Power Internet Of Things Research Articles

Deep Learning Aided SID in Near‐Field Power Internet of Things Networks With Hybrid Recommendation Algorithm

ABSTRACTIn the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning‐based semi‐autoencoders in conjunction with a hybrid recommendation algorithm to enhance SID tasks. Our proposed method utilizes the deep learning‐based semi‐autoencoder to effectively capture and learn complex patterns from high‐dimensional power IoT data, facilitating the identification of anomalies indicative of potential security threats. The hybrid recommendation algorithm, which combines collaborative filtering and content‐based filtering, further refines the detection process by cross‐verifying the identified anomalies with historical data and contextual information, thereby improving the accuracy and reliability of the SID tasks. Through extensive simulations and practical data evaluations, our proposed framework demonstrates superior performance over conventional methods, achieving higher detection accuracy. In particular, the detection accuracy of the proposed scheme is more than 20% higher than that of the competing schemes.

A dynamic resource allocation optimization method for compilation subsystems in power IoT environment

Abstract Affected by the resource allocation skew problem, which leads to the low utilization rate of resources, we propose to study the optimization method of dynamic resource allocation of compiler subsystem under the environment of electric power IoT (Internet of Things). The skewness willingness function is constructed to reflect the imbalance degree of the compiler subsystem partitioning in the power IoT environment, and at the same time, quantitative indexes are provided for evaluating the effectiveness of the compiler subsystem partitioning algorithm in the power IoT environment. In the resource allocation stage, combined with the compiler subsystem resource tilt allowable threshold, the dynamic allocation of resources is carried out based on the existing compiler tasks in accordance with the priority, with the goal of minimizing the additional waiting time (tilt). In the test results, the compiler subsystem is tested for the distribution of ηEE under different types of requests is always within 0.25 J/bit. Compared with the control group, it has obvious advantages in processing performance and stability.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Deep Learning‐Based Anomaly Diagnosis Method for Capacitive Voltage Transformer Measurement Error in PIoT

ABSTRACTIn the era of power Internet of Things (PIoT), the accuracy of capacitive voltage transformers (CVTs) is crucial for maintaining the reliability of voltage measurement and protection systems in smart grids, thereby contributing to overall grid stability and efficiency. Accurate and timely detection of anomalies in CVT measurement errors is essential for preventing equipment failures, reducing maintenance costs, and improving overall system reliability. However, existing anomaly diagnosis methods often rely on statistical analysis and rule‐based approaches, which have limitations in capturing complex patterns and adapting to evolving anomaly types. This paper proposes a novel deep learning‐based anomaly diagnosis method for CVT measurement error in PIoT, called LSTM‐CVT. The proposed method leverages a long short‐term memory (LSTM) neural network architecture with three key strategies: bidirectional temporal dependency capture, hierarchical feature learning, and joint anomaly diagnosis and error estimation. The experimental results demonstrate the superior performance of LSTM‐CVT compared to state‐of‐the‐art baseline methods.

PIoT‐oriented multi‐target recognition of substation infrared images driven by deep learning

AbstractSubstation infrared imaging plays a crucial role in condition monitoring and fault detection of Power Internet of Things (PIoT). However, the accurate and efficient recognition of multiple targets in substation infrared images remains a challenging task. This paper proposes a deep learning‐based multi‐target recognition framework for substation infrared images in PIoT. This paper presents a method for recognizing various electrical equipment in infrared images of substations using a faster region‐based convolutional neural network (Faster RCNN). The optimization of Faster RCNN includes class rectification inspired by non‐maximum suppression (NMS), enabling the correction of misclassified equipment parts and enhancing recognition accuracy. The approach combines NMS and class rectification to retain region proposals with optimal recognition performance. Experimental results demonstrate the effectiveness of the proposed method in improving the recognition accuracy of electrical equipment in infrared images.

Additive engineering for Sb2S3 indoor photovoltaics with efficiency exceeding 17%

Indoor photovoltaics (IPVs) have attracted increasing attention for sustainably powering Internet of Things (IoT) electronics. Sb2S3 is a promising IPV candidate material with a bandgap of ~1.75 eV, which is near the optimal value for indoor energy harvesting. However, the performance of Sb2S3 solar cells is limited by nonradiative recombination, which is dependent on the quality of the absorber films. Additive engineering is an effective strategy to fine tune the properties of solution-processed films. This work shows that the addition of monoethanolamine (MEA) into the precursor solution allows the nucleation and growth of Sb2S3 films to be controlled, enabling the deposition of high-quality Sb2S3 absorbers with reduced grain boundary density, optimized band positions, and increased carrier concentration. Complemented with computations, it is revealed that the incorporation of MEA leads to a more efficient and energetically favorable deposition for enhanced heterogeneous nucleation on the substrate, which increases the grain size and accelerates the deposition rate of Sb2S3 films. Due to suppressed carrier recombination and improved charge-carrier transport in Sb2S3 absorber films, the MEA-modulated Sb2S3 solar cell yields a power conversion efficiency (PCE) of 7.22% under AM1.5 G illumination, and an IPV PCE of 17.55% under 1000 lux white light emitting diode (WLED) illumination, which is the highest yet reported for Sb2S3 IPVs. Furthermore, we construct high performance large-area Sb2S3 IPV minimodules to power IoT wireless sensors, and realize the long-term continuous recording of environmental parameters under WLED illumination in an office. This work highlights the great prospect of Sb2S3 photovoltaics for indoor energy harvesting.

Key technologies and applications of collaboration between digital power grid and Internet of Things

AbstractThe integration of electricity technology and information technology, such as the Internet of Things (IoT), enables the construction of new power systems, along with the innovation of application scenarios and business scope. The key technologies of power IoT and the data flow process are summarised first. The IoT technology and application scenario requirements of power generation, transmission, loading, and storage of new power systems are studied. Thus, the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems. The key problems and solutions faced by the power IoT under the digital transformation are described, and the cross‐integration of key technologies and promotion of application scenario innovation are prospected. Finally, the key issues of future technological development were discussed, providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.

A Multi-Hop End-Edge Cooperative Computing Scheme for Power IoT

With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge cooperative computing (MHCC) scheme to address inspection task processing in power IoT. We formulate a multi-hop task offloading problem that minimizes the energy consumption of inspection SDs with delay constraints. We develop a JDPSO algorithm to solve the multi-hop task offloading problem and evaluate the algorithm’s performance based on numerous simulation experiments. The experimental results show that JDPSO reduces the system’s energy consumption by 56.30%, 48.56%, 78.87%, 50.89%, 30.85%, and 68.31%, and also reduces delay by 50.69%, 42.78%, 58.67%, 44.84%, 10.22%, and 62.88% compared to GA, RSA, SSA, MFO, DOA, and ALC schemes.

Enhancing power marketing audit through IoT and multi-sensor information fusion: A substation scenario analysis

The demand for electricity in economic development is increasing, so it is necessary to establish a unified power marketing audit evaluation system, standardize the power marketing audit workflow, and formulate scientific audit evaluation standards to make the power marketing audit more scientific and objective. This paper introduces multi-sensor cooperative sensing from IoT technology and uses multi-source data fusion technology for complete multi-sensor information fusion. The objective is to illustrate how multi-sensor information fusion and Internet of Things (IoT) improvements may improve the power industry's auditing process, especially at substations, resulting in increased efficiency, accuracy, and risk management. We focus on analyzing the environmental characteristics of the power IoT sensing system in the substation scenario and compare the advantages and disadvantages of various types of networking algorithms. Experiments show that our method has a low bandwidth footprint, energy consumption, and high data accuracy.

An Active Distribution Grid Exceedance Testing and Risk-Planning Simulation Based on Carbon Capture and Multisource Data from the Power Internet of Things

In order to achieve peak carbon and carbon neutrality targets, a high number of distributed power sources have been connected to distribution networks. How to realize the planning of a distribution network containing integrated energy under the condition of carbon capture and complete the exceedance test of the distribution network under the condition of accessing a large number of distributed generators has become an urgent problem. To solve the above problem while promoting sustainable development, this work proposes an active distribution network risk-planning model based on multisource data from carbon capture and the Power Internet of Things. The model calculates the semi-invariants of each order of the node state vectors and branch circuit current vectors and then utilizes Gram–Charlier-level expansion to obtain the exceeding probability density function and the probability distribution functions of the node voltages and line powers in the distribution network. Combined with multisource data, an active distribution network with an integrated energy system designed for carbon capture was modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model were simplified by second-order cone relaxation, and the optimal planning scheme of the distribution network was solved by combining the Gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verified the effectiveness of the proposed model.

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis.

At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Computing task allocation for power Internet of Things including renewable energy sources

Power Internet of Things (PIoT) is the key technology to build a new power system based on new energy. Focusing on the problem that the large amount of data leads to the long computation delay of cloud computing in the operation control process of PIoT including renewable energy sources, this paper establishes a cloud-edge-end collaborative optimization calculation model for PIoT based on edge computation. Combined with data collected by a variety of smart terminal devices (STDs) arranged around the power generation equipment, the edge computation framework of PIoT is analyzed, and a computing task allocation model based on minimizing average system latency is established. The corresponding simulation model is built for simulation verification. Compared with other baseline schemes, it has been demonstrated that the average system latency of all tasks can be significantly decreased by using the proposed optimization scheme.

A 8.1-nW, 4.22-kHz, −40–85 °C relaxation oscillator with subthreshold leakage current compensation and forward body bias buffer for low power IoT applications

This paper presents a relaxation oscillator (RxO) utilizing subthreshold transistor leakage current compensation (SLC) technology to expand the operating range from 70 °C to 85 °C at high temperatures. The compensated RxO outperforms the uncompensated counterpart by 25 times for temperature coefficient (TC). Additionally, forward body bias (FBB) technology optimizes the digital buffer delay with temperature drift at low supply voltage, extending the oscillator’s low-temperature range from −20 °C to −40 °C. Fabricated in a 65 nm CMOS process, the oscillator consumes a mere 8.1 nW at a 0.4 V supply voltage. At room temperature, it operates at 4.22 kHz with an energy efficiency of 1.92 nW/kHz. The oscillator demonstrates a temperature coefficient (TC) of 114.01 ppm/°C within the range of −40 °C to 85 °C, rendering it a prospective choice for low-power Internet of Things (IoT) applications.

Learning-Based Multi-UAV Assisted Data Acquisition and Computation for Information Freshness in WPT Enabled Space-Air-Ground PIoT

The explosion of information data in the power Internet of Things (PIoT) poses difficulties for data acquisition and computation of power devices located in remote areas with limited computing capability and energy. Therefore, we propose a multi-unmanned aerial vehicle (UAV) assisted wireless power transmission (WPT) enabled space-air-ground PIoT framework, in which the UAVs provide energy for data acquisition of power devices through WPT, and use mobile edge computing (MEC) technology to calculate the data. Finally, they act as relays to forward the data to the low earth orbit (LEO) satellite. For keeping the data fresh, we jointly optimize the number of UAV hovering positions, the hovering positions, the connection between UAVs and power devices, the energy transmission and data acquisition time of power devices, the computing resources, flight speed and trajectories of UAVs, aiming to minimizing the average age of information (AoI) of power devices. Through theoretical derivation the problem is decoupled into five independent subproblems, which are solved using improved K-means algorithm, Lagrangian dual decomposition method, interior point method, adaptive optimization, and Q-Learning algorithm, respectively. Extensive numerical simulations demonstrate the superior properties of our solution comparing to benchmark algorithms.

Research on Power IoT Technology Integrating 5G Communication and Distributed Computing

Abstract With the development of 5G technology and distributed computing, the Internet of Things (IoT) for electric power shows excellent potential in realizing efficient and secure energy management. The objective of this study is to investigate how 5G communication technology can be combined with distributed computing to enhance the performance of power IoT. This paper examines the power of IoT technology that integrates 5G communication and distributed computing. The study is designed to enhance the power system’s efficiency and security. Through experiments and simulations, this study utilizes 5G technology to optimize IoT communication while using distributed computing to enhance data processing. According to the results, the integrated system improves the data transfer rate by 50% and reduces the potential security risk by 25%. The conclusion demonstrates that combining 5G and distributed computing can significantly enhance the efficiency of power IoT, which is crucial for the construction of future smart grids.

ALICA: A Multi-S-Box Lightweight Cryptographic Algorithm Based on Generalized Feistel Structure

With the development of science and technology, IoT devices have already become ubiquitous in the public eye. Through the perception layer, the collected data are displayed or transmitted to the server backend for analysis. Due to the increasing integration of IoT devices into people’s daily lives, privacy issues, such as data leaks, have received more attention. Most sensor nodes, such as temperature and pressure sensors in marine environments, have low computing power, storage capacity, and significant underlying heterogeneity, making it challenging to implement a standardized data security protection solution. Data security in the nodes is seriously challenged as a result. It is of great significance to design a lightweight block cipher for the Internet of Things (IoT) environment, which can ensure the security of node information. A new lightweight block cipher algorithm called ALICA is proposed in this paper, which is well-suited for the low computing power and heterogeneous device environment at the lower layers. A generalized Feistel structure and a linear structure with XOR and shift operations are used to achieve ease of software implementation. Two different S-boxes are used to create the nonlinear structure of the password, thereby enhancing the robust security of the cipher. In addition, the cipher adopts a design approach that prioritizes software efficiency while also considering hardware implementation efficiency. This makes it more suitable for low computing power, storage capacity, and resource-limited IoT sensor nodes at the lower layers.

Design and implementation of a vulnerability mining system for power internet of things terminal based on fuzzy mathematical model simulation platform

With the rapid development of the economy, the power supply has also shown an increasing trend year by year, and many loopholes and hidden dangers have emerged during the operation of the power grid. The power grid may be subject to malicious attacks, such as hacker attacks, power theft, etc. This may lead to security risks such as power grid system paralysis and information leakage. In order to ensure the quality of power supply, it is necessary to optimize the distribution of electricity and improve power supply efficiency. This article pointed out the security performance issues of power Internet of Things (IoT) terminals and analyzed the design and implementation of a vulnerability mining system for power IoT terminals based on a fuzzy mathematical model simulation platform. This article used a fuzzy mathematical model to quantitatively evaluate the security performance of power IoT terminals, providing an effective theoretical basis for vulnerability mining. Based on the analysis of vulnerability mining technology classification and vulnerability attack process, this article characterizes vulnerability parameters through fuzzy mapping. Based on the collected vulnerability data and the online and device status of power IoT terminals, fuzzy logic inference is used to determine and mine potential vulnerability issues in power IoT terminals. This article aimed to improve the security performance of power IoT terminals and ensure the safe and stable operation of the power system. By testing the number of system vulnerabilities, vulnerability risk level, and vulnerability mining time of the power IoT terminal vulnerability mining system based on fuzzy mathematical models, it was found that the power IoT simulation platform based on fuzzy mathematical models has fewer terminal vulnerabilities. The fuzzy mathematical model can reduce the vulnerability risk level of the power IoT simulation platform system, and the time required for vulnerability mining was reduced; the time was reduced by 0.48 seconds, and the speed of vulnerability mining was improved. Fuzzy mathematical models can promote the development of the power industry, which provides strong support for the security protection of power IoT terminals.

Research on electromagnetic vibration energy harvester for cloud-edge-end collaborative architecture in power grid

With the deepening of the construction of the new type power system, the grid has become increasingly complex, and its safe and stable operation is facing more challenges. In order to improve the quality and efficiency of power grid management, State Grid Corporation continues to promote the digital transformation of the grid, proposing concepts such as cloud-edge-end collaborative architecture and power Internet of Things, for which comprehensive sensing of the grid is an important foundation. Power equipment is widely distributed and has a wide variety of types, and online monitoring of them involves the deployment and application of a large number of power sensors. However, there are various problems in implementing active power supplies for these sensors, which restrict their service life. In order to collect and utilize the vibration energy widely present in the grid to provide power for sensors, this paper proposes an electromagnetic vibration energy harvester and its design methodology based on a four-straight-beam structure, and carries out a trial production of prototype. The vibration pickup unit of the harvester is composed of polyimide cantilevers, a permanent magnet and a mass-adjusting spacer. The mass-adjusting spacer can control the vibration frequency of the vibration unit to match the target frequency. In this paper, a key novel method is proposed to increase the number of turns in a limited volume by stacking flexible coils, which can boost the output voltage of the energy harvester. A test system is built to conduct a performance test for the prototype harvester. According to the test results, the resonant frequency of the device is 100 Hz, the output peak-to-peak voltage at the resonant frequency is 2.56 V at the acceleration of 1 g, and the maximum output power is around 151.7 mu W. The proposed four-straight-beam electromagnetic vibration energy harvester in this paper has obvious advantages in output voltage and power compared with state-of-the-art harvesters. It can provide sufficient power for various sensors, support the construction of cloud-edge-end architecture and the deployment of a massive number of power sensors. In the last part of this article, a self-powered transformer vibration monitor is presented, demonstrating the practicality of the proposed vibration energy harvester.