Recent Advances in Electrical & Electronic Engineering - Volume 16, Issue 5, 2023

Introduction: A more modern, extremely applicable method for signal acquisition is compression sensing. It permits effective data sampling at a rate that is significantly lower than what the Nyquist theorem suggests. Compressive sensing has a number of benefits, including a muchreduced demand for sensory devices, a smaller memory storage need, a greater data transfer rate, and significantly lower power usage. Compressive sensing has been employed in a variety of applications because of all these benefits. Neuro-signal acquisition is a domain in which compressive sensing has applications in the medical industry. Methods: The novel methods discussed in this article are FFT-based CoSaMP (FFTCoSaMP), DCT-based CoSaMP(DCTCoSaMP) and DWT-based CoSaMP (DWTCoSaMP) based on sparse signal sequences / dictionaries by means of Transform Techniques, where CoSaMP stands for Compressive Sampling Matching Pursuit with respect to Objective Quality Assessment Algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for Compressive Sampling Matching Pursuit. Results: DWTCoSaMP is giving the PSNR values of 40.26 db, for DCTCoSaMP and FFTCoSaMP, PSNR is 36.76 db and 34.76 db. For DWTCoSaMP, SSIM value is 0.8164, and for DCTCoSaMP and FTCoSaMP, SSIM 0.719 and 0.5625 respectively. Finally, for DWTCoSaMP, RMSE value is 0.442, and for DCTCoSaMP and FFTCoSaMP, SSIM 0.44 and 0.4425, respectively. Conclusion: Among Compressed sampling techniques DWTCoSaMP, DCTCoSaMP and FFTCoSaMP discussed in this paper, DWTCoSaMP reveals the best results.

Background: In renewable energy system applications, the power converter is essential for transferring energy into the load centre. The AC network voltage is much higher than the renewable energy source voltage; therefore, a transformer is commonly used to step up the voltage. However, the size and cost of the system are larger due to the presence of a bulky transformer, and also the efficiency is deteriorated due to PWM voltage being applied to the transformer. Objective: To eliminate the requirement of the transformer, DC link voltage is boosted up to a higher level using a high-gain converter. Various DC-DC converters have been proposed in recent studies; however, the involvement of the active switches is high to produce higher voltage gain, which reduces efficiency. In order to overcome this limitation, this particular paper proposes a novel switched hybrid-voltage doubler high gain DC-DC converter, which helps to achieve higher gain with a minimum number of components count compared to other reported converters. It also reduces the device's stress, thereby increasing its efficiency. Methods: This paper describes the detailed operational studies along with CCM and DCM modes. The mathematical analysis under each operating state is reported in this paper. A detailed comparative study on the performance merits of the proposed converter, along with other recently reported converters, is presented. Results: Finally, the simulation results are presented to validate the operation of the proposed converter. Furthermore, a 500 W laboratory setup is developed to verify the operation details and practical feasibility of the converter through experimental results and presented in this paper. Conclusion: From the measured results, it is concluded that the proposed converter can play the most promising role in the transmission phase in renewable energy system implementations since it boosts the voltage to the adequate point as required by the system.

Background: The 5G cellular networks are expected to provide high data rates for indoor environments by utilizing the mmWave bands. Due to a lack of general path loss models of these bands, various works have been carried out to find the most appropriate model that can provide accurate values of path loss over distance for different indoor scenarios. This paper focuses on some well-known path loss models, e.g, mixed and OpenOffice models introduced by 3GPP; the dualstripe model was presented by Alcatel-Lucent, picoChip Designs, and Vodafone at the 3GPP meeting; single and dual ABG models were designed by leading academic and industrial labs. Methods: Two network scenarios, e.g., single-cell and 3-cell networks, are utilized in simulation to analyze the effects of path loss models on network performance. Results: The simulation results indicate that the single-slop and dual-slop models can derive a similar performance when only a cell is deployed. However, in a 3-cell network system with intercell interference, the achieved performance in these systems is significantly different. Conclusion: The dual-slop model is better than the single–slope model in terms of path loss modeling since it can capture more characteristics of wireless transmission links, particularly the Light-of- Sight (LoS) and Non-LoS (NLoS) effects.

Background: Precise modeling and control system design are the basis of highperformance control of dual-winding bearingless brushless DC motors (BL-BLDCM), and the existing research is relatively few. Objective: This paper takes 12-slot/tooth 4-pole BL-BLDCM as the research object, establishes a set of a detailed mathematical model, and designs the control system of BL-BLDCM. ; Methods: Firstly, the structure of BL-BLDCM and the radial suspension force generation principle were introduced. Secondly, a set of an accurate mathematical model of BL-BLDCM was established which was finally verified by static and dynamic finite element method (FEM), the motor body model and control system were built by Matlab/Simulink software, and the simulation analysis of control performance was completed. Results: The established mathematical model is correct. The BL-BLDCM can generate stable electromagnetic torque and radial magnetic suspension force, and the control system has strong ability of resisting load torque disturbance and radial force load disturbance. Conclusion: It has certain reference significance for the accurate modeling and control system design of later BL-BLDCM.

Background: With the further opening of the electricity sales market, based on the current power reform situation, it is more emphasized to focus on the market and customers and carry out value marketing. Therefore, further mining the user value has become a necessary means for the transformation of power grid enterprises, and the construction of power user behavior portraits has very practical significance for the business expansion of power grid companies and the improvement of customer service levels. Objective: In order to further explore user value, a method of user electricity behavior portrait based on QFPAK-means (quantum flower pollination K-means) clustering is proposed. Methods: Through the quantum flower pollination algorithm, considering the overhead cost, the optimal classification number is automatically determined, and on this basis, K-means clustering is completed. In the meantime, the typical power consumption patterns of users are extracted by using the K-means clustering algorithm based on quantum flower pollination, and the features are extracted as the power consumption behavior portraits of users. Results: Through the comparative simulation of different methods, the effectiveness of the proposed algorithm is verified, which can provide outstanding guidance for power grid companies to expand their business and improve customer service levels. Conclusion: A method of user electricity behavior portrait based on QFPAK-means clustering is proposed to further explore user value, and the experimental results demonstrate the effectiveness and advantage of the proposed method.

Background: Electric load forecasting plays an essential role in the dispatching operation of power systems. It can be divided into long-term, medium-term, and short-term according to the forecast time. Accurate short-term electric forecasting helps the system operate safely and reliably, reduces resource waste, and improves economic efficiency. Objective: To fully use the time-series characteristics in load data and improve the accuracy of short-term electric load forecasting, we propose an improved Informer model called Nysformer. Methods: Firstly, the input of data is improved, and the information is input into the model in the form of difference. Then, the Nystrom self-attention mechanism was proposed, approximating the standard self-attention mechanism using an approximation with O(n) time complexity and memory utilization. Results: We conducted experiments on a publicly available dataset, and the results show that the proposed Nysformer model has lower time complexity and higher accuracy than the standard Informer model. Conclusion: An improved informer network is proposed for short-term electric load forecasting, and the experimental results demonstrate the proposed model Nysformer can improve the accuracy of short-term electric load forecasting.

Introduction: The power system is translating to the “generation, network, load, and storage multiple coordination control” model, which helps to increase the proportion of renewable energy consumption and reduce the operation cost. Materials and Methods: The residential electrical system is an important component in the power system, which includes distributed photovoltaic systems, flexible loads, electric vehicles, and battery storage systems, which have the potential to realize multiple coordination control and minimize the net cost of the electricity consumption in a residential electrical system by the coordinated cooperation of generation, flexible loads, and the battery storage system. The comfort level of the residents is also maintained during control. Therefore, a nonlinear economic model is introduced to represent coordinated cooperation of generation, flexible loads, and the battery storage system, and a nonlinear economic predictive controller is proposed to solve the problems of this model. Results: Based on the forecasted generation/load for a tumbling prediction window, the impact of different control trajectories is estimated, then the minimum cost trajectory for flexible loads and battery is selected, and only the first control action is implemented. Then, the prediction window is moved to the next time interval, and the same process is repeated. The case study shows that the proposed method reduces about 24.36% of the net cost of the residential electrical system. Conclusion: Meanwhile, the hot-water temperature and indoor temperature of the household are maintained without affecting the end-user comfort, and the state-of-charge (SOC) for the electric vehicle and the battery system is always kept under constraints without affecting usage. The proposed method has a good performance for the coordinated cooperation of generation, flexible loads, and battery storage system in the residential electrical system. The proposed method also increases the consumption of PV, with the implementation in more residential buildings that will benefit in achieving carbon neutrality by 2060 in China.

Background: Electronic voltage transformer (EVT) is an essential metering device for future substation automation systems. One of the main drawbacks of EVT is its poor long-term stability, which affects its measurement accuracy. This will, in turn, adversely affect the entire protection and control systems it is employed for. Objective: Aiming at reducing the EVT measurement error over long-term operation, an EVT error prediction method combining Prophet, temporal convolutional network (TCN) and selfattention is proposed in this paper. Methods: The proposed method is based on building prophet and TCN error prediction models to estimate preliminary prediction values. On this basis, self-attention is introduced to further extract features and make full use of the useful information in historical data. Then the secondary prediction can be achieved, and the final predicted value can be reported as an output. Results: The proposed method is validated by applying the error data of an EVT in a substation to its historical operation. The results show that the model can effectively predict the error trend of EVT. Conclusion: The prediction results of this method are similar to the fluctuations of the actual values, indicating that it provides a new reliable method for error prediction of EVT.

Background: In order to upgrade a motor based on the YXKK450-2 for high power density with high efficiency, low energy consumption, and low weight with the same volume, its power is increased from the original 900 kW to 1250 kW. To ensure good ventilation and heat dissipation characteristics, it is necessary to redesign the ventilation and cooling system of the entire machine. Methods: Based on the finite volume method, a physical model of the entire flow field of the motor's inner and outer air passages was established. The internal and external cooling systems of the motor were simulated, and the fluid flow law of the cooling system was realized. By extracting the pressure difference between the inlet and outlet of the cooler and the circulating cooling air velocity in the inner air passage, the boundary conditions for calculating the flowheat coupling field were determined. The temperature rise trend and overall temperature distribution were obtained. Results: The results of the test resistance method were in good agreement with the numerical simulation test results, The temperature rise error was 4%, and the wind speed value error was 2.7%, all within allowable limits (<5%), verifying the accuracy and effectiveness of the calculation method. Conclusion: An accurate prediction of motor temperature rise, fluid flow law, and temperature distribution is achieved.