Multi-source System Research Articles

Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis.

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

Energy Management Strategy for Distributed Photovoltaic 5G Base Station DC Microgrid Integrated with the CF-P&O-INC MPPT Algorithm

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems.

Microstructural and optoelectronic properties of sputtered Al:ZnO films and Al:ZnO/Cu bilayer structures: Effects of substrate and Cu thickness

In this study, using a multi-source confocal radio frequency magnetron sputtering system, we synthesized aluminum doped zinc oxide (AZO) single layers and AZO/Cu bilayers on glass and crystalline quartz substrates with a constant AZO thickness of 65 nm and variable Cu bottom layers with a thickness of 4–13 nm. The microstructural, morphological, optical, and electrical characteristics of all samples were investigated using a variety of spectroscopic techniques. The X-ray diffraction results show that all films prepared on both substrates have a hexagonal wurtzite crystal structure and exhibit the preferred orientation along the (002) plane with some microstructural variations. The atomic force microscopy images revealed that the thickness of the Cu layer and substrate type greatly affect the films' topography and surface roughness. The transmittance spectra indicate that the single AZO layer on both substrates possess the highest average visible transmission; however, all the samples deposited on glass substrates show greater transmittance than that on quartz. Photoluminescence analysis put into evidence a decrease in ultraviolet emission with Cu thickness for both substrates, with intensities varying according to substrate type. Hall Effect measurements reveal that all samples have n-type conductivity as well as that Cu thickness has a significant impact on their electrical characteristics. Moreover, a higher value of the figure of merit is achieved with the AZO/Cu bilayer structure grown on a glass substrate with 13 nm of Cu layer thickness.

Utilization and performance comparison of several HESSs with cascade optimal-FOD controller for multi-area multi-source power system under deregulated environment

Electric power system network is evolving rapidly towards a more flexible, robust and secured interconnected network which can provide better power quality within a competitive environment commonly known as deregulated power system (DPS). For safeguarding DPS, energy storage systems (ESSs) and an efficient control method are required to maintain equilibrium between load demand and generated power known as automatic generation control (AGC). This study highlights an attempt of performance comparison of several hybrid ESSs (HESSs) for AGC of multi-area multi-source power system under deregulated scenario. The considered system comprises of thermal, hydro and gas (THG) generating units in each area of a two-area DPS. A cascade optimal controller-fractional order derivative (COC-FOD) is suggested for AGC performance advancement and its performance is compared with optimal controller (OC). A nature inspired salp swarm algorithm (SSA) is utilized to optimise secondary FOD controller gains while primary OC is designed via full state feedback control approach. The time domain analysis and bode plot reveal the eminence of COC-FOD over OC by returning 66 % lower value of cost function, 24 % lower undershoot, 98 % lower overshoot, at least 24 % lesser rise time for area frequency deviation, and 30 % greater stability margins with comparable settling time and lesser number of oscillations. The performances of various HESSs like ultra-capacitor (UC) + redox flow battery (RFB), superconducting magnetic energy storage (SMES) + RFB and flywheel energy storage (FES) + RFB in the attendance of OC and COC-FOD controller are compared and performance of COC-FOD in the presence of SMES+RFB is found superior to other HESS combinations by giving 75 % lesser value of cost function and at least 43 % lesser undershoot for area frequency deviation compared to the scenario where COC-FOD is simulated without ESSs. To present a more realistic scenario, system nonlinearities are considered. Finally, COC-FOD controller proved its resilience and efficacy by providing satisfactory stabilized performance under wide variations in the system parameters.

Research on Big Data Intelligent System for Fault Diagnosis of Computer Aided Ship Power System

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models

Hybrid energy systems, including hybrid power generation and hybrid energy storage, have attracted considerable attention as eco-friendly solutions to meet the increasing global energy demands while minimizing environmental impacts. The economic viability and resilience of hybrid energy system solutions depend on careful consideration of economic and reliability factors during the design phase. This paper explores the optimization and design of a wind turbine (WT)/photovoltaic (PV) system coupled with a hybrid energy storage system combining mechanical gravity energy storage (GES) and an electrochemical battery system. An adaptive energy management strategy linked to an optimization process has been proposed for the optimal integration of the WT/PV system with the hybrid Gravity/Battery storage system. Forecast models have been employed to predict solar and wind generation. Simulation models are developed for each component of the multi-source power plant to predict energy flow behavior based on real-world industrial load demand scenarios. The renewables contribution to 100 % reliability is approximately 18 % for photovoltaic and 92 % for wind turbine, primarily attributed to the great wind energy potential of the selected site. The optimal cost of energy is 0.28 €/kWh for a reliability of 100 %, which reduces to 0.03€/kWh when reliability level is decreased to 20 %. The incorporation of forecast models into the study and the hybridization of storage options improve the design accuracy and optimal operation of the multi-source power system leading to reliable and efficient use of renewable energy.

Validation of Sensor Data Integrity in OT Environments Through Multisource Data Sensors

This research paper focuses on detecting cyber threats from the OT environment by combining data from multiple sources. Monitoring cyber security or hybrid threats in an industrial OT environment is difficult due to different equipment, protocols, environments, personnel management and training, etc. However, the OT environment can also be observed with a multisource sensor system, which can be used to collect data. By combining IT and OT data, additional cyber threats can be found. Especially concerning the integrity of OT command-and-control data. We deal with the key concepts and differences of the industrial operating environment, which create challenges compared to the traditional IT environment. This is important because the policies defined at the European level for the NIS2 regulation are coming to touch all member countries, regardless of what the national implementation schedule is. The increased standards for OT environment cyber security implementation and development will also have an impact on the personnel management and training to support the onboarding of the standards in practice. Critical infrastructure protection is important because, without the protection of critical infrastructure, vital functions cease to function. Hostile actors cause security challenges among Western actors. In this study, we delve into whether it is possible to find threats concerning OT command-and-control process. The increased data surface collected from the IT/OT environment improves the capabilities for the system to detect malicious attacks towards the OT system. With the help of test equipment, the goal is to demonstrate that it is possible to find threats by combining data from multiple sources. With the help of test equipment, we find out IT and OT capabilities, which we load with various attacks and anomalies. We produce added value compared to traditional monitoring method test cases by comparing data obtained from different sources. The research paper shows the importance of detecting OT threats. By monitoring IT and OT environments and combining their data, we can find hidden threats. Only one test equipment configuration has been used in the study, but the results can be generalized and classified. The study also provides guidelines for how the detection of cyber threat capabilities should be developed.

Experimental and simulation study on the performance of a solar assisted multi-source heat pump drying system in Zhengzhou area

Zhengzhou of Henan province is an important agricultural production base. To improve the performance of the combined drying system and promote the development of agricultural products in Zhengzhou area, a solar assisted multi-source heat pump (SMSHP) drying system was proposed based on a traditional solar assisted air source heat pump (SASHP) drying system. The simulation models were developed for the air source heat pump (ASHP), SASHP, and SMSHP drying system respectively. The coefficient of performance, energy consumption and specific moisture extraction rate of the drying systems under different seasons were analyzed. Moreover, to validate the simulation models, an experimental platform of the combined drying system was designed and set up, and the experimental tests were carried out on the ASHP, SASHP, and SMSHP drying system. The simulation results revealed that the SMSHP drying system had lower energy consumption, higher coefficient of performance and specific moisture extraction rate than that of the SASHP drying system. The most significant performance enhancement was observed in spring, where the SMSHP system demonstrated a 22.69 % increase in coefficient of performance, a 19.2 % increase in specific moisture extraction rate, and a 16.11 % decrease in energy consumption compared to the SASHP drying system. Comparing with the experimental data, the simulation model errors of the energy consumption, coefficient of performance, and specific moisture extraction rate of the SMSHP drying system were 3.19 %, 0.28 %, and 2.27 %, respectively. Therefore, the accuracy of the simulation results was confirmed. To better calculate the heat provided by the solar collection system, the concept of the tank heating guarantee rate was first introduced, and it was found to be more scientific than the solar energy guarantee rate. At last, The economic and environmental benefit of the drying systems were analyzed during the life cycle, and the SMSHP drying system exhibited better comprehensive performance than the SASHP drying system. This paper provides valuable insights into the comprehensive utilization of solar energy and the control logic of solar assisted air source heat pump combined drying system under different seasons.

Cascaded Fractional Control Approach for Frequency Regulation of Multi-Source Power System Integrating Renewable Energy Resources

Cascaded Fractional Control Approach for Frequency Regulation of Multi-Source Power System Integrating Renewable Energy Resources

Simulation method for multi-source information fusion space target

Current space target simulation systems suffer from limitations, such as a single simulated spectral band, inability to allocate spectral ratios between bands, and poor imaging quality of multi-spectral simulated images. We propose a multi-source information fusion spatial target simulation method system that features a “series-parallel” hybrid connection. We optimize the design of a multi-spectral common aperture optical system and experimentally verify the proposed method. The experimental results show that the proposed system can simulate a wide range of spectral bands ranging from ultraviolet to long-wave infrared. Furthermore, it achieves precise control over the ratio of simulated radiant energy among spectral bands, with a spectral simulation error of less than 4.8%. Notably, it effectively images stars against the cosmic background and spacecraft across different spectral bands. This system serves as a theoretical cornerstone and provides crucial technological support for performance testing and rapid iterative development of multi-source information fusion space exploration systems.

The energy management strategy of the multi-source parallel power system for the self-reconfigurable ground vehicle

The self-reconfigurable ground vehicle (SRGV) has the ability of self-assembly and self-disassembly, which is a disruptive innovation to the traditional fixed configuration ground vehicle. The basic component of the SRGV is defined as a cell unit (CU), which is a complete system capable of working independently and has the basic function of the ground vehicle. The reconfiguration of the SRGV is not only the connection of the mechanical systems but also the integration between the power sources of different CUs. To this end, this paper proposes a novel multi-source parallel power system (MSPPS) for the SRGV, whose key characteristics are multi-branch and co-bus. The MSPPS can extend any number of power sources, which greatly improves the power level of SRGV. In this paper, the MSPPS with battery power source is discussed. The disassembly and assembly of the SRGV could lead to some inconsistencies such as SoC between the battery packs of each CU. To prolong the lifetime of the battery packs and working time of the SRGV, a hierarchical proportional control (HPC) strategy and a filtered model predictive control (FMPC) strategy are proposed. Both energy management strategies can reasonably allocate the output energy between different battery packs to meet the power demand and reduce battery inconsistencies. To verify and compare the effectiveness of the proposed two strategies, numerous simulations are carried out. The simulation results show that the FMPC strategy has faster convergence speed and lower power fluctuations in the energy management process. A SRGV prototype consisting of three CUs is developed, and the experimental platform for the power system of the SRGV is successfully established. The feasibility of the proposed MSPPS architecture is validated. The proposed HPC strategy is deployed in the rapid ECU. The experiment results are similar to the simulations and effectively demonstrate the real-time performance.

Overheating fault traceback of Regenerative thermal oxidizer in Multi-Source waste gas treatment system via dynamic cumulative correlation analysis

Regenerative thermal oxidizers (RTO) are commonly used in the pharmaceutical and chemical industries to treat emissions of volatile organic compounds but are prone to overheating risks stemming from variations in the flow and concentration of exhausted gases from diverse workshops. This paper proposes a dynamic analysis method based on correlation between various sensor-point data to determine the cause of the RTO overheating in such a multi-source waste gas treatment system. This method continuously adjusts the length of the data in the correlation analysis to comprehensively capture the dynamic changing characteristics of the data. And a strategy for the accumulation of association degrees is introduced to reveal long-standing potential causal relationships. The effectiveness of proposed method was validated in an industrial exhaust gas treatment system of a pharmaceutical company with over 1000 sensor points. The fault diagnosis results are consistent with those of the actual on-site investigation in 5 real cases. Our proposed method can provide relatively accurate results within 2–3 h, much faster than the manual investigation of the workers (2 days on average). Moreover, compared to traditional static correlation analysis methods, our method can narrow down the investigation scope by more than 5 times. This study provides a practical and effective method for tracing the source of anomalies in complex dynamic systems, and also contributes to the safe and stable operation of chemical engineering systems.

Novel tuning rules for IMC-high-order PID load frequency controller of power systems

The problem of load frequency control (LFC) for power grids has received widespread attention in power industrial control. Ensuring the stability of the frequency is the primary concern that constrains the development of the electric power system. This paper presents a novel internal model control (IMC)- proportional-integral-derivative (PID) controller design method for LFC control systems, centered on pole-zero conversion. This method aims to simplify the system model's complexity and improve the system's performance. The IMC controller is approximated as a PID series compensator (high-order PID), with exact tuning formulas provided for various scenarios. These include single-area without or with reheat turbines, hydro turbines with transient droop compensators, wind turbines, and gas turbines. The tuning formulas are characterized by a single parameter, determined under robustness constraints and integrated time absolute error (ITAE). The proposed tuning method considers the generator rate constraint (GRC) and applies to multi-area single-source and multi-area multi-source power systems. The results of the simulation confirm that the controller tuning method presented in this paper has significant advantages over the existing design methods concerning robustness and anti-interference performance.

Multi-source information contrastive learning collaborative augmented conversational recommender systems

Conversational Recommender Systems (CRS) aim to provide high-quality items to users in fewer conversation rounds using natural language. Despite various attempts that have been made, there are still some problems: Previous CRS only learned item representations in a single knowledge graph and ignored item tags; information gaps exist in the same items from different knowledge graphs and information popularity both affect user preferences; system generated responses lack descriptiveness and diversity. To address these problems and fully utilize external knowledge, we propose a Multi-source Information Contrastive Learning Collaborative Augmented method (MCCA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{MCCA}$$\\end{document}), which aims to mine the potential tag preferences of users in dialogues as well as enhance the accuracy of item representation and user preference modeling. Specifically, we utilize the obtained items and their tags to construct a new knowledge graph that incorporates movie tags. We design a Multi-source Item Fusion mechanism (MIF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{MIF}$$\\end{document}) to bridge the information gaps between items from different knowledge graphs and then utilize unsupervised contrastive learning to enhance the items’ representation capability after MIF. Additionally, a Multi-Tag Fusion mechanism (MTF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{MTF}$$\\end{document}) is designed to combine user-perceived information (i.e., tag popularity) and keywords obtained from reviews to co-enhance user preference representations through items and tags, and to incorporate fused item and tag features into the conversation module. Extensive experiments on two datasets show that MCCA significantly outperforms state-of-the-art methods. The source code will be available at https://github.com/lhy-cqut/MCCA.

Study and sizing of a multi-source pumping system

Study and sizing of a multi-source pumping system

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

A Multi-Source Braking Force Control Method for Electric Vehicles Considering Energy Economy

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance.

A multi-feedback system integrated simulation-based teacher training to scaffold pre-service teachers’ teaching skills: A phenomenological approach

AbstractBefore taking full responsibility for a real classroom, pre-service teachers (PTs) can safely experience a rich learning atmosphere in a simulated virtual classroom environment and receive feedback on their lesson planning and teaching performance. This research aims to support and examine the process of structuring PTs’ teaching skills with a multi-source feedback system integrated with simulation-based teacher training (SimInTeach-FBS). A phenomenological approach was employed in order to understand the nature of PT’s experiences from a feedback system focusing on teaching skills. Seventy PTs, twelve mentor teachers (MTs), and six teacher educators (TEs) participated in the study. Various data collection tools were used to gain a deeper understanding of the participants' experiences: reflective journals, open-ended feedback forms, and observation notes. The results of the content analysis showed that teaching experience with SimInTeach-FBS raises self-awareness and improves PTs’ teaching skills. It was revealed that teaching skills were mutually structured, feedback given to the PTs multiplied the effect of the development process, and teaching experience facilitated the transfer of acquired teaching skills to real life.