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To investigate the relationships among the chemical compositions of fog droplets, atmospheric pollutants, and the fog microphysical characteristics, four fog cases were sampled in a comprehensive field experiment conducted at the Donghai site in an agricultural city, China, in the winter of 2020. The pH, electrical conductivity (EC), total ion concentration (TIC), and chemical compositions of the fog water were all size-dependent. High concentrations of non-sea-salt calcium (nssCa2+) and NH4+ led to the alkaline pH of 6.13–7.32. The TIC of fog water was relatively high, especially in small droplets of diameter within 4–16 μm (463527.9 μeq/L). The relatively high NO2− concentration was also found in the fog water, dominated by the non-homogeneous chemical reaction between NO2 and fog droplets. An interesting finding is that the TIC of fog water measured on December 12 was much higher than that measured on December 11. Compared to the first fog, the stronger thermal inversion and local emissions led to the higher concentration of atmospheric particles in the second fog. Generally, the relatively high concentration of atmospheric particles resulted in a large number of small-sized droplets, which had fast liquid-phase oxidation reaction rates and high gas-liquid conversion efficiencies, leading to a higher TIC in the fog water during the second fog.

The realization of fault location method based on traveling wave natural frequency depends on the extraction of traveling wave natural frequency. When the fault occurs on the power system with one or more nodes, there will be multiple reflections at the discontinuous wave impedance, which makes it difficult to extract the natural frequency principal component that represents the fault distance in the spectrum. Upon this foundation, a traveling wave natural frequency extraction method based on PSO and VMD is proposed in this paper. Firstly, PSO algorithm was used to calculate the envelope entropy of VMD at different parameters, and to select the optimal parameter combination corresponding to the minimum envelope entropy. Then, the fault traveling wave is decomposed by VMD based on the optimal parameter combination, and to extract principal components of natural frequency by analyzing spectrum. Finally, the simulation model of T-type transmission line is built to verify the reliability of this method. The simulation results show that this method can effectively suppress the influence of spectrum aliasing, accurately extract the natural frequency principal component including fault information, and the ranging error is small.

To make an appropriate compromise between the solving speed and solution quality for a distribution network with distributed generations (DGs), an efficient reconfiguration algorithm based on vector shift operation (VSO) considering DGs is proposed. The nodal equivalent power model considering the influence of DGs is established. Power moment is selected as an index to decide whether load flow calculation or VSO should be conducted. In the former case, all bus voltages are updated and then be regarded as the initial condition for VSO. In the latter case, the power and resistance vectors are first formulated based on the structure and parameters of the distribution system, and the variation of power loss after branch exchange is then calculated based on these vectors instead of the time-consuming load flow calculation. The power and resistance vectors are updated by simple element shift operation after each step of branch exchanging. The final optimal solution is obtained when the open-branch set remains unchanged after branch exchanging. The proposed VSO-based reconfiguration algorithm is applied to a practical distribution system in Taiwan. The influences of the DGs and network reconfiguration on power loss and nodal voltages are illustrated. Test results show that the proposed reconfiguration algorithm is feasible and efficient.

Knots are common characteristics of sawn lumber that can adversely affect its strength properties. Clusters of knots and knots adjacent to each other are known to have combined lumber strength-reducing effects. However, grading rules that stipulate the allowable sizes and distances among distinct knots are somewhat qualitative and subjective, as the spatial interaction of knots and their resulting relationship to strength properties are not fully understood. This thesis’s objective is to investigate and quantify the strength-reducing effects of the spatial distribution of knots on Douglas Fir (DF) lumber, via high-resolution scans of its surfaces. A new approach for modelling lumber strength is proposed with an important extension that incorporates all relevant knot information, rather than a single strength-reducing knot. To complete the dataset needed for the new approach, we identify the spatial locations and sizes of the knot faces for individual lumber specimens using the knot detection and localization algorithms developed concurrently. The knot matching algorithm recently developed in our lab is used to reconstruct the knots’ internal three-dimensional structures. Non-destructive testing is conducted to obtain the transverse vibration measurement of Modulus of Elasticity (MOE). The destructive strength testing is then carried out to each specimen to obtain the strength measurement of its ultimate tensile strength (UTS) with the mode and location of failure recorded. We adopt the Bayesian approach to handle the model inference problem. The fitted model provides a predictive distribution for lumber tensile strength that accounts for the spatial distribution of knots and their strength-reducing characteristics. The efficacy of the approach is demonstrated via simulation studies and then applied to the data collected by testing a sample of 113 DF lumber specimens. The predictive performance of the model is investigated, and comparisons are made to the baseline regression models. The insights gained on the strength-reducing effects of knots and their spatial locations are described. We then discuss how the new approach could contribute to the improvement of lumber grading practices.

A large number of distributed generators access to low resistance grounding system may have great influence on the fault characteristics of single phase ground. In view of low resistance ground active distribution network, the connection mode of Inverter-interfaced Distributed Generators (IIDG) is discussed comprehensively based on operation safety and protection requirements. The equivalent model of IIDG and simplified zero sequence network model of low resistance grounding system contain multi-IIDG for single phase ground fault are established. The amplitude and phase characteristics of zero sequence current in single phase grounding fault of the system and the characteristics of high-resistance ground fault are analyzed. Simulative results by MATLAB/Simulink verifies the correctness of the analysis results of fault characteristics. The conclusions could be provided as reference for solving the grounding protection of system with high penetration of distributed generator.

Transformer differential protection may be of malfunction at the emergence of inrush current, and it will affect the normal operation of the transformer. So the paper puts forward a new application of wavelet energy spectrum entropy-neural network theory in transformer microcomputer protection, in which the multi-resolution analysis of wavelet transform and information entropy technology are combined firstly and forms a new conception named wavelet energy spectrum entropy, and it will be put into neural network theory as the feature vector, forms the new algorithm in the end. This method decomposes signal through wavelet transform, and extracts the high frequency part of energy in each scale of wavelet transform from inrush current signal and the short circuit current signal, and calculates the wavelet energy entropy value, which will be as the input feature vector of modified BP neural network. And this feature vector is used as training characteristic value for training in BP neural network. According to the measured data of the system, it has achieved good effect. At the same time, for the large amount of calculation and the high requirements of signal sampling rate in wavelet energy entropy - neural network algorithm, a new idea which uses the high-speed hardware platform of FPGA to realize the algorithm application is put forward, and it will break the bottleneck of traditional microcomputer protection that the MCU would not give consideration to both the speed and the accuracy of the protection at the same time. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3524

In this paper, carrier based islanding protection is taken as the research objects, and the signal injection equipment and the islanding protection equipment are designed to implement the protection. First, it makes demands on signal injection equipment, and chooses proper signal injection mode, signal frequency and signal source according to the demands; then, it makes demands on islanding protection equipment, and chooses optimal signal detection method, signal processing algorithm and the measurement of voltage and frequency; finally, it establishes the simulation model of DG interconnection system with signal injection and islanding protection using matlab to validate the feasibility of signal injection equipment and islanding protection equipment. Simulation results show that the designed equipments satisfy the requirements and carrier based islanding protection can be implemented reliably.