Search Results (7)

To enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. By analyzing the non-periodic and periodic components of short-circuit currents, the half-cycle elimination method and fast Fourier transform are utilized to compute these two components, respectively. The double-sampling CFI sequence approach is designed to fully utilize the response and waiting times of relay protection. Following the first sampling to estimate the target zero-crossing point, the remaining response and waiting times are allocated for a second sampling and recalculation to enhance the precision of zero-crossing prediction. MATLAB R2023a is employed to conduct multi-scenario simulations, and the algorithm’s performance is evaluated using actual recorded waveform data. The results demonstrate that the proposed algorithm accurately predicts the target zero-crossing point after a short circuit, with a computational error of less than 0.2 ms. Furthermore, the double-sampling sequence method is shown to improve the accuracy of open-circuit zero-crossing point calculations by an order of magnitude. This work provides a novel technical approach for the fast and precise handling of short-circuit faults in power systems. Full article

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics. Full article

The porosity of differently wetted carbonaceous material with disordered mesoporosity was investigated using low-field ¹H NMR relaxometry. Spin–spin relaxation (relaxation time T₂) was measured using the CPMG pulse sequence. We present a non-linear optimization method for the conversion of relaxation curves to the distribution of relaxation times by using non-specialized software. Our procedure consists of searching for the number of components, relaxation times, and their amplitudes, related to different types of hydrogen nuclei in the sample wetted with different amounts of water (different water-to-carbon ratio). We found that a maximum of five components with different relaxation times was sufficient to describe the observed relaxation. The individual components were attributed to a tightly bounded surface water layer (T₂ up to 2 ms), water in small pores especially supermicropores (2 < T₂ < 7 ms), mesopores (7 < T₂ < 20 ms), water in large cavities between particles (20–1500 ms), and bulk water surrounding the materials (T₂ > 1500 ms). To recalculate the distribution of relaxation times to the pore size distribution, we calculated the surface relaxivity based on the results provided by additional characterization techniques, such as thermoporometry (TPM) and N₂/−196 °C physisorption. Full article

Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales. Full article

The charging time of Li-ion batteries connected in series (LBCSs) power supply is the main concern in an electromagnetic propulsion system. However, the capacity loss of a Li-ion battery is inevitable due to the repetitive operation of LBCSs power supply, which leads to the decrease in the average current. Thus, the voltages of symmetrically distributed pulse capacitors of LBCSs power supply will not reach the setting value in the specified time. This paper proposes a novel closed-loop control method to solve the problem. By collecting the pulse capacitor voltage and the circuit current, the time sequences of Li-ion batteries are recalculated in real time in a closed-loop to increase the average current. The time-domain model of the circuit topology of the LBCSs power supply and an innovative closed-loop control method based on the time sequences recalculation algorithm are described first. Then, the circuit model is built in PSIM for simulation analyses. Finally, a series of experiments are conducted to confirm the effectiveness of the method on the megawatt LBCSs power supply platform. Both the simulation and experimental results validate that the proposed method not only shortens the charging time, but also increases the average current. In practical experiments, the charging time is shortened by 4.5% and the average current is increased by 4.8% using the proposed method at the capacity loss of 50 V. Full article

Research Highlights: One of the priorities of the European Commission is to build up an effective circular economy based on recycling and multiple use of materials. Wood biomass is a renewable raw material and can be used several times in a cascading sequence. Each country has a unique situation regarding the availability and utilization of wood sources. Background and Objectives: The objective of this study was to analyze wood flow in the Czech Republic using the cascading principle of biomass use. The specific situation in the Czech Republic lies in a lack of valid and reliable input data from official statistics. Therefore, the reverse input method was applied. Materials and Methods: Input data analyses of roundwood sources and foreign trade were based on official statistical data. The calculation of raw wood volume consumption in primary processing was performed based on the data after our own correction and recalculation. It was then possible to build up a basic model of multi-stage cascade wood use. The input volume of roundwood was divided among all types of primary processing production using conversion factors. Results: Cascading use of wood (CUW) showed the level of efficiency of the resource. Official statistical input data and the reversed input data regarding raw wood volume entering wood processing revealed differences at a level of 27%. The overall CUW in the Czech Republic indicates a high rate of wood use in primary processing with low added value and in generating energy. Conclusions: The reverse input method reveals the real situation of wood consumption irrespective of the level of official statistical data. It is suitable to apply in an environment of incomplete or incorrect input data. CUW in Czechia showed an opportunity for increasing the efficiency of wood utilization. The structure of wood use needs to be optimized towards creating greater added value. Full article

When a blackout occurs, it is important to reduce the time for power system restoration to minimize damage. For fast restoration, it is important to reduce taking time for the selection of generators, transmission lines and transformers. In addition, it is essential that a determination of a generator start-up sequence (GSS) be made to restore the power system. In this paper, we propose the optimal selection of black start units through the generator start-up sequence (GSS) to minimize the restoration time using generator characteristic data and the enhanced Dijkstra algorithm. For each restoration step, the sequence selected for the next start unit is recalculated to reflect the system conditions. The proposed method is verified by the empirical Korean power systems. Full article