Iet Generation Transmission & Distribution最新文献

Evaluating power flow fluctuations under uncertainties is an important problem in power systems with a high proportion of renewable energy. However, the computational efficiency of the existing interval algorithms for nonlinear power flow equations is challenging. This paper proposes an interval power flow analysis method based on an interval AC-LPF model and affine arithmetic. The proposed method can guarantee accuracy and reduce time complexity without iterations. By Linearising AC power flow and introducing the intervals of injected power, this paper proposes an interval AC Linearised power flow model. Furthermore, an affine arithmetic-based algorithm is designed to reduce conservation and time complexity in the interval calculation. The proposed method is validated on six IEEE standard systems and a modified real-world power system in Northwest China. Numerical results show the effectiveness of the proposed method.

In this paper, a large-signal synchronisation analysis method based on saddle point characteristics is proposed to discuss the stability of the phase-locked loop (PLL)-based reactive current injection system. Firstly, the large-signal model of the PLL-based reactive current injection is established. An equilibrium point analysis shows that the PLL-based reactive current injection has an infinite number of stable points and saddle points. Then, based on the characteristic that trajectories converging to saddle points form the boundaries of convergence regions for stable points, the large-signal stability analysis of the PLL-based reactive current injection is obtained. It is concluded that the PLL-based reactive current injection has infinite convergence regions. Each convergence region has one stable point, and the position of the stable point is relevant to the grid voltage amplitude and reactive current reference. When the states of the reactive current injection system lie within a convergence region, the final states will converge to the stable point of this region. In addition, the shapes and sizes of the convergence regions are strongly influenced by the grid voltage amplitude and the reactive current reference. When the grid voltage amplitude drops or the reactive current reference increases, the size of the convergence regions reduces. In particular, when the grid voltage amplitude and/or reactive current reference changes largely, the sizes of the convergence regions decrease significantly. In this case, the system states at the original stable point will fall outside the new convergence region, and the PLL-based reactive current injection will lose its synchronisation stability. The effectiveness of the proposed theoretical analyses is verified by simulations.

In power system restoration (PSR), networks with various voltage levels have different decision-making constraints and restoration characteristics. Specifically, the restoration plan for the lower voltage level network is more adaptable to uncertainty of wind power output, owing to its greater flexibility. First, the restoration scheme decision-making is divided into two parts for the main network level (MNL) and the regional network level (RNL) respectively, according to the voltage levels. Second, a hierarchical coordinated optimisation model is established based on a two-stage framework. In the first stage, the plants/lines restoration sequence of the MNL and the subsystem partitioning scheme are determined. Furthermore, the plants/lines restoration sequence of the RNL and the restoration power scheduling scheme of the MNL and RNL are obtained in the second stage of optimisation, which can be flexibly adjusted to adapt to uncertain wind power outputs. The coordination and allocation of frequency regulation resources across subsystems are considered. Finally, the nested column and constraint generation algorithm is applied to solve the two-stage robust model. Case studies using the IEEE standard and a provincial system in China show that the algorithm converges in 2–3 iterations. Compared to non-hierarchical approaches, the proposed method improves cumulative restored energy by 2% and 5.3% in case 1 and case 2, respectively, while maintaining robustness against wind power uncertainty, highlighting its effectiveness in multi-level PSR.

In this paper, a reduced switch approach with cross clamped multilevel configuration is presented for high power penetration in the utility grid. The cross arm converter gives a new solution to feed MW scale power with fewer count of semiconductor switches. The less device approach makes it a compact solution with modular functionality to handle power efficiently. A cross arm connects two solar PV arrays with only six switches to have symmetric level contribution in converter voltage. Topological level explanations and modes of converter modules are presented for the 13-level based new renewable energy system. Each cross connected module operates two PV arrays in individual MPPT mode to feed power efficiently. Efficiency enhancement is ensured with the nearest level round function to provide a fundamental switching logic. Fundamental modulation ensures fewer losses in switching transitions and efficient power control is achieved in day and night duration. Active and reactive power dynamics are shown to ensure reactive power compensation under sudden changes in grid voltage levels. Dynamic performance assessment shows that the converter operates smoothly with changes in irradiance set points. The system is modelled in MATLAB and RT-LAB environments, testing MW-scale feasibility of the new solar multilevel configuration for high power applications.

The detection and localisation of high impedance faults (HIFs) in overhead distribution networks, particularly those caused by tree contact, remain challenging due to their low fault current magnitude, nonlinear behaviour, and high waveform variability. This study addresses these challenges by experimentally validating and parameterising a two-variable-resistor series model for HIFs. Controlled tests were conducted at the 13.8 kV Extra High Voltage Laboratory (LEAT) of the Federal University of Pará (UFPA), Brazil, using three tree types to collect waveform data. The faults were modelled in ATPDraw software and validated through waveform comparisons, achieving an R² coefficient above 0.89, demonstrating excellent agreement between simulated and measured signals. Additionally, comprehensive parameterised data tables detailing resistance variations over time and voltage are provided. These findings not only reduce the need for costly experimental setups in early-stage studies but also provide a solid foundation for developing and testing advanced HIF detection and localisation algorithms, thus contributing to improved power distribution system reliability.

Corona discharge on high-voltage direct current (HVDC) transmission lines generates space charges that affect the ion flow electric field on the buildings nearby. According to China's national standards, the electric field on building platforms must be controlled under the limit. Due to the variety of buildings near HVDC lines, extensive calculations are needed to obtain the distribution of the electric field. A sub-model method for the upstream finite element method is proposed to improve the calculation efficiency of the 3-D ion flow electric field by setting a reasonable cut-boundary condition, which is obtained by calculating the complete model without buildings. By analytic derivation, the potential and charge density errors at the cut-boundary are proved to have nearly linear influence on the electric field calculation results. Then the setting principle of the cut-boundary is determined by analysing various calculation results. The sub-model method was found to be over 14.7 times faster than the complete model after calculating 432 models, and the results are in good agreement with the complete model. Finally, the enhancing and weakening characteristics of the electric field on one-layer and two-layer building platforms near HVDC lines are obtained. The research results are helpful for the design of HVDC projects with buildings nearby.

With the advancement of automation in industrial production, the sensitivity of user equipment and processes to voltage sags has progressively increased. Voltage sags are highly likely to cause adverse consequences for production. Voltage sag events exhibit different characteristics, resulting in varying consequence states for users. Accurately identifying these consequence states is essential for determining the user's voltage sag mitigation needs and planning the optimal mitigation strategy. However, due to the low-frequency, high-damage nature of voltage sags, events with more severe consequences are less likely to occur, resulting in insufficient sample data. Furthermore, users are unable to provide detailed sample data due to the protection of production information, making data-driven assessments of industrial process voltage sag consequences even more challenging. To address these challenges, this paper proposes a method for identifying the consequence state of voltage sags in industrial users based on a data-driven method. First, voltage sag event features and consequence category labels for industrial processes are established. An improved semi-supervised fuzzy C-means (SSFC) algorithm is introduced to classify the consequence states of industrial processes. Second, a data augmentation technique based on the least squares generative adversarial network (LSGAN) is applied to expand the dataset of voltage sag samples with the consequence category labels. Next, based on the augmented dataset, a recognition model with VTC-Attention-bidirectional long short-term memory (VA-BiLSTM) is developed to explore the latent features of voltage sag consequences in industrial processes. A recognition library for voltage sag consequence states is created, allowing industrial users to input easily obtainable voltage sag data and obtain corresponding consequence states. Finally, a case study involving a manufacturer in South China is conducted to validate the effectiveness of the proposed method.

With the proliferation of harmonic sources, network operators face significant challenges in identifying and interpreting sudden changes in harmonic emission behaviour due to the large volume of power quality data and the lack of automated analysis tools. This article introduces a novel algorithm that detects and characterises atypical harmonic emission patterns based on a comprehensive framework that includes data preparation, anomaly detection, and knowledge acquisition stages. By employing context-based features, the underlying data properties of both typical and atypical patterns are captured effectively. Sliding-window thresholds enable a flexible adaption of the algorithm to variations caused by seasonality and trends. In the knowledge acquisition stage, the significance and properties of atypical patterns are summarised using aggregated anomaly scores, significance categories, and a classification scheme. The algorithm's effectiveness is demonstrated through its application to over 5000 harmonic time series collected in the transmission system in Germany.

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored.  The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs.

The increasing integration of power electronic-based generation has significantly weakened power system frequency control due to reduced system inertia. In this context, the load contribution, through its frequency droop and inertia, plays a crucial role in stabilizing frequency fluctuations. However, the integration of solid-state transformers (SST) prevents this contribution from occurring, because the DC-link in the power converters decouples load dynamics from the main grid. Existing solutions attempt to mimic load contributions through synthetic demand responses; they often overlook both to meet grid code requirements and optimize control actions. This paper proposes a predictive frequency control algorithm to maximize load contribution via SSTs, constrained to grid code requirements. The proposal introduces two key innovations: from a transmission perspective, the control formulation is designed to minimize the rate of change of frequency (RoCoF) of the system frequency response; from a distribution feeder perspective, it incorporates constraints on voltage, frequency, and the RoCoF to ensure power quality and prevent the disconnection of distributed generation units (DG). Simulation results are obtained through a model of the involved dynamics, which are compared with a proportional controller. The proposed strategy achieves better performance indicators in all analysed scenarios.