Iet Generation Transmission & Distribution最新文献

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.

Currently, for distribution line voltage measurement, there is no effective and convenient dynamic calibration method of coupling capacitance for capacitive coupling-based sensors. Therefore, a differential non-contact voltage measurement method based on harmonic injection is proposed in this paper. Based on the single-probe measurement, a shield is used to reduce the interference of external stray capacitance on the measurement; the probe is improved and a differential circuit structure is introduced, which eliminates the input capacitance of the op-amp; then harmonics are injected into the measurement circuit, and the discrete Fourier transform is used to extract the fundamental frequency and harmonic components in the response signal, and the coupling capacitance parameter is solved by the ratio of the harmonic source to the harmonic response signal and then substituting it into the fundamental equation to achieve the dynamic calibration of the coupling capacitance and fundamental response signal, and the coupling capacitance dynamic calibration and fundamental response signal dynamic calibration, capacitance dynamic calibration and the measurement of fundamental voltage signal; finally, the measurement method proposed in the article can meet the measurement requirements of a 400 V distribution network as shown in the simulation results, and the maximum voltage measurement error is less than 0.4%.

The conventional DC bus signaling (DBS) coordination control strategy for islanded DC microgrids (IDCMGs) faces challenges in coordinating multiple distributed generators (DGs) and fails to effectively incorporate the state of charge (SOC) information of the energy storage system, reducing system flexibility. In this article, a two-layer fuzzy control-based coordination strategy is proposed for multi-PV islanded DC microgrids. The first layer fuzzy logic controller (FLC) quantifies and selects the optimal system operating mode, adaptively adjusting the number of PV units operating in maximum power point tracking (MPPT) mode to manage system power surplus or deficit, thereby simplifying system design and enhancing flexibility. The second layer FLC adaptively adjusts the output of distributed energy sources based on SOC and current system conditions to better align the energy storage system's output with overall system operation, resulting in at least a 4% improvement in SOC level and effectively preventing overcharging or over-discharging issues seen in traditional control. Additionally, for PV units operating in droop mode, the droop coefficient is recalculated based on their maximum generation capacity under changing external conditions, thereby achieving more efficient power distribution and preventing system instability caused by power exceeding the limits of individual PV units. Finally, the effectiveness of the proposed control strategy is validated through RT-lab hardware-in-the-loop (HIL) simulations.

In modern power systems with high levels of distributed generation (DG), traditional protection schemes face challenges in ensuring reliable and efficient fault detection due to the complexities introduced by DG, particularly low-inertia sources such as wind power. This paper presents an advanced protection scheme that integrates voltage relays (VRs) rather than overcurrent relays (OCRs) to improve coordination with distance relays (DRs) and enhance fault detection across multiple protection zones. By utilizing voltage measurements instead of conventional current-based methods, the proposed scheme addresses issues such as low fault currents and mis-coordination, which are common in DG-integrated systems. The VR-DR coordination improves system reliability by increasing fault detection sensitivity and selectivity, reducing the risk of mis-coordination, and minimizing reliance on potentially inconsistent current measurements. VRs trigger faster fault isolation by operating before backup DRs, thus improving overall response times and system resilience. The VR scheme significantly outperforms traditional overcurrent relay schemes, with tripping times ranging from 0.002 to 0.956 s, compared to 0.035 to 1.184 s in the traditional scheme for three-phase faults in a CIGRE power network. Additionally, the total tripping time is reduced from 10.5 s in the traditional scheme to 3.2 s with the VR scheme in networks with DGs under line to line to ground fault. The study demonstrates that no mis-coordination events occurred with DRs in zone two, further emphasizing the effectiveness and reliability of the VR scheme. This innovative approach offers substantial improvements in fault management, ensuring quicker fault resolution and enhanced system stability in modern, DG-integrated power grids.

Fair sharing of carbon responsibility is crucial to achieve the goal of low-carbon transformation and dual-carbon power system. In response to the current issue of a certain proportion of joint responsibility between source and load, it does not comply with the principle of “who causes who bears”. This thesis proposes a source storage transport coordinated planning method for allocating carbon responsibility from a planning perspective. Firstly, based on the basic principle of “who causes, who bears”, it classifies and holds accountable various stakeholders, establishes reasonable and fair allocation principles including reward and punishment indicators, and a dual carbon responsibility cost allocation rule for electricity balance and power balance in the power system; Secondly, based on the previous works, a source storage network joint planning model which comprehensively considers the investment cost of conventional thermal power installation, energy storage investment, wind abandonment penalty cost, transmission line expansion cost, and carbon emission responsibility cost is constructed. Finally, taking the Garver-6 node system as an example, the effectiveness and feasibility of the proposed method were verified by comparing it with the source storage and transportation planning method that does not consider carbon responsibility allocation.