Recent Advances in Electrical & Electronic Engineering - Volume 15, Issue 1, 2022

Background: The generator is a mechanical device that converts other forms of energy into electrical energy. It is widely used in industrial and agricultural production and daily life. Methods: To improve the accuracy of generator fault diagnosis, a fault classification method based on the Bare-bones Cuckoo Search (BBCS) algorithm combined with an artificial neural network is proposed. For this BBCS method, the bare-bones strategy and the modified Levy flight are combined to alleviate premature convergence. After that, the typical fault features are obtained according to the vibration signal and current signal of the generator, and a hybrid diagnosis model based on the Back- Propagation (BP) neural network optimized by the proposed BBCS algorithm is established. Results: Experimental results indicate that BBCS exhibits better convergence performance in terms of solution quality and convergence rate. Furthermore, the hybrid diagnosis method has higher classification accuracy and can effectively identify generator faults. Conclusion: The proposed method seems effective for generator fault diagnosis.

This paper highlights the notable individual contributions of the researchers in the field of first-order universal filters, and additionally, segregates the reviewed works amongst various scientifically chosen categories. Relevant first-order universal filter circuits reported in the last two decades are chosen for the review. The segregation categories are based on the well-recognized signal processing features, such as the mode of operation, number of active and passive elements, grounded nature of the passive components, simultaneous availability of the outputs, and the provision for resistorless realization. These works are also analyzed for various other performance enhancing attributes, like support for cascadability, transistor count, load insensitivity, matching constraints, etc. Moreover, exhaustive observations regarding existing works are also included individually in tabular form to mainly emphasize newness, uniqueness and downsides of reported ideas, which further enrich the quality of the review paper.

Objective: Ironless, permanent magnet, synchronous linear (IPMSL) motors are applied widely in precision servo control for the nonexistence of cogging forces and comparatively small fluctuations in thrust and speed. Methods: The air and water cooling structures are designed by assuming the heat loss in the motor operations is the source for the distribution of the temperature field in the analysis under natural cooling. Results and Conclusion: The temperature fields of the linear motor under the two cooling modes are compared and analyzed, which helps monitor the temperature of linear motors during development and operations.

Background: The infrared image of electrical equipment often contains snow or is blurred, which makes it difficult to detect and analyze its state. Methods: A prior infrared image denoising and restoring method based on L2-relaxation L0 analysis is proposed. Through the prior image estimation, the problem of image de-blurring and denoising is transformed into the problem of solving the maximum entropy of a posteriori probability, and then the parameters are jointly optimized to widely degrade the image, so that the image is locally sparse from the strip and edge to the linear predictable texture, and the target object to be extracted is obtained by using the alternative iterative solution, to achieve the purpose of denoising and restoring of the original fuzzy infrared image with noise. Two kinds of infrared images with different brightness levels in a 220kV booster station are used for the experiments. Results: Compared with BM3D, TwIST, TVL1C, TVL2C, the experimental results show that the denoising and restoration effect of the proposed method is clearly better than the four methods. The PSNR, ISNR, and SSIM of the proposed method are greater than the others, and the calculation time is shorter. Conclusion: This method can not only enhance the sparsity of the infrared image target and improve the estimation accuracy, but also has the advantages of minimum image distortion, fast convergence speed, and preserving the target detail edge. This method can provide a new idea for other types of infrared image denoising and restoration.

Background: The condition of the power transformer directly affects the reliability and efficiency of the power system. The dissolved gas analysis (DGA) has been widely recognized as one of the effective methods in the field of transformer fault diagnosis. Objective: To tackle the problem of insufficient single transformer fault data and weak generalization ability of the diagnosis model, this paper proposes a transformer fault diagnosis model based on data cleaning and transfer learning. Methods: 21 kinds of dissolved gas characteristics of the to-be-Diagnosed Transformer (TDT) and Auxiliary Transformers (ATs) are selected as fault features to detect transformer fault. The first data cleaning is used for Auxiliary Fault Data (AFD) based on similarity analysis between Target Fault Data (TFD) and AFD. Then the TFD and AFD are all cleaned to remove the singular edge interference data for the second cleaning. The transfer learning algorithm is applied to extract effective information from AFD and train the fault diagnosis model. Results: Test results show that the proposed method can improve the efficiency of fault diagnosis and the accuracy of fault identification. Conclusion: The two data cleanings complement each other, and both play a role in eliminating bad data and ensuring the accuracy of the fault diagnosis. Transfer learning can effectively extract effective information from AFD and train a better transformer fault diagnotor to improve fault diagnosis accuracy.

Background: Interferometer is the most preferred technique for high accuracy direction finding. For broadband direction finding (DF) applications, multiple antennas forming a number of baselines are used to achieve better accuracy in measuring the direction of arrival (DOA). Ambiguity resolution in multi-element interferometers is a crucial algorithm for the computation of DOA. Objective: This paper introduces a fast, algebraic Common Minimum Modulo Search (CMMS) algorithm for ambiguity resolution and DOA computation. LabVIEW-based tool for Monte Carlo simulation of the algorithm over various parameters is described. Methods: The CMMS is a computationally efficient algorithm, where the longest baseline phase difference is used for accuracy, and the phase difference data from all other baselines are used for resolving the ambiguities in the longest baseline. A hybrid approach using correlation and minimum algebraic search is used to determine the best fit set of ambiguities to the measured phase difference data. Results: Case studies have been presented in the literature and the performance of the CMMS algorithm was validated against established algorithms. This paper discusses the FPGA implementation as well as its simulation results. Analysis is performed on the 6-18 GHz frequency band. Moreover, the sources of errors and resulting limitations regarding the applicability of the algorithm are also discussed. Conclusion: Simulation results establish the algorithm's effectiveness in ambiguity resolution and applicability to diverse array configurations. The short nine-clock cycle processing latency in FPGA is demonstrated by the simulation results.

Background: CCCII implementation of the classic seventh-order Chebyshev filter is relatively rare. This work is a supplement to it. Methods: By a floating negative simulating resistance using single the second generation currentcontrolled conveyor (CCCII), we design a new current buffer to employ one CCCII and one multiple output CCCII (MOCCCII), and a new grounded MOCCCII-employed the Frequency-Dependent Negative Resistance (FDNR). Moreover, by employing a classic seventh-order Chebyshev passive filter as a prototype and utilizing adjoint network theory, a seventh-order Chebyshev active low-pass filter is realized through applying the Bruton transform, frequency and impedance scaling technique, FDNR, and component replacement method. The filter employs only grounded capacitors and nine CCCIIs, and performance can be controlled through adjusting bias currents of the CCCIIs. Results: The designed circuit is simulated with Multisim, which shows that the filter is correct and effective. Conclusion: A seventh-order Chebyshev active low-pass filter with Amax = 0.5 dB and fc = 10 kHz, is realized.

Background: DC-DC converters are utilized in a wide range of industrial and commercial applications such as automobiles, renewable energy systems, DC motor control, portable chargers, uninterruptable power supply, etc. because of their efficiency, ease of use, simpler circuits, and cheaper solution. The most common ones are the buck, boost, and buck-boost converters. Methods: In this paper, the implementation of these converters has been proposed using advanced controllers: Proportional plus Integral (PI), Sliding Mode Control (SMC), and Fuzzy Logic Control (FLC). The controllers have been implemented with practical circuit values in MATLAB Simulink. Results: The results show that all proposed controllers can track the input set-point voltage. The PI controller response was superior in eliminating the steady-state error in all converters. However, it showed greater overshoot and ripple factor in boost operation. The SMC controller response was superior in terms of the shortest settling time. However, it showed the greatest peak time in buck converter and boost mode of the buck-boost converter. The FLC controller showed the highest settling time and steady-state error in all operations. Conclusion: The proposed work is novel as compared to others available in the literature such that no such comprehensive study was found with all types of DC-DC converters together up to our best knowledge. The proposed work is significant as it will give complete guidance regarding simulation implementation of DC-DC converters with advanced intelligent control algorithms.