protection schemes and depends on multiple system preconditions for reliable operation—many of which may no longer hold in systems with a high penetration of inverters. This paper investigates the impacts of GFM inverters on distance protection, with the main objective of providing an improved understanding of the topic. Important interoperability issues are highlighted with simulation results and elaborated upon based on the theory behind the distance relay model and the behaviours of GFM inverters during faults. The simulations consider numerous fault types and two GFM inverters with different current-limiting control techniques in their fault-ride through strategies. Results indicate several challenges that state-of-the-art distance relays may face with GFM inverters.

As the demand for frequency regulation resources in power systems increases, collaborative optimization of flexible resources with rapid frequency regulation response capabilities, particularly by enabling scalable prosumers in local areas to participate in the frequency regulation ancillary service market, can effectively enhance safety, stability, and frequency regulation ability of power system. Therefore, this paper first establishes a collaborative optimization framework for scalable prosumers in frequency regulation and describes the operation model of prosumers. Considering the uncertainties that can impact prosumers' power decisions during frequency regulation, a scenario-augmented dataset generation method based on a denoising diffusion probabilistic model is proposed to improve decision adaptability under extreme scenarios with insufficient regulation capabilities. Additionally, to enhance the scalability and applicability of the training method in scalable prosumer collaborative optimization scenarios, a multi-agent attention proximal policy optimization algorithm combined with a global attention mechanism is introduced. The effectiveness of the proposed method in improving decision timeliness, operation benefits, scalability, and policy adaptability during scalable prosumers’ participation in frequency regulation ancillary services is validated using the IEEE standard node test system under various scales and scenarios.

Wind energy systems require fault diagnosis that identifies faults despite data inconsistencies. This study addresses challenges in supervisory control and data acquisition (SCADA) systems for monitoring wind turbine conditions from imbalanced data representation and error vulnerability. It examines the efficacy of adaptive elite-particle swarm optimization (AEPSO)-tuned extreme gradient boosting (XGBoost) on an imbalanced SCADA dataset for wind turbine fault classification. The methodology integrates the resampled dataset with t-distributed stochastic neighbour embedding represented deep learning features. Employing AEPSO-XGBoost classifier trained on merged SCADA and deep learning data from a physics-informed deep convolutional neural network forms the basis of the fault (alarm) classification model. The AEPSO-XGBoost regressor is validated across three distinct rear bearing temperature datasets, facilitating parameter optimization and model robustness. Also, this study explores supervised and unsupervised anomaly detection models using PDCNN and AEPSO-XGBoost with rear-bearing temperature data. Findings exhibit substantial fault classification and prediction enhancements by merging resampled SCADA data with deep learning features. Moreover, results show that applying AEPSO-XGBoost can significantly improve anomaly detection metrics. Through AEPSO-XGBoost's efficacy in enhancing fault prediction within imbalanced SCADA datasets, the study proposes an integrated framework for fault classification and anomaly detection as an innovative predictive maintenance system for wind energy systems.

This article presents the stability analysis of a resonant grounded power distribution system (RGPDS) in which a nonlinear model predictive controller (NMPC) with a nonlinear extended state observer (NLESO) is used to achieve the desired fault current compensation through the residual current compensation (RCC) inverters. The detailed model of the system is developed to appropriately model nonlinearities so that these can be represented as an extended state and estimated using the ESO. The closed-loop model of the system is developed in the discrete-time to conduct the frequency-domain analysis using two different methods, for example, the describing function (DF) method and Tsypkin criterion. The main target of these analyses is to gain useful insights on different control parameters and get an idea about their effects on the overall stability of the RGPDS with an REFCL. The relationship between the Nyquist plot and the trajectory of the key control parameter is used to determine the boundary up to which the control parameter can be adjusted to maintain the stability of the system. Finally, it is identified that the Tsypkin criterion allows more flexibilities compared to the DF method for selecting the control parameter.

In line with the latest protection configuration requirements for 35 or 10 kV distribution grids, current differential protection is recommended for distribution lines connected to photovoltaic power sources. However, unlike traditional synchronous generators, photovoltaic power sources provide fault currents with amplitudes typically less than 1.2 to 2 times the rated current, and their phase angles are controlled and capacitive. This often results in the differential current being insufficient to trigger the current differential protection even during internal faults. This paper analyzes the issues with applying traditional current differential protection to photovoltaic power sources connected lines and deduces the threshold for the ratio restraint coefficient. An adaptive protection strategy is proposed, where the restraint coefficient is adjusted based on the amplitude ratios and phase angle differences of the fault currents. This ensures correct operation, particularly in cases of high photovoltaic power sources penetration. The strategy is tested through simulations conducted in Power Systems Computer Aided Design (PSCAD), showing improved sensitivity compared to traditional methods.

Dynamic reactive power sources can efficiently address the voltage stability problem of a wind-penetrated power system and the possibility of cascading failures, which are caused by the decreasing inertia and the increasing complexity of the system dynamics. However, their applications are limited by the high investment cost. Identifying appropriate buses for the deployment can improve the economy and efficiency of reactive power source configuration and reduce the complexity of deployment models. Here, a new metric is proposed to guide the selection of candidate buses, based on the improved spectral learning technique and the quantitative assessment of the short-term voltage stability. Specifically, a new short-term voltage stability metric is developed to assess the dynamic voltage responses of different stages after a contingency. Then, an improved spectral learning algorithm with objective priorities assigned to different buses is used for the bus selection, aiming to identify the most influential buses, in terms of short-term voltage stability and propagation potentials. A two-dimensional decision-making methodology is proposed, considering both the capacity sensitivity and the bus's structural characteristics. The effectiveness of the proposed methodology is validated on a New England 39-bus system using an electromechanical transient model.