To enhance the fault handling capacity of low-voltage DC (LVDC) distribution networks and mitigate the detrimental effects of faults on converter apparatus, this research introduces an innovative modular fault suppression and handling strategy. The proposed approach entails the integration of multi-port fault limiting modules (FLM) with DC circuit breakers (DCCBs) and DC transformers, thereby enhancing the system’s current and voltage regulation. This integration allows for the use of economical mechanical DCCBs while improving the fault-ride-through (FRT) capabilities of the conversion equipment. Under normal operating conditions, the FLM are bypassed to reduce system disturbances; in the event of a fault, it is engaged to curtail fault currents and preserve voltage stability. The validity of the proposed method is verified through simulations in PSCAD/EMTDC. Simulation results demonstrate that the proposed strategy effectively reduces the peak fault current and limits the transient voltage compared to conventional schemes. Furthermore, the system successfully achieves a fault ride-through by maintaining the fault current within a safe range of the rated current, ensuring robust protection and rapid recovery.

The global energy landscape is undergoing a profound transformation, marked by an unprecedented integration of renewable sources. This paradigm shift brings forth the challenge of low inertia in power systems, posing significant uncertainties to grid stability and reliability. This paper addresses these challenges and proposes innovative solutions to ensure the resilience of future transmission networks. The paper leverages advanced modeling techniques, including dynamic simulation models and control methods to analyze real-world case studies, mainly focusing on wind power plants operating as hybrid plants with integrated energy storage systems and participating in reserves markets to provide frequency response. The analysis includes adapting the Nordic equivalent power system model, allowing a deeper understanding of the dynamics of low-inertia environments and the impact of renewable energy integration. The aim is to provide valuable insights into the complex interactions within low-inertia power systems and highlight the importance of adapting power systems to ensure resilience in evolving energy scenarios. The paper contributes to the ongoing discourse on building sustainable and reliable future transmission networks through empirical analysis and theoretical modeling. It emphasizes technical strategies, operational advancements, and policy considerations essential for navigating the challenges posed by the transition to renewable energy sources.

With the global energy transition and the development of smart manufacturing, modern power systems are characterized by a high penetration of renewable energy and power electronic devices, leading to a declining resilience against voltage sags. Accurate post-event source identification and event inversion estimation form the foundation for managing and mitigating voltage sag issues, which is of great significance for enhancing high-quality power supply in the grid. This paper proposes a method for voltage sag source identification and inversion estimation based on disturbance energy and pattern matching, aiming to achieve precise localization and full-process inversion estimation of voltage sag events, thereby providing key technical support for refined grid management and high-quality power supply. First, by analyzing the changes in energy flow direction at each monitoring point before and after a fault, upstream and downstream identification of the sag source is achieved. Combined with judgment results from multiple monitoring points, the exact location of the voltage sag is accurately determined, overcoming the challenge of insufficient source identification accuracy caused by multi-directional power flow from renewable energy sources in active looped systems. Second, pattern matching technology is introduced to construct a voltage sag event inversion estimation model. Using the Monte Carlo simulation method, a voltage sag source identification pattern library is generated. The measured data from limited monitoring points are intelligently matched with the pre-generated pattern library based on characteristics, enabling post-event inversion of voltage sag events and determining the voltage sag severity at unmonitored nodes. Finally, the IEEE 30-bus system is used as a case study to simulate and verify the effectiveness and accuracy of the proposed method in precise voltage sag source localization and inversion estimation.

This study explores the design and evaluation of an economical hardware platform aimed at enhancing load break switches (LBS) in distribution systems. Simulations conducted using DIgSILENT Power Factory and PSCAD validated the proposed method. The Provincial Electricity Authority of Chiang Mai Province successfully implemented this hardware platform in the Ban Khun Pae Smart Microgrid project. The hardware platform underwent rigorous laboratory testing using an overcurrent algorithm to detect fault currents. It successfully identified all potential issues, including saturation currents on feeders, and autonomously transmitted data to operators for timely corrective action. The findings confirmed that single line-to-ground fault currents were the most frequent fault type. The Intelligent Electronic Devices relay and the InHand Wireless Overhead-Line System fault current detectors at the site worked just as well as the improved LBS, showing that they are strong and reliable ways to find faults. This economical and efficient platform not only enhances operational reliability but also paves the way for improved fault monitoring and management in distribution systems, ensuring faster response times, and minimizing downtime during faults. The seamless integration with existing feeder infrastructure further highlights its practical utility and scalability, providing an innovative solution to the challenges faced in modern distribution networks.

With the development of power electronics technology, AC/DC hybrid distribution networks have gradually become an important form of distribution networks in the future and have attracted widespread attention. The high proportion access of distributed new energy represented by distributed photovoltaic (PV) brings great challenges to the safe and stable operation of AC/DC hybrid distribution networks, meanwhile the randomness and fluctuation of PVs’ power output put forward higher requirements for the real-time and dynamic response of the AC/DC hybrid distribution network optimal voltage control. Firstly, the Q-V voltage control model for PV in AC network and P-V voltage control model for PV in DC network are established. Then, the dynamic optimal voltage control model of AC/DC hybrid distribution network is constructed. After that, the data-driven and model-driven control strategies are combined, the voltage control controller is trained through the Multi-agent Gradient Descent Strategy (MADDPG) to solve the optimal adjustment action value of the PV droop parameter in the real-time state, so as to realize the online dynamic voltage control of AC/DC hybrid distribution network.

The inherent intermittency and fluctuation of renewable energy generation can lead to uncertainty in electricity carbon emission intensity. This uncertainty poses challenges to the low-carbon scheduling of integrated energy systems and impacts the economic benefits of system operation. To this end, this paper proposes an optimization model for integrated electricity-heat-gas energy system operation considering uncertain indirect carbon emission intensity. First, a regional integrated electricity-heat-gas energy system model considering the tiered carbon trading mechanism is formulated. Then, polyhedral uncertainty sets are introduced to analyze and qualify the uncertainties in renewable energy and indirect carbon emission intensity. Finally, a stochastic optimization method is introduced to deal with the uncertainty in the integrated energy scheduling model. Extensive case studies are conducted to verify the competence of the proposed models in enhancing the environmentally friendly and economic benefits of integrated energy systems.

Abstract This study evaluates the operational performance, environmental impact, and regulatory compliance of the WUPA Sludge Treatment Plant, with a focus on sludge and water quality management. Sludge and water samples from the WUPA River were analysed for key physicochemical parameters, including pH, turbidity, dissolved oxygen, and heavy metal concentrations. The results indicate high pollutant removal efficiency, with turbidity, suspended solids, and biochemical oxygen demand reduced by 66.27%, 91.42%, and 91.43%, respectively. While the treated sludge demonstrates suitability for agricultural application, nitrate concentrations exceed permissible limits for direct water discharge. Total dissolved solids removal and electrical conductivity reduction exhibited minor inefficiencies, resulting in an overall plant performance efficiency of 57.73%. Environmental impact assessment revealed minimal risk of leaching or odour to surrounding areas; however, turbidity and trace metal concentrations in river water exceeded WHO drinking water guidelines. The study highlights the need for enhanced nitrate reduction, heavy metal monitoring, and process optimisation to improve both operational efficiency and environmental compliance. Additionally, the findings provide insights into potential energy recovery from sludge and offer transferable strategies for UK wastewater utilities to advance low-carbon, net-zero, and sustainable sludge management practices.

Equitable carbon responsibility distribution is pivotal for the low-carbon transformation of the power system and the realization of dual-carbon goals, fostering fair benefits and balanced development for all entities within the system. Addressing the current practice of a fixed ratio sharing between generation and load sources, which deviates from the principle of “the polluter pays”, this paper proposes a coordinated planning method for generation, storage, and transmission, considering carbon responsibility allocation from a planning perspective. Firstly, a adhering to the fundamental principle of ” who causes, who bears, ” the various stakeholders are classified and held accountable, and the reasonable and fair sharing principle and reward and punishment index are established. Then, on this basis, a two-layer carbon responsibility cost allocation rule for power system power balance and power balance is established. Secondly, a source-storage-network joint planning model considering the investment cost of conventional thermal power installation, energy storage investment cost, wind curtailment penalty cost, transmission line expansion cost and carbon emission responsibility cost is constructed. Finally, the effectiveness of the proposed method is verified by an example of a regional power grid transmission project system in Northeast China, and compared with the source-storage-transmission planning method without considering carbon responsibility allocation, the necessity of considering carbon responsibility allocation is further explained.

According to the constraints of frequency safety indices, rationally utilizing the energy reserve provided by wind power and energy storage to meet the system's frequency regulation demand is inevitable for the development of a large-scale new energy power system. Firstly, frequency response characteristics and frequency regulation safety indicators required by new energy generation systems were analyzed. Secondly, the frequency dynamic response model of the system with wind power and energy storage was established, and the extreme value time for the virtual inertia response of the system was calculated. Using the extreme time at which the frequency drop is maximum, the virtual inertia time constant of the wind-storage system was evaluated. Additionally, the system inertia and the primary frequency regulation demand were obtained considering the frequency safety indices, and a novel coordinated control strategy for wind power and energy storage to provide the required frequency support was proposed. Finally, a grid-connected wind-storage simulation system was built to verify the superiority of the proposed control strategy. The obtained results indicate that the proposed control scheme flexibly meets the system frequency safety requirements.

We present a practical O(1)-per-query method for non-uniform scalar quantisation when decision boundaries are static and reused across large query volumes. Real-valued boundaries are demoted to an FP12 finite universe (U=212) and indexed using a compact van Emde Boas–style predecessor structure, yielding predecessor retrieval via a constant number of table lookups independent of the number of bins. We show that the coarse lookup is corrected by a bounded local check against neighbouring original boundaries, recovering the exact bin. Experiments show up to a 5× speedup versus binary search for high-volume lookup workloads.

The identification of vulnerabilities within distribution networks, impacted by complex factors such as ring configurations and the integration of distributed energy resources, remains a significant challenge. This study presents a novel enhanced hierarchical analysis fault chain comprehensive assessment model to address this issue. The proposed model develops four new weak indicators of assessing the nodes of a distribution network based on complex network theory: an improved indicator of node degree, improved indicator of node PageRank, power flow increment impact indicator, and node voltage stability indicator. It introduces the weighting method with the binomial coefficient for optimal distribution of weight in the fault chain framework to further enhance effectiveness in refined hierarchical analysis, aimed at improving objectivity and precision. Besides, it employs an Integrated Analytic Hierarchy Process in which system indicators and operational parameters under various conditions can be integrated and dynamically calculate the fault rate of a weak link in the network. These further enhance the adaptability and precision of the model in solving inherent complexities within modern distribution networks. Simulation case studies performed using IEEE-69 node complex active distribution network have demonstrated the efficacy and superiority of the proposed method for correct identification of weak nodes.

Range sidelobe modulation (RSM) limits the performance of pulse-agile radars. This letter proposes an expectation–maximization (EM)-based minimum mean-square error (MMSE) estimator that adaptively learns the clutter and target distributions and adjusts a range–Doppler (RD) prior map. By optimizing the shrinkage strength via prior weighting, the proposed method improves RSM suppression in the region of interest. Simulation results validate the effectiveness of the proposed approach.

With the development of smart grid, transmission line UAV intelligent inspection technology has been widely used. Pin defect detection is a common task in the intelligent inspection process, but due to the small size of the pin bolts in the inspection image, it is difficult for the existing detection algorithms to accurately recognize the pin defects in the complex background. In this paper, an Adaptive Focusing Multi-scale Feature Network (AFMFNet) is proposed. First, the Path-Interleaved Deformation Convolution (PIDC) is proposed to further enhance the feature extraction ability for the irregular pose of pin bolt. Second, the Small Target Enhanced Pyramid (STEP) is constructed. It realizes the effective fusion of multi-scale features of small targets through the differentiated processing between different layers and the global perception capability granted by CSP_OmniKernel. Finally, the improved Wise-MPDIoU loss function is utilized to improve the convergence speed and regression accuracy of the model. AFMFNet enhances detection accuracy for normal and defective pins by 7.5% and 13.4% respectively compared to the baseline model, achieving a reasoning speed of 141.2 f/s on PC, meeting real-time detection needs. Its robustness is verified in complex scenarios, offering a new intelligent approach for transmission line inspection.

Loss of Excitation (LOE) is one of the most critical faults in synchronous generators, which, if not detected promptly, can lead to power system instability and severe damage to the generator. The main challenge in distinguishing LOE from Stable Power Swings (SPS) lies in the time delay, which is a common issue in most recent protection methods. This paper presents a novel method for LOE detection in synchronous generators. The method utilizes a new criterion based on the variation of the generator rotor speed, where this criterion increases above one per unit after the occurrence of LOE and remains in this state. Unlike traditional methods that require complex settings, this approach operates without the need for threshold adjustments, ensuring accurate and fast performance under various conditions. In the simulations, generators of various sizes were subjected to various loads and network scenarios. The results of the simulations, which were carried out in MATLAB/Simulink, verify that the suggested approach not only diminishes the detection time but also preserves high accuracy when system disturbances occur, protecting system stability under severe conditions.

During prolonged extreme meteorological events such as cyclones, low-level rapids, thunderstorms, etc., the output of wind farms may undergo noticeable fluctuations within short timeframes, leading to wind power ramp events (WPREs). This paper proposes an optimization method for allocating electrochemical energy storage (EES) to tackle WPREs considering the correlation among offshore wind farms. Firstly, the correlation of offshore wind farms’ output is verified and analysed. Multiple scenarios are sampled using Latin Hypercube Sampling (LHS) and then rearranged using Cholesky decomposition ordering method (CDOM) to obtain output scenarios that incorporate the varying correlations among adjacent wind farms. Secondly, an improved multi-parameter segmentation algorithm is proposed to detect WPREs for the different correlation scenarios and assess the demand of EES for WPREs. Following this, a bi-level optimization model of EES coordinated with thermal power units (TPUs) for WPREs is formulated. Finally, through an example analysis, the effectiveness of the proposed model is verified. The simulation results show that stronger correlation among wind farms lead to fewer WPREs and lower requirements for TPUs and EES capacity, thereby reducing the investment and operation costs.

Medium-voltage back-to-back (MVB2B) converters can connect two distribution systems and quantifiably transfer power between them. This function can enable the MVB2B converter to exchange distributed energy resource (DER)-generated power between two systems and bring significant value to enhancing distribution system DER adoption. Our previous work analyzed and demonstrated the value the MVB2B converter can bring to DER integration. As continuous work, this paper presents a methodology that helps address the MVB2B converter sizing and location selection problem in distribution systems with high DER penetrations. The proposed methodology aims to address three critical problems for MVB2B converter implementation in the real world: 1) which distribution systems are better to be connected, 2) what converter size is appropriate for connecting the distribution systems, and 3) where the optimal connection points are in the systems for connecting the MVB2B converter. The proposed methodology has been demonstrated by case studies that include various scenarios involving distribution systems with different dominated load types and high photovoltaic penetrations.

To mitigate secondary damage caused by reclosing a distribution line with a permanent fault, a highly sensitive fault property identification scheme using detection-based reclosing is proposed in this paper. Firstly, the analytical model for single-phase detection-based reclosing in distribution networks is established to reveal the differences between transient and permanent faults. Given that the equivalent fault resistance of permanent ground faults differs significantly from the insulation resistance of transient faults, an equivalent fault resistance calculation method is proposed to improve the sensitivity of fault property identification. Furthermore, to reduce the impact of fault resistance and load capacity on phase-to-phase fault property identification sensitivity, a permanent fault identification method utilizing the phase-to-phase impedance ratio is proposed. Finally, a comprehensive detection-based reclosing scheme is developed. Validation results based on PSCAD simulations and RTDS closed-loop tests demonstrate that the proposed scheme exhibits both wide applicability across various scenarios and high sensitivity.

This paper proposes a novel method to tackle the growing problem of system instability in microgrids, which is brought on by the widespread adoption of renewable energy sources (RESs) and distributed generators (DGs). Connecting RESs and DGs to microgrids through power electronic interfaces leads to a decrease and fluctuation in the system’s inertia. This reduction in inertia leads to increased uncertainty and instability in the system, necessitating either load shedding or the curtailment of renewable generation, which are undesirable for both utilities and customers. This approach involves finding minimum capacity of battery energy storage systems (BESSs) as virtual inertia with optimum droop coefficients for stabilizing grid and avoiding any load shedding in a realistic microgrid Broome city in Australia. The proposed approach is formulated within the context of a multi-objective optimization algorithm, by utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in an integrated DIgSILENT and MATLAB framework. The minimum sizing of BESSs and their optimum droop coefficient are obtained for different scenarios including step load change, cloud event, and synchronous generator outage.The outcome of this research is a critical decision-making tool for the microgrid owner to cost-effectively decide the virtual inertia sizing and their parameters for stabilization of microgrid.