Iet Generation Transmission & Distribution最新文献

Parallel restoration is a common method to bring the power grid back online after a blackout. The initial step in this process involves sectionalizing the system into multiple subsystems to have an efficient restoration. In this regard, this paper proposes a new method. First, the generators are grouped so as to simultaneously minimize the differences in total maximum generation capacity among the subsystems and the unavailable energy capacity (UEC). Dijkstra's algorithm is then applied to calculate the shortest paths between generators. This helps in the identification of the bus groupings within the system. To determine tie lines, the status of other buses is assessed, and all possible solutions satisfying network constraints are identified. Sectionalizing is carried out by six key criteria: energy not supplied (ENS), maximum standing phase angle (SPA) among all subsystems, restoration time, subsystem recovery time balance index (H index), quality index (Q index), and power exchange among subsystems. The analytic hierarchy process (AHP), method which is a common approach for ranking and prioritizing criteria is used for the first time to determine the sectionalizing strategy based on these criteria. This paper considers time-varying load conditions and solves the sectionalizing problem independently for each hour of the day. Also, results are obtained and compared for two scenarios: with and without the integration of renewable energy sources. The New England 39-bus power system is used to validate the proposed method, in which the results demonstrate its superiority performance for network restoration in comparison with other methods.

The paper addresses the analytical calculation challenges for magnetic saturation current characteristics in high-voltage shunt reactors, considering the air-gapped core structure and nonlinear magnetisation characteristics under novel oscillatory conditions in renewable energy power systems. A piecewise analytical method is proposed to determine winding and neutral reactor currents based on magnetic circuit operational states. Electromagnetic equations are established for shunt reactors under various magnetic circuit states during oscillation. Using knee-point as state boundaries, piecewise solutions yield time-domain analytical expressions for winding and neutral reactor currents. On this basis, the influence of oscillatory parameters on the characteristic components of the winding current is analysed, and a method for determining the critical saturation oscillatory frequency and amplitude of the shunt reactor is proposed accordingly. Finally, a parameter-refined shunt reactor simulation model is constructed using the MATLAB/Simulink platform to validate the accuracy and effectiveness of the proposed method, as well as its adaptability and robustness under different operating conditions.

In recent years, smart distribution networks have developed rapidly. However, complex electrical equipment and multisource monitoring data present great challenges for efficient fault detection in distribution networks. Accordingly, this study designs a multistage fault diagnosis framework based on a modified convolutional neural network (MCNN). First, an adaptive multiresolution S-transform (MST) model is applied to detect the initiation and recovery times of feeder line faults efficiently. Then, feeder line fault waveforms are converted into 2D images on the basis of the results of MST and equal-interval sampling. Next, a convolutional neural network (CNN) combined with a parallel network is designed as a robust fault classifier. The structure of the classifier model can help enhance accuracy, while the modified activation function can achieve fast convergence. Finally, simulation data obtained from an IEEE model and field data collected from a city power system are used to validate the effectiveness and practicality of the proposed MCNN model. The average 10-fold cross-validation results of the fault diagnosis model based on MCNN are better than those of the CNN model in terms of related indicators. Meanwhile, the average 10-fold cross-validation accuracy of the proposed classifier based on simulation and field data is 97.3% and 95.6%, respectively.

The IEEE Std 519–2022 IEEE Standard for harmonic control in electric power systems is widely used for controlling the harmonics in the electric power system. It uses the total demand distortion (TDD) for current harmonic evaluation at the point of common coupling (PCC). According to IEEE 519–2022, current harmonic assessment for site installations with inverter-based resources (IBRs) or distributed energy resources (DERs) is based on a decision tree. As per the decision tree, any site installation with a combined rated site generation exceeding 10% of the annual average load demand shall utilize the total rated current distortion (TRD) method, as specified in IEEE Std 1547, for evaluating current harmonics. For a combined DER rating from 10% to 90% of annual average load demand, TRD evaluates the higher current harmonic distortion, leading to an additional/higher rating of harmonic mitigation devices (i.e., harmonic filters) to comply with the current harmonic limits. The authors of this paper propose a new index, called ‘total distortion (TD)’, to evaluate current harmonic distortion and overcome the shortcomings mentioned above. Modeling and harmonic analysis are performed in DigSILENT PowerFactory software to calculate harmonic distortions. The effectiveness of the TD over the TDD and TRD index is analysed for an example of an e-Bus charging station with a combined rated generation (i.e., DERs) of different sizes. Using the TD method for current harmonic evaluation minimizes the additional harmonic filter requirements (1 × 195 kVAr tuned for a 245 Hz maximum value) to comply with the IEEE Std 1547 limits, as compared to the TRD method. The authors propose a new decision tree for evaluating current harmonics at the site installation, considering both internal loads and DERs/IBRs, and recommend it to IEEE Std 519, IEEE Std 1547, and IEEE Std 2800.

Phase-locked loops (PLLs) are critical for synchronizing modular multilevel converter high voltage direct current (MMC-HVDC) systems with alternating current (AC) grids, yet their stability impacts remain underexplored. This study systematically compares the impacts of the synchronous reference frame-PLL (SRF-PLL) and the inertia PLL (IPLL) on small-signal stability and large-signal synchronization. A unified framework is adopted to enable unbiased comparison by aligning their bandwidths. A small-signal model, incorporating PLL dynamics, is developed, based on which PLL impacts are examined using impedance-based generalized Nyquist criteria. The analysis results show that system stability depends primarily on PLL bandwidth rather than structure. This reveals inherent limitations of PLL-based synchronization and motivates PLL-less designs for improving stability. To enhance large-signal synchronization, a phase correction pathway is introduced for the SRF-PLL. This approach improves synchronization while avoiding steady-state phase detection errors inherent to the IPLL. Finally, all results are verified by non-linear time-domain electromagnetic transient simulations in PSCAD. This work advances quantitative understanding of PLL characteristics and stability impacts in MMC-HVDC systems, providing motivation for adopting PLL-less system designs.

Accurate evaluation of typical industry customers’ demand response potential (DRP) is of great significance for promoting the electricity retail companies to achieve DR targets and supporting the balance regulation of the power system with a high penetration of renewable energy resources. Existing DRP evaluation methods ignore the differences in customers’ DR features and the correlation between DR features in different time periods. Moreover, the characterisation of DR willingness only considers the impact of electricity prices, which reduces the accuracy of DRP evaluation results. Given this background, a DRP evaluation method based on integrated empirical mode decomposition (IEMD) and the multi-head convolutional self-attention algorithm (MCSA) for typical industry customers is proposed in this paper. Firstly, an IEMD and DR willingness-based method for extracting DR features of industry customers is proposed. Then, an MCSA-based DRP evaluation method for typical industry customers, utilising the extracted DR features, is developed to realise accurate DRP evaluation by electricity retail companies. Finally, case studies on the industry customers in Zhejiang province, China, show that the proposed method can obtain higher accuracy in evaluating the typical industry customers’ DRP, thus providing technical support for the electricity retail companies to fully mobilise the flexible resources of the demand side.

Global energy transition drives large-scale renewable sources and diversified load integration into distribution networks, introducing significant uncertainty and challenging operational flexibility. Although AC/DC hybrid distribution networks offer advantages, existing methods inadequately model complex spatiotemporal correlations of source load uncertainties and lack systematic frameworks to quantify supply–demand flexibility imbalances under unique AC/DC interconnection modes. This study addresses these gaps by developing a Copula-Markov chain model for temporal correlations and an R-Vine Copula model for spatial correlations to generate renewable scenarios, formulating a daily-load-index-driven optimisation model for stochastic demand fluctuations, and establishing an electric vehicle (EV) charging model incorporating user behaviours. A novel flexibility assessment framework is proposed, defining supply–demand metrics and integrating security constraints via power flow verification. Case studies demonstrate the model's high fidelity, achieving a scenario coverage rate exceeding 0.99, a scenario width of approximately 0.002 and a slope similarity of around 0.06. The assessment reveals critical flexibility gaps; for instance, urban networks showed a moderate upward shortfall probability of 4.30%, whereas rural networks exhibited severe inadequacy with a shortfall probability of approximately 99.31%, underscoring the bottleneck effect of VSC capacity and resource availability.

Fault location is increasingly essential in inverter-based active distribution networks. This is due to the large number of branches and laterals in such networks, as well as the presence of inverter-based distributed generators (IBDGs). Several techniques are used for locating faults in distribution networks, including impedance-based approaches, traveling wave-based schemes, and artificial intelligence (AI)-based approaches. AI-based schemes are superior to others in terms of speed and accuracy, and they do not demand high-frequency devices. However, there is a lack of AI-based schemes that can effectively address scenarios involving a high number of branches, a limited number of measurement instruments, the presence of IBDGs, and high fault resistance. Accordingly, this paper introduces a modified one-dimensional convolutional neural network (1-D CNN) that combines residual connections with 1-D CNNs. The suggested approach includes two elements for fault location: (i) determining the fault distance and (ii) identifying the section of the network that is faulty. The results indicate that this approach effectively pinpoints faults with varying resistance levels at different locations, even in the presence of IBDGs. Ultimately, the proposed solution demonstrates enhanced accuracy in networks featuring multiple distributed generators, numerous sub-branches, unbalanced load conditions, and diverse fault scenarios.

Microgrids offer a viable solution to power outages by integrating distributed energy resources (DERs) to ensure a reliable, localised power supply. This paper presents a two-layered power management system (PMS) for a microgrid cluster, utilising a wide area measurement system (WAMS) to enhance operational reliability. In the proposed two-layer PMS, the first layer implements a centralized PMS for individual microgrid operation and the second layer employs a supervisory PMS for microgrid cluster management. The proposed PMS addresses the critical challenge of power imbalance arising from rapid load fluctuations and the stochastic nature of renewable-based DERs. The system is implemented on a modified IEEE 33-bus test system, configured as a microgrid cluster comprising residential and commercial microgrids. Two algorithms, a load prioritization algorithm (LPA) and a conventional load shedding algorithm (LSA), are developed and implemented within the phasor data concentrator (PDC) to manage power deficit and surplus. Both algorithms facilitate the power transactions by prioritising loads according to a priority factor and their performance is compared against each other. To ensure practical equivalence to real distribution systems, diverse load categories are incorporated. The system is validated through comprehensive case studies in MATLAB/Simulink and also demonstrated via real-time validation using an OPAL-RT (OP4510).

In this study, a sequence impedance model for a virtual synchronous machine (VSG) grid-connected inverter that incorporates fractional-order characteristics of real inductive and capacitive components was developed, utilising the harmonic linearisation method. The model's accuracy was validated through simulation. The impact of altering the sequence of the inductor and capacitor on the impedance characteristics was further examined, system's stability was assessed using the generalised Nyquist criterion. The outcomes of hardware-in-the-loop studies indicate that the fractional-order features of the components influence the stability of the VSG grid-connected system. In the interim, the parameter-optimised fractional-order model exhibited superior stability compared to the integer-order model.