Search Results (3,861)

The study presents a concept assessment of a crossflow turbine with vertical-axis application in a “zero-head” system for rivers with high discharge. The evaluation of system output parameters, such as generated power and efficiency, is performed through numerical simulations over a virtual prototype. The approach used is validated in previous studies through a physical prototype of the downscaled system. The focus is on the virtual prototyping results for a single module of two Bánki–Michell/Ossberger turbines across a range of rotational speeds to assess system robustness. An overall efficiency of about 60% is calculated, indicating opportunities for design improvement. The obtained results relate to a complete channel system with five site stations, each with four modules. The study also includes a preliminary financial assessment of parameters, such as the investment period and the overall financial efficiency of such a solution. The main result of the study is an evaluated concept, with certain directions for further improvement in the next stage of detailed design development, using the validated simulation model. Full article

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

The high penetration of renewable energy brings significant power fluctuations and operational uncertainties to distribution networks. Traditional power allocation methods for hybrid energy storage systems (HESSs) exhibit strong parameter dependency, limited frequency-domain recognition accuracy, and poor dynamic coordination capability. To overcome these limitations, this study proposes an adaptive power allocation strategy for HESSs under renewable energy integration scenarios. The proposed method employs the Grey Wolf Optimizer (GWO) to jointly optimize the mode number and penalty factor of the Variational Mode Decomposition (VMD), thereby enhancing the accuracy and stability of power signal decomposition. In conjunction with the Hilbert transform, the instantaneous frequency of each mode is extracted to achieve a natural allocation of low-frequency components to the battery and high-frequency components to the supercapacitor. Furthermore, a multi-objective power flow optimization model is formulated, using the power commands of the two storage units as optimization variables and aiming to minimize voltage deviation and network loss cost. The model is solved through the Particle Swarm Optimization (PSO) algorithm to realize coordinated optimization between storage control and system operation. Case studies on the IEEE 33-bus distribution system under both steady-state and dynamic conditions verify that the proposed strategy significantly improves power decomposition accuracy, enhances coordination between storage units, reduces voltage deviation and network loss cost, and provides excellent adaptability and robustness. Full article

Aiming at the challenges of complex spatial-temporal correlation and strong nonlinearity in the power prediction of large-scale wind farm clusters, this study proposes a short-term wind power prediction method that combines a dynamic graph structure and a Kolmogorov–Arnold Network (KAN) enhanced neural network. Firstly, a spectral embedding fuzzy C-means (FCM) cluster partition method combining geographic location and numerical weather prediction (NWP) is proposed to solve the problem of insufficient spatio-temporal representation ability of traditional methods. Secondly, a dynamic directed graph construction mechanism based on a stacked wind direction matrix and wind speed mutual information is designed to describe the directional correlation between stations with the evolution of meteorological conditions. Finally, a prediction model of dynamic graph convolution and Transformer based on KAN enhancement (DGTK-Net) is constructed to improve the fitting ability of complex nonlinear relationships. Based on the cluster data of 31 wind farms in Gansu Province of China and the cluster data of 70 wind farms in Inner Mongolia, a case study is carried out. The results show that the proposed model is significantly better than the comparison methods in terms of key evaluation indicators, and the root mean square error is reduced by about 1.16% on average. This method provides a prediction tool that can adapt to time and space changes for engineering practice, which is helpful to improve the wind power consumption capacity and operation economy of the power grid. Full article

The increasing demand for high-power electric vehicle charging systems with Vehicle-to-Grid (V2G) capability highlights the need for modular, scalable power converters. This paper proposes a hierarchical control strategy for a high-power, multi-port electric vehicle charging station. The system, based on a Series-Parallel Modular Multilevel Converter (SP-MMC) with isolated modules, is managed by a coordinated control strategy that integrates proportional-integral-resonant regulators, nearest-level control with voltage sorting, and single-phase-shifted modulation. The proposed system enables simultaneous, independent regulation of multiple bidirectional, isolated direct current ports while maintaining grid-side power quality and internal variables of the SP-MMC. The proposed control is validated using real-time Model-In-the-Loop (MIL) simulations that include sequential port activation, bidirectional power flow, and charging operation. MIL results demonstrate stable operation with controlled DC-link voltage ripple, accurate per-port current tracking, and near-unity grid power factor under multi-port operation. Full article

Existing docking methods for mobile robots rely on a LiDAR sensor or image processing using a camera. Although both demonstrate excellent performance in terms of sensing distance and spatial resolution, they are sensitive to environmental effects, such as illumination and occlusion, and are expensive. Some environments or conditions require low-power, low-cost novel docking solutions that are less sensitive to the environment. In this study, we propose a guidance and navigation solution for a mobile robot to dock into a docking station using the values of the angle of arrival and received signal strength indicator between the mobile robot and the docking station, measured via wireless communication based on Bluetooth low energy (BLE). This proposed algorithm is a LiDAR- and camera-free docking solution. The proposed algorithm is used to run an actual mobile robot and BLE transceiver hardware, and the obtained result is significantly close to the ground truth for docking. Full article

With the increasing integration of renewable energy into power grids, voltage source converter-based high-voltage direct current (VSC-HVDC) stations often adopt hybrid grid-following (GFL) and grid-forming (GFM) control strategies to improve adaptability to varying grid strengths. In many existing schemes, the hybrid coefficient changes abruptly, which may produce large transient current overshoots and compromise the safe and stable operation of converters. An adaptive hybrid GFL-GFM control framework equipped with a hybrid coefficient transition regulation is proposed. Small-signal state–space models are established and eigenvalue analysis confirms stability over the considered short-circuit ratio (SCR) range. The regulating method is activated only during coefficient transitions and is inactive in steady-state, thereby preserving the operating-point eigenvalue properties. Dynamic equations of the converter current change rate are derived to reveal the key role of the hybrid-coefficient change rate in driving transient current overshoots, based on which a real-time hybrid coefficient regulating method is developed to shape coefficient transitions. Simulations on a 500 kV/2100 MW VSC-HVDC project demonstrate reduced transient current overshoot and power oscillations during SCR variations, with robustness under moderate parameter deviations as well as representative SCR assessment error and update delay. Full article

With the rapid increase in renewable energy penetration and the expansion of multi-regional interconnected power systems, there is a growing need to forecast the power output of renewable energy power plant clusters within a region. Existing methods primarily utilize spatio-temporal correlations between stations to directly predict cluster output, but they still have the following shortcomings: (1) lack of analysis and utilization of the similar output characteristics between wind and solar power stations; and (2) inadequate integration of individual plant characteristics and adaptability across different prediction spatial scales. Therefore, this study proposes a method for forecasting and correcting daily power generation zones of wind–solar clusters based on output similarity clustering. First, the output similarity characteristics of wind and solar plants within the cluster are evaluated, and a similarity matrix is constructed to cluster the plants into sub-clusters. Second, a single-site power prediction model based on the BiLSTM model and multi-task learning is constructed to aggregate preliminary power prediction results from individual sites within sub-clusters. Finally, a cluster power prediction correction model based on the GCN-Transformer model is developed to refine preliminary predictions using spatio-temporal correlations between sub-clusters. Simulation results demonstrate that the proposed method, through its integrated approach combining clustering partitioning, multi-task learning, and spatio-temporal correlation correction within a comprehensive forecasting workflow, achieves improvements of 15.2323%, 19.0581%, and 0.0283% over the baseline GCN model in terms of MAE, RMSE, and R-score, respectively. This effectively enhances the accuracy of power forecasting for wind-solar power plant clusters. Full article

An accurate in situ stress field is a prerequisite for evaluating the stability of surrounding rock in underground caverns of a pumped-storage power station (PSPS) and ensuring the long-term safe operation of underground powerhouses. However, in situ stress measurements in the field are typically characterized by a limited number of measurement points, strong data randomness, and high testing costs. Meanwhile, conventional regression inversion methods often yield stress fields with insufficient accuracy or unstable spatial distributions. To address these issues, this paper proposes an in situ stress field inversion method based on the particle swarm optimization–support vector regression (PSO-SVR) algorithm. Stress boundary conditions are formulated in terms of lateral stress coefficients combined with shear stresses, and PSO is employed to optimize the hyperparameters of the SVR model. The stress boundary conditions predicted by the PSO-SVR algorithm are then imposed on a numerical model to compute the stresses at the measurement points, and the optimal boundary conditions are identified by minimizing the root mean square error (RMSE) between the inverted and measured in situ stresses. On this basis, the stress components at the measurement points and the in situ stress field in the study area are obtained. The results demonstrate that the inverted in situ stresses agree well with the field measurements, exhibiting good consistency and spatial regularity. Specifically, compared with the traditional multiple linear regression (MLR) method, the PSO-SVR algorithm reduces the RMSE and mean absolute error (MAE) of the in situ stress measurement data by 48.21% and 47.01%, respectively, and produces inversion results with higher accuracy, more stable spatial patterns, and markedly fewer anomalous zones. Consequently, the PSO-SVR algorithm is well suited for in situ stress inversion in PSPSs and provides a reliable stress-field basis for subsequent optimization of underground cavern excavation and support. Full article

Pumped-storage power stations (PSPSs) are vital for grid stability, yet pump-turbines (PTs) operating in the S-shaped region often induce severe hydraulic instability. To reveal the mechanism of these self-excited oscillations, this study establishes a nonlinear mathematical model based on rigid water column theory and a cubic polynomial approximation of the PT’s nonlinear characteristics. Both analytical derivations and numerical simulations were conducted. Analytical results indicate that, in the absence of surge tanks, self-excited oscillations occur when the PT’s negative hydraulic impedance modulus exceeds the pipeline impedance. With a single surge tank, the system behaves analogously to the Van der Pol oscillator, exhibiting oscillations that converge to a stable limit cycle governed by system parameters. Numerical simulations for a dual-surge-tank system further reveal that, due to initial negative damping, the PT transitions to alternative stable equilibria. Crucially, the transition direction is governed by the polarity of the initial disturbance: negative perturbations lead to the regular turbine region, while positive ones lead to the reverse pump region. Additionally, pipe friction causes the steady-state discharge to deviate slightly from the theoretical static value, with deviations remaining below 2.96%. This work provides a theoretical basis for stability prediction in PSPSs. Full article

This paper proposes a complete solution, implementing a low-cost, energy-independent, network-connected, and scalable environmental air parameter monitoring system. It features a remote sensing module which provides environmental data to a cloud-based server and a software application for real-time and historical data processing, standardized air quality indices computations, and a comprehensive visualization of environmental parameters evolutions. A fully operational prototype was built around a low-cost micro-controller connected to low-cost air parameter sensors and a GSM modem, powered by a stand-alone renewable energy-based power supply. The associated software platform has been developed by using Microsoft Power Platform technologies. The collected data is transmitted from sensors to a remote server via the GSM modem using custom-built JSON structures. From there, data is extracted and forwarded to a database accessible to users through a dedicated application. The overall accuracy of the air quality monitoring system has been thoroughly validated both in controlled indoor environment and against a trusted outdoor air quality reference station. The proposed air parameters monitoring solution paves the way for future research actions, such as the classification of polluted sites or prediction of air parameter variations in the site of interest. Full article

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances. Full article

Conventional approaches for designing and integrating charging stations into the grid are time-consuming and computationally expensive. For the purpose of power quality enhancement of EVCS, more focus has been paid on charging station design infrastructure, hence neglecting the need for the technical design of grid topology. Therefore, this paper focuses on the design and development of multiple distribution grid topologies for topology-aware characterization of power quality in grid-tied solar-powered EV charging stations. The control and energy management strategy is implemented solely to enable consistent grid-PV-EV interaction. The models have been successfully developed and tested for four modes of operations, PV to EV, PV to Grid, V2G and G2V, in MATLAB/Simulink 2022b. From the results, it is clear that the grid voltage THD during V2G remains at 0.01%, 0.08% and 0.01% and the grid-connected current THD remains at 0.19%, 1.88% and 0.19% for three different grid topologies, GT1, GT2 and GT3, respectively, while, during G2V, the voltage THD are valued at 0.02%, 0.05% and 0.03% and the grid-connected current THD at 0.45%, 1.28% and 0.75% for grid topologies GT1, GT2 and GT3 respectively. The results demonstrate that grid topology-aware analysis is required for consistent harmonic characterization of PV-integrated EV charging stations under V2G, G2V and PV-assisted operating modes. Full article

With the continuous and rapid growth of global photovoltaic (PV) installed capacity, the fluctuation, intermittence, and randomness of its output aggravate the inertia loss of traditional power systems, which poses severe challenges to grid voltage stability, frequency regulation, and safe operation of equipment. Stability control of PV power stations has become a necessary aspect of technical support for the construction of new power systems (NPSs). In this paper, a technical analysis framework of stability control of photovoltaic power stations is systematically constructed. First, the core stability problems of photovoltaic systems are sorted out. Then, a technical review of the three control levels, namely the equipment, system, and grid, is carried out. At the same time, the application potential of emerging technologies such as data-driven and AI control, digital twin predictive control, and advanced grid-forming (GFM) inverters is described. Based on existing reviews, this paper proposes an equipment–system–grid hierarchical analysis framework and explicitly integrates emerging technologies with classical methods. This framework provides references for the selection, engineering deployment, and future research directions of stability control technologies for photovoltaic power plants, while also offering technical support for the safe and efficient operation of high-penetration renewable energy power grids. Full article

The metric for probing the variation in atmospheric refractive indices is radio refractivity (RR), which is a key factor in determining the losses associated with a radio signal as it traverses from one atmospheric layer to another. Ten years (2015–2024) of surface hourly data of temperature (K), pressure (P), and relative humidity (RH) obtained from ERA-5 reanalysis were used for RR computations based on ITU-R models. Twelve major cities of South Africa were benchmarked for the study. Time series plots of the overall ten-year RR hourly mean were generated for the cities. The correlation coefficient (R) between RR and RH was investigated. The results indicate the highest and lowest RR of 360.94 and 301.09 (N-Units) in Pietermaritzburg and Kimberly, respectively, with a range of 59.85 over the country. In the southern coast, Pietermaritzburg recorded the highest and lowest values of 360.14 and 325.52 (N-Units) at 21:00 and 11:00 hrs., followed by Durban with 348.55 and 339.44 at 17:00 and 10:00 hrs., Bhisho with 346.88 and 320.622 at 00:00 and 11:00 hrs., and Cape Town with 328.54 and 322.47 (N-Units) at 00:00 and 10:00 hrs., respectively. In the central region, Bloemfontein recorded values of 344.97 and 305.58 at 04:00 and 13:00 hrs., respectively, while Kimberly recorded 338.06 and 301.09 at 04:00 and 13:00 hrs., respectively. In the northern region, Johannesburg recorded the highest and lowest values of 358.79 and 318.56 (N-Units) at 03:00 and 13:00 hrs., respectively; Pretoria recorded values of 352.25 and 316.76 at 04:00 and 13:00 hrs., respectively; Emalahleni recorded values of 358.79 and 318.95 at 03:00 and 13:00 hrs., respectively; and Polokwane recorded values of 357.59 and 320.82 at 03:00 and 13:00 hrs., respectively. Mahikeng recorded values of 346.70 and 311.37 at 04:00 and 13:00 h, while Mbombela recorded values of 360.11 and 329.17 (N-Units) at 00:00 and 12:00 h, respectively. The implications of these results are a higher refractive attenuation effect of terrestrial transmitted radio signals in cities with higher RR and during the early morning, evening, and night hours of the day. A high positive (R) of 0.84 to 0.99 was observed between RR and RH across the country. A geo-spatial RR contour map was generated for the study stations for practical applications and could be helpful in cities where the contour passes within South Africa. These findings should be taken into consideration in the design and reappraisal of terrestrial radio-link and power budgets to ensure quality of service. The overall findings provide practical applications for mitigating RR-prone attenuation on terrestrial radio channels, such as Radio and Television broadcasting, GSM, and microwave link systems, among others, across South Africa and other countries with similar geography and climate. Full article