Power System Monitoring Research Articles

Improved Monitoring of Wind Speed Using 3D Printing and Data‐Driven Deep Learning Model for Wind Power Systems

This study presents a novel method for airflow rate (i.e., wind speed) sensing using a three‐dimensional (3D) printing‐assisted flow sensor and a deep neural network (DNN). The 3D printing of thermoplastic polyurethane can realize multisensing devices for different flow rate values. Herein, the 3D‐printed flow sensor with an actuating membrane is used to simultaneously measure two electrical parameters (i.e., capacitance and resistance) depending on the airflow rate. Subsequently, a data‐driven DNN model is introduced and trained using 6,965 experimental data points, including input (resistance and capacitance) and output (airflow rate) data with and without external interferences during capacitance measurements. The mean absolute error (MAE), mean squared error (MSE), and root mean squared logarithmic error (RMSLE) measured using predicted flow rate values by the DNN model with multiple inputs are 0.59, 0.7, and 0.18 for continuous test dataset without interference and 1.16, 3.95, and 0.73 for test dataset with interference, respectively. Compared to the prediction results using single‐input cases, the average MAE, MSE, and RMSLE significantly decrease by 70.37%, 88.74%, and 72.26% for test datasets without interference and 51.91%, 53.01%, and 12.20% with interference, respectively. The results suggest a cost‐effective and accurate sensing technology for wind speed monitoring in wind power systems.

Development of a stand-alone photovoltaic (PV) energy system with multi-storage units for sustainable power supply

The sizing of the energy components is essentially designed to prevent outages and ensuring the reliability of the power supply. This paper focuses on the development of a stand-alone photovoltaic/battery/fuel cell power system considering the demand of load, generating power, and effective multi-storage strategy using a probabilistic sizing algorithm. A computer program was developed and used in the design of component sizing configuration of a stand-alone power system that comprises of a photovoltaic generator (PV), battery, water electrolyzer, a storage gas tank, a fuel cell, and an inverter for a reliable power supply. This program manages the energy flow through the various components of a stand-alone PV/battery/fuel cell power system and provide an optimal technical configuration. The optimum system configuration of a residential building with daily power demands of 69 kWh/day energy consumption is composed of PV arrays resulting in total rated power of 15 kW, 16 units of 6 V, 225 Ah battery bank, 5.5 kW fuel cell, 5.5 kW Water Electrolysis, 16.5 kg hydrogen tank, and a 5.5 kW inverter. Based on the simulation results conducted, it was shown that the sizing and development of a stand-alone PV/battery/FC energy system have been achieved with system reliability (loss of power supply equal to zero). This program could be used as a power monitoring and control system for a stand-alone PV/battery/fuel cell power system.

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. This paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

UEFI-based Research on the Inner Operation Mechanism and Characteristics of Firmware Vulnerabilities in Key Devices of Electric Power Monitoring Systems

Abstract With the large number of computer technology and modern communication technology used in power monitoring systems, its security protection is constantly facing new challenges. The UEFI firmware is used to construct the physical connection structure of key devices in the power monitoring system in this paper. Using fuzzy testing methods to mine the vulnerabilities existing in the power monitoring system by generating a large number of variant test cases as the monitoring object, based on the collection of information of the basic blocks covered during the test run of the vulnerability seed to determine the target point to which the seed belongs. The coverage weight of the seed is determined with the help of the simulated annealing algorithm in order to accomplish task division of the target point. The fuzzy test method is used to analyze the operation mechanism and characteristics of the vulnerabilities in the power monitoring system, and the firmware attack mechanism of different HOOKs under UEFI is explored to summarize the characteristics of the scenarios in which the vulnerabilities appear in the power system as well as their impacts. The results show that the impact caused by vulnerabilities in the power monitoring system on the generation side and transmission side is mainly to damage the integrity and availability of information, the integrity and availability of the vulnerabilities in the generation side of the production side of the device with a risk rating of 63.74, 71.73, respectively, and the vulnerabilities in the transmission side of the SCADA with a risk rating of 79.04, 69.36, respectively. The vulnerabilities detected 608 security vulnerabilities were implanted in the UEF module, and 653 possible security problems were reported by the detection, of which the statistical underreporting rate was 1.48% and the false alarm rate was 9.05%.

Design and Development of a Micro Inverter Power Monitoring System to Enhance Photovoltaic System Performance by Integrating LoRaWAN and IoT

This research examines the significance of utilizing Photovoltaic Solar Power Systems (PVSPS) with Micro Inverters and Internet of Things (IoT) technology, particularly LoRaWAN, in addressing challenges related to efficiency and power management. PVSPS has emerged as a leading solution to tackle the increasing global energy consumption and the adverse effects of greenhouse gas emissions. The rapid growth of the PVSPS industry underscores its relevance. This study explores the impact of shading on solar panels connected in series and parallel configurations, emphasizing the importance of proper placement. The results indicate that shading significantly reduces energy production, especially in panels connected in series. Micro Inverters play a crucial role in maintaining stable output voltage, even under shading conditions. However, attention must be paid to their influence on output current. Efficient monitoring and management of PVSPS through IoT technology such as LoRaWAN are key in supporting the transition to sustainable energy. This data underscores the importance of continuous monitoring to identify issues and optimize PVSPS performance, in the face of environmental challenges and climate change.

Power System Oscillation Monitoring and Damping Control Redesign under Ambient Conditions and Multiple Operating Points

Using prediction-error identification methods, this paper proposes a measurement data-driven approach to monitor power system oscillations at a power plant, identify a data-based model using an input signal and redesign the plant’s power system stabilizer damping controller to mitigate the observed oscillations under ambient conditions and multiple operating points. The advantage of the proposed methodology is that the damping performance can be monitored continuously and the redesign only requires measurements at the power plant along with the controllers structure, which are known by the power plant operator.

Power Monitoring System Using The Internet of Things for Photovoltaic Powered Fertigation System

This research paper explains the concept of photovoltaic (PV) systems. Among renewable energy technologies, PV systems rank third after hydropower and wind in terms of usage. They find significant application in agricultural activities, contributing to income generation for daily livelihoods. However, certain plant species demand meticulous attention to achieve optimal efficiency, necessitating a larger workforce for their care. By incorporating the Internet of Things (IoT), users gain the ability to remotely monitor PV system performance through a dedicated application. The central goal of this project involves crafting a simulation of an IoT system. This system is intended for monitoring the functionality of PV systems and devising an IoT-based model for overseeing the power aspects of PV systems in conjunction with fertigation systems (watering plants with nutrient solutions). A key feature of this system is its capacity to gauge the IoT model's effectiveness by contrasting outcomes derived from hands-on observation and simulated scenarios. The outcomes of this study reveal thatthe developed IoT system effectively tracks real-time parameters like current, voltage, generated power, and solar PV temperature. It successfully establishes a link between users and the IoT infrastructure for system monitoring. Looking ahead, the gathered PV output data will be instantly accessible on mobile devices. This empowers users to assess the performance of their PV systems concerning fertigation.

Research on Edge-Computing-Based High Concurrency and Availability “Cloud, Edge, and End Collaboration” Substation Operation Support System and Applications

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information system, and other business platforms are getting higher and higher, and this paper puts forward a high concurrency and availability “cloud-side-end collaboration” based on edge computing. This paper proposes a high concurrency and availability “cloud, edge and end collaboration” architecture based on edge computing for substation operation support systems. First, this paper summarizes the development status of domestic substation operation support systems and analyzes the advantages and disadvantages of various technical architectures. Then, a “cloud-side-end cooperative” substation operation support system architecture with “high real-time, high concurrency, high security and high stability” is proposed, which focuses on remote inspection, remote operation, and remote safety control of substation businesses from the perspective of engineering applications. It realizes transparent monitoring of equipment operation, unified management of operation data, and integration of production command and decision-making; solves the problems of dispersed coexistence of multiple systems for dispatching, monitoring, analysis, management, and other businesses, switching between multiple systems, and insufficient real-time and stability of the system; and controls the risks of the grid, reduces the potential safety hazards, and solves the contradiction between the continuous growth of the grid equipment and the shortage of production personnel. The results of engineering application examples show that the proposed architecture compared with the existing system architecture has greater advantages and can meet the requirements of large-scale access to the substation, with feasible popularization and application.

Calibration and Validation of INA219 as Sensor Power Monitoring System using Linear Regression

Electricity demand which increases up to 2.7%, needs to be evaluated to prevent power wastage. This paper proposes an INA219 sensor and a power monitoring solution based on the ESP8266. Power Monitoring stores and displays real-time data in Google Sheets via Blynk version 1.0.1. The system has been calibrated with a fixed LED and resistor as a voltage calibration load. Meanwhile, the lamp and shunt resistors calibrate the shunt voltage. The measuring tools for comparison in calibration are digital multimeters, oscilloscopes, and power data loggers. Calibration using the linear regression technique with accuracy, precision, and uncertainty analysis are determined by Mean Absolute Percent Error (MAPE), Relative Standard Deviation (RSD), and Gaussian distribution. Successively, the sensor coefficient of determination (R2), accuracy, precision, and uncertainty of the load voltage and shunt voltage are 0.999 and 0.997, 99.27% ​​and 93.71%, 99.82% and 99.55%, 0.37 V and 0.89 mV.

Development of Wireless Smart Current Sensor for Power Monitoring System

The traditional distribution network must be replaced by a smart grid, durable, and dependable, due to the rising demand for power by the customer. The smart grid will need a smart monitoring system based on the smart current sensor at all buildings to determine the power consumption with its costs. This paper proposed a wireless monitoring system for measuring the three-phase currents in the building by using three HW-666 current sensors with Arduino and ESP32 microcontrollers to transmit the data according to the Internet of Things (IoT) technique to the Telegram app on the mobile phone. The power consumption is determined by assuming the constant voltages and power factor at the building that is tested with this monitoring system. The results are scheduled on the personal computer (PC) by using the serial monitor of the Arduino software and also by receiving the message every 20 minutes on the telegram app furthermore, real-time data can be obtained immediately upon receiving transmitted commands from the Telegram application. In addition, this monitoring system is designed to cut off power to the building and sound an alarm if abnormal currents are detected. The proposed monitoring system is given the same results when a comparison with the ammeter with errors not exceeding 10%.

ДИСТАНЦИОННОЕ ОБНАРУЖЕНИЕ УТЕЧЕК В ТЕПЛОЭНЕРГЕТИЧЕСКИХ ТРУБОПРОВОДНЫХ СИСТЕМАХ

The features of the development of technical diagnostic methods for remote control of the parameters of the operation of thermal power pipeline systems are considered, with a main focus on heat supply systems, including leak detection, as well as in the creation of software for automated control systems. Mathematical models for remote leak detection are proposed, implemented using software for regular monitoring and maintenance to ensure reliable operation of the systems under consideration. It is shown that the creation of algorithms and a system is required to detect leaks, including determining their location and scale. The created software can be used for automated control and monitoring of a thermal power system based on the results of the study. Based on the collected data related to the parameters of the heat supply system, such as pressure, gas flow and others, it is possible to build a statistical model that may include the probability distribution, expected values and variances of the parameters. The solution to the problem of specific leak detection may vary depending on the type of heat supply and the technologies used. It is shown that the study will contribute to improving the reliability and safety of heat supply systems by providing more effective detection and control of leaks and other anomalies in the operation of pipeline systems.

Development of a Low-Cost, Open-Source LoRA-based SCADA System for Remote Monitoring of a Hybrid Power System for an Offshore Aquaculture Site in Newfoundland

In this article a low-cost and open-source Internet of Things (IoT) based Supervisory, Control and Data Acquisition (SCADA) system for remote monitoring of the hybrid power system for an offshore aquaculture site is presented. The selected site is situated 2 km away from the coastline where there is no electrical utility infrastructure and limited communication options are available. The hardware of the designed system primarily consists of six field sensors, Arduino Leonardo as Remote Terminal Unit (RTU), LoRA (Long Range) gateway, cables, AC/DC current and voltage supplies. Arduino IDE, AWS, Influx DB, and Grafana provide the software support. The field sensors are responsible for measuring the solar, battery, inverter & generator currents, along with battery voltage and temperature. All of the field sensors except the temperature sensor send the data to RTU which further delivers it to The Things Network (TTN) cloud. With the help of influx DB, AWS cloud computing services, and Grafana, the data can be stored and visualized through interactive yet informative graphs. The graphs display the historical and live data of each sensor. Further, it also gives the option to set alarms and alerts on user-defined conditions to improve control over the hybrid power system. The complete hardware is assembled and tested in Memorial University’s Power lab. The developed system was supplied with variable current/voltage supplies and the data was logged for three continuous hours. However, the data can be stored for a much longer duration as per user’s requirement. The hardware and the results presented here are a testament that the proposed design system is capable of providing a remote monitoring solution for the offshore aquaculture site.

The Design, Fabrication, and Evaluation of a Phase-Resolved Partial Discharge Sensor Embedded in a MV-Class Bushing.

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signal. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) for voltage measurement, the D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kilovolts (kV)) were less than 0.2% and 10 min, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus.

Low cost power monitoring system with electric bill calculation

A power monitoring system with electric bill calculation has been developed to measure voltage, current, power, kilowatt-hours, and prices per hour of electrical appliances and to help users to reduce the monthly bill. The prototype uses a voltage and current sensor, ESP32 Node MCU, STM32 microcontroller and a pulse width modulation (PWM) signal to control a rectifier circuit that converts alternating current (AC) to direct current (DC) that available in the market and the development took about 6 months. The prototype successfully collected electricity data and provided the energy needed for monitoring system. The prototype had a percentage accuracy of 92.22% between the calculation bill compare to IoT prototype measurement. The final prototype is a portable device that can accurately measure and record the relevant data and provide real-time monitoring and data visualization with the usage of the electricity to a certain load.

Power Consumption Minimization of a Low-Cost IoT Data Logger for Photovoltaic System

This paper introduces an innovative IoT-based data logger for photovoltaic (PV) system monitoring, emphasizing low power consumption and affordability. The system comprises a PV panel, a charging controller, and a backup battery, focusing on monitoring their voltages and currents through a network of voltage and current sensors. Data is stored on an SD card and displayed in real-time on a web server. The FireBeetle 2 ESP32-E microcontroller, chosen for its efficient deep-sleep mode power management, is central to the data logger's design. This study employs several low-power strategies, notably reducing supply voltage and CPU frequency to decrease power consumption significantly. A data buffering mechanism stores sensor readings in the microcontroller's flash memory, transferring them to the SD card hourly to balance power efficiency and data security. Wi-Fi connection intervals are optimized to 45 seconds, balancing power use and system monitoring frequency. The data logger averages a power consumption of 122.78 mW and demonstrates its efficacy against the commercial DI-145 model. Priced at C$ 55.05, the system is both cost-effective and scalable, capable of monitoring multiple PV panels and batteries, reducing per-unit costs. The study underscores the successful integration of affordability, low-power operation, and efficient monitoring in a PV system data logger, showcasing its potential in future renewable energy research.

Design and Implementation of IoT based PV Integrated Power Monitoring System

The awareness of usage of power and effective utilization of power is based on intelligent decision, which can help the user as well as supplier. In this paper the design and implementation of IOT based PV integrated system is presented. A hardware prototype is developed with PV module connected to boost converter, Arduino Uno controller, ESP 8266 Wi-Fi module, and Thing speak to monitor a real-time power. The Arduino Uno serves as the central controller, gathering data from smart power meters and PV modules for solar energy generation. The collected data is wirelessly delivered to the ThingSpeak, which provides cloud-based storage, data analysis, and visualization. With these users may remotely monitor usage of power, track solar energy generation, and get notifications for energy utilization.

Comparative Studies between Phasor Measurement Units (Synchrophasors) & SCADA System

bstract: The increasing demand of load without considerable increase in transmission resources has posed numerous constraints and challenges in the power system monitoring and performance. Modern power system grid monitoring tools use data from remote terminal units (RTUs), protective relays, and transducers to provide information to system operators. This information is vital for the operation of the power system on a daily basis and under system contingencies. However, the method presently available to retrieve data from the devices is asynchronous and relatively slow. The asynchronous nature of the data does not provide accurate angle difference information from two nodes on the network. Moreover, the low data rate may be too slow to capture many short-duration disturbances on the grid. Alternatively, Synchrophasors based technology can be used to provide high-speed and coherent real-time information of the power system that is not available from legacy supervisory control and data acquisition (SCADA) systems.

Secure Hydrogen Production Analysis and Prediction Based on Blockchain Service Framework for Intelligent Power Management System

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis by integrating blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. Combining blockchain with intelligent power management systems makes the efficient utilization of hydrogen resources feasible. Using smart contracts and distributed ledger technology facilitates secure data analysis (SDA), real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus advancing clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, specifically on secure hydrogen data analysis and intelligent power management systems.

An upscaling minute-level regional photovoltaic power forecasting scheme

Along with the increasing penetration of photovoltaic (PV) power generation, regional power forecasting becomes more and more critical for stable and economical operation of power system. The key challenge of regional PV power forecasting technology is the lack of complete and accurate historical power data since not all PV plants are equipped with the precise real-time output power monitoring system. Besides, the computation burden will be heavy when the number of PV plants in the target region is large. This paper therefore proposes an upscaling minute-level regional PV power forecasting scheme using the data of the selected reference PV plants. In this paper, a novel method of reference PV plants selection is proposed by comprehensively considering the prediction accuracy of artificial neural network (ANN) as well as Pearson correlation coefficient. The reference PV plant selection coefficient μ is introduced as the comprehensive indicator for reference PV plant selection, which incorporates Pearson correlation coefficient and MAPE. In addition, a PV output power correction method is assumed to guarantee the proper operation of regional power forecasting. Besides, this paper proposes a flexible approach to effectively decrease the accumulated error of rolling forecasting by integrating the forecasting results under different temporal resolutions. In specific, the power forecasting results in temporal resolutions of 1 min, 5 min and 15 min are simultaneously derived and the performance between the traditional rolling forecasting and the proposed method is compared. The validity of the proposed method is finally verified using the collected historical power data of PV plants installed in a city of Eastern China. For time resolution of 1 min, 5 mins and 10 mins, the corresponding RMSE are 6.56, 5.73 and 4.85 and corresponding MAPE are 4.04%, 3.45% and 2.86%.

Study on Smart Designed Power Monitoring System Using IoT Devices

This study has clearly proved that a well-designed power monitoring system is quite effective and necessary in the current year. During the same time, the integration of IOT devices with this system increased its effectiveness and use in a variety of disciplines. On the other hand, using this monitoring system makes it simple to maintain direct and effective connection with GSM and embedded controllers. Several sensors in smart design monitoring systems come in handy in this circumstance. Aside from that, the study's critical discussion revealed that smart power monitoring systems have a favourable influence on motion direction, home security, augmented reality, and technological disruption. The researcher gets data from 100 respondents out of a total population of 500. The sample for the study is chosen by the researcher using the basic random selection approach. The researcher prefers simple random sampling because it is the simplest way for selecting samples for the study. The researcher distributes the questionnaire to the study's respondents and so collects their responses in order to perform the research.