Search Results (10,649)

Hydropower stations, as critical infrastructure for basic energy supply, play a pivotal role in ensuring the reliability of power systems through their safe and stable operation. Grounding grids operating long-term in complex soil environments are prone to corrosion and degradation, disrupting current distribution balance and causing spatial asymmetry in the voltage field, thereby compromising system safety. Corrosion branch resistance increment identification based on the electrical network method is typically modeled as a parameter inversion optimization problem. However, this problem exhibits underdetermination and other characteristics, making it difficult for traditional analytical methods to obtain stable solutions. To address this, this paper proposes a quantum perturbation scheduling candidate pool-guided sine–cosine algorithm (QSPSCA). Building upon the classical sine–cosine algorithm framework, it incorporates a dynamic candidate pool with multi-source attractor points and a quantum-inspired long-tail scheduling local refinement operator. This achieves an enhanced and smooth transition between global exploration and local refinement. Comparative experiments based on the CEC2017 benchmark and a hydropower station grounding grid corrosion diagnosis case demonstrate that QSPSCA outperforms multiple comparison algorithms in terms of average optimality and result stability. Furthermore, QSPSCA is applied to three typical engineering-constrained optimization problems. Results demonstrate that, whilst satisfying engineering constraints, this method consistently yields higher-quality feasible solutions with superior convergence accuracy and stability compared to alternative algorithms. Therefore, QSPSCA is not only applicable to underdetermined inversion diagnostics but also provides a solution framework with broad applicability for complex engineering optimization problems under structural symmetry perturbations. Full article

Piezoelectric cantilever beam harvesters are widely considered for self-powered bridge monitoring, yet their performance is often constrained by resonance detuning under low-frequency ambient vibrations. This issue is particularly pronounced in bridge environments, where the dominant vibration frequencies are typically low and narrowly distributed. While several frequency tuning strategies have been proposed, their relative effectiveness under bridge-relevant conditions has not been systematically evaluated within a unified experimental framework. This study experimentally evaluated four tuning strategies for cantilever piezoelectric energy harvesters, i.e., spring tuning, magnetic tuning, tip mass adjustment, and beam length modification, to identify effective methods for matching the dominant frequency of bridge deck vibrations. A unified test platform using a common harvester configuration was established, and performance was quantified by resonant frequency alignment, maximum output voltage, and −3 dB bandwidth. Among the four methods, root-based spring tuning showed the best overall performance, achieving frequency matching while retaining strong electrical output, with a maximum voltage of 9.01 V and a bandwidth of approximately 1.5 Hz. Magnetic tuning also provided accurate frequency control, but reduced voltage by 15–25%. By contrast, tip mass and beam length tuning produced larger resonance shifts but caused voltage reductions of up to approximately 50%. Full article

The accelerating adoption of electric vehicles (EVs) alongside renewable distributed generators (RE-DGs), particularly solar photovoltaic (PV) and wind-based systems, is reshaping the operational and planning paradigms of modern power distribution networks. In this study, an optimal allocation framework is developed for the simultaneous integration of EV charging stations (EVCSs) and RE-DGs within a looped configuration of the IEEE 33-bus distribution system. Two advanced metaheuristic techniques—Improved Grey Wolf Optimizer (IGWO) and Metaheuristic COOT-Based Optimization (MCBO)—are employed to determine the optimal siting and sizing of these resources. The optimization objectives focus on minimizing active power losses while enhancing voltage stability and reducing overall voltage deviation across the network. Simulation results reveal that the MCBO algorithm demonstrates superior performance, yielding a maximum reduction of 82.49% in active power losses with the integration of standalone PV, and 78.14% when PV is deployed in conjunction with EVCSs. Similarly, wind turbine generator (WTG) integration resulted in a loss reduction of 85.74% without EVCSs and 81.57% with EVCS integration using the same approach. The findings further indicate that looped network configurations consistently outperform traditional radial systems in both loss reduction and voltage profile enhancement, underscoring their suitability for accommodating future EV and renewable energy penetrations in smart distribution grids. Full article

This paper proposes a collaborative optimal scheduling strategy based on asymmetric Nash bargaining for the integrated electricity–heat–hydrogen multi-microgrid system, which can minimize the overall system operation cost while guaranteeing the dynamic fairness of multi-microgrids energy transactions with full consideration of wind–solar uncertainty. First, a scenario generation method based on temporally correlated Latin hypercube sampling and Wasserstein probability distance-based scenario reduction is adopted to construct representative wind–solar uncertainty scenarios, which effectively mitigates the operational risks arising from wind and solar power output fluctuations in the coordinated dispatch of multi-microgrids. Then, an asymmetric Nash bargaining-based cooperative game model for energy trading is established, with each microgrid’s optimal independent operation cost as the negotiation breakdown point. The alternating direction method of multipliers is used for a distributed solution to obtain the optimal scheme that balances total system cost and trading fairness. Simulation results verify that the proposed strategy can effectively suppress operation risks from renewable uncertainty, significantly cut total system cost by 36.85%, and fully ensure trading fairness among multi-microgrid entities, with favorable engineering application value. Full article

The public deployment of electric vehicle charging stations must simultaneously balance construction economics, user accessibility, queueing pressure, feeder security, tail risk under demand uncertainty, and spatial fairness. These criteria are strongly coupled, yet most existing studies either rely on static optimization with limited behavioral realism or use multi-agent reinforcement learning for short-term charging operation rather than for long-term siting. This paper proposes a preference-conditioned multi-agent deep deterministic policy gradient (PC-MADDPG) framework for the urban charging station siting problem in a coupled traffic–distribution environment. Candidate charging sites are modeled as cooperative agents under centralized training and decentralized execution. Each agent outputs a continuous pile-allocation action, which is repaired into an integer expansion plan under a budget constraint. The environment evaluates each plan through attraction-based demand assignment, queue approximation, LinDistFlow-style feeder analysis, and a six-objective performance vector, including annual net cost, travel burden, service inconvenience, grid penalty, CVaR of unmet charging demand, and equity loss. On a reproducible benchmark with 12 demand zones, 10 candidate sites, an 11-bus radial feeder, and 16 stochastic daily scenarios, the proposed framework generates a non-dominated archive with 42 unique feasible plans. A representative PC-MADDPG solution opens 5 of 10 candidate sites and installs 20 fast-charging piles, achieving 99.88% mean demand coverage with an annual profit of 2.083 M$ and a maximum line utilization of 0.999. Relative to the NoGrid ablation, the selected full model reduces grid penalty by 23.87% and equity Gini by 51.08%, with only a 0.35% profit concession. Relative to the NoRisk ablation, the CVaR of unmet demand is lowered by 69.70%. Compared with a demand-greedy baseline, the proposed method reduces grid penalty by 11.72% and equity Gini by 25.19% while preserving similar demand coverage. These results provide proof-of-concept evidence, on a reproducible coupled benchmark, that preference-conditioned multi-agent learning can serve as a practical many-objective siting engine for charging-infrastructure planning when coupled traffic and feeder constraints are explicitly modeled. Full article

In distribution networks, traditional fault location methods have insufficient anti-interference capability and low accuracy in locating high-resistance grounding faults. To address these issues, a distribution network fault location method on the basis of SimAM-GraphSAGE-GAT is proposed. Firstly, the distribution network topology structure is converted into an adjacency matrix, and the electrical parameters of the faulty line are incorporated as node features into the graph structure of the network. Subsequently, the sampling and aggregation mechanism of GraphSAGE is used for learning node representation. Features are refined using SimAM. As a parameter-free attention mechanism, SimAM improves the ability of the model to capture important fault information. Then, the multi-head attention mechanism of GAT is introduced to enhance the representation of neighborhood relationships. Finally, GraphSAGE is utilized once again for deep aggregation, with a view to localizing faults by node classification. An IEEE 33-node distribution network model is adopted to verify the effectiveness of the algorithm in the experiment. The results show that this method can maintain high positioning accuracy even under the tested conditions, such as high-resistance grounding, noise interference, and data loss. Full article

This study investigated the immobilization of Chlorella sp. in a nylon matrix to analyze its retention behavior and monitor biomass adhesion. Image capture and processing techniques were combined with capacitance measurements over time, using a Python-based data analysis code. The experiment was carried out in a 2 L photobioreactor under controlled conditions (24 °C, continuous aeration at 9.31 L/min, and light intensity of 71 μmol m⁻² s⁻¹). The methodology allowed for quantification of biomass distribution on the matrix surface, as well as changes in the capacitance and optical density of the microalgal culture. The results indicated maximum growth around day 15, showing a strong correlation between optical density (absorbance at 686 nm), image analysis of the matrix, and capacitance records. At this point, absorbance reached 3.913, coverage of 24.56% on the nylon matrix, and capacitance of 375.9 μF. Capacitance measurement proved to be a useful indirect tool to estimate biomass adhesion, while image analysis provided spatial distribution. The observed upward trend in process variables highlights the potential of electrical parameters, such as capacitance, for monitoring microalgal immobilization in suspended systems without altering biofilm structure. This approach supports future applications in scaling processes for bioactive compound production or environmental treatment systems. Full article

Biomethane is emerging as a key renewable gas in both mature and developing energy systems worldwide. Driven by climate-neutrality objectives, energy-security concerns, and rising waste-to-energy ambitions, global biomethane production is expected to expand rapidly in the coming decade. In Europe, this growth is accelerated by the REPowerEU target of 35 billion m³ by 2030. However, as biomethane production increases and natural gas demand declines over time, distribution networks face growing operational challenges, including pressure build-up and biomethane curtailment caused by supply and demand mismatches. This study evaluates whether surplus biomethane can be converted into electricity as a multi-commodity strategy to alleviate these constraints. Using hourly operational data from two Dutch Distribution System Operators (DSOs), a simulation model was developed to assess the impact of generator-based biomethane-to-power conversion on both gas and electricity distribution networks. The results show that, for RENDO, the approach increases effective biomethane injection by 49.0%, reduces natural gas deliveries from the transmission system by 20.0%, and lowers electricity imports by 9.2%. For Coteq, the corresponding impacts are 106.8%, 30.6%, and 16.2%, respectively. These findings indicate that multi-commodity coupling through biomethane-to-power conversion provides a promising strategy for increasing biomethane injection and renewable electricity generation. Full article

Uncertainties in generation and dynamic load behavior provide new problems for radial distribution systems (RDS) caused by the growing integration of renewable distributed generators (RDGs), including solar photovoltaic (PV) systems and wind turbines (WTs), as well as electric vehicle charging stations (EVCS). This article offers a thorough techno-economic evaluation of how to best distribute RDG resources (solar PV, wind, and EVCS) inside a 28-bus distribution test system in India, taking into account generation volatility due to the seasons. Optimization of installation and operating costs, enhancing voltage stability, and decreasing active power loss are done all at once using a new Catch Fish Optimization Algorithm (CFOA). Integrating beta and Weibull distributions, respectively, into the probabilistic modeling of solar irradiance and wind speed allows for economic analysis to adhere to recognized approaches from contemporary multi-objective optimization frameworks. The simulation findings confirm that the proposed CFOA-based placement method improves economic efficiency, decreases energy loss, and increases system performance. Full article

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a /Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH estimation method is proposed based on alternating electrical signals. First, an aging test was carried out using commercial 18650-type batteries. Considering the current uncertainty in practical applications, tests under different discharge conditions were conducted to obtain the capacity and wide frequency band impedance data of each battery throughout its life cycle. Then, important features at specific frequencies were extracted from the impedance data, and an interpretable analysis of the features was performed using the distribution of relaxation times (DRTs). Finally, the impedance features were combined with the Gaussian process regression algorithm in machine learning to estimate and validate the SOH. The results show that using fixed-frequency impedance features can achieve accurate estimation. The average value of the maximum absolute error of each battery under different working conditions can be controlled within 1.59%. With the development of embedded chips and online measurement technology, battery management systems can obtain important impedance features by applying alternating electrical signals within a certain frequency range, thus achieving online estimation of SOH. Full article

Electromagnetic hydrodynamic (EMHD) mixed convective flow of tetra hybrid nanofluid (TeHNF) in a Darcy-Forchheimer porous medium in a vertical channel with thermal radiation is considered in the paper. The electric and magnetic fields are homogeneous, magnetic perpendicular to the walls of the channel, and electric perpendicular to the plane formed by the directions of the magnetic field and the basic current. The channel walls are impermeable, and they are at constant but different temperatures. The basic equations that describe this problem are ordinary nonlinear differential equations (ODEs), and they are transformed into dimensionless ODEs by introducing dimensionless quantities, which are analytically solved using the homotopy perturbation method (HPM). The relations for velocity and temperature distributions, Nusselt numbers and shear stresses on the channel walls were determined. These relations are functions of introduced physical parameters that characterize the observed problem. For TeHNF, where the base fluid is water and the nanoparticles are made of aluminum oxide, titanium dioxide, magnesium oxide and magnetite, a part of the obtained results is given. Velocity and temperature plots are presented in the form of graphs, and Nusselt numbers and shear stresses are presented in the form of tables. Based on the analysis of the obtained results, appropriate conclusions were drawn. It was concluded that an increase in the Hartmann number as well as an increase in the porosity factor decrease the fluid velocity and shear stress, and increase the fluid temperature and Nusselt numbers. Higher values of the Forchheimer factor and higher heat radiation correspond to lower fluid velocities, lower temperatures, lower values of shear stresses and Nusselt numbers. By increasing the value of the Grashof number, the velocity of the fluid increases, and so do the shear stresses. TeHNF shows advantages over simpler hybrid nanofluids and commercial fluids. Full article

To address the issue of vehicle instability and veering during braking when a single wheel fails in an electric vehicle’s electromechanical braking (EMB) system, an integrated application-oriented control framework based on adaptive sliding mode control (ASMC) is proposed. To address the shortcomings of SMC—such as difficulty in suppressing oscillations and the high workload associated with parameter tuning—a novel composite reaching law function was designed, and the TD3 algorithm was employed to optimize the sliding mode control parameters. When a failure in the EMB system is detected, the upper-layer control uses an improved ASMC algorithm to calculate the vehicle’s additional yaw moment. The lower-layer control employs an optimal control algorithm to distribute braking force, taking into account braking intensity, yaw moment, and tire utilization. This approach is integrated with sliding mode steering control to enhance vehicle stability during braking. To meet the driver’s braking requirements, a backpropagation (BP) neural network is first employed to identify braking intent. Based on this, the additional yaw moment is calculated by the upper-layer controller, and the brake force distribution is optimized through the lower-layer controller, thereby improving the vehicle’s stability. Through co-simulation analysis using Simulink-2024a and CarSim-2019.1, the results show that, compared to traditional algorithms, the proposed hierarchical control strategy reduced the maximum sideslip angle by 51.4%, decreased the maximum yaw rate by 47.2%, and reduced the maximum lateral offset by 45.6%. This control strategy enables enhanced stability across various braking intensity conditions. Full article

In ultra-high-voltage GIS and GIL systems, epoxy resin insulators are still the mainstream choice. However, as a thermosetting material, epoxy resin is difficult to recycle after disposal, which limits its environmental benefits. Thermoplastic insulators, due to their recyclability, are potential alternatives. This study focuses on 30% glass fiber-reinforced PET and PA6 materials. Their injection molding behavior, hydraulic pressure performance, and insulation performance were systematically analyzed using Moldflow, ANSYS, and COMSOL, respectively. For injection molding, Moldflow simulations were conducted for filling, packing, and cooling stages. Melt temperature was varied from 260 to –310 °C (PET) and 250–300 °C (PA6), while mold temperature was varied from 80 to –130 °C (PET) and 70–120 °C (PA6). An optimization objective function, Y = Δp/20 + Δx/0.5 + Δs/1.8, was developed to determine optimal processing parameters. Based on this function, the optimal parameters identified are: PET at 290 °C melt temperature and 120 °C mold temperature; PA6 at 250 °C melt temperature and 70 °C mold temperature. For hydraulic testing, Moldflow–ANSYS coupled simulations were performed under 2.4 MPa pressure with the compliance criteria of bulk stress < 90 MPa and insert-contact stress < 20 MPa. PA6 passed within a processing window of melt temperature < 270 °C and mold temperature < 120 °C. PET failed under all tested conditions, with insert-contact stress ranging from 24.25 to 27.55 MPa, consistently exceeding the 20 MPa threshold. In terms of insulation performance, this paper utilizes COMSOL to study the electric field distribution of thermoplastic insulators in SF₆ GIS/GIL and provides optimization suggestions for insulator geometry design. This study systematically compares the injection molding processes and hydraulic pressure performance of PET and PA6 thermoplastic insulators. These results provide important process insights and design guidance for evaluating thermoplastic materials as potential alternatives to epoxy resin in GIS/GIL applications. Full article

Power transformers are fundamental devices in electrical power transmission and distribution systems as they regulate voltage levels, which helps reduce system losses. However, their operation can be affected by temperature, with increases in temperature causing a decrease in their efficiency and lifetime. In addition, the presence of harmonics in the electrical current can cause overheating and distortions in transformer performance. A significant proportion of these harmonics are caused by the increasingly widespread use of DC renewable energies. To control such renewable sources, power inverters are used, which generate harmonics and cause overheating in transformers connected to the grid. One solution to this problem is to reduce the harmonic content generated by these converters to avoid transformer overheating and improve their lifetime. In this work, a modulation technique for H-bridge multilevel inverters is presented with the aim of reducing both harmonics and transformer heating. To test the technique’s effectiveness, the recommendations of a standard have been followed, which include the use of a dry transformer prototype for temperature measurements. The proposed technique has been compared with classical techniques for H-bridge multilevel inverters, and the experimental results indicate a reduction in the hottest-spot temperature. Full article