To enhance the charging and discharging strategy of the energy storage system (ESS) and optimize its economic efficiency, this paper proposes a novel approach based on the enhanced whale algorithm. Recognizing that the standard whale algorithm can sometimes suffer from local optima in high-dimensional multiobjective optimization, this study introduces chaotic mapping and individual information exchange mechanisms to address this challenge. The proposed algorithm explores optimal configurations for different energy device placements and capacities through encircling and bubble searches, evaluating various multiobjective functions for optimization. In addition, the algorithm refines both the system operation model and the ESS configuration model, with the objective function being the analysis of the average annual revenue of the ESS. Model testing results demonstrate that this algorithm yields more moderate energy storage (ES) capacity decay, extending operational time to 3,124 days and achieving a full-life cycle benefit of the ESS as high as 1,821,623.68 yuan. Also, our algorithm demonstrates high efficiency, with minimal test time (68.36 seconds) and quick optimization (0.031 seconds per cycle), regardless of problem complexity.

Meizhen Gao

Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

In order to optimize the performance of the transmission network (TN), this paper introduces the random fractal search algorithm based on the beetle antenna search algorithm, thus proposing the random fractal beetle antenna algorithm (SFBA). The main work of this research is as follows: (1) in the beetle antenna search algorithm, this study used a population of beetles and introduced elite members of the population in order to make the algorithm more stable and to some extent improve the accuracy of its answers. (2) Utilizing the elite reverse learning method and the leader’s multilearning strategy for elites helps to strike a balance between the global exploration and local development of the algorithm. This strategy also further improves the ability of the algorithm to find the optimal solution. (3) Experiments on real experimental data show that the SFBA algorithm proposed in this paper is effective in improving TN performance. In summary, the research content of this paper provides a good reference value for the performance optimization of TN in actual production.

Junlei Liu

Zhu Chao

Xiangzhen He

Bo Bao

Xiaowen Lai

Power Transmission Network Optimization Strategy Based on Random Fractal Beetle Antenna Algorithm

The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

Mustefa Badri Usman

Ram Sewak Singh

S Rajkumar

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

This paper aims to solve the optimization problems in far-field wireless power transfer systems using deep reinforcement learning techniques. The Radio-Frequency (RF) wireless transmitter is mounted on a mobile robot, which patrols near the harvested energy-enabled Internet of Things (IoT) devices. The wireless transmitter intends to continuously cruise on the designated path in order to fairly charge all the stationary IoT devices in the shortest time. The Deep Q-Network (DQN) algorithm is applied to determine the optimal path for the robot to cruise on. When the number of IoT devices increases, the traditional DQN cannot converge to a closed-loop path or achieve the maximum reward. In order to solve these problems, an area division Deep Q-Network (AD-DQN) is invented. The algorithm can intelligently divide the complete charging field into several areas. In each area, the DQN algorithm is utilized to calculate the optimal path. After that, the segmented paths are combined to create a closed-loop path for the robot to cruise on, which can enable the robot to continuously charge all the IoT devices in the shortest time. The numerical results prove the superiority of the AD-DQN in optimizing the proposed wireless power transfer system.

Yuan Xing

Riley Young

Giaolong Nguyen

Maxwell Lefebvre

Tianchi Zhao

Haowen Pan

Liang Dong

Optimal Path Planning for Wireless Power Transfer Robot Using Area Division Deep Reinforcement Learning

Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

Hongyang He

Yue Gao

Yong Zheng

Yining Liu

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

Conventional magnetically coupled resonant wireless power transfer systems are faced with resonant frequency splitting phenomena and impedance mismatch when a receiving coil is placed at misaligned position. These problems can be avoided by using uniform magnetic field distribution at receiving plane. In this paper, a novel 3D transmitting coil structure with improved uniform magnetic field distribution is proposed based on a developed optimization method. The goal is to maximize the average magnetic field strength and uniform magnetic field section of the receiving plane. Hence, figures of merit (FoM1 and FoM2) are introduced and defined as product of average magnetic field strength and length or surface along which uniform magnetic field is generated, respectively. The validity of the optimization method is verified through laboratory measurements performed on the fabricated coils driven by signal generator at operating frequency of 150 kHz. Depending on the allowed ripple value and predefined coil proportions, the proposed transmitting coil structure gives the uniform magnetic field distribution across 50% to 90% of the receiving plane.

Davor Vinko

Domagoj Bilandžija

Vanja Mandrić Radivojević

Optimization of a Two-Layer 3D Coil Structure with Uniform Magnetic Field

Wireless power transfer has been proved promising in various applications. The homogeneous winding method in loosely coupled transformers incurs unnecessary intense magnetic field distribution in the center and causes extra magnetic loss. An inhomogeneous winding method is proposed in this paper, and a relatively homogeneous magnetic field distribution inside the core is achieved. This paper investigated the magnetic loss of homogeneous winding and inhomogeneous winding for wireless power transfer. A theoretical model was built to evaluate magnetic loss under inhomogeneous winding. The coupling coefficient and magnetic loss were investigated individually and comparisons were made between different width ratio combinations. Theoretical analysis was validated in experiments.

Jing Zhou

Jiazhong He

Fan Zhu

Inhomogeneous Winding for Loosely Coupled Transformers to Reduce Magnetic Loss

The widespread deployment of the Internet of Things (IoT) requires the development of new embedded systems, which will provide a diverse array of different intelligent functionalities. However, these devices must also meet environmental, maintenance, and longevity constraints, while maintaining extremely low-power consumption. In this work, a batteryless, low-power consumption, compact embedded system for IoT applications is presented. This system is capable of using a combination of hybrid solar and radiofrequency power sources and operates in the 900 MHz ISM band. It is capable of receiving OOK or ASK modulated data and measuring environmental data and can transmit information back to the requester using GFSK modulated data. The total consumption of the system during its sleep state is 920 nW. Minimum power required to operate is −15.1 dBm or 70 lux, when using only radiofrequency or solar powering, respectively. The system is fully designed with components off the shelf (COTS).

John Nicot

Ludivine Fadel

Thierry Taris

An Autonomous Wireless Sensor Node Based on Hybrid RF Solar Energy Harvesting

In this paper, a multiantenna wireless transmitter communicates with an information receiver while radiating RF energy to surrounding energy harvesters. The channel between the transceivers is known to the transmitter, but the channels between the transmitter and the energy harvesters are unknown to the transmitter. By designing its transmit covariance matrix, the transmitter fully charges the energy buffers of all energy harvesters in the shortest amount of time while maintaining the target information rate toward the receiver. At the beginning of each time slot, the transmitter determines the particular beam pattern to transmit with. Throughout the whole charging process, the transmitter does not estimate the energy harvesting channel vectors. Due to the high complexity of the system, we propose a novel deep Q-network algorithm to determine the optimal transmission strategy for complex systems. Simulation results show that deep Q-network is superior to the existing algorithms in terms of the time consumption to fulfill the wireless charging process.

Bin Xu

Cristiano Tapparello

Wei Shi

Xuejun Liu

Timothy Lu

Optimal Wireless Information and Power Transfer Using Deep Q-Network

An approach to improve wireless power transfer (WPT) to implantable medical devices using loop antennas is presented. The antenna exhibits strong magnetic field and dense flux line distribution along two orthogonal axes by insetting the port inside the antenna area. This design shows excellent performance against misalignment in the y-direction and higher WPT as compared with a traditional square loop antenna. Two antennas were optimized based on this approach, one wearable and the other implantable. Both antennas work at both the ISM (Industrial, Scientific, and Medical) band of 433 MHz for WPT and the MedRadio (Medical Device Radiocommunications Service) band of 401–406 MHz for communications. To test the WPT for implantable medical devices, a miniaturized rectifier with a size of 10 mm × 5 mm was designed to integrate with the antenna to form an implantable rectenna. The power delivered to a load of 4.7 kΩ can be up to 1150 μW when 230 mW power is transmitted which is still under the safety limit. This design can be used to directly power a pacemaker, a nerve stimulation device, or a glucose measurement system which requires 70 μW, 100 μW, and 48 μW DC power, respectively.

Muayad Kod

Jiafeng Zhou

Yi Huang

Muaad Hussein

Abed P. Sohrab

Chaoyun Song

An Approach to Improve the Misalignment and Wireless Power Transfer into Biomedical Implants Using Meandered Wearable Loop Antenna

Wireless energy harvesting is an effective way to power condition monitoring sensors which are the basis of smart grid. In this paper, a new free-standing I-shaped core is designed to scavenge electromagnetic energy from large alternating current. An I-shaped core can guide more magnetic flux by adding a pair of magnetic flux collector plates at both ends of the rod core. It weakens the core demagnetization field and enables more energy to be collected. Since a magnetic field line can be bent with high-permeability soft magnetic materials, a highly efficient grid-shaped coil is proposed. Compared with the I-shaped coil, its weight is lighter and power density is higher. A Mn-Zn ferrite with high relative permeability and ultralow conductivity can effectively reduce eddy current loss, which proves to be the most suitable material. The measured open circuit voltage agrees well with the theoretical value. The experimental results show that the output power can reach 4.5 mW when the I-shaped coil is placed in a magnetic flux density of 6.5 μTrms. The power density is 7.28 μW/cm3. Therefore, the proposed design can be very effective for supplying condition monitoring sensors.

Hao Wang

Guangyu Shi

Congzheng Han

A Free-Standing Electromagnetic Energy Harvester for Condition Monitoring in Smart Grid

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Wireless Power Transfer&nbsp;publishes original&nbsp;research and industrial developments relating to wireless power.

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Chief Editor Jiafeng Zhou is Senior Lecturer at the Department of Electrical Engineering and Electronics, University of Liverpool, UK. His research interests include wireless power transfer, energy harvesting, microwave power amplifiers, filters and metamaterial devices.

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title

Wireless Power Transfer publishes original research and industrial developments relating to wireless power.

Wireless Power Transfer