Power System Monitoring Research Articles

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

Construction and Test of Baler Feed Rate Detection Model Based on Power Monitoring

The existing methods of measuring the baler feed rate seldom consider the influence of machine vibration on the sensor signal during field operation, which leads to the low detection accuracy and poor stability of feeding quantity detection. We established a feed rate detection model of a baler based on power monitoring of the pickup platform. Through the dynamic analysis of the pickup platform, the functional relationship between the working power of the pickup platform and the feed rate was constructed. A power monitoring system of the pickup platform was developed, and the model construction experiment of the working power and the feed rate was performed. The influence mechanism of different running speeds on the torque noise signal of the power input shaft of the pickup platform was explored. The frequency of the noise signal was mainly concentrated at 0.5–6 Hz and 9–13 Hz employing a fast Fourier transform, and the noise signal was eliminated by the frequency-domain-filtering method. The function model of working power and feed rate of the pickup platform was established based on signal processing, and the determination coefficient R2 of the model was 0.9796. The field experiment results show that when the feed rate of the baler is between 1.6 and 4.88 kg/s, the determination coefficient R2 and RMSE between the actual and predicted feed rate are 0.989 and 0.2, respectively. The relative error range of feed-rate prediction is −9.37–8.77%, which indicates that the model has high detection accuracy and good stability and meets the requirements of feed-rate monitoring of a baler in field operation.

An Efficient Variable Step Solar Maximum Power Point Tracking Algorithm

The classic Photovoltaic system maximum power point tracking technique cannot concurrently take into account the dynamic response speed and steady-state accuracy when the light intensity changes. To address this issue, a new composite variable step MPPT control algorithm is developed in this study. Based on the three-stage variable step incremental conductance method, the algorithm adds the Kalman filtering algorithm to pre-process the photovoltaic cells output signal, and uses a new calculation approach to adjust the variable step coefficient. As a result, the perturbation step can be automatically modified according to changes in the external environment, which resolves the issues with poor dynamic reaction speed when the classic variable step algorithm started and the light changed. Compared to conventional MPPT control algorithms, the improved MPPT strategy can be easily realized using a hardware control system since it has a simplified control logic and requires less data to be calculated. In this study, the hardware circuit of the enhanced MPPT control algorithm is built using the ESP32 as the primary control chip. This chip can be utilized in conjunction with the Internet of Things to enable remote monitoring of the solar power system’s operational state. According to test results, the algorithm can instantly detect the maximum power point in all lighting circumstances with tracking accuracy of up to 99.6% and a reduction in dynamic response time of the system to 0.12 s.

Multi-exposure electric power monitoring image fusion method without ghosting based on exposure fusion framework and color dissimilarity feature.

To solve the ghosting artifacts problem in dynamic scene multi-scale exposure fusion, an improved multi-exposure fusion method has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera response model was applied to preprocess the input image sequence. Second, the initial weight map was estimated by multiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation.

Optimal placement of PMU for enhancing power system monitoring

Phasor Measurement Units (PMUs) have emerged as crucial tools in the field of power system monitoring, control, and safety. Extensive research has been conducted on enhancing the capabilities of PMUs. Traditional SCADA techniques, which have been employed for many years, gather data on power system parameters at intervals of 4 to 10 seconds, offering a static perspective of the system's behavior. However, relying solely on static data may limit the effectiveness of the monitoring and control. On the other hand, PMUs provide real-time information about the dynamic state of the system, enabling more comprehensive insights. For the optimal and cost-effective installation of PMUs, it is vital to strategically position them in locations that effectively monitor the state of the system. The Instruction Level Parallelism (ILP) approach was employed to identify the ideal PMU placement for power system observability. The ILP approach determines the most suitable positions for PMUs by considering factors such as the redundancy of measurements and cost efficiency. To validate the effectiveness of the ILP approach, it was tested using the widely recognized IEEE 14 bus system, which serves as a standard benchmark for power-system analysis. Through the ILP approach, the optimal placement of the PMUs can be determined, thereby enhancing the observability of the power system. This in turn improves situational awareness, enables effective fault detection, and facilitates coordinated control strategies. By integrating PMUs and the ILP approach, the power system monitoring and control can be significantly enhanced. Ongoing research and development in PMU technology and placement methodologies continues to advance the field, enabling more efficient and effective management of modern power grids.

Dynamic Synchrophasor Estimation Based on Weighted Real-Valued Sinc Interpolation Method

Phasor measurement units (PMUs) are the cornerstone of intelligent sensing and monitoring of power systems. The superiority of the sinc interpolation function-based least squares (SLS) method, a signal processing algorithm for PMUs, has been proven in harmonic phasor estimation. However, the SLS-based estimators suffer from spectral interferences and heavy computational burden. To this end, a real-valued sinc weighted least squares (SWLS-R) method is proposed to track the dynamic synchrophasor accurately and quickly. First, the weighted vector, i.e., window function, is introduced to enhance the SLS estimator’s behavior by mitigating the impact of spectral interference. Then, the complex-valued system matrix is transformed into the real-valued system matrix to lower the computational burden by avoiding complex matrix multiplication. Finally, an explicit and fast solution is designed to calculate the pseudoinverse matrix of the SWLS-R, which aims to further improve computational efficiency. The accuracy and effectiveness of the proposed method are validated by simulations. These are designed, under steady and dynamic conditions, according to the M-class requirements of the Standard IEC/IEEE 60255-118-1.

Practical Implementation and Operational Experience of Dynamic Mode Decomposition in Wide-area Monitoring Systems of Italian Power System

This study presents the assumptions and strategies for the practical implementation of the dynamic mode decomposition approach in the wide-area monitoring system of the Italian transmission system operator, Terna. The procedure setup aims to detect poorly damped interarea oscillations of power systems. Dynamic mode decomposition is a data-driven technique that has gained increasing attention in different fields; the proposed implementation can both characterize the oscillatory modes and identify the most influenced areas. This study presents the results of its practical implementation and operational experience in power system monitoring. It focuses on the main characteristics and solutions identified to reliably monitor the interarea electromechanical modes of the interconnected European power system. Moreover, conditions to issue an appropriate alarm in case of critical operating conditions are described. The effectiveness of the proposed approach is validated by its application in three case studies: a critical oscillatory event and a short-circuit event that occurred in the Italian power system in the previous years, and a 15-min time interval of normal grid operation recorded in March 2021.

User Station Security Protection Method Based on Random Domain Name Detection and Active Defense

The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.

Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

The invention of Phasor Measurement Units (PMUs) produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible. PMUs are used in transmitting data to Phasor Data Concentrators (PDC) placed in control centers for monitoring purpose. A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid. This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data. The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid. As a result, detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical. A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper. The proposed method consists of three modules namely, offline Gaussian Mixture Model (GMM), online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies. The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset, adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies. The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

Newton-Raphson Method Based Power Flow Analysis and Dynamic Security Assessment

China will accelerate the construction of a new power system under the goal of "carbon neutrality and emission peak". With the change of energy structure and system characteristics, the uncertainty on both the supply and demand sides of the power system has increased significantly, and the problem of small disturbance stability has gradually emerged. In this paper, by performing power flow calculation for the power system, the nodes requiring voltage regulation and the lines with the highest active power losses are identified, and adjustment measures are provided according to their location. In that way, the terminal voltage of each power-using device can be maintained within the specified fluctuation range, reducing the active power losses in the network while satisfying customer demand, which lowers the cost of electrical energy and reduces the waste of energy resources. This paper first introduces the basic mathematical model of the power system and the principle of the Newton-Raphson method for solving power flow. Then simulates the IEEE standard node system and proposes grid operation optimization recommendations such as adjusting the output of generating units or reactive power compensation in conjunction with the simulation results of the calculation example. It enables efficient and targeted measures to be taken to improve system safety and economy in real-time monitoring of power systems, ensuring safe and economic operation.

A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems

A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed which combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.

A Powerful Tool for Power System Monitoring: Distributed Dynamic State Estimation Based on a Full-View Synchronized Measurement System

Modern power systems are witnessing a rapid transition with the increasing penetration of intermittent nonsynchronous renewable generation and distributed energy resources. According to the National Energy Administration, China’s installed wind and solar energy capacity accounted for 26.7% of the country’s total power generation capacity at the end of 2021. In terms of electricity generation, wind and solar energy accounted for 11.8% percent of the total electricity use that year. In the renewable energy development plan in the 14th Five-Year Plan (2021–2025), China aims for renewables to meet 33% of national power consumption and for nonhydro renewables to satisfy 18%. This transition poses challenges for system operators to grasp the actual status of power systems accurately and comprehensively.

An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform

The problem of detecting illegal pollutants in wastewater is of fundamental importance for public health and security. The availability of distributed, low–cost and low–power monitoring systems, particularly enforced by IoT communication mechanisms and low-complexity machine learning algorithms, would make it feasible and easy to manage in a widespread manner. Accordingly, an End-to-End IoT-ready node for the sensing, local processing, and transmission of the data collected on the pollutants in the wastewater is presented here. The proposed system, organized in sensing and data processing modules, can recognize and distinguish contaminants from unknown substances typically present in wastewater. This is particularly important in the classification stage since distinguishing between background (not of interest) and foreground (of interest) substances drastically improves the classification performance, especially in terms of false positive rates. The measurement system, i.e., the sensing part, is represented by the so-called Smart Cable Water based on the SENSIPLUS chip, which integrates an array of sensors detecting various water-soluble substances through impedance spectroscopy. The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. The corresponding dataset has been made publicly available for download. The obtained results demonstrate an excellent classification ability, achieving an accuracy of more than 95% on average, and are a reliable “proof of concept” of a pervasive IoT system for distributed monitoring.

Design and implementation of real-time laser power monitoring system in laser ranging

Design and implementation of real-time laser power monitoring system in laser ranging

Application for testing and optimising the performance of electric power systems in a hybrid “MELEX” vehicle

This paper presents an application for monitoring power systems in a Melex type vehicle. The application is intended to optimise the energy use of the modified hybrid drive system used on this vehicle. It was written in Python programming language and is used to display vehicle operating parameters on a touch screen located on the control panel. A system for monitoring voltages and currents, of the electrical part of the drive train of a Melex vehicle, was designed and built, together with a data acquisition system. Tests were carried out on the basis of the completed measuring system, a selected section of which is presented in this paper.

Novel PMU Model for Dynamic State Disturbance Analysis with Effective Data Handling System

In this paper a hybrid DFT phasor calculation method is presented. This method is used to calculate the fundamental component phasor value of the harmonic signal without any physical filter. With this method the computational time for each phasor value calculation is reduced and the calculated phasor value has the constant magnitude and rotating phase angle. This calculated phasor values are used for the disturbance or fault identification in the power system based on Total Vector Error (TVE). The %TVE-based fault identification is more effective, because the TVE value is calculated with reference phasor value. If any fault/disturbance occurs or frequency changes then %TVE value changes. This change in TVE value is reflected in the calculation of the next sample (1/f.*N sec), giving the advantage to this method as compared to the other magnitude-based fault identification systems. Normally with the phasor calculation large data is produced, which requires large memory for the storage of this data and makes the analysis difficult. To avoid large data storage system conditional-based data storage system is proposed, where the data is stored during only the disturbance conditions or at every one second. With this technique, the data to be stored is reduced and the analysis of this data also becomes simpler for the post disturbance and for future load prediction. The performance of the proposed method is evaluated in terms of accuracy of calculation and its implementation ability. The simulation results are as per the IEEE C37.118.1a2014 for the power system monitoring and fault identification.

Prototype of Electric Power Monitoring System and Load Control in Residential Houses Using IoT-Based ESP8266

The use of the Internet of Think (IoT) for a system that can monitor the use of load and electrical power in a residential house will be very necessary. This is because homeowners who often go out and leave their homes with loads that are still active, for example, house lights that are always turned on will be a waste of electrical energy. The purpose of making this article is to create a system that can control electricity use automatically using the Internet Of Think (IoT) and monitor the electricity data in real time, calculate the amount of current, voltage, and power in an entire load. Calculating the comparison of sensor and manual values by looking at the percentage of errors on the sensor, and analyzing whether the sensor is suitable for use or not. The systems used are: Pzem 004t as an overall reader of electrical power, LDR sensor as light input to turn on the lights automatically when it is dark, and nodemcu esp8266 as input/output process and sends data to the internet via a wifi network which will be monitored through the application. Blynk Iot 2.0 ABSTRAKPenggunaan Internet of Think (IoT) untuk sistem yang dapat memonitoring penggunaan beban dan daya listrik  pada sebuah rumah tinggal akan sangat diperlukan. Hal tersebut karena pemilik rumah yang sering berpergian keluar dan meninggalkan rumahnya dengan beban yang masih aktif contohnya lampu rumah yang selalu dihidupkan akan menjadi pemborosan energi listrik tersebut. Tujuan dari pembuatan artikel ini adalah yaitu membuat sistem yang dapat mengontrol penggunaan listrik secara otomatis menggunakan Internet Of Think (IoT) serta memonitoring data listrik tersebut secara realtime, menghitung jumlah arus, tegangan, dan daya pada suatu keseluruhan beban yang ada. Menghitung perbandingan nilai sensor dan manual dengan cara melihat persentase kesalahan pada sensor tersebut, dan menganalisa pada sensor tersebut layak digunakan atau tidak. Adapun sistem yang digunakan yaitu: Pzem 004t sebagai pembaca keseluruhan daya listrik, sensor LDR sebagai input cahaya untuk menghidupkan lampu secara otomatis ketika hari sudah gelap, dan nodemcu esp8266 sebagai proses input/ouput dan pengirim data ke internet melalui jaringan wifi yang akan dimonitoring lewat aplikasi Blynk Iot 2.0.

Feed-Forward Neural Network for health monitoring of a parallel hybrid electric power system

Hybrid engines are becoming more and more widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, the number of components is higher and this results in a decrease in reliability. With Engine Health Monitoring (EHM) we mean the set of techniques used to monitor the health status of a system based on the values assumed by some related parameters. Artificial Intelligence (AI) methods are widely used nowadays in this discipline. In this paper, an EHM approach was developed to monitor the health status of some components constituting an hybrid turboshaft. The dynamic model of the hybrid electric power system is described in an accompanying paper. Feed-Forward Neural Network (FFNN) is used as AI tool to built the just cited system. The engine modelled with Simulink, was used to perform a series of steady-state simulations implementing a degradation condition in some selected components. The degradation condition was simulated by changing the value of the Performance Parameters (PPs) related to each of the selected components. The results of the simulation were used to obtain a dataset useful to train the FFNN to predict the values of the same PPs in a degraded case.

RLS-based BeiDou Satellite High Precision Positioning for Power Device

The BeiDou-3 navigation satellite system (BDS-3) has been officially put into use, which will provide wider, more accurate, and smarter industrial applications. BDS-3 positioning has advantages of stability, economical reliability and most importantly, high positioning accuracy, which is widely used in power systems, covering power dispatching, energy management, data acquisition, and system monitoring. However, in the positioning process of BDS-3, positioning accuracy will be affected by spatial and temporal interference factors. This paper addresses the problem of poor positioning accuracy under the interference influence of BDS-3, and proposes a Recursive Least Squares (RLS)-based adaptive high-precision positioning scheme to effectively reduce the influence of interference factors.