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A novel hybrid bandpass filter (BPF) with wideband and high selectivity is proposed in this paper. The hybrid BPF is composed of two film bulk acoustic resonator (FBAR) cells and three lumped component resonators realized by the integrated passive device (IPD) technology. The ABCD matrix to S-parameters matrix method is used to calculate the frequency response of the BPF. Moreover, the expressions of transmission zeros (TZs) have been extracted. In the design process, an iterative design approach is proposed to improve the circuit and layout of the hybrid filter based on the packaged acoustic-electric hybrid simulation effect. Finally, two parts of the filter are packaged based on the flip-chip method, and two prototypes for the BPF are measured. The measured results of two chips with 3 dB fractional bandwidth of 13.7% and 15.8% are designed and fabricated, which verifies the validity of the proposed design principle. [ABSTRACT FROM AUTHOR]

With the fast growth of electronic technology today, the hybrid circuit of analog and digital circuits has become a trend in the growth of electronic technology. To address fault diagnosis in analog circuits, Haar wavelet is applied for fault feature extraction. The K-means clustering method and pseudoinverse algorithm were used to optimize the center value and weight value of the radial basis function neural network, respectively. The adaptive step size was improved in the wolf pack algorithm, the parameters of the radial basis function neural network were optimized based on the wolf pack algorithm, and a wolf pack algorithm optimized radial basis function neural network model was constructed. The test results show that this model converges after 40 times of training, with an error value of 10−3 and an average value of the mean squared error of 0.45. Comparing the fault diagnosis rates of the original model, genetic algorithm optimized radial basis function neural network model, and wolf colony algorithm optimized radial basis function neural network model, the last model has the best fault diagnosis rate, reaching 95.52%. The wolf colony algorithm is utilized to optimize the radial basis function neural network model to diagnose the faults in the standard filter circuit, and the fault diagnosis rate reaches 96.17%. The findings express that the radial basis function neural network model optimized by the wolf colony algorithm has a good diagnosis effect for different faults in the analog circuit. [ABSTRACT FROM AUTHOR]

In recent years, low-voltage direct current (LVDC) microgrids are becoming more attractive because they represent a solution to integrate renewable sources, storage, and electronic loads bringing some advantages in comparison with traditional AC grids. However, the protection of such a network involves many challenges, especially in the case of LVDC microgrids with more than one feeder and multiple energy sources. Indeed, the traditional protection breakers used for an AC grid cannot isolate the faults and protect the components of a DC grid, while the use of solid-state circuit breakers increases energy losses. This paper deals with the analysis and design of the protection schemes for LVDC microgrids through the combination of mechanical circuit breakers and hybrid circuit breakers. This solution has the advantage of energy loss reduction but introduces further issues due to the slow transition times of the mechanical circuit breakers. Thus, a completely decentralized control system capable of overcoming the fast fault-clearing time, cost-effectiveness, and selectivity issues is designed to protect from pole-to-pole faults. The proposed control strategy is compared with a centralized protection scheme available in the literature through numerical simulation. The two algorithms show similar performances, with a mean voltage dip duration of less than 30 ms and a maximum voltage dip duration of about 100 ms in the most severe fault condition, but the proposed solution is more reliable and flexible since it does not depend on the communication system. [ABSTRACT FROM AUTHOR]