Power System Monitoring Research Articles

Solar, fuel, and battery cell-based small-scale hybrid power systems for long-term environmental monitoring using wireless sensors

Solar, fuel, and battery cell-based small-scale hybrid power systems for long-term environmental monitoring using wireless sensors

Cybersecurity in Cyber–Physical Power Systems

The current energy transition combined with the modernization of power systems has provided meaningful transformations in the transmission, distribution, operation, planning, monitoring, and control of power systems. These advancements are heavily dependent on the employment of new computing and communications technologies, which, combined with traditional physical systems, lead to the emergence of cyber–physical systems (CPSs). In this sense, besides the traditional challenges of keeping a reliable, affordable, and safe power grid, one must now deal with the new vulnerabilities to cyberattacks that emerge with the advancement of CPSs. Aware of this perspective and the severity of the ongoing challenges faced by the industry due to cyberattacks, this paper aims to provide a comprehensive survey of the literature on cybersecurity in cyber–physical power systems. For this, clear definitions, historical timelines, and classifications of the main types of cyberattacks, including the concepts, architectures, and basic components that make up, as well as the vulnerabilities in managing, controlling, and protecting, a CPS are presented. Furthermore, this paper presents defense strategies and future trends for cybersecurity. To conduct this study, a careful search was made in relevant academic and industrial databases, leading to a detailed reporting of key works focused on mitigating cyberattacks and ensuring the cybersecurity of modern CPSs. Finally, the paper presents some standards and regulations that technical and international institutions on cybersecurity in smart grids have created.

A Multiobjective Intelligent Decision-Making Method for Multistage Placement of PMU in Power Grid Enterprises

The wide area measurement system (WAMS) based on synchronous phasor measurement technology plays an increasingly important role in dynamic monitoring and wide area protection of modern power systems. If the phasor measurement unit (PMU) is placed on all buses of the power system, the voltage and branch current of all buses can be directly observed. However, due to the high placement cost of PMU and its ability to measure the voltage phasor of the installed bus and the current of the associated branch, it is unrealistic and unnecessary to install PMU on all buses of the system. This paper discusses the incomplete observability under single PMU loss (N-1) contingencies and its effect on PMUs placement. An improved two-archive algorithm is proposed to solve the five-objective placement optimization model. In addition, a fuzzy decision-making method combining subjective and objective is proposed to help power grid enterprises select the most appropriate solution. The proposed method is tested on several IEEE bus systems and Polish 2383-bus system, and the test results verify its effectiveness.

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

Electric vehicles (EVs) represent a promising green technology for mitigating environmental impacts. However, their widespread adoption has significant implications for management, monitoring, and control of power systems. The integration of renewable energy sources (RESs), commonly referred to as green energy sources or alternative energy sources, into the network infrastructure is a sustainable and effective approach to addressing these matters. This paper provides a comprehensive review of the integration of RESs and EVs into power systems. The bibliographic analysis revealed that IEEE Access had the highest impact among journals. In order to enhance the classification of the reviewed literature, we have provided an analytical summary of the contributions made by each paper. The categorization facilitated the recognition of the primary objectives explored in the reviewed works, including the classification of EVs and RESs, the incorporation of RESs and EVs into power systems with an emphasis on emissions, the establishment of EV charging stations and parking facilities, EV batteries and battery energy storage systems, strategies for managing the integration of RESs with EVs, EV aggregators, and the financial implications. In order to provide researchers with a valuable synopsis of the implementation particulars, the papers were bifurcated into two primary classifications, namely mathematical algorithms and heuristic algorithms. The mixed integer linear programming algorithm and particle swarm optimization algorithm were commonly utilized formulations in optimization. MATLAB/Simulink was the primary platform used for executing a considerable portion of these algorithms, with CPLEX being the dominant optimization tool. Finally, this study offers avenues for further discourse and investigation regarding areas of research that remain unexplored.

Framework design and application perspectives of digital twin microgrid

In order to realize the energy supply-side reform and promote the “double carbon” goal, the power supply structure of China is being optimized and the proportion of renewable energy sources (RES) is gradually increasing, resulting in the traditional mechanism model analysis methods can hardly satisfy the requirements of monitoring and operation optimization of new power systems. Digital twin technology utilizes sensors, big data, artificial intelligence and other technologies to establish the mirroring from physical space to virtual space, and it is an effective way to analyze the evolution law of dynamic behavior of key equipment and power systems, and propose optimal control strategies. Based on the technical connotation of digital twin, this paper proposes a framework for the construction of digital twin microgrids with high percentage of RES. A real-time digital simulator (RTDS) is used to build a grid-level digital twin microgrid to digitally reproduce the equipment, environment and other key aspects of the physical grid. A digital twin framework for power equipment is proposed to provide a systematic structural support for the digital management of microgrid power equipment. Finally, the advanced application module of digital twin microgrid is prospected to provide lessons and references for the construction of digital twin microgrid.

Detection and mitigation of GPS Spoofing Attacks on Phasor Measurement Units using deep learning

The widespread use of Phasor Measurement Units (PMUs) is considered one of the most important contributions to the quality of power system monitoring. PMUs are digital devices that provide synchronized phasor measurements time-referenced by the Global Positioning System (GPS). The lack of encryption in conventional GPS receivers used in PMUs makes them vulnerable to GPS Spoofing Attacks (GSAs) that cause the GPS receiver to lose track, resulting in a phase shift of all measurements of the affected PMU. Even small phase shifts can downgrade the performance of the state estimator, indicating their ability to propagate throughout the entire system. Therefore, ensuring the integrity of PMU measurements is of utmost importance. State-of-the-art statistical methods are insensitive to small phase shifts, and they become computationally intensive as the system size and the number of simultaneous attacks increase. This paper presents the first application of deep learning to simultaneously detect and mitigate the effects of GSAs against PMUs. The performance of the proposed method is evaluated on IEEE 14-, 57-, and 118-bus systems and compared to state-of-the-art statistical and deep learning methods.

Design of Low Power Temperature Sensor Based on 180 nm Complementary Metal Oxide Semiconductor Technology

CMOS temperature sensor is widely used in power monitoring system, power consumption is an important index. The digital filter power consumption is one of the main sources of the temperature sensor power consumption, and limiting the Digital filter power consumption becomes an important method to realize the low power consumption of the temperature sensor. Based on this, a low power digital filter for CMOS temperature sensors is designed, and a precision adaptive digital filter is proposed, the filter is cascaded by a recursive CIC filter and a FIR filter based on a shift adder, the order of CIC filter and FIR filter can be adjusted according to the difference between the measured temperature and the threshold temperature range set by the user, when the measured temperature is outside the threshold temperature range, the operation unit in the filter is selectively switched off, which makes the power consumption of the filter decrease. For the temperature range that does not need to be monitored, the requirement of temperature measurement accuracy is usually not high, if high-precision temperature monitoring is still carried out, it will have a lot of unnecessary power consumption, in this paper, an adaptive precision digital filter is used to solve the problem. In order to further reduce the power consumption of the temperature sensor, according to the characteristics of the slow change of the temperature signal, a single temperature conversion combined with idle off mode is adopted, the FIR filter power consumption is reduced by 5.5% by optimizing the single temperature conversion operation. The temperature sensor is realized by 180 nm CMOS process. The results show that the sensor can achieve an accuracy of 0.47 °C in the temperature range of −55–115 °C when the measured temperature is in the threshold temperature range, under 1.8 V supply voltage, the power consumption of the digital part of the sensor is 20.15 μw. When the measured temperature is outside the threshold temperature range, the power consumption of the digital part of the sensor can be reduced by 11.3%.

DYNAMIC CARRYING RATING ON OVERHEAD ELECTRIC LINES. MONITORING METHODS AND FIELD APPLICATION

This paper presents different methods for overhead transmission line monitoring and the calculation of dynamic line rating. Some of these techniques are based on the reading or the prediction of the whole set of meteorological variables involved in the corresponding equations, whereas other methods use direct or indirect measurements of the conductor temperature. Additionally, a correction method will be described for incomplete monitoring situations. Finally, a real case study is presented for a 220-kV overhead line, where a double monitoring technique is considered for the calculation of the dynamic line rating, with a remarkable improvement in the utilization of the assets, compared to the seasonal rating. Keywords: Dynamic line rating, overhead transmission lines, grid operation and planning, electric power system monitoring.

Power monitoring data access control system based on BP neural network

Abstract With the rapid development of social economy, the demand for electric power engineering is gradually increasing. The power supply system is constantly developing in the direction of large space and automation, and various high and new technologies are also constantly improving. The power monitoring data access control system is used to monitor and control the power production and supply process and improve the power supply efficiency. The further development of the region also has a higher demand for power and energy supply. For the problem that the natural environment of transmission and distribution lines in various power grids is uncertain, which makes the line operation unsafe. This paper proposed a power monitoring data access control system based on BP (back propagation, abbreviated as BP) neural network. This paper described the related concepts of BP neural network and power monitoring system, and described the functions and construction methods of power monitoring data access control system. On this basis, relevant experiments were carried out to verify the performance of the proposed system. The experimental results showed that the fault detection accuracy of the traditional algorithm was about 93 %, while the fault detection accuracy of the algorithm in this paper was more than 98 %. The highest accuracy rate was 99.88 %, and the accuracy rate of fault detection was greatly improved.

Analysis on Network Information Security Protection Technology of Intelligent Terminal Power Monitoring System of Substation under the Support of Multimodal Multimedia Information

According to the perspective of multimode multimedia data, the protection technology of network information security is mainly discussed. The theoretical basic research is outlined, its meaning, level, reasons, and consequences are expounded, and the future research content and development trend are discussed. With the rapid development of online services and mobile technology, the world has entered the era of multimedia big data. There have been a large number of scientific studies in the multimedia industry, which explain the various fields of big data, including the capture, storage, reference, excavation, and search of multimedia big data. The article expounds the specific concept of power monitoring system network information security, establishes the safety regulations of electric power monitoring system, formulates the corresponding ideas for the safety guarantee level of electric power monitoring system, and promotes the long-term development of China’s electric power enterprises. The safe operation of the power monitoring system is directly related to the stable operation of all power supply systems and even the social economy. At this stage, the situation of network security management in China’s power monitoring system is still not optimistic. We take the multimode multimedia data, organize the research dynamics of the network security protection technology, and discuss the future research trend.

Power Monitoring and control System Using IoT

Internet of things has been a great use in the modern world and the importance of IoT is increasing day by day. The wastage of energy is a very serious concern, and energy consumption of all our appliances are in huge level. So, proper usage of this resource is of immense important. And this increase in power requirement has pushed the researchers and industrialist to design low power systems with smart power management. In this paper, design of a smart system for energy conservation is proposed to reduce the energy consumption which is based on some distributed components like sensors, micro controller and google sheets. The smart power management system can monitor, measure and control electricity usage in real-time. With this proposed system, users can automatically and manually control the unwanted real-time electricity usage, which can also be viewed through graphical representation. Key words: - Sensors, IoT, Arduino IDE, ESP32, Google sheets, Power management system

AI-enabled and multimodal data driven smart health monitoring of wind power systems: A case study

The development of AI has enabled the fault detection of industrial components to be achieved through the combination with deep learning. A detection method combined with deep learning has also emerged for the fault detection of fan blades, such as models based on neural networks using the appearance or sound of the blades. However, the detection model obtained from a single data type often has limitations, such as low accuracy and overfitting. This is also the problem with fan blade detection. In contrast, multimodal data fusion detection models are often more stable. The modality diversity of blade diagnosis is strong, and it can be achieved from multiple modalities such as image, sound, and vibration. To improve the accuracy of fault diagnosis of fan blades, this article proposes a multimodal double-layer detection system (MTDS) based on decision-level and feature-level fusion. The system includes a wind turbine simulation platform and a multimodal detection system. It mainly obtains different modal data of the simulated wind turbine from the image, sound, and vibration signals, including blade images through unmanned aerial vehicle photography, blade vibration signals through electronic vibrators, and blade sound signals through microphones. The highly correlated sound and vibration modal data are fused at the feature level, and a detection model based on the sound and vibration mixed mode is implemented using a sound-vibration-CNN (SV-CNN) proposed in this case. Then, a detection model of the image mode is trained based on the blade image using a Convolution Block Attention Module ResNet (CBAM-ResNet) network. Finally, the detection input of the two modal models is fed into a perceptron to obtain the final prediction result, and the decision-level fusion is implemented to achieve fan blade detection based on multimodal, namely the implementation of MTDS.

Research on cloud-edge AC Power Monitoring Systems based on MODBUS

With the development of the Internet of Things and automation technology in the industry, the types of equipment in the industrial field are increasing, and the data parameters of the industrial process are gradually becoming complex. It puts forward higher requirements for the system perception layer’s real-time performance, reliability, and maintainability. The original data acquisition architecture is not easy to ensure the reliable transmission and real-time processing of massive industrial data. In order to solve these problems, this paper designs an AC Power Monitoring System. It proposes a cloud edge computing framework based on MODBUS: First, the data is collected and sent to the edge server by MODBUS through the RS485 bus. Second, combined with the advantages of the powerful service capability of cloud computing resources and the low transmission delay of edge computing, the edge side and cloud side will work together. Finally, the functions of data reading, uploading and sending, correction, configuration, and fault alarm are realized. The field operation shows that using a cloud edge computing framework can reduce the processing pressure of cloud computing, reduce network latency and lower requirements for network bandwidth and make the system efficient and safe for collecting and analyzing big data. The cooperation of MODBUS protocol enhances the reliability and practicability of communication. The cloud edge computing system based on MODBUS is easy to design and maintain and is suitable for many monitoring systems, which have high promotion value.

Evaluation of a Soft Sensor Concept for Indirect Flow Rate Estimation in Solenoid-Operated Spool Valves

Concepts, such as power and condition monitoring or smart systems, are becoming increasingly important but require extensive insight into the process or machine, which is mostly gained by additional sensors. However, this development is contrasted by growing global competition and cost pressure. The measurement concept of a soft flow rate sensor presented here, addresses this discrepancy by means of a cost-effective software-based sensor, utilizing the dependence between flow rate and flow force in hydraulic spool valves. In the presented work, the feasibility of the introduced approach and the anticipated challenges, such as the modeling of disturbances in the mechanical, fluidic and electromagnetic subdomain, are assessed. For this purpose, important phenomena influencing measurement accuracy are identified on the basis of previous work. With the help of these findings, a greatly simplified version of the soft sensor is built and evaluated with a commercial valve to perform an initial test of the concept. Regardless of future implementations, the soft sensor concept presented here may have limitations in terms of dynamics and accuracy. However, the aim is not to replace a classic flow rate sensor, such as a gear sensor, but rather to create a cost-effective way to determine the flow rate without any additional integration effort.

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

Design and Implementation of Node-Red Based Open-Source SCADA Architecture for a Hybrid Power System

At present, hybrid renewable power systems (HRPS) are considered reliable combinations for power generation under various conditions. The challenge facing researchers and engineers today is designing and implementing a reliable, efficient, and applicable SCADA system for adequate monitoring and control of hybrid power systems. In order to analyze, observe, and control the essential parameters of an HRPS, a SCADA system is crucial. As part of this study, a low-cost, low-power, open-source SCADA (Supervisory, Control, and Data Acquisition) system for hybrid renewable energy systems is presented. The system utilizes two remote terminal units (RTUs), an Arduino Mega2560 and a Wio terminal, to communicate with all actuators and measure vital system characteristics such as voltage, current, and power. Using the Firmata protocol, a laptop serves as the main terminal unit (MTU) to communicate with the Arduino. In addition to being the system’s central component, Node-Red is utilized for processing, analyzing, storing, and displaying data. In contrast, a Wio terminal is used to display the values of all sensors in real-time on its LCD screen. As a whole, the proposed SCADA system is designed to keep the HRPS running smoothly and safely by displaying vital parameters, reporting any significant faults, and controlling the generator so that the batteries can be charged and discharged correctly. This article presents a complete description of all algorithms, experimental setups, testing, and results.

Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining

In underground mines, physical layer security (PLS) technology is a promising method for the effective and secure communication to monitor the mining process. Therefore, in this paper, we investigate the PLS of an amplify-and-forward relay-aided system in power monitoring and communication systems for mining, with the consideration of multiple eavesdroppers. Explicitly, we propose a PLS scheme of cooperative jamming and precoding for a full-duplex system considering imperfect channel state information. To maximize the secrecy rate of the communications, an effective block coordinate descent algorithm is used to design the precoding and jamming matrix at both the source and the relay. Furthermore, the effectiveness and convergence of the proposed scheme with high channel state information uncertainty have been proven.

System State Estimation Using Weighted Least Square Method

State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

Power System State Estimation using Weighted Least Square Method

State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

Energy Management Switch for a Self-Powered Wireless Sensor in Monitoring Power Systems

Energy Management Switch for a Self-Powered Wireless Sensor in Monitoring Power Systems