Recent Advances in Electrical & Electronic Engineering - Volume 17, Issue 4, 2024

Background: The dynamics research of gear transmission systems mainly revolves around "excitation-model-response". The increasing number of dynamic incentive factors considered in the study has brought new problems to selecting modeling and solution methods and analyzing dynamic characteristics. Objective: This study aims to sort out the main research content of gear transmission system dynamics. Moepver, the commonly used analysis models, modeling, and solution methods are compared, and references for method selection and in-depth research are provided. Methods: This paper reviews the representative papers and patents related to the dynamic analysis of the gear system. The main contents of the dynamic excitation, dynamic model and dynamic characteristic analysis of the gear system are discussed, and suggestions for future development directions are given. Results: The dynamic excitations mainly considered in the current research are internal excitations and external excitations; random excitations are rarely considered. This paper analyzes and summarizes the commonly used modeling methods, model classification, solution methods, and dynamic characteristics research content. The advantages and disadvantages of several commonly used analysis models, modeling, and solution methods and their applicable occasions are summarized for the reference of researchers. Conclusion: The dynamics study of the gear system is a systematic work. It requires comprehensively considering the influence of dynamic excitations and selecting appropriate methods to establish and solve the model to obtain the dynamic characteristics that can better reflect the actual working conditions of the gear system. It is of great significance to improve the performance of the gear system.

Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

Background: With the increasing penetration of new energy resources such as wind farms and photovoltaic power plants, there is concern about the stability of power systems. Methods: Aiming at the low-frequency oscillation issue of the wind-PV hybrid system, a coordinated optimal design strategy based on Static Var Compensator (SVC) and Power Oscillation Damping (POD) control is proposed. Modal analysis is used to locate the controller, then the Whale Optimization Algorithm (WOA) is introduced to optimize the SVC-POD controller parameters. To solve the wind-PV hybrid energy system's low frequency oscillation issues, the damping ratio and angular velocity of the generator rotor are considered in the objective function. Results: Taking IEEE four-machine two-area system as an example, several working conditions are designed, including changing the power of the contact line. The eigenvalue analysis and simulation findings verify that the suggested proposed method can successfully raise the damping ratio of the system, keep the generator speed stable, and restrain the occurrence of low-frequency oscillation. Conclusion: Simulation results demonstrate that this method efficiently suppresses the lowfrequency oscillation and increases the robustness of the system.

Aims and Background: Focusing on the low-frequency oscillation and system power loss problem in wind-thermal-bundled (WTB) transmission systems and the interaction problem due to different controllers, this study aimed to improve the low-frequency oscillation characteristics of WTB transmission system while ensuring the economy of operation and suppressing the adverse interaction between controllers. Methods: For this purpose, a coordination and optimization strategy for a power system stabilizer (PSS), static synchronous series compensator with additional power oscillation damping controller (SSSC-POD), and static synchronous compensator with additional power oscillation damping controller (STATCOM-POD) is proposed based on multi-objective salp swarm algorithm (MSSA). The controller regulation characteristics are taken into account in the coordination method. Results: Several designed scenarios, including changing the transmission power of the tie line and increasing wind power output, are considered in the IEEE 4-machine 2-area to test the proposed method. The power flow analysis, characteristic root analysis, and time-domain simulation are used to analyze the simulation results. Conclusion: Simulation results demonstrate that the proposed approach can effectively suppress the low-frequency oscillation of the WTB system while reducing its net loss. The application in engineering issues for MSSA is supplemented.

Introduction: The introduction of virtual resistance can effectively suppress the circulating current between micro-sources and improve power allocation in low-voltage microgrid, but it also causes the voltage deviation of micro-sources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation at the same time in this paper. The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. The effects of switching load and micro-source on the optimization results are analyzed through the simulation of low-voltage microgrid, and the simulation results show that the virtual resistance optimization method can significantly suppress circulating current while improving power quality. When distributed generators are connected to utility grid through inverters and feeders, differences in feeder parameters and inverter control strategies easily cause circulating current and uneven power distribution among micro-sources. The introduction of virtual resistance can effectively suppress the circulating current between micro-sources and improve power allocation in low-voltage microgrids, but it also causes the voltage deviation of micro-sources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation. Methods: The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. Results: In the simulation scenario of two micro-sources and three micro-sources, the virtual resistance obtained by the method proposed in this paper has more obvious improvement on the system operation index, and is not affected by the load type. Conclusion: The method of optimizing virtual resistance based on the hybrid index can achieve the effect of restraining circulating current and improving power sharing degree on the premise of guaranteeing power quality and satisfying system stability. The optimization of virtual resistance is affected by the number of feeders. It is necessary to re-optimize the virtual resistance after changing the number of feeders, but the process of switching micro-source and adjusting load does not affect the optimized resistance value.

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

Background: With the gradual construction of new power systems, new energy sources, such as wind and photovoltaic power, will gradually dominate positions in the power supply structure, directly leading the new power system to rely heavily on accurate meteorological forecasts. High-precision and high-resolution meteorological forecasts are important technical methods to improve the safe, stable, and economic operation of the new power system. Objective: Since the analysis of meteorological elements is the basis of meteorological forecasting, in this paper, the effect of different meteorological elements including temperature, relative humidity, air pressure, wind speed, wind direction, and radiation on the performance of power forecasting, was analyzed by using 7 machine learning algorithms in 5 provinces in southern China. Methods: About 5 provinces in southern China were selected as the research objects, and 7 typical machine learning algorithms were applied and compared, including support vector machine (SVM), decision tree (DT), random forest (RFR), K-nearest neighbor (KNN), Linear Regression (LR), Ridge Regression (RR), and Lasso Regression (Lasso R). At the same time, the influence of different meteorological elements, such as temperature, relative humidity, air pressure, wind speed, wind direction, and radiation amount, on the prediction performance of wind power and photovoltaic power was considered. Then, the performance of different regression models was further investigated and analyzed. Results: Based on the data of 10 new energy stations in 5 regions, the research on the prediction performance of 7 machine learning methods shows that the performance of models in different regions varies greatly. Among the 10 selected new energy stations, the RFR model and KNR model have superior overall performance. Conclusion: This study shows how variable importance and prediction accuracy depend on regression methods and climatic variables, providing effective methods to assess the interdependence of meteorological variables and the importance of meteorological variables in predicting output power.