Search Results (939)

The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based diagnosis methods. To improve the representation and classification capability of fault signals, this paper proposes a fault type identification method based on wavelet packet transform and an improved CNN-BiGRU model with a channel attention mechanism. First, three-phase voltage, three-phase current, and zero-sequence voltage signals are decomposed by wavelet packet transform, and the corresponding time–frequency matrices are constructed. Then, these matrices are integrated and converted into time-frequency images, so that multi-source fault information can be represented in a unified form. On this basis, CNN is used to extract local spatial features from the time-frequency images, while BiGRU is employed to capture bidirectional dependency information of fault features. Furthermore, a channel attention mechanism is introduced to enhance informative feature channels and suppress redundant information, thereby improving the fault classification performance. Simulation results based on a 10 kV DG-integrated distribution network show that the proposed method achieves high recognition accuracy under different DG capacities and access configurations. Compared with CNN, BiGRU, and CNN-BiGRU models, the proposed CNN-BiGRU-Attention model shows better classification accuracy and adaptability, demonstrating its effectiveness for fault type identification in active distribution networks. Full article

The increasing penetration of distributed generation (DG) in distribution systems poses significant operational challenges, including increased power losses, voltage profile deviations, and variations in short-circuit currents. These issues may compromise network safety, reliability, and the selectivity of protection schemes under different operating scenarios. This paper proposes a hybrid optimization methodology for the optimal placement and sizing of DG, aiming to minimize active power losses while ensuring voltage regulation and keeping short-circuit currents within permissible limits. An integrated approach is proposed that combines a mesh-to-radial network reconfiguration strategy with a modified Ant Lion Optimization algorithm, known as ALO-DG, enabling the simultaneous optimization of network topology and the allocation of distributed generators at candidate buses. The problem is formulated taking into account power balance constraints, voltage limits, distribution network capacity limits, and short-circuit current limits. The proposed methodology achieved substantial reductions in active power losses in the IEEE 33-bus and 69-bus test systems, reaching 84.42% and 91.56%, respectively. These improvements were accompanied by enhanced voltage profiles while preserving the radial operating structure of the distribution networks. Furthermore, the proposed hybrid methodology serves as a tool for the planning and operation of distribution systems with high DG penetration, particularly in scenarios where grid security and protection coordination are critical considerations. Full article

Cross-scene Hyperspectral Image (HSI) classification faces substantial domain shifts caused by sensor heterogeneity, acquisition variation, and scene diversity. While benchmark annotations are assigned to individual center pixels, local patches often contain implicit multi-label semantics due to spectral mixing and spatial overlap. This mismatch distorts prediction structure, exacerbates generalization errors, and limits the effectiveness of standard domain generalization (DG) techniques focused solely on feature or prediction invariance. We propose Class-Aware Semantic Calibration (CASC), a systematic semantic structure calibration framework that addresses three complementary distortions induced by mismatched patch supervision: (i) Balance corrects class frequency bias via reweighted supervision; (ii) Separability enhances boundary decision stability through margin-based logit calibration; and (iii) Independence reduces domain-specific spurious co-occurrence via prediction covariance decorrelation. To preserve calibrated semantics under pseudo-source shift, we further introduce a complementary DualAlign (DA) module, which jointly aligns feature statistics and prediction distributions, enforcing consistency at both representation and semantic levels. Extensive experiments on three cross-scene benchmarks (Houston, Pavia, and WHU-Hi) demonstrate that CASC-DA consistently improves performance over strong baselines, achieving an average gain of 3.0% in overall accuracy and 4.9% in Kappa coefficient compared with the best-performing baseline on each dataset. These results underscore the importance of semantic structure calibration for domain-generalized HSI classification. Full article

In this work, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to address the optimal scheduling of non-dispatchable sources in a microgrid energy management system (MGEMS) for residential areas under utility-induced demand-side management (DSM) programs. While taking economic and environmental aspects into account, the goal is to maximize energy management by integrating a variety of distributed generation (DG) units with an energy storage device. Using real-time meteorological data, two case studies were analyzed and simulated using MATLAB/Simulink R2025b. The simulation results reveal that the optimum optimization outcome among the case studies is obtained at a higher DSM load participation level of 10%. Without the involvement of DSM, MG’s producing units in the first case had the highest carbon emissions of 797.110 kg and an overall operating cost of 267.10 €. Similarly, with the involvement of DSM, the second case had the lowest overall operating cost of 155.01 € and the lowest carbon emissions of 748.731 kg. The second case, which has optimal DG scheduling, is the suggested way to improve microgrid efficiency and provide a dependable power supply with low operating costs and emission reduction. Full article

The integration of renewable-based distributed units into distributed systems has been aided by recently developed technologies based on renewable energy, changes to utility infrastructure, and progressive government regulations. In this paper, an improved version of the golden jackal optimization (IGJO) is implemented to incorporate distributed generators (DGs) and capacitor banks (CBs) into the distribution system. The existing studies give only DG unit insertion, but in this work, simultaneous integration of different kinds of DG with a capacitor bank is used to analyze the impact. The main emphasis of this study is to minimize power loss along with the upgradation of the voltage profile. Improvement in voltage stability index and minimization of total voltage deviation (TVD) were also achieved by placing the DG and CB units in a suitable position. Load modeling is also considered here to validate the results. Seven types of loading, including constant power (half load and heavy load), constant current, constant impedance, residential, industrial, and commercial loads, are used to show the effect of integration of DG and capacitor bank into a 33-bus and 118-bus radial distribution system. Comparison of the proposed method with previous studies shows the better performance of the implemented method over other techniques. Full article

To address the insufficient consideration of load temporal characteristics, load growth, distributed generation (DG) integration, and business-expansion load connection in existing available-capacity assessment methods, this paper proposes a load-connection decision-making method for active distribution network. Firstly, considering the load temporal characteristics, load growth, DG, and the bearing capacity of transformer distribution districts, a time-series bearing capacity analysis model of transformer distribution districts is proposed. In addition, a heuristic topology search strategy considering dynamic capacity constraints is developed to identify feasible power-supply paths and evaluate the dynamically validated available capacity. Secondly, considering the integration of DG and energy storage systems (ESSs), as well as key indicators such as load balance, temporal characteristic matching and comprehensive economic performance, a business-expansion load-connection decision-making method for active distribution network is proposed. Finally, the effectiveness of the proposed model and method is validated through a case study. The results show that after DG and ESS integration, the load balancing degree and temporal characteristic matching index are improved by approximately 31.42% and 18.21%, respectively. Compared with the peak-capacity method, single-capacity-index method, and loss-priority method, the proposed method achieves the highest or jointly highest comprehensive decision value under different operating scenarios. The improved branch-and-bound method reduces the number of actual evaluations while obtaining the same optimal decision result. The proposed method can optimize load-connection schemes and provide theoretical foundation and practical decision support for active distribution network planning and business expansion. Full article

The exhaustion of fossil fuels, environmental concerns, and difficulties in deploying smart grids have expedited the development of renewable energy distributed generators (REDGs) and electric vehicles (EVs). In recent decades, there has been a notable rise in the production and marketing of EVs. Previous research has proposed reactive power control solutions, including the use of power electronic converters associated with distributed generators (DGs) to alleviate voltage fluctuations. This research presents a strategy for the best integration of electric vehicles through bidirectional charging and renewable energy distributed generators inside power systems, with the objective of efficiently managing voltage, active power, and reactive power flows at interconnection points. Furthermore, it entails determining appropriate locations and dimensions for electric car charging stations through a comparative examination of computing time and iterations between the Hybrid Genetic Algorithm Improved Particle Swarm Optimization (HGAIPSO) and several other optimization methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Improved Particle Swarm Optimization (IPSO). This analysis was performed on the IEEE-118 bus system, incorporating Vehicle-to-Grid (V2G), Grid-to-Vehicle (G2V), and REDG allocations. The simulation results indicated that the suggested HGAIPSO approach is more rapid and effective regarding calculation time for complex networks, attaining optimal solutions with greater efficiency. Full article

The intermittent nature of distributed generators based on renewable energy sources (DGs-RESs), together with the time-varying behavior of load demand, introduces significant uncertainty into the planning and operation of active distribution networks. These uncertainties make the optimal siting and sizing of DGs-RESs and D-STATCOMs a challenging multi-objective optimization problem. This paper proposes a multi-objective Coati Optimization Algorithm (MOCOA) for the coordinated allocation of DGs-RESs and D-STATCOMs in radial distribution networks under uncertainty. The proposed framework aims to minimize total active power losses (TAPLs) and enhance the voltage stability index (VSI) while satisfying the operational constraints of the distribution system. First, the load sensitivity factor (LSF) is employed to identify the most suitable candidate buses, thereby reducing the search space and improving the computational efficiency of the optimization process. Then, MOCOA is applied to determine the optimal placement and sizing of DGs-RESs and D-STATCOMs. The uncertainties associated with load demand, solar irradiance, and wind speed are modeled using probabilistic representations, and reduced representative scenarios are considered to evaluate system performance under uncertain operating conditions. The proposed method is validated using modified IEEE 33-bus and IEEE 69-bus radial distribution networks. The simulation results demonstrate that the coordinated integration of DGs-RESs and D-STATCOMs significantly reduces TAPLs, improves the VSI, and enhances the voltage profile. In particular, increasing the number of DG/D-STATCOM units and using wind energy reduces the TAPL by 26.95% and increases the 24 h cumulative VSI from 20.16781 p.u. to 20.4162 p.u. Comparative results with other optimization techniques confirm the effectiveness, robustness, and superior performance of the proposed MOCOA for uncertainty-aware planning of active distribution networks. Full article

As a clean heating solution, electric heating loads (EHLs) have become a critical flexible load resource on the demand side in recent years. To enhance the power grid’s frequency regulation capability and mitigate the impacts of both EHLs and high-penetration renewable energy on the power grid, a symmetry-guided distributed control strategy for active distribution networks (ADNs) considering demand response (DR) of EHLs is proposed from the perspective of source–load bilateral coordination. Based on the symmetry of information interaction and control structure between distributed generators (DGs) and EHLs, a thermodynamic dynamic model of EHLs and a source–load coordinated response control framework are established. An improved consensus-based distributed control algorithm and a temperature queue sorting-based distributed response strategy are designed to maintain symmetrical power allocation and symmetrical response coordination between DGs and EHLs, achieving rapid and stable source–load coordination. Finally, comprehensive simulations verify the effectiveness of the proposed strategy. The results show that the proposed strategy improved the convergence speed by 27.5%, achieved fast and effective control of DGs and EHLs, maintained the steady-state frequency above 49.95 Hz under various interferences, effectively eliminated frequency deviation caused by source–load interference, and significantly improved the stability and frequency support capability of ADNs. Full article

To mitigate large-scale blackout risks in urban distribution systems under typhoon-induced extreme weather and to reduce post-disaster restoration costs, this study proposes a resilience-oriented spatiotemporal co-optimization framework integrating transportation networks, power grids, and distributed energy resources. First, a city-scale typhoon spatiotemporal model is established, integrating static wind field, dynamic evolution, and trajectory-based mobility with urban-geometry-driven wind speed correction to characterize the spatiotemporal progression of extreme wind hazards. Second, the time-varying failure rates of distribution network components are quantified by explicitly accounting for network topology correlations, while the spatiotemporal dispatchability and output characteristics of distributed resources under disaster conditions are systematically modeled. Third, a pre-disaster proactive deployment model is formulated to minimize load curtailment costs and resource allocation expenditures. The model integrates active network reconfiguration with coordinated placement of distributed generation (DG) and mobile energy storage systems (MESSs), enabling resilience-enhancing pre-positioning strategies. Subsequently, a post-disaster restoration scheduling model is developed with the objective of minimizing unserved load. By embedding traffic flow constraints and optimal path computation under disrupted transportation conditions, the proposed framework realizes spatiotemporal coordination among MESSs, DG, and electric vehicles (EVs), thereby accelerating system-level recovery. Finally, the effectiveness of the proposed strategy is validated on a 51-node urban distribution system located in eastern coastal China, demonstrating significant improvements in restoration performance and resilience enhancement. Full article

Optimal allocation of distributed generation (DG) and feeder reconfiguration are critical strategies for improving the operational efficiency and voltage stability of modern radial distribution networks under increasing penetration of renewable resources. However, the simultaneous optimization of DG placement, sizing, and network topology constitutes a highly nonlinear multi-objective problem subject to electrical, operational, and radiality constraints. Unlike existing studies that treat DG allocation and feeder reconfiguration as separate or weakly coupled problems, this work introduces a unified mixed-integer nonlinear optimization framework that captures their strong interdependency. In addition, a hybrid Big Bang–Big Crunch (HBB-BC) algorithm is proposed, combining stochastic contraction with adaptive learning mechanisms to improve convergence robustness in highly nonlinear search spaces. This contribution addresses the limitations of conventional metaheuristics in handling coupled topology–generation optimization problems and provides a scalable solution for modern active distribution networks. We propose a coordinated optimization framework for optimal DG placement and feeder reconfiguration aimed at minimizing real power losses while enhancing voltage stability and reducing both operational cost and environmental impact. The problem is formulated as a constrained multi-objective optimization model and solved using an improved hybrid Big Bang–Big Crunch metaheuristic algorithm which integrates exploration and exploitation mechanisms to achieve fast convergence and robust global search performance. The proposed method is validated on both IEEE 33-bus and IEEE 69-bus radial distribution systems under multiple operational scenarios. The results demonstrate that the coordinated optimization consistently achieves significant performance improvements across different network scales, confirming the robustness and scalability of the proposed framework. Full article

To address the challenges of power fluctuations caused by the integration of distributed generation (DG) and the difficulty in simultaneously managing peak-valley load regulation due to diverse user energy demands in a microgrid system, this paper presents a coordinated optimal configuration method for serving a hybrid energy storage system (HESS), which explicitly considers the differentiated requirements from both the supply-side and the demand-side. In the presented method, an improved empirical mode decomposition (EMD) method is first presented to decompose the DG power into high-frequency, medium-frequency, and low-frequency bands. Based on the complementary technical and economic characteristics of different energy storage types, a coordinated regulation strategy for HESS in the multiple time-frequency domains is developed. Second, a coordinated optimal configuration model for HESS is further established. This model integrates key performance indicators, including maximum fluctuation and renewable energy utilization rate on the supply-side and the peak-valley difference reduction rate on the demand-side. Finally, a distributed optimization algorithm based on an improved alternating direction method of multipliers (ADMM) is developed to solve the coordinated configuration model. The experimental results demonstrate that the presented method can effectively smooth the DG power fluctuations and reduce the load peak-valley difference. The renewable energy utilization rate reaches 100%, and the peak-valley difference reduction rate reaches approximately 80%. The presented method successfully achieves the coordinated optimal configuration of HESS on both the supply and demand sides, providing a theoretical underlying infrastructure for the configuration of energy storage in the microgrid system with high penetration of renewable energy. Full article

The rapid expansion of distributed generation (DG) in radial distribution networks introduces bidirectional power flows that fundamentally disrupt traditional unidirectional protection coordination. This paper proposes a multi-criteria optimization method for the optimal placement of reclosers in distribution networks with DG. The approach incorporates analytical short-circuit current calculations to determine the critical DG capacity required to maintain protection sensitivity and avoid protection maloperation. The method is applied to a rural medium-voltage feeder. The results demonstrate the existence of a permissible DG capacity threshold beyond which relay sensitivity is compromised; the optimal placement of a recloser reduces the annual energy not supplied by 14.3%, while the integration of DG further improves supply reliability and can eliminate the annual energy deficit. The study confirms that reliability improvement measures must be coordinated with protection constraints to ensure the safe and reliable transition toward decentralized power systems. Full article

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster. Full article

Distribution networks are being transformed by the growing penetration of distributed generation (DG), which changes power flows, voltage profiles, and the optimal operating point of the feeder. This study proposes a hybrid technique that combines the Gray Wolf Optimizer (GWO) with a neural network (NN) surrogate model to solve the distribution network reconfiguration (DNR) problem. The method minimizes active power losses while improving voltage regulation and preserving radial operation under operational constraints. The GWO performs global exploration of discrete switch configurations, whereas the NN accelerates local refinement by screening candidates before exact AC power flow validation. This manuscript presents benchmark results for the IEEE 33-bus and IEEE 69-bus distribution test systems. For the IEEE 33-bus benchmark, DG units are installed at buses 14, 25, and 30. For the IEEE 33-bus case, losses are reduced from 282.94 kW in the base case to 120.65 kW with DG and to 87.08 kW after hybrid reconfiguration, while the minimum voltage magnitude improves from 0.8829 p.u. to 0.9587 p.u. For the IEEE 69-bus case, total active losses decrease from 224.95 kW to 82.22 kW with DG and to 29.92 kW after reconfiguration while concurrently improving the voltage profile and line loading. From a sustainability perspective, the main contribution of the proposed workflow is to reduce technical losses at the distribution level, thereby improving energy efficiency for a given demand. Overall, the results show that the combined use of DG and surrogate-assisted reconfiguration can yield substantial efficiency gains across benchmark feeders of varying sizes, while broader multi-feeder validation and more detailed surrogate error quantification remain necessary before claiming general applicability. Full article