Proceedings of The 6th International Conference on Clean Energy and Electrical Systems

As the proportion of renewable energy generation in the power grid continues to increase, integrating it into the grid will have a significant impact on the existing power system. Therefore, countries around the world have introduced strict standards for renewable energy grid integration, especially when grid faults occur. Renewable energy sources must be able to inject reactive power to maintain grid voltage at required levels via smart inverters in case low voltage occurs. In the past, inverter control primarily relied on Proportional Integral (PI) controllers, and parameter tuning was often done through trial and error or empirical methods. This approach was time-consuming, costly, and lacked robustness. Long Short-Term Memory (LSTM) networks, on the other hand, offer advantages of recurrent neural networks while addressing the issues of vanishing and exploding gradients. This paper utilizes LSTM networks followed by fully connected layers as the primary controller for the inverter. The paper uses both an outer loop and an inner loop to off-line optimize the LSTM network controller, replacing traditional PI controllers. The inner loop employs the Adam optimizer to optimize the weights and biases of the LSTM network and the fully connected layers, while the outer loop uses the Particle Swarm Optimization algorithm to optimize the initial learning rate and batch size of Adam optimizer and the number of hidden units in the LSTM network. Through simulation and experimentation, the proposed method achieved smaller averages of Root Mean Squared Error (0.0243) and Mean Absolute Error (0.0106) compared to those (0.0407 and 0.0330) obtained by the traditional PI controller, respectively. These results indicate that the proposed method outperforms traditional PI controllers.

When the vacuum circuit breaker interrupts the high current, the anode surface experiences a significant influx of energy, leading to rapid heating and subsequent melting. Simultaneously, a substantial quantity of metal vapor is generated through evaporation, entering the arc column to interact with the cathode plasma. This paper establishes a vacuum arc burning model that considers the impact of anode melting pool protrusions on the characteristics of the vacuum arc. The results demonstrate that as the number of protrusions increases, the density of neutral atomic vapor also rises. This is due to the larger tip area of big protrusions, resulting in more widely distributed jets. Additionally, larger protrusions worsen the uneven distribution of ions, causing accumulation near the electrode’s edge. When the protrusions are symmetrically distributed around the melting pool, the ion pressure distribution is also symmetrically distributed, and the pressure peak increases with the increase in the number and size of protrusions. When the protrusions are symmetrically distributed on both sides of the anode melting pool, the distribution of their axial ion velocity is also symmetrical. The shape and geometric features of large protrusions may lead to exacerbating asymmetries during ion transport.

Inverter-based power sources that connect renewable energy to the power grid are being increasingly implemented in recent years. Among them, grid-connected inverters that use battery energy to perform virtual synchronous generator (VSG) control to simulate the inertial characteristics of synchronous generators (SGs) have gained considerable interest. The current-controlled VSG (iVSG™) discussed in this study exhibits the characteristics of grid-forming (GFM) inverter control that enables both grid-connected operation as well as paralleled stand-alone operation, while employing the current feedback control as the primary control method. However, when distributed power sources connected via a common grid-following (GFL) inverter generate power, they may cause interference with the connected grid. This interference can be avoided by adjusting the control parameters of each power source, even in microgrids that use iVSG™ as the primary power source. In this study, we reproduced this interference situation through mathematical and experimental analyses to identify the eigenvalues that cause the interference and the parameters that participate in the interference. The obtained guidelines for adjusting the control parameters of iVSG™ are reported.

Solid-state soft starters are widely utilized to lessen the high inrush starting currents and large starting torque pulsations resulting from the starting of induction motors (IMs). Applications of solid-state soft starters span across industries such as manufacturing, water treatment, HVAC (heating, ventilation, and air conditioning), and more. Therefore, this paper provides a novel control technique for selecting the optimal soft-starting voltage profile to limit the starting current at a specified value and subsequently minimize starting torque pulsations. For 3 kW three-phase IM, the control model is implemented using MATLAB/SIMULINK. Simulation results show the efficacy of the soft starting technique under various loading conditions. Moreover, the proposed technique has the advantages of a minimal computational burden as well as good accuracy when compared to the mathematical calculations and the trial-and-error approaches.

The issues of resource depletion and global warming require us to transit towards the use of hybrid production systems integrated with batteries, as a result of intermittent characteristics or renewable energy. Efficient management needs adequate control of the energy distribution system. This work aims to improve the functionality of the PIMENT laboratory management tool, known as SMARTEN, by incorporating calculations for various energy subsystems. This tool version was developed in a simple environment to minimize computation time. Due to the complex nature of the examined energy system, it is crucial to adopt a comprehensive approach to physical models rooted in system analysis. A comprehensive case study will be demonstrated also. To find a viable alternative for storage systems, pumped-storage energy transfer stations (PETS) will be implemented with an energy consumption system connected to electric vehicle advancement. The energy system we intend to examine will be established in the southern region of La Réunion, specifically at Vincendo in the Commune of Saint Joseph. The chosen site exhibits an elevation disparity of around 100 m, and possesses continuous and favorable levels of sunlight and precipitation, making it well-suited for the proposed project.

As wind energy gains a larger footprint in global energy systems and demonstrates increasing utility in microgrid applications, the imperative for in-depth research into primary frequency regulation in microgrids incorporating wind power grows. Addressing this need, this paper proposes an innovative deep learning optimized Global Adaptive Model Predictive Control (MPC) algorithm, which is specifically designed to improve the primary frequency synthetic inertia control (SIC) in microgrids with integrated wind power. The paper first establishes a comprehensive response model for primary frequency SIC in such microgrids, incorporating mechanical energy compensation from wind turbines aimed at preventing secondary frequency drops. Then identify the limitations of the conventional MPC in controlling wind power’s contribution to primary frequency regulation. In response, the paper proposes the Global Adaptive MPC algorithm. This novel algorithm is engineered for real-time, adaptable adjustment of SIC parameters. Furthermore, the paper leverages cutting-edge deep learning techniques to expedite the MPC optimization process. This advancement significantly enhances the system’s real-time performance and stability. The efficacy and superiority of the proposed algorithms are substantiated through extensive experimental validation, which shows a marked improvement in the performance indices of primary frequency regulation.

Distributed energy resources (DERs) have been widely integrated into the distribution system for energy conservation and environmental protection. A state estimation method with high accuracy and reliability is critical to ensure the secure operation of the distribution system with DERs. Due to the limited real-time measurements available, pseudo-measurements are typically used to improve redundancy in state estimation. However, pseudo-measurements with large errors may affect the accuracy of the estimation results. A user-centered bounded distribution system state estimation (UCB DSSE) model is developed based on operators’ requirements for real-time measurement residuals to guarantee the quality of the estimated solution. A dynamical system derived from the UCB DSSE model is employed to establish the theoretical foundation, while a theory-based state estimation method is developed with a robust computational mechanism. Numerical evaluations are conducted to demonstrate the performance of the theory-based state estimation method with promising results.

The history of electrical energy consumption is important information to assess its behavior. The study was an initial stage in the conduct of an electrical energy audit that determines the actual consumption associated with the facility and the potential savings associated with the consumption. The execution of the electrical energy audit can provide enormous benefits in different capacities as it can help in the reduction of energy cost in each facility brought about by the evaluation results. This study was an evaluation of electrical energy used within the system among four higher educational institutions in the Philippines with the objective of assessing its individual consumption considering the base energy load, per capita consumption, and energy utilization index. University campuses are representation of diverse buildings with varied energy consumptions and can provide excellent testbeds for electrical energy audit to comprehend the consumption based on the different profiles. The complete analysis of the consumption of electrical energy was conducted by assessing the per capita consumption and the energy utilization index provided by certain standards and code. The per capita consumption of the four HEIs in the year 2018 and 2019 are all acceptable and each person consumed energy within its ideal consumption versus that of Statistica Research Department. However, the analysis of the consumption per given area based on the Philippine Green Building Code shows that there was poor electrical energy performance. To provide benchmark for energy conservation, HEIs may have adopted the average consumption of 2018 as its base energy load in 2019 and thus, savings may have been made in electrical energy consumption among all these HEIs.

A solid oxide fuel cell (SOFC) fueled by reformate obtained from an ethanol partial oxidation (POX) was investigated regarding electrical and the thermal performances. Performance analysis was performed through Aspen Plus simulator. Gas compositions obtained from POX and SOFC were computed through the Gibbs free energy minimization method. Three voltage losses that include activation, ohmic and concentration losses was considered in an electrochemical model. Effects of operating parameters in both POX and SOFC on performance of SOFC-POX system were examined. From the simulation results, it was found that the suitable condition of POX was at the reformer temperature of 700 ℃ and oxygen to ethanol molar ratio of 0.1. While, the operating temperature of SOFC should be 900 ℃. Under these operating conditions, the maximum electrical and thermal efficiencies of 67.24% and 25.77%, respectively, can be achieved. Finally, the heat integration of the SOFC-POX system was considered using pinch analysis. The minimum cold utility is 88.1 kW while the hot utility is no need for this system.

Lithium-rich layered oxides (LLOs), described as \(x\) Li₂MnO₃·(1–x)LiTMO₂ (TM = transition metal, Ni,Mn, Co, etc.) have recently been identified as promising cathode materials for high-energy storage applications. In this research, a range of cathode materials containing varying proportions of LLOs were created using the sol-gel technique to explore on structure and electrochemical properties. X-ray diffraction analysis was used to examine the crystal structure of the samples, revealing that the LLOs cathode possessed composite characteristics. Increasing the Li₂MnO₃ content was observed to enhance the stability of the lithium-rich structure, thereby boosting the capacity. However, the irreversible reaction of Li₂MnO₃ would reduce the capacity. The optimized cathode composition is 0.5Li₂MnO₃·0.5LiNi_1/3Mn_1/3Co_1/3O₂ and its discharge capacity is 250 mAh/g.

Under the carbon neutrality goal, wind and solar power have become one of the most important options for decarbonizing the power system. This article takes the power system predominated by wind and solar power as an example to construct a two-stage production simulation model. The load duration curve is used to optimize hydropower and other power sources that need to consider energy balance in a time period. The time-series load curve is used to optimize coal-fired power, nuclear power and other conventional power sources output. A provincial level large-scale power system is taken as example, and different capacity combination scenarios of wind and solar power are set up to do daily simulation and yearly simulation. The proposed production simulation model is used to study the energy storage configuration and power supply cost changes along with the increase of capacities and generations of wind and solar power while approaching to near-zero emissions. Research results show that even if the total capacities of wind and solar power reach 226% of the maximum power load, fossil energy generation still accounts for 9%. System operating costs rise steadily as the proportion of wind and solar power capacity increasing. There has a saturation effect on replacing fossil energy generation by increasing wind and solar power capacities, as well as the energy storage to promote the consumption of wind and solar power.

Fault Detection and Diagnosis (FDD) plays a crucial role in maintaining the integrity and efficient operation of modern industrial systems, from manufacturing sectors to process industries. FDD involves identifying and classifying abnormal conditions that could lead to equipment failure, production inefficiencies, or safety hazards. However, traditional FDD techniques face challenges in handling vast data and complex system dynamics and ensuring timely and accurate fault detection in dynamic environments. Manual inspections and heuristic approaches are inadequate, and statistical process control methods have limitations in capturing complex relationships and adapting to evolving process conditions. To overcome these challenges, advanced techniques such as deep learning-based approaches have emerged, leveraging the capabilities of neural networks for fault detection and diagnosis. These approaches have shown promising results in handling high-dimensional, nonlinear, and time-varying process data. This paper reviews the advancements, challenges, and prospects of deep learning in FDD in industrial systems. Firstly, it discusses the emergence and development of deep learning methods applied to FDD and their applications in relevant fields. Secondly, a new development path that combines deep learning with big data is proposed to address the increasing production data in modern industrial settings. Finally, the opportunities and limitations of deep learning in FDD are clarified, providing insights for future research and development in this area.

The growing adoption of electric vehicles (EVs) is driving the integration of photovoltaic (PV) and battery energy storage systems (BESS) in homes to mitigate energy costs. Designing such a system is critical for balancing energy generation and consumption, employing the net-zero energy concept. Additionally, the application of the vehicle-to-home (V2H) concept to support home energy demand while achieving a net-zero energy state has not been thoroughly investigated. This paper proposes a design methodology for a net-zero energy electric vehicle (NZEV) charging station integrated with a V2H control strategy capable of operating in both off-grid and on-grid schemes. The objective is to maintain a balance between PV-generated energy and consumption from both the EV and home loads. The charging station is installed for a practical test in an off-grid scheme and analyzed to operate in an on-grid scheme with and without V2H function. The data collected from the testing and analysis are used to evaluate the capability to achieve a net-zero energy state. The practical results demonstrate that the off-grid NZEV achieves weekly net-zero energy by utilizing the PV to store sufficient power in the BESS for EV charging. The energy analysis results indicate that the on-grid NZEV, operating in conjunction with V2H, imports less energy from the grid and achieves a net-zero energy state to a greater extent than operation without V2H. This research underscores the potential of the NZEV charging station to enhance energy sustainability by integrating V2H principles.

In this paper, a novel type of asymmetric interior permanent magnet synchronous motor (AIPM) is presented. The motor's topology is based on an interior U-shaped structure, with an asymmetric slot-type stator and dissymmetrical permanent magnets (PMs) in the rotor. The influences of both stator and rotor variables are investigated using finite element analysis. The Taguchi Algorithm is applied for optimization under constant copper loss, resulting in a significant enhancement of torque quality. A further comparison between the proposed AIPM and IPM is conducted, including output torque, air gap flux density, and dual-direction rotation performance. It is demonstrated that the proposed AIPM effectively reduces cogging torque and torque ripple, while also enhancing average output torque to a certain extent.

Partial shading of photovoltaic (PV) modules reduces the output power of a PV system. Electrically reconfigurable arrays have been proposed to increase the power output of shaded PV systems. In this paper, an electrically reconfigurable PV array was built using the total cross-tied (TCT) topology. The PV array was then tested manually using different shading patterns. Furthermore, the PV array was deployed and allowed to autonomously reconfigure in an environment where shading due to clouds and foliage is expected. Results from the I-V and P-V curves of the PV array show that the power improvements expected of the array after reconfiguration were met. However, when connected to the balance of system components, improvements in the power output were not met. During pilot deployment, the speed of changing shading patterns also affected the outcome. Thus, when designing a PV system that incorporates a reconfiguration of PV modules, the following need to be considered: first, identify the potential causes of partial shading at the target location, which include the expected shading patterns and speed of changing patterns, and second, investigate the effects of the balance of system components, such as component efficiencies, speed of maximum power point tracking, and system type.

Increasing electricity demand has led to the construction of distribution substations in dense urban areas, raising concerns about the health and well-being of the nearby public. These concerns relate to potential environmental and electrical safety impacts. This study assesses and compares electromagnetic fields (EMFs) exposure levels, including electric field (EF) and magnetic field (MF), from three 115/22 kV substations in urban areas: air-insulated switchgear, gas-insulated switchgear, and mixed-technology switchgear. The environmental impact assessment considers air quality, noise, and heat. EMF field measurements are compared with ICNIRP Guidelines to evaluate general public and occupational exposure levels. The correlation between the exposure levels from each substation and their daily load curves is examined. Results show that EMF exposure levels are within acceptable limits, ensuring the safety of the general public and substation personnel. Additionally, measurements for particulate matter less than 10 microns (PM10), 24-h-averaged-noise level (Leq24h), and wet bulb globe temperature (WBGT) indicate that the environmental impact is within the relevant Thai standards.

This work focuses on a typical 110 kV indoor substation transformer room in the Yellow River Basin. Safety alarms frequently occur during the summer, significantly impacting the safe operation of equipment. The Computational Fluid Dynamics (CFD) results revealed an apparent airflow short-circuits and uneven flow distribution in the existing system, leading to localized high temperatures near the transformer. To address this issue, a hybrid ventilation of involving both natural and mechanical ventilation was proposed. Firstly, considering the principles of thermal pressure, the actual placement, as well as the heat dissipation characteristics of the main transformer, the natural ventilation system was reconfigured. Five ventilation layouts of outlet were proposed. The research results indicated even arrangement of outlets into six showed the highest heat-dissipating capacity, with a minimum average temperature of 39.60 ℃ on the same vertical Z-axis horizontal section, significantly lower than the original system of 43.39 ℃. This layout increased the natural ventilation flow from 10,886 m³/h to 12,700 m³/h by enhancing thermal pressure, resulting in a more uniform distribution of airflow. Based on the optimized natural ventilation layout, two different mechanical ventilation modes were investigated: mechanical exhaust and mechanical supply. The former mode displayed a lower exhaust temperature of 39.6 ℃ than the latter of 41.6 ℃. This work provides a reference for the efficient heat dissipation and safe operation of main transformer rooms in the relevant area.