Optimal Power Research Articles

Joint Power Optimization and Subcarrier Allocation for URLLC in Cell-Free Massive MIMO System

Joint Power Optimization and Subcarrier Allocation for URLLC in Cell-Free Massive MIMO System

Simultaneous Optimization of Discrete and Continuous Parameters Defining a Robot Morphology and Controller.

The morphology and controller design of robots is often a labor-intensive task performed by experienced and intuitive engineers. Automatic robot design using machine learning is attracting increasing attention in the hope that it will reduce the design workload and result in better-performing robots. Most robots are created by joining several rigid parts and then mounting actuators and their controllers. Many studies limit the possible types of rigid parts to a finite set to reduce the computational burden. However, this not only limits the search space, but also prohibits the use of powerful optimization techniques. To find a robot closer to the global optimal design, a method that explores a richer set of robots is desirable. In this article, we propose a novel method to efficiently search for various robot designs. The method combines three different optimization methods with different characteristics. We apply proximal policy optimization (PPO) or soft actor-critic (SAC) as the controller, the REINFORCE algorithm to determine the lengths and other numerical parameters of the rigid parts, and a newly proposed method to determine the number and layout of the rigid parts and joints. Experiments with physical simulations confirm that when this method is used to handle two types of tasks-walking and manipulation-it performs better than simple combinations of existing methods. The source code and videos of our experiments are available online (https://github.com/r-koike/eagent).

Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network

Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network

Joint Trajectory and Power Optimization for Energy-Efficient UAV Redeployment Against an Eavesdropper Under Consistent Fairness Constraint

Joint Trajectory and Power Optimization for Energy-Efficient UAV Redeployment Against an Eavesdropper Under Consistent Fairness Constraint

Bend-twist adaptive control for flexible wind turbine blades: Principles and experimental validation

This study proposes a new methodology for optimizing the power curve of a wind turbine at low wind speeds. The principles of bend-twist coupling and the mechanism of energy exchange between the structure and inflow are analyzed. For the blade’s geometric nonlinearity, the virtual displacements and strain fields are described using the Green-Lagrange strain theorem, retaining third-order terms in the energy expressions. The equations of motion are derived using Hamilton’s principle. The bend-twist coupling effects and large deformations of the blade are analyzed using the Updated Lagrange method. Notably, the angle of attack for a single blade section is influenced by bend-twist deformation, causing variations in the rotor’s maximum power coefficient from its optimal value. Additionally, the projection length of the blade, influenced by centrifugal forces, also affects the bend-twist deformation. Based on these findings, an aero-elastic coupling control strategy, termed “Bend-twist Adaptive Control”, is proposed and validated through experiments. The results demonstrate that the proposed control strategy could increase annual power production by 2.3 %. These conclusions offer a promising outlook for future wind turbine blade design and power optimization.

Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge Devices

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application’s workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings.

Experimental comparison of the effects of the two designed probes for exfoliation of graphene by sonication.

In the present research, comparative simulation and experimental investigation were carried out for two different probes to improve the efficiency and quality of few-layer graphene utilizing liquid-phase exfoliation. Two differently designed shaped probes are fabricated for this study, i.e., a simple probe and a stepped probe. The acoustic pressure distribution in the vessel is simulated for both probes at different output powers. In the experiment, both probes exfoliate graphite powder in a mixture of deionized water and ethanol. Different sonication times and output power were studied for different pulse modes. The graphene layers were characterized using Ultraviolet-visible spectroscopy (UV‒Vis), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Raman Microscope. The simulation shows that the total displacement on the tip of the stepped probe is 5.7% greater than that of the simple probe. Also, the pressure difference produced by the stepped probe is 9.15 × 106 pa compared to the pressure difference of the simple probe which is 8.23 × 106 pa. Consequently, the stepped probe was more effective in exfoliating graphene. The experimental results show that the absorbance peak in the stepped probe is approximately 32% greater than the absorbance peak in the simple probe with the same output power. Also, the graphene with better quality is produced with the stepped probe compared to the simple probe, which verifies the simulation findings. Additionally, the optimum output power, pulse duration, and sonication time to produce graphene with less time and energy consumption are obtained. The best graphene flakes were obtained via sonication with a pulse duration of 0.7 s, an output power of 282 W, and a sonication time of 45 min using a stepped probe. UV-Vis., SEM and TEM analysis show increases in quantity and quality of the exfoliated flakes. These results are approved by the peak ratio of I2D/IG around 1 in Raman spectra revealed the production of the few-layer graphene by the simple probe and the bilayer graphene by the stepped probe. So, this method is suitable for the production of graphene flakes from graphite in suitable quantity and quality.

Smelling-based hunting optimization for battery–Super/Ultracapacitor hybrid energy storage station in wind/solar generation system using Siamese neural network

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics.

Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm

The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network. The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field. The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.

A Power Grid Topological Error Identification Method Based on Knowledge Graphs and Graph Convolutional Networks

Precise and comprehensive model development is essential for predicting power network balance and maintaining power system analysis and optimization. The development of big data technologies and measurement systems has introduced new challenges in power grid modeling, simulation, and fault prediction. In-depth analysis of grid data has become vital for maintaining steady and safe operations. Traditional knowledge graphs can structure data in graph form, but identifying topological errors remains a challenge. Meanwhile, Graph Convolutional Networks (GCNs) can be trained on graph data to detect connections between entities, facilitating the identification of potential topological errors. Therefore, this paper proposes a method for power grid topological error identification that combines knowledge graphs with GCNs. The proposed method first constructs a knowledge graph to organize grid data and introduces a new GCN model for deep training, significantly improving the accuracy and robustness of topological error identification compared to traditional GCNs. This method is tested on the IEEE 30-bus system, the IEEE 118-bus system, and a provincial power grid system. The results demonstrate the method’s effectiveness in identifying topological errors, even in scenarios involving branch disconnections and data loss.

Large circuit models: opportunities and challenges

Within the electronic design automation (EDA) domain, artificial intelligence (AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level (RTL) designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems’ capabilities.

HAC-SAGIN: High-altitude computing enabled space-air-ground integrated networks for 6G

The space–air–ground integrated networks (SAGINs) provide a new paradigm for the evolution of the Internet of Things (IoT) networks by enhancing coverage and deploying computing resources near IoT devices, especially in emergency situations and disaster-hit regions. In the context of the IoT networks, aerial platforms such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) present in the air layer of SAGINs with access and aerial computing (AC) capabilities have the potential to significantly expand coverage, enhance performance, reduce delay and handle complex computation tasks for IoT devices. Seeking the stated prospect, we propose a high-altitude computing (HAC)-enabled SAGIN leveraging millimeter waves (mmWave) frequency range in which the IoT devices are provided access services by low-earth orbit satellites (LEO-SATs) and HAPs while the HAPs offer AC facility as well. Non-orthogonal multiple access (NOMA) is used as a multiple access scheme with different clustering mechanisms in uplink (UL) and downlink (DL) communication. We aim to establish high-rate data transmission in DL along with minimizing the execution time of IoT devices offloading their data to the HAPs in UL communication. The mmWaves range is targeted to have high-rate data transmissions and NOMA implementation further enhances the bandwidth available for an individual IoT device. For efficient offloading in UL communication, we formulate an optimization problem aiming to minimize the execution time by using the Lagrangian function-based approach. Execution time is minimized by reducing the transmission and computation time, which is attained by the optimization of allocated power and computation resources. Simulation results demonstrate that the proposed HAC-SAGIN is able to establish high-rate transmissions in DL and exhibits a significant decrease in execution time in UL in contrast to the no optimization case. Optimum power assignment improves the achievable rate, leading to reduced transmission time, while optimum core assignment efficiently reduces the computation time. In addition, the offloaded data size-driven NOMA implementation in UL prominently improves the system effective throughput.

Optimum power control and coordinate sizing for the stand-alone wind-energy storage integrated hydrogen production system

The stand-alone wind-energy storage integrated hydrogen production technique is becoming a key and emerging technique to achieve carbon neutrality. However, the conflict between the random wind power fluctuation and the stable power demand of the electrolyzer lowers the system's operation stability and reliability. To address this issue, a modified output power smooth control strategy for the wind turbine that considers the dynamic performance of the electrolyzer is proposed. The output power of the wind turbine can be smoothed following the need of the electrolyzer, which helps reduce the electrolyzer's start-stop times and the battery's charging and discharging cycles. Eventually, the efficiency and life span of the hydrogen production system is enhanced. Afterward, an operational strategy for the hydrogen production system is provided, and a coordinated sizing strategy for the wind turbine, energy storage system, and electrolyzer is proposed. The results of case studies, which use the real data obtained from a hydrogen production demonstration project, prove that the overall lifecycle revenue for the hydrogen production system is remarkably increased. Finally, a long-term simulation model for a stand-alone wind-energy storage integrated hydrogen production system was developed. Simulation results validate the effectiveness of the proposed method.

Combined Economic Emission Dispatch Including Variable Energy Resources

Sustainable energy solutions are becoming more and more necessary as the world's energy needs increase as a result of population expansion and industrialization. Using renewable energy sources has become essential to meeting these needs and reducing the negative effects on the environment. The increasing incorporation of renewable energy sources (RES) like wind and solar into microgrid systems poses a notable obstacle to attaining optimum power dispatch because of their intrinsic unpredictability. The combined economic emission dispatch (CEED) issue may become inefficient as a result of this fluctuation, especially in islanded microgrid systems. In particular, in areas with significant RES potential, resolving this problem is essential to improving the sustainability and dependability of the energy supply. In this work, the optimization of the CEED issue in an islanded microgrid system with wind, solar, and thermal energy sources is the main emphasis. By employing a weighted sum approach and a Butterfly Optimization Algorithm (BOA), the research aims to provide an efficient solution to the multi-objective CEED dilemma. The proposed method outperforms traditional optimization techniques, offering a more robust framework for integrating RES into microgrids. This research reveals a number of limitations that affect the effectiveness of energy dispatch systems, such as thermal unit ramp rates and operating restrictions. Subsequent investigations have to concentrate on delving deeper into these limitations and devising tactics to augment the flexibility of optimization algorithms such as the Butterfly Optimization Algorithm (BOA) approach.

Coordinated active-reactive power optimization considering photovoltaic abandon based on second order cone programming in active distribution networks

On the basis of predecessors’ coordination optimization of active and reactive power in distribution network, For the necessity of the optimal operation in the distribution network, part of power generated from photovoltaic (PV) cannot be sold to users, and cannot enjoy subsidies. Similarly, the network loss in the power transmission will also bring a certain economic loss. This paper comprehensively considers the economic loss caused by the network loss and PV abandon of the distribution system, and establishes a model to minimize the economic loss. To solve this problem efficiently, the method of DistFlow equation and mixed integer second order cone programming (MISOCP) is used to solve the problem, in this method, the original mixed integer nonlinear programming non-convex problem is transformed into a convex problem, which makes the optimization problem easy to solve. The modified IEEE 33 and IEEE 69 distribution networks are tested by the above method. The optimized results are able to meet the target and have very small relaxation gaps, and the voltage level is also optimized. This coordinated optimization approach helps to optimize the economic operation for active distribution networks with PVs.

A novel hybrid genetic algorithm and Nelder-Mead approach and it’s application for parameter estimation

Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first tested on 15 benchmark functions commonly used to evaluate optimization strategies. The effectiveness of GANMA is also demonstrated through its application to parameter estimation problems, showcasing its practical utility in real-world scenarios. Results GANMA outperforms traditional optimization methods in terms of robustness, convergence speed, and solution quality. The hybrid algorithm excels across different function landscapes, including those with high dimensionality and multimodality, which are often encountered in real-world optimization issues. Additionally, GANMA improves model accuracy and interpretability in parameter estimation tasks, enhancing both model fitting and prediction. Conclusions GANMA proves to be a flexible and powerful optimization method suitable for both benchmark optimization and real-world parameter estimation challenges. Its capability to efficiently explore parameter spaces and refine solutions makes it a promising tool for scientific, engineering, and economic applications. GANMA offers a valuable solution for improving model performance and effectively handling complex optimization problems.

IoT Weather Station Optimized for Energy Efficiency

This work presents an economical IoT weather station with energy-efficient capabilities. It offers a multi-sensor platform, with balanced functionality and power optimization that ensures an extended- wireless range. The developed system features diverse sensors measuring temperature, humidity, pressure and light intensity. Employing power-saving techniques like sleep/wake cycles and controlled sampling rates makes the station economical and power-efficient. This setup allows distant observation via a web browser interface, addressing the challenge of low power and extended-range deployments of embedded systems. The adopted energy optimization strategy enabled autonomous long-range functioning, with real-time data collection and visualization. The recoded peak power consumption is only 2022 A for all the embedded operations.

Optimization of laser-tool distance and laser power in laser-assisted milling under material softening and surface quality constraints

Optimization of laser-tool distance and laser power in laser-assisted milling under material softening and surface quality constraints

Design and implementation in power-efficient and thermal conductivity of vedic processor for embedded applications

An embedded system that handles critical applications requires power economy, performance and thermal management, and an efficient Vedic processor with highly efficienct thermal conductivity. The proposed contemporary processor architecture has been designed to use Vedic mathematics and elaborate power and thermal optimization approaches to formulate an Arithmetic Logic Unit (ALU). This ALU is based on Vedic Sutras to enable quick computation, high precision floating point operations, and low power consumption. Since thermal management leads to energy loss in the form of heat, the embedded processor’s thermal conductivity is enhanced using novel materials and unique ways of heat dissipation. This improves performance as well as power efficiency. The proposed approach uses extensive simulation thermal analysis by EDA tools to build an optimised embedded processor that consumes 35% less power, has 40% high thermal conductivity, is 25% faster, and utilizes 15% less area when compared to conventional embedded processors. This paper helps in furthering embedded systems research by showing how old mathematical ideas can be used in a new engineering approach to solve current technology issues. The suggested novel embedded system is more efficient and thermally resistant for IOT, mobile computing, and industrial control systems.

Density-guided and Adaptive Update Strategy for Multi-objective Particle Swarm Optimization

Abstract As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy to fall into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms.