Power System Monitoring Research Articles

Graph topology-constrained BILP for optimal PMU placements

In the monitoring and control of power systems, the deployment of Phasor Measurement Units (PMUs) is of paramount importance, yet their high installation costs have limited their widespread application. To address this issue, this paper proposes an innovative method based on graph topology constraints, known as the Graph Topology-constrained Binary Integer Linear Programming (GT-BILP) approach, aimed at achieving the optimal configuration of PMUs. This method not only takes into account cost-effectiveness but also ensures the complete observability of the system. By quantifying the contribution of each node and bus to the system's observability within the network, a novel contribution matrix is constructed. This matrix integrates the dynamic characteristics of network topology and information flow, providing precise decision support for the deployment of PMUs. By combining the matrix with the network's planning model, the GT-BILP model is obtained, which can effectively solve the PMU layout requirements for large power grids. For the special case of zero-injection buses (ZIBs), the Topology Transformation algorithm has been improved, significantly enhancing the solution efficiency and quality. Through simulation experiments on multiple IEEE standard test systems, the effectiveness and superiority of the proposed GT-BILP model have been verified. This research is of significant importance for improving the monitoring and control capabilities of power systems and provides a new perspective for technological advancement in this field.

Combination of a Rabbit Optimization Algorithm and a Deep-Learning-Based Convolutional Neural Network–Long Short-Term Memory–Attention Model for Arc Sag Prediction of Transmission Lines

Arc droop presents significant challenges in power system management due to its inherent complexity and dynamic nature. To address these challenges in predicting arc sag for transmission lines, this paper proposes an innovative time–series prediction model, AROA-CNN-LSTM-Attention(AROA-CLA). The model aims to enhance arc sag prediction by integrating a convolutional neural network (CNN), a long short-term memory network (LSTM), and an attention mechanism, while also utilizing, for the first time, the adaptive rabbit optimization algorithm (AROA) for CLA parameter tuning. This combination improves both the prediction performance and the generalization capability of the model. By effectively leveraging historical data and exhibiting superior time–series processing capabilities, the AROA-CLA model demonstrates excellent prediction accuracy and stability across different time scales. Experimental results show that, compared to traditional and other modern optimization models, AROA-CLA achieves significant improvements in RMSE, MAE, MedAE, and R2 metrics, particularly in reducing errors, accelerating convergence, and enhancing robustness. These findings confirm the effectiveness and applicability of the AROA-CLA model in arc droop prediction, offering novel approaches for transmission line monitoring and intelligent power system management.

AN IOT-BASED FRAMEWORK FOR EFFICIENT SOLAR POWER GENERATION AND INTEGRATION IN AUTOMOTIVE

Objective: We suggest an Internet of Things (IoT)-based system that uses edge intelligence to anticipate power production effectively and monitors electricity substations and smart solar installations. It ensures dependable and effective power distribution inside industrial Internet of things settings, improving sustainability, safety, and energy management in smart buildings. It also improves decision-making and reduces volatility. Method: Create and execute an IoT-enabled power monitoring system for smart solar panels and substations that incorporates edge intelligence for instantaneous prediction and decision-making. Deploy an IoT-enabled solar charging station for smart homes and Industry 4.0 applications, and use the cloud for sensor data analysis and control. Findings: In order to effectively manage load for commercial, electric, residential, and industrial vehicles, the suggested framework improves the efficiency and dependability of power production and distribution in industrial IoT contexts. The system increases overall efficiency via the mitigation of power fluctuations and eventualities. Furthermore, IoT integration enhances smart building energy management safety and sustainability of energy resources as well as reduced the overall cost by 95% when comparing to the traditional devices. Novelty: For smart solar systems and substations, a novel framework combines edge intelligence with IoT. It includes a sophisticated IoT-based control system that improves power distribution network decision-making. In addition to taking an integrated strategy to energy management and enabling real-time monitoring and prediction of power production in industrial IoT contexts, it emphasizes sustainability, safety, recycling, and reuse in smart buildings.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart grid involves application of information and communication technology (ICT) for monitoring, protection, operation and control of interconnected power systems under various scenarios. Smart grid control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats and attacks under central and distributed control architectures. Cybersecurity of SGC, especially, load frequency control (LFC), is an important issue that is addressed in this article. The state-of-the-art in cybersecurity and attacks on SGC and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic vulnerability assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for VA of the standard 39-bus New England test system and the 96-bus reliability test system (RTS) to illustrate the concept of cybersecurity and VA of smart grid LFC.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Machine learning for base transceiver stations power failure prediction: A multivariate approach

The widespread deployment of cellular networks has improved communication access, driving economic growth and enhancing social connections across diverse regions. Base Transceiver Stations (BTSs), are foundational to mobile networks but are vulnerable to power failures, disrupting service delivery and causing user inconvenience. This paper proposes a machine-learning-based framework for preemptive BTS power failure prediction using multivariate time-series data from power and environmental monitoring systems. We employ a combination of deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, to achieve accurate and timely predictions of BTS power failures. CNNs were selected for extracting dependencies among features of a multivariate time-series data, while LSTMs effectively capture temporal dependencies, making them suitable for predicting power failures.The proposed models exhibit noteworthy predictive performance, with the LSTM network emerging as the most accurate model (MSE: 0.001, MAPE: 2.528), followed by the hybrid CNN-LSTM (MSE: 0.001, MAPE: 2.843) and the CNN (MSE: 0.223, MAPE: 2.843). This work demonstrates deep learning’s effectiveness in preemptive BTS failure prediction, enabling proactive maintenance and improved network resilience.

Generating Payloads of Power Monitoring Systems Compliant with Power Network Protocols Using Generative Adversarial Networks

In the network environment of power systems, payload generation is used to construct data packets, which are used to obtain data for the security management of network assets. Payloads generated by existing methods cannot satisfy the specifications of the protocols in power systems, resulting in low efficiency and information errors. In this paper, a payload generation model, LoadGAN, is proposed by using generative adversarial networks (GANs). Firstly, we find segmentation points to cut payloads into different segment sequences using sliding window schema based on Bayesian optimization. Then, we use different payload segments to train several child generators to generate corresponding parts of a whole payload. Segment sequences generated by these generators are assembled to form a whole new payload that is compliant with the specifications of the original network protocol. Experiments on the Mozi botnet dataset show that LoadGAN achieves precise payload segmentation while maintaining a high payload effectiveness of 85.5%, which is a 40% improvement compared to existing methods.

DLCH-YOLO: An Object Detection Algorithm for Monitoring the Operation Status of Circuit Breakers in Power Scenarios

In the scenario of power system monitoring, detecting the operating status of circuit breakers is often inaccurate due to variable object scales and background interference. This paper introduces DLCH-YOLO, an object detection algorithm aimed at identifying the operating status of circuit breakers. Firstly, we propose a novel C2f_DLKA module based on Deformable Large Kernel Attention. This module adapts to objects of varying scales within a large receptive field, thereby more effectively extracting multi-scale features. Secondly, we propose a Semantic Screening Feature Pyramid Network designed to fuse multi-scale features. By filtering low-level semantic information, it effectively suppresses background interference to enhance localization accuracy. Finally, the feature extraction network incorporates Generalized-Sparse Convolution, which combines depth-wise separable convolution and channel mixing operations, reducing computational load. The DLCH-YOLO algorithm achieved a 91.8% mAP on our self-built power equipment dataset, representing a 4.7% improvement over the baseline network Yolov8. With its superior detection accuracy and real-time performance, DLCH-YOLO outperforms mainstream detection algorithms. This algorithm provides an efficient and viable solution for circuit breaker status detection.

Empowering energy conservation: A prototype of household energy consumption monitoring based on Internet of Things

Abstract One of the obstacles to control household energy consumption is unavailability electricity instruments/tools to monitor electricity energy consumed by appliances causing a lack of awareness of household energy saving. Energy meter installed on buildings only show electricity consumption in total. This study aims to design and create a prototype system that can monitor and control the electrical appliances usage utilizing the Internet of Things (IoT)-based electronic devices where electricity usage data is stored in Firebase real-time database. The methods carried out are arranged in several stages, starting from data collection, design, configuration, and implementation. The tools used in this research will utilize the ESP8266 as the microcontroller, the PZEM-004T as current sensor and the Blynk as the web server. The tool will send current voltage (Volt (V)) and capacity (Watt (W)) data and then converted to energy and also the costs incurred from electricity usage based on the basic electricity tariff of National Electricity Company (PLN) and the results can be seen directly through IoT (i.e. smartphone, PC, laptop). The accuracy of power measurement by the system is 97.4% compared to measurement used by watt-checker. The results of this study show that an electrical power monitoring system makes people easier to monitor electrical power consumption by appliances used. The pilot measurement shows that a unit system can monitor electricity usage for four units of electronic equipment i.e. water pump, Television, fridge, electrical iron, rice cooker, etc. precisely and succeeded to control and increase energy saving awareness to reduce electricity consumption at the end.

Research on improved underwater cable image processing technique based on CNN-GAN

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems.

Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring.

Wavelet-based convolutional neural network for non-intrusive load monitoring of next generation shipboard Power Systems

In this study, a non-intrusive load monitoring (NILM) framework is developed for next generation shipboard power systems (SPS) based on a discrete wavelet transform signal processing and a convolutional neural network (CNN). We have applied the developed NILM method to a four-zone medium voltage direct current (MVDC) SPS to evaluate the effectiveness of the proposed method. Each zone of the MVDC SPS consists of multiple components, such as propulsion load, pulsed load, high ramp rate load, cooling load, and hotel load. The current signals from the main generators are the main inputs to the NILM model. The current signals are first processed through a discrete wavelet transform to create a coefficient vector that reflects the status of all the components in each zone. Then, a multi-class classification problem is formulated and solved using a CNN architecture model to monitor the load statuses in real time. The results of case studies show that the developed NILM model in comparison with benchmark methods can (i) accurately monitor the status of all components with a total accuracy of over 98%, (ii) identify unique pulsed loads with a total accuracy of over 99%, and (iii) sustain the functionality of load monitoring under extreme events such as cyber/physical attacks, load uncertainty, and noisy inputs.

Design of Power Outage Monitoring and Warning System Based on Internet of Things

With the rapid development of distribution network and the high popularity of distributed generation, the research on risk assessment of distribution network is accelerated. When quantifying the degree of harm caused by abnormal situations, it is necessary to consider the severity of the harm and the impact on society to measure the severity of power failure and reduce the loss caused by power risk. To provide data support for the fine perception of the situation and its development trend, the monitoring system constructed by the power microservice further improves the ability and accuracy of sudden fault prediction. In this paper, through the monitoring system information to explore the transmission line fault early warning, the data analysis model is established. The particle swarm optimization algorithm and BP neural network are used to build the early warning model to predict the potential fault situation and cause analysis of the power grid under various indicators in a certain period in the future, which is to provide more scientific and reasonable risk management and control for the power grid. Through the statistical analysis of all kinds of information data collected by the power microservice, a power monitoring system based on early warning of power outage risk is established to enhance the operation adaptability of the power grid system under uncertain conditions and prevent the occurrence of tripping blackouts so that this study has an important function of intelligent decision-making. The experimental results show the feasibility and high efficiency of this study. The comparison of algorithm results shows that the performance of this method is improved by more than 10%. The stability is improved by 17%. The response time to various situations is shortened by 32% to judge the abnormal operation state of the power grid more efficiently. It can make emergency preparations in time, reduce the damage caused by accidents to the power grid and provide practical tools for the power grid to further enhance the disaster prevention ability of transmission equipment and effectively resist risk events.

Real-time Stability Monitoring of Power Systems Using Instantaneous Lyapunov Exponent and Malthusian Parameter

Real-time Stability Monitoring of Power Systems Using Instantaneous Lyapunov Exponent and Malthusian Parameter

Prompt-critical power excursion measurements with optical fibers in the CABRI experimental reactor

This paper presents our experimental work to assess the capability of estimating the transient-induced power distribution evolution in the CABRI experimental reactor, using Cherenkov radiation in optical fibers. The CABRI reactor is designed to produce power transients from 100 kW up to 21 GW in less than 100 ms, in order to irradiate a test fuel pin under conditions representative of a Reactivity Insertion Accident in pressurized water reactors. Because of the wide response range and short response time required to follow the reactor power evolution during a complete transient, the usual means of detection, such as ionization or fission chamber, are rendered inoperable, especially for in-core or core vicinity measurements. For this reason, we suggest measuring the Cherenkov light produced within the optical fibers. The Cherenkov light emission is linked to local electron production, which is proportional to local gamma flux through the Compton or pair production cross-section. In other words, the intensity of Cherenkov radiation is related to the photon flux intensity. Knowledge of the fission photons emitted by the reactor gives direct insight into the fission rate, hence a spatial power density distribution could be reconstructed by using the measurement of Cherenkov light at different points in the reactor. The radio-luminescence measurements taken using photodiodes show very good linearity with the reactor power monitoring system despite the transient radiation induced attenuation observed in the optical fibers. The radio-luminescent light shows steady values with around 5% of variations over the 15 transients for low OH fibers and high OH fibers. Low OH tends to overestimate the FWHM of the power peak by 1–4%.

A Design and implementation of a sustainable microcontroller based solar power automatic water irrigation control and monitoring system

The major cause of the shortage of land reserve water owing to the lack of rainfall makes the proper operation of irrigation systems extremely important. A major concern for farmers these days is water scarcity. Pakistan is an important agricultural nation and major part of economy depend on agriculture. This study analyses the soil moisture monitoring and automatic plant watering system since it is really helpful in all kinds of weather. In this study a prototype is design and implement using Arduino-based automation sensors are employed for the control and observation of a solar powered smart irrigation system. An Arduino based remote irrigation system developed for the agricultural plantation, which is placed at a remote location and requires water, provides for plantation when the moisture of the earth drives under the set-point rate which turns on the pump on autopilot. An estimation was carried for solar power energy potential agriculture system condition using soil and humidity sensors. The measurements and mathematical formulation were observed to monitor effectivity of solar based power generation. This analysis is conducted for 20 Watt power panel integrated with 5-watt electrical drive with battery capacity 1200 m-Ah. A modified energy model was used to determine the agricultural load for a 5-watt dc motor. The results showed a cumulative analysis to implement this system inspired by smart sustainable clean renewable energy technologies which meet the farmer’s demands significantly increase the productivity of farming and reduce wastage of water and energy.

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

INCREASING THE ACCURACY OF THE POWER LINES MODELING BASED ON PMU

The article discusses the modeling of power lines based on PMU data to assess the state of overhead lines. Current approaches to solving the problem are analyzed. The advantages and disadvantages of these approaches are presented. It has been established that when modeling power lines are traditionally represented in the form of a - circuits scheme, which leads to methodological modeling errors. To reduce the methodological error of modeling, overhead lines (OHLs) can be presented in the form of equations with distributed parameters, which allows for maximum accuracy. However, distributed line equations contain hyperbolic functions and reduce performance in real-time simulations. Based on this, it is noted that the selection of an adequate mathematical model of overhead lines with the appropriate accuracy of synchronized vector measurements in real time when assessing the state of power lines is an important stage of modeling. The article discusses overhead line models using equations with distributed parameters, a -circuits, an equivalent - circuits and chain -circuits depending on the number of links. The results of calculating the mode based on the modeling accuracy using the example of a 500 kV overhead line are presented, as well as the results of comparing various equivalent circuits. It is shown that when modeling overhead line modes based on the results of PMU measurements, representing power lines as chain circuits with links 50 km or less in length is appropriate, since it makes it possible to obtain a model accuracy that is adequate to the PMU measurements. Methodological errors in modeling ultra-high voltage transmission lines using the equations of long lines have been studied. The obtained results can be used for monitoring, analysis and operational management of the electric power system

EXPERIMENTAL STUDY: IMPLEMENTING IOT TECHNOLOGY FOR MONITORING ENTERPRISE POWER SUPPLY SYSTEMS

This study has demonstrated the necessity and effectiveness of a well-designed power monitoring system in the current year. The integration of IoT devices with this system has enhanced its effectiveness and applicability across various disciplines. Moreover, using this monitoring system facilitates easy and efficient connections with GSM and embedded controllers. The incorporation of multiple sensors in smart design monitoring systems proves beneficial in this context. Additionally, the study's critical discussion highlights that smart power monitoring systems positively impact motion direction, home security, augmented reality, and technological disruption. Data was collected from 100 respondents out of a total population of 500, selected using a simple random sampling method. This method was preferred due to its simplicity in selecting samples for the study. The researcher distributed questionnaires to the respondents and collected their responses to conduct the research.

Research on condition operation monitoring of power system based on supervisory control and data acquisition model

In order to better detect and identify the running state of the wind turbine equipment and its generator in the power system wind farm, and then improve the operation reliability of the wind turbine in the power system wind farm, the monitoring method of power system state operation based on Supervisory Control and Data Acquisition (SCADA) model was studied. The SCADA model pre installs different types of sensors on the units of the power system to collect, record and store the power data of the power system. The empirical wavelet transform method is selected to extract the signal characteristics from the state operation signals collected by the SCADA model. ReliefF algorithm is used to select features related to equipment status monitoring from the extracted status operation signal features. Fuzzy C-means clustering algorithm is used to analyze the correlation of feature selection results and identify power system operating conditions. According to the condition identification results of power system, support vector regression prediction algorithm is selected to output the monitoring results of power system state operation. The experimental results show that the monitoring time of the generator and other equipment is less than 100 ms, which provides a strong guarantee for the reliable operation of the power system. This result not only validates the validity of the monitoring and data acquisition model proposed in this paper, but also provides a new idea and method for the intelligent operation and safety management of power system.