Journal of Electrical Engineering & Technology最新文献

We investigated the use of photoplethysmography (PPG) features to assess the severity of both intraoperative and postoperative pain. PPG data was collected from 386 patients undergoing routine surgery. We extracted 180 pain assessment features based on PPG waveform characteristics identified in previous studies. Pain assessment involves a two-step process. First, we evaluated the presence of pain using the extracted features. If significant pain was detected, we then conducted a severity analysis. Pain severity was categorized into three groups: no pain, moderate, and severe. Intraoperative and postoperative pain labeling were based on clinical judgment and numerical rating scale criteria, respectively. For intraoperative pain presence, we performed statistical tests to identify significant changes in features before and after both intubation and skin incision. Postoperative pain presence analysis compared preoperative and postoperative periods. Statistical analysis revealed 106 and 124 features significant for intraoperative and postoperative pain presence, respectively. Among the pain-related features, 27 related to PPG amplitude, area, and slope were significant for all severity comparisons (no pain vs. moderate, no pain vs. severe, and moderate vs. severe) during intraoperative assessment. Postoperative severity assessment identified 12 significant features related to PPG amplitude, area, and pulse interval. These results suggest the potential of PPG-based features for assessing pain severity.

Although the heavy non-powered trailer can be easily moved and driven by the prime traction vehicle, positioning and parking are not easy. This is because it requires highly skilled driving skills. The electric mover system can drive the heavy non-powered trailer itself without a prime vehicle using the electric driven roller at the trailer wheel. During the driving mode, the mover roller is detached from the trailer wheel to reduce the traction vehicle load. The roller is solid-firmed to the trailer tire and then rotates the trailer wheel by the electric motor to adjust the parking position during the parking mode.

To suppress the proper friction between the mover roller and trailer tire, the actuator motor has to be stopped at the proper braking point of the roller. And, this can be implemented by the actuator motor current detection. A simple over-current with the holding time is generally used to determine the actuator braking point between the roller and the tire. The conventional over-current detection is very simple and powerful, but the actual frictions at each tire are not similar due to the load and operating conditions of each side mover. The different frictions at each side tire increase the motion and moving errors at the actual positioning and parking motion.

To balance the friction between the mover rollers and each tire, the non-linear disturbance force observer based on the sensorless speed estimation of the actuator motor is proposed. To keep the same friction force, the actuator motors are stopped at the same pre-fixed friction level using the estimated disturbance force and estimated actuator motor speed.

Compared to the conventional over-current holding time method, the proposed method shows improved friction control performance at each wheel and mover roller. And, the moving torques are improved due to the balanced friction.

The high electric field generated by conductive particles significantly reduces the strength of air insulation. Thus, understanding the effects of conductive particles on the insulation characteristics of air is essential to ensure the operation and stability of a power facility based on air insulation. The purpose of this paper is to provide knowledge to understand the breakdown process caused by conductive particles. This paper presents the insulation characteristics of air in an electrode structure with fixed-multiple conductive particles, and proposes a breakdown development model based on the corona discharge mechanism. Experiments on ac breakdown in atmospheric air were conducted by varying the electrode gap and by using several types of fixed-multiple particle arrangements. It is interesting that the breakdown voltages of air were almost equal for the electrode structures with fixed-single and multiple particles. For understanding the breakdown process, the current waveforms were measured during the corona development. Corona discharges associated with multiple particles are first observed in the positive period of the applied voltage. The breakdown development model including this corona characteristics was proposed based on the electron generation mechanism during the positive and negative period of the applied voltage.

Non isolated multilevel inverters (MLI) with reduced switch components are becoming more popular to attain higher voltage levels. However, this kind of MLI increases the number of DC sources and has some issues like higher charging current, a high peak VA rating of the switches, and a high capacitor ripple voltage. This manuscript presents a modified multicell based multilevel boost inverter with a multistage DC output. It has three sections namely Boost converter, level shifter and h-bridge inverter. The proposed circuit is powered by solar photovoltaic (PV); using the Perturb and Observe Maximum Power Point method. The suggested circuit working in asymmetric mode and produces a 31-level output waveform. This topology helps to reduce the Total Harmonic Distortion, increases the output voltage levels, and reduces the common mode voltage. This topology is compared with other multilevel inverters presented in recent literature and the analysis is presented. Simulation results for various irradiance values are presented with other key factors. The experimental prototype of a 1.5 kW, single phase, 31-level inverter is designed and verified and the proportional results are substantiated.

This study investigates an approach related detection of series arc faults in the DC lines through the utilization of features extracted from the difference between odd and even components of the signal, integrated with intelligence models in diverse domains. Series DC arc faults pose significant safety risks in various electrical systems, necessitating robust detection methods. In this research, the authors propose a novel approach that leverages the unique characteristics of the signal’s odd and even components to enhance fault detection accuracy. The methodology involves preprocessing the signal to extract relevant features capturing the discrepancy between odd and even components, which are then used as inputs for AI models. These models are trained to classify fault and non-fault conditions based on the extracted features. The integration of feature extraction from odd and even signal components with AI models offers a promising solution for heightening the reliability and efficiency of DC arc error recognition systems in various industrial and residential applications.

In order to improve the thrust characteristics of the multi-phase permanent magnet synchronous linear motor, the five-phase U-shaped consequent-pole permanent magnet synchronous linear motor (FUCP-PMSLM) with 15-slot 16-pole is taken as the research object to analyze the motor thrust and optimize the design parameters. Firstly, the equivalent magnetic circuit method is used to compare the air gap flux density of the U-shaped permanent magnet motor with the non-salient permanent magnet motor, and the influence of the motor phase number changes on the motor thrust is analyzed from the perspective of the magnetomotive force. Secondly, the Taguchi method is used for sensitivity analysis, and four sensitive parameters are selected from seven design parameters as optimization variables, the mathematical model between the motor optimization objectives and optimization variables is obtained by response surface methodology fitting, the multi-objective genetic algorithm is used to solve the problem. The finite element simulation results show that, compared with the initial motor, the average thrust of the optimized motor is increased by 5.8% and the thrust ripple is decreased by 71.3%. Finally, an experimental prototype is fabricated and tested, the test results are basically consistent with the simulation results, which verifies the effectiveness of the design parameter optimization.

This paper is a one-year case study of day-ahead prediction of PV output at Changwon in South Korea. We are focused on day-ahead hourly PV power forecasting and long-term experiments in this paper. We introduce three machine learning based forecasting methods that predict hourly PV power for the next day at midnight, and show performance of them for a 51 kW PV system located at Changwon for a year. Our methods learn relationship of historical meteorological factors, and then predict 24 h PV power considering the trained relationship and weather forecasts from weather forecasting organizations. We show monthly performance of all the proposed methods and a persistence model for a year. Since South Korea is located in a temperate zone with four distinct seasons, and has complex climate characteristics, it is difficult to show actual performance of PV forecasting methods by short-term experimental results. We believe that long term experimental results in this paper are valuable data for the next studies.

The large-scale development of electric vehicles has made accurate short-term charging load prediction increasingly important for ensuring the safe operation of the power grid. To address issues of poor generalization ability and overfitting in single models, this paper proposes an integrated stacking prediction algorithm that combines three models: category boost (CatBoost), light gradient boosting machine (LGBM), and ridge regression (RR), using a stacking integration framework. The Cat–LGBM–RR model uses an internal stacking mechanism, where the RR model calculates the final prediction results after the CatBoost and LGBM models generate the necessary metadata. The effectiveness of the proposed model is demonstrated using load data from a new energy charging pile organization in a province of China. This paper’s contributions include: (1) proposing a stacking integration-based prediction algorithm; (2) providing a more thorough feature construction approach; (3) comparing and verifying the performance using enterprise real data sets and a variety of reference models. Numerical examples show that the mape of the Cat–LGBM–RR model was 4.52%. Compared with other models, it has precision advantage.

The large scale integration of renewable energy sources and energy storage technologies is driven by energy transition. The integrated technologies pose multiple uncertainties and challenges to System operator such as inconsistency, instability, and economic infeasibility in the Unit Commitment (UC) problem. It requires addressing multiple uncertainties in UC problem while ensuring reliable and cost-effective grid operation. In this paper, a net load demand model is proposed for incorporating multiple uncertainties. Uncertainties pertaining to photovoltaic (PV) generation, load forecasts and energy storage (ES) are modeled with a joint chance constraint approach for solving stochastic day ahead UC. The chance constraint is employed to limit the probability of joint uncertainty within the predefined bounds. The next day UC schedule and costs for IEEE 39-bus system are solved by Mixed Integer NonLinear Programming (MINLP). Three case studies are performed to validate effectiveness of proposed model. Case 1 is base-system analysis of UC costs without uncertainties. Case 2 describes impacts of load forecast uncertainty on UC. In Case 3 impact of joint chance constrained multiple uncertainties on UC cost and schedule are studied with coordinated PV-ES operation. Results prove the efficacy of proposed net load demand model for optimizing system UC with multiple uncertainties.

This article proposes a methodology for designing Model Predictive Control (MPC) systems utilizing data-driven neural network model. The performance of MPC systems is highly dependent on the precision of the underlying model. While linearizing a vehicle’s dynamic model is feasible under certain conditions, accurately capturing the vehicle’s nonlinear dynamics, such as cornering stiffness and the interaction between tires and road surface, remains challenging. To address these nonlinear dynamics, this study proposes the estimation of a second-order lateral dynamic vehicle model via Long Short Term Memory (LSTM) model. The data-driven model utilizing LSTM has demonstrated better accuracy in simulating lateral vehicle motion when compared to conventional linearized dynamics model that employs nominal parameters. Ultimately, this data-driven LSTM model was incorporated into an MPC framework. This LSTM model-based MPC revealed enhanced performance in tracking accuracy over traditional MPC systems that utilize linear models for vehicle lateral dynamics.