Improve System Performance Research Articles

Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters

Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.

Environmental assessment of industrial aquaponics in arid zones using an integrated dynamic model

Land desertification, water scarcity, and food security challenges in arid zones are intensifying, driving the need for sustainable agricultural solutions like aquaponics. This study investigated innovative water and energy-saving strategies using an integrated dynamic model for an on-demand industrial aquaponics system in Israel. The model evaluated the performance of a recirculating aquaculture system (RAS), hydroponics system (HPS), and desalination unit (DU) by adjusting physical and operational parameters to optimize water and nutrient use efficiency, energy consumption, and yield. Optimizing the system design resulted in an aquaponics system with approximately 420 m3 RAS, 6.85 ha HPS and 40 m3/d DU, achieving phosphorus use efficiency of 96 %, a water use efficiency of 97 %, freshwater input of 1.5 L/day/m2, and energy consumption of 0.56 kWh/day/m2. To mitigate the challenges of extreme arid climates, evaporative cooling combined with outdoor shading and mechanical cooling was found to be a feasible option to control temperature and humidity in the greenhouse. Dehumidification technologies further improved system performance by recovering 22 % freshwater from seawater and increasing nitrogen use efficiency by 18 %. Achieving daily energy self-sufficiency required 4500 m2 photovoltaic panels and 5000 m2 solar heating system. While the system model was initially devised with a specific focus on conditions in Israel, it has been designed with scalability, allowing it to be adapted and applied extensively across diverse peri-urban regions and arid zones globally.

Development of Phased Array Ultrasonic Testing System Using Distributed Architecture

Abstract With more rigorous requirements for reliability and safety of structures and materials, high resolution and fast imaging have been urgent needs of Phased Array Ultrasonic Testing (PAUT). In order to improve the detection resolution and efficiency of complex structures and materials such as ultra-thick carbon steel and honeycomb plate, a PAUT system is proposed in this paper. The system uses a distributed architecture, which not only divides hardware resources, improves system performance, but also is scalable. PA, single- and multi-channel conventional UT, and TOFD (Time Of Flight Diffraction) modalities are combined, and PA modality is carried by the fully parallel non-multiplexed 128 PE (Pulse Echo) electronics with independently adjustable emitting and receiving parameters. In addition, the ultrasound analog front-end circuit is designed using discrete devices, providing more powerful analog performance than integrated chips. GTX (Gigabit Transceiver) and PCIe (Peripheral Component Interconnect Express) are applied to high-speed data transmission, providing raw ultrasound data. Characterization and verification results indicate that the system is ideal for challenging inspections in many industries.

Enhancement of power density and hydrogen productivity of the reverse electrodialysis process by optimizing the temperature gradient between the working solutions

Reverse electrodialysis (RED) is an emerging technology that converts the salinity gradient energy (SGE) embedded in two solutions of different concentrations into electricity or hydrogen productivity. Given that two solutions containing SGE in nature are often at different temperatures, this study experimentally investigated the effects of the temperature and salinity ratio of the working solutions on the performance of RED systems. The results show that the power density and hydrogen productivity of RED systems could be markedly improved by adjusting the imposed temperature difference on the basis of the salinity gradient. The degree of improvement mainly depends on the magnitude and direction of the temperature difference between the high- and low-concentration solutions. The enhancement effect was more pronounced under a negative temperature difference (NTD) than under a positive temperature difference (PTD). Additionally, the improvement in system performance owing to the temperature difference was not affected by the endothermic or exothermic effects of different solutes in the water. At a NTD of 25℃ and a concentration of 0.1 M aqueous NaOH as the electrode solution, a RED system based on LiBr solution with a salinity ratio of 4.5 M/0.02 M as the working solution achieved a maximum power density of 0.754 ± 0.006 W∙m−2 and the highest hydrogen productivity of 1.128 ± 0.019 mol∙m−2∙h−1, which was 15.1% and 5.5% higher than the hydrogen productivity under LiCl and NaCl solution conditions, respectively. These results show the great potential of LiBr solution in the field of RED hydrogen productivity.

HTSA: A novel hybrid task scheduling algorithm for heterogeneous cloud computing environment

Cloud computing provides users and programs with scalable resources and on-demand services virtually in real time, making it a fundamental paradigm in modern computing. The concept for using remote computing resources is novel. Cloud computing relies on task scheduling to boost system performance, reduce execution time, and optimize resource use. Due to exponential task increase and problem complexity, the search space is huge. Optimization tasks like this are NP-hard. This work aims to find a near-optimal solution for a multi-objective task scheduling problem in the cloud while lowering search time. Using the Genetic Algorithm (GA) and Gravitational Search Algorithms (GSA) benefits while avoiding their drawbacks, we offer a standard cloud computing task scheduling method to improve system performance and optimize the Quality of service (QoS) parameters like energy, makespan, resource utilization and throughput. We use CloudSim to test standard functions, real-time, and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. The designed technique outperforms Gravitational Search Algorithms (GSA), Ant Colony Optimization(ACO), and Particle Swarm optimization(PSO) in Degree Of Imbalance (12%), resource utilization (9%), Mean Response Time (7%) and energy consumption (6%).

Hybrid edge–cloud energy management system for an industrial-scale green hydrogen refilling station: Lessons learned and findings

Green hydrogen is a potential contributor to a more sustainable transportation system. In this paper, we present our experiences and insights gained from an industrial-scale green hydrogen refilling station, marked by the innovative hybrid Edge–Cloud electrolyser system (EMS) employed in this context. Hybrid EMS can improve system performance, adapt to dynamic conditions, and continuously improve efficiency, even under unpredictable load and low storage situations. It also allows the centralized monitoring of future hydrogen production sites to facilitate the operation and maintenance and, in the long-term vision, to enable the joint coordination of the plants flexibilities in the service market as a virtual plant, thus increasing the cost efficiency. Through a real-time digital-model based assessment of the EMS, this study offers valuable lessons learned and findings, contributing to a deeper understanding of EMS in green hydrogen infrastructure.

Do long-term acoustic-phonetic features and mel-frequency cepstral coefficients provide complementary speaker-specific information for forensic voice comparison?

A growing number of studies in forensic voice comparison have explored how elements of phonetic analysis and automatic speaker recognition systems may be integrated for optimal speaker discrimination performance. However, few studies have investigated the evidential value of long-term speech features using forensically-relevant speech data. This paper reports an empirical validation study that assesses the evidential strength of the following long-term features: fundamental frequency (F0), formant distributions, laryngeal voice quality, mel-frequency cepstral coefficients (MFCCs), and combinations thereof. Non-contemporaneous recordings with speech style mismatch from 75 male Australian English speakers were analyzed. Results show that 1) MFCCs outperform long-term acoustic phonetic features; 2) source and filter features do not provide considerably complementary speaker-specific information; and 3) the addition of long-term phonetic features to an MFCCs-based system does not lead to meaningful improvement in system performance. Implications for the complementarity of phonetic analysis and automatic speaker recognition systems are discussed.

Optimizing Indoor Airport Navigation with Advanced Visible Light Communication Systems.

This study presents a novel approach to enhancing indoor navigation in crowded multi-terminal airports using visible light communication (VLC) technology. By leveraging existing luminaires as transmission points, encoded messages are conveyed through modulated light signals to provide location-specific guidance. The objectives are to facilitate navigation, optimize routes, and improve system performance through Edge/Fog integration. The methodology includes the use of tetrachromatic LED-equipped luminaires with On-Off Keying (OOK) modulation and a mesh cellular hybrid structure. Detailed airport modeling and user analysis (pedestrians and luggage/passenger carriers) equipped with PINPIN optical sensors are conducted. A VLC-specific communication protocol with coding and decoding techniques ensures reliable data transmission, while wayfinding algorithms offer real-time guidance. The results show effective data transmission and localization, enabling self-localization, travel direction inference, and route optimization. Agent-based simulations demonstrate improved traffic control, with analyses of user halting and average speed. This approach provides reliable indoor navigation independent of GPS signals, enhancing accessibility and convenience for airport users. The integration of VLC with Edge/Fog architecture ensures efficient movement through complex airport layouts.

A Heterogeneous Glider System with Underwater Acoustic Communication and Positioning

Abstract Autonomous underwater vehicles are increasingly frequently used in the fields of ocean observation, data collection, and tactical surveillance. Underwater navigation and near real-time communication are the most important factors limiting the performance of these vehicles. In this article, an underwater acoustic communication and positioning system (UACPS) based on heterogeneous gliders is presented. The system consists of an acoustic wave glider and an acoustic underwater glider, and includes an ultrashort baseline and an acoustic modem. The program and communication protocol of the communication and positioning algorithm are independently developed, and the hardware circuit design of the communication and ultra-short baseline positioning, as well as the embedded surface wave gliders and gliders are independently implemented. Sea trial results indicate that the communication distance of the system is more than 3 km and the positioning error is less than 5%. With the further improvement of system performance, the low cost, long time, long distance and near real-time communication characteristics of our UACPS can be used to integrate multiple autonomous unmanned platforms as part of intelligent surveillance robot networks.

Activated sludge microbial community assembly: the role of influent microbial community immigration.

Wastewater treatment plants (WWTPs) are host to diverse microbial communities and receive a constant influx of microbes from influent wastewater. However, the impact of immigrants on the structure and activities of the activated sludge (AS) microbial community remains unclear. To gain insight on this phenomenon known as perpetual community coalescence, the current study utilized controlled manipulative experiments that decoupled the influent wastewater composition from the microbial populations to reveal the fundamental mechanisms involved in immigration between sewers and AS-WWTP. The immigration dynamics of heterotrophs were analyzed by harvesting wastewater biomass solids from three different sewer systems and adding to synthetic wastewater. Immigrating influent populations were observed to contribute up to 14% of the sequencing reads in the AS. By modeling the net growth rate of taxa, it was revealed that immigrants primarily exhibited low or negative net growth rates. By developing a protocol to reproducibly grow AS-WWTP communities in the lab, we have laid down the foundational principles for the testing of operational factors creating community variations with low noise and appropriate replication. Understanding the processes that drive microbial community diversity and assembly is a key question in microbial ecology. In the future, this knowledge can be used to manipulate the structure of microbial communities and improve system performance in WWTPs.IMPORTANCEIn biological wastewater treatment processes, the microbial community composition is essential in the performance and stability of the system. This study developed a reproducible protocol to investigate the impact of influent immigration (or perpetual coalescence of the sewer and activated sludge communities) with appropriate reproducibility and controls, allowing intrinsic definitions of core and immigrant populations to be established. The method developed herein will allow sequential manipulative experiments to be performed to test specific hypothesis and optimize wastewater treatment processes to meet new treatment goals.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

Optimal Control Problems in Complex Systems: The Combination of Mathematical Modeling and Optimization Strategies

Optimal control in complex systems is a challenging and practical field. Traditional control methods can not meet the needs of complex systems, so mathematical modeling and optimization strategies are needed to improve system performance and stability. The optimal control theory can solve the control problem in complex system effectively and improve the operation efficiency of the system by establishing mathematical model and combining optimization strategy. From mathematical modeling method to hybrid optimization strategy research, this paper discusses the theory and practice of optimal control in depth. In the future, it is necessary to further improve the research methods to solve the uncertainty of complex system models and make greater contributions to realizing more intelligent control schemes.

Performance Improvement of Wireless Power Transfer System for Sustainable EV Charging Using Dead-Time Integrated Pulse Density Modulation Approach

The recent developments in electric vehicle (EV) necessities the requirement of a human intervention free charging system for safe and reliable operation. Wireless power transfer (WPT) technology shows promising options to automate the charging process with user convenience. However, the operation of the WPT system is designed to operate at a high-frequency (HF) range, which requires proper control and modulation technique to improve the performance of power electronic modules. This paper proposes a dead-time (DT) integrated Pulse Density Modulation (PDM) technique to provide better control with minimal voltage and current ripples at the switches. The proposed technique is investigated using a LCC-LCL compensated WPT system, which predominantly affects the high-frequency voltage and current ripples. The performance analysis is studied at different density conditions to explore the impact of the integrated PDM approach. Moreover, the PDM technique gives better control over the power transfer at different levels of load requirement. The simulation and experimental analysis was performed for a 3.7 kW WPT prototype test system under different modes of operation of the high-frequency power converters. Both the simulated and experimental results demonstrate that the proposed PDM technique effectively enhances the efficiency of the HF inverter while significantly reducing output current ripples, power dissipation and improving the overall WPT system efficiency to 92%, and leading to a reduction in the power loss in the range of 10% to 20%. This leads to improved overall system control and performance.

Aggregated planning to solve multi-product multi-period disassembly line balancing problem by considering multi-manned stations: A generic optimization model and solution algorithms

Disassembly lines are crucial in the recovery process of end-of-life (EOL) products, facilitating the systematic extraction of parts to meet predetermined objectives. While previous studies have made significant advancements, they have not addressed the disassembly line planning and balancing problem by focusing on the real-life significance of combining long-term decision-making and short-term operational missions. This study fills this gap by combining multi-product, multi-period, and multi-manned disassembly line concepts into a novel integrated approach. Initially, a generic optimization model is formulated, incorporating the concept of multi-manned stations. Subsequently, genetic algorithm (GA)-based solution approaches are developed to address the problem’s complexity. Three tactical-level policies, including economic disassembly quantity (EDQ), just-in-time (JIT), and random (R) based lot size policies, are explored within the algorithms. Additionally, simulated annealing-based local search algorithms and two crossover operators, CR1 and CR2, are incorporated to enhance solution quality. Through computational analysis, six algorithms are evaluated, with the GACR1-JIT algorithm demonstrating significant cost reductions compared to alternatives. The findings underscore the growing importance of JIT-based lot size policies, particularly with an increasing number of periods. This research bridges theoretical and practical considerations by highlighting the strategic importance of combining long-term disassembly line planning and short-term lot sizing decisions to improve system performance.

Optimized TRIAD-Based Nanosatellite Attitude Determination Using Horizon Sensor and Magnetometer

The aim of this article is to investigate the accuracy of vector measurement-based attitude determination methods for a nanosatellite. Measurements from the horizon sensor and magnetometer are therefore modeled on the body frame. The triaxial Attitude Determination (TRIAD) technique is a widely used and effective method to determine the attitude of a nanosatellite. In this study, the TRIAD method is used with three different approaches to obtain the smallest orientation error of a nanosatellite equipped with magnetometers and horizon sensors. Analysis of covariance is conducted to evaluate the validity and reliability of the attitude determination process. Three modifications of the TRIAD algorithm were tested for accuracy and the most accurate was determined. The analysis provides information on the sources of error and uncertainty associated with the measurement and estimation process. This information is used to improve system performance and the accuracy of attitude outputs.

Role of inertial nonlinearity and coupling stiffness on a series of coupled harvesters

This study investigates the effect of coupling configuration on a state-of-the-art harvesting system consisting of a series of spring-coupled piezoelectric nonlinear monostable tip-loaded cantilever-based vibration energy harvesters (VEHs). The investigation finds favorable coupling configurations that exploit complex nonlinear interaction for improving system performance. The system model incorporates the beam's geometric nonlinearities in the governing equation using inertial and stiffness nonlinear terms. The study first analytically investigates the effect of grounding stiffness on a single harvester's performance. Then, it numerically investigates the effect of increasing array size and changing stiffness distribution. The study discovers a novel hardening/softening transition phenomenon at a critical grounding stiffness value induced by the inertial nonlinear effect. This critical event also maximizes peak output power near the largest eigenfrequency at an optimum grounding stiffness value. At larger arrays, the peak power shows a complex bifurcation behavior, hinting at the presence of multiple attractors. The numerical study of different stiffness configurations and linearized eigenanalysis reveals the necessity of a multilevel optimization strategy focused on both the stiffness ratio between the coupling springs' and their overall stiffness level. This study bridges the gap between previous studies on spring-coupled linear and bistable arrays, offering new insights into coupled nonlinear interactions and proposing novel optimization strategies.

A Diagnostic Case Study for Manufacturing Gas-Phase Chemical Sensors

In this work, we describe the design, manufacturing development, and refinement of a chemical detection platform designed to identify specific odorants in the natural gas industry. As the demand for reliable and sensitive volatile organic compound (VOC) detection systems is growing, our project aimed to construct multiple prototypes to enhance our detection capabilities and provide portable detection platforms. Throughout the development process across nominally identical and duplicated instruments, various failure modes were encountered, which provided insight into the design and manufacturing challenges present when designing such platforms. We conducted a post hoc root cause analysis for each failure mode, leading to a series of design modifications and solutions. This paper details these design and manufacturing challenges, the analytical methods used to diagnose and address them, and the resulting improvements in system performance. In the end, a debugging flow chart is presented to aid future researchers in solving the possible issues that could be encountered. Our findings show the complexities of bespoke chemical sensor design for unique applications and highlight the critical importance of iterative testing and problem-solving in the development of industrial detection technologies. Achieving consistency across devices is essential for optimizing device-to-device efficiency. The work presented is the first step towards ensuring uniform performance across a production run of chemically sensitive devices. In the future, a universal device calibration model will be implemented, eliminating the need to collect data from each individual device.

Genetic algorithm-based optimization of combined supercritical CO2 power and flash-tank enhanced transcritical CO2 refrigeration cycle for shipboard waste heat recuperation

The shipping industry significantly contributes to global trade and economy, but is also responsible for approximately ∼3 % of global greenhouse gas (GHG) emissions. Harnessing waste heat from marine vessels to perform useful work is a potential solution to reduce these emissions. Although the Organic Rankine Cycle (ORC) and CO2 cycles have been studied separately for waste heat recovery, a combined power and refrigeration systems, utilizing both cycles have not been reported. Herein, a combined system, “Marine Energy Recovery System (MERS)”, that integrates the supercritical CO2 Brayton power cycle with a flash-tank-enhanced transcritical CO2 refrigeration cycle, is proposed, aiming to generate power and provide cooling simultaneously. The power cycle incorporates an ORC, and a flash-tank is introduced within the refrigeration cycle to improve system performance. Both cycles share a low-temperature recuperator and a gas cooler to further utilize the heat between them. The MERS is evaluated from both a 1st and 2nd law perspective against various performance metrics under different operating conditions, such as: gas cooler pressure, evaporation temperature, and turbine inlet temperature and pressure. Results elucidated that the MERS achieved energy and exergy efficiencies of 54.62 % and 54.90 %, respectively, representing significant improvements over similar systems. The net work output improved by 717.55 kW and the net cooling capacity by 515.7 kW relative to the reference system. Furthermore, a COP of 2.99 outlines its potential as a cooling system. To further enhance MERS’s performance, a multi-objective optimization approach is employed, utilizing Artificial Neural Network (ANN) and Genetic Algorithm (GA). Optimal operational conditions yielded an energy efficiency of 74.95 % while maintaining a net work output of 6890.55 kW with gas cooler pressure, turbine inlet temperature, and gas cooler outlet temperature being 9.5 MPa, 461.76°C and 50°C respectively. These findings support the reliability and improved design of the MERS for future marine applications.

Experimental study on a solar thermoelectric power generation system under non-uniform irradiation

Non-uniform irradiation caused by high light concentration significantly affects the performance of the solar thermoelectric power generation system, but the research remains at the theoretical stage. This paper establishes an experimental setup for the solar thermoelectric system and conducts a comprehensive experimental study of the system operating under non-uniform irradiation. The temperature, power and efficiency of the systems are tested for different input powers, cooling methods and structures, and irradiation uniformity. The results indicate that the output power of the solar thermoelectric system slightly increases and then decreases as the irradiation uniformity increases. The system’s output power is minimized when the irradiation uniformity approaches 100%. The optimum irradiation uniformity of the solar thermoelectric system is 47.4% and the maximum efficiency is 1.495%, which is an improvement of 7.6% compared to the efficiency of 98.4% irradiation uniformity. The performance of the solar thermoelectric system can be further improved by using a double-layer TE structure. At a concentration ratio of only 12.4, the double-layer system connected in series achieved a maximum efficiency of 2.06%, which is 0.55% higher than the single-layer system. The importance of optimizing irradiation uniformity to improve system performance is experimentally revealed. These results are important for clarifying the operating principle of the solar thermoelectric system under non-uniform irradiation and can guide the subsequent design of high-efficiency solar thermoelectric systems.

Design and performance optimization of a novel CAES system integrated with coaxial casing geothermal utilization

At present, the heat storage sub-system in the adiabatic compressed air energy storage system generally has low heat storage density and limited heat storage temperature, resulting in mediocre power generation efficiency of the system. To address these issues, this paper proposes a novel approach for coupling the energy storage system with geothermal energy utilization. This study innovatively uses coaxial casing wells to extract geothermal energy and to preheat compressed air in front of the expander. Detailed numerical investigations and dynamic thermodynamic analysis are carried out to evaluate system performance. Sensitivity and optimization are analyzed to identify the operational and design parameters of the proposed system. In addition, an economic analysis is conducted to estimate the investment and payback of the energy storage system. The results show that geothermal energy utilization can directly and significantly enhance the electric, heating and cooling energy output of compressed air energy storage system. During one 24-hours operation cycle, the electricity output is 104.45GJ, the heating supply output is 81.59 GJ, and the cooling supply output is 16.69 GJ. The RTE and energy efficiency are 64.26 % and 122.68 %, respectively. Economic analysis shows that the static payback period is 12.17 years, and the net present value can reach 3.46 million USD. It can be concluded that geothermal energy plays a greater role in energy storage systems than geothermal power production alone. The compressed air energy storage system combined with geothermal energy addresses current issues and leads to improved system performance.