Power Network Monitoring Research Articles

Condition Monitoring of Distribution Power Network Assets Using PMU Measurements

Condition Monitoring of Distribution Power Network Assets Using PMU Measurements

Retracted: Application of the Lagrange Equation for Intelligent Sensor Vibration Control for Power Network Monitoring

Retracted: Application of the Lagrange Equation for Intelligent Sensor Vibration Control for Power Network Monitoring

Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid

LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis.

Research on security situation awareness method of power network Monitoring system based on data mining

In the traditional security situation awareness methods of power network monitoring systems, there are deviations in the analysis of different sample data sets, which affect the security situation threat value perception results of power network monitoring systems. Therefore, this paper designs a security situation awareness method for a power network monitoring system based on data mining. The identification structure of security situation elements is determined, the self-encoder is used for mapping, the security situation elements of the power network monitoring system are extracted according to the obtained parameter samples, and the security situation awareness model of the power network monitoring system is established. The RBF neural network is used for data mining of the monitoring system, and the design of the security situation awareness method of the power network monitoring system based on data mining is completed. The experimental results show that the recognition accuracy of the design method for samples with different threat levels is more than 90%. Compared with the traditional methods, the security situation awareness results obtained by the design method are closer to the expected output threat value.

Prevention and detection of FDIA on power-network protection scheme using multiple support set

Usage of ICT tools for monitoring and control of power networks warrants mechanisms for robust security of supervisory control and data acquisition (SCADA) system against false data injection attack (FDIA) and detection of intruder induced manipulation of sensor information. Motivated by the aim of improving network resilience against FDIA, the present work aims at the twin objectives of providing robustness to one of the principle control actions in power networks i.e. protection relaying against FDIA and early detection of FDIA. In this context, a computationally cheap and economically viable schema based on logical analysis of data(LAD) has been proposed. The proposed scheme identifies a minimal set of secured sensors for which the network observability necessary for fault detection and classification is maintained. While preserving the observability, the mapping between sensor information and network state (healthy/faulty) allows for detecting FDIA and providing immunity to the protection scheme, irrespective of the type and extent of FDIA on the unsecured sensors.

Safety Protection Technology of Power Monitoring System Based on Feature Extraction Algorithm

The network security protection technology of power monitoring systemsis of great significance. Aiming at the power network monitoring and pro-tection technology problem, the paper proposes an active monitoring andprotection strategy based on a feature extraction algorithm. The algorithmcan calculate the transfer degree of security incidents based on evidencetheory. First, the paper obtains a specific state transition diagram based onthe security topology of a generalized random power communication net-work. Then, we analyze the relationship between power system informationsecurity and engineering security based on the system’s operating results andfeature extraction algorithms. The experimental results demonstrate the rapideffectiveness of this method.

Optimization of Phasor Measurement Unit Placement Using Several Proposed Case Factors for Power Network Monitoring

Recent developments in electrical power systems are concerned not only with static power flow control but also with their control during dynamic processes. Smart Grids came into being when it was noticed that the traditional electrical power system structure was lacking in reliability, power flow control, and consistency in the monitoring of phasor quantities. The Phasor Measurement Unit (PMU) is one of the main critical factors for Smart Grid (SG) operation. It has the ability to provide real-time synchronized measurement of phasor quantities with the help of a Global Positioning System (GPS). However, when considering the installation costs of a PMU device, it is far too expensive to equip on every busbar in all grid stations. Therefore, this paper proposes a new approach for the Optimum Placement of the PMU problem (OPP problem) to minimize the installed number of PMUs and maximize the measurement redundancy of the network. Exclusion of the unwanted nodes technique is used in the proposed approach, in which only the most desirable buses consisting of generator bus and load bus are selected, without considering Pure Transit Nodes (PTNs) in the optimum PMU placement sets. The focal point of the proposed work considers, most importantly, the case factor of the exclusion technique of PTNs from the optimum PMU locations, as prior approaches concerning almost every algorithm have taken PTNs as their optimal PMU placement sets. Furthermore, other case factors of the proposed approach, namely, PMU channel limits, radial bus, and single PMU outage, are also considered for the OPP problem. The proposed work is tested on standard Institute of Electrical and Electronics Engineering (IEEE)-case studies from MATPOWER on the MATLAB software. To show the success of the proposed work, the outputs are compared with the existing techniques.

Research on Power Network Monitoring Technology Based on Power Supply System

Research on Power Network Monitoring Technology Based on Power Supply System

Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison

Phasor Measurement Units are the most advanced instruments for power network monitoring, since they allow phasors, frequency and rate of change of frequency (ROCOF) to be measured in predetermined time instants with respect to an absolute time reference. The employed estimation algorithm plays a key role in overall performance under off-nominal conditions; the challenge to be faced is combining high steady-state accuracy with fast responsiveness in dynamic conditions, small reporting latency and reduced computational burden. Under regular operation, AC power networks are weakly unbalanced three-phase systems. Based on this consideration, the recent literature has proposed native three-phase estimation algorithms that effectively exploit this property to accurately identify the positive sequence synchrophasor, frequency and ROCOF. In this respect, the present paper describes three among the most promising three-phase algorithms based on the Space Vector transformation. By means of numerical simulations, it compares the achieved performance in terms of response time and estimation error both under steady-state and dynamic conditions. All the considered approaches enable a flexible design that allows balancing accuracy and responsiveness. For this analysis, the reporting latency has been limited to about one and half nominal cycles, i.e., 30 ms at 50 Hz; the P-class algorithm suggested by IEC/IEEE Std 60255-118-1 has also been included as comparison benchmark.

Lineage Chain Mark Fault-Tolerant Method for Micro-Batching Monitoring Data in Distribution Power Network

Aiming at the problem of lacking efficient distributed fault tolerant mechanism for data explosion in the distributed distribution power automation system, based on the record-level fault tolerance of a resilient distributed dataset (RDD) and micro-batch computing, a new lineage chain mark (LCM) fault-tolerant method is proposed. Using the distribution power network monitoring dataset as cache object, the programming model based on the micro-batch processing framework is comprehensively used to design the topology example of the distribution power network monitoring data, which can shorten the derivative chain of RDD. This paper takes the monitoring data in a dispatching and monitoring system of the Shijiazhuang-Dezhou distribution power network as the data source, which has deployed a flow calculation test cluster of one primary node and three working nodes. Taking the most frequent fault of single data node failure condition as an example, the test results based on 3 million monitoring data in RDD show that, the CPU usage volatility of this model in cluster nodes was less than 1.5%, the network consumption of read and write execution is decreased by about 3.1% and 5.0%, respectively, compared with the original model, and the disk usage was reduced by 4.2%; the iterative test shows that the number of iterations is positively correlated with the computational time, which is consistent with the calculation formula for the worst execution time of the lineage chain labeling model. When the number of iterations is more than 400, the greater the number of iterations, the more significant the LCM method improves the performance of the calculation. The results show that the long pedigree chain can be cut off by using the pedigree chain marker sequence, so that multi-computer parallel processing can be carried out in the nodes, which reduces the computational overhead of iterative operation and effectively improves the performance of fault-tolerant computing.

Conic Relaxations for Power System State Estimation With Line Measurements

This paper deals with the nonconvex power system state estimation (PSSE) problem, which plays a central role in the monitoring and operation of electric power networks. Given a set of noisy measurements, PSSE aims at estimating the vector of complex voltages at all buses of the network. This is a challenging task due to the inherent nonlinearity of power flows (PFs), for which the existing methods lack guaranteed convergence and theoretical analysis. Motivated by these limitations, we propose a novel convexification framework for the PSSE problem using semidefinite programming (SDP) and second-order cone programming (SOCP) relaxations. We first study a related PF problem as the noiseless counterpart, which is cast as a constrained minimization program by adding a suitably designed objective function. We study the performance of the proposed framework in the case where the set of measurements includes: 1) nodal voltage magnitudes and 2) branch active PFs over at least a spanning tree of the network. It is shown that the SDP and SOCP relaxations both recover the true PF solution as long as the voltage angle difference across each line of the network is not too large (e.g., less than $90^{\circ }$ for lossless networks). By capitalizing on this result, penalized SDP and SOCP problems are designed to solve the PSSE, where a penalty based on the weighted least absolute value is incorporated for fitting noisy measurements with possible bad data. Strong theoretical results are derived to quantify the optimal solution of the penalized SDP problem, which is shown to possess a dominant rank-one component formed by lifting the true voltage vector. An upper bound on the estimation error is also derived as a function of the noise power, which decreases exponentially fast as the number of measurements increases. Numerical results on benchmark systems, including a 9241-bus European system, are reported to corroborate the merits of the proposed convexification framework.

Opportunities to use satellite technologies for asset condition monitoring of power networks under the European Space Agency's Integrated Applications Promotion programme

Network operators in the UK have stated their intention to target network reliability through improved procedures for asset management. This involves having a better understanding of how an asset deteriorates over time and their current health. The availability of good quality data is essential to this process. At the distribution level the majority of asset condition data comes from inspection, sometimes coupled with a form of continuous condition monitoring. Both are relatively expensive, with inspection also being very personnel-intensive. Satellites provide a variety of enabling capabilities that are applicable across many sectors. They can potentially support the inspection process of many of the assets present within distribution and transmission networks. This study presents an overview of potential satellite applications within the power industry, with particular emphasis on how they can be used for asset inspection and monitoring. A workshop was organised by European Space Agency and the Power Networks Demonstration Centre with UK network operators as participants to further explore some of the potential applications. The main areas identified are briefly outlined in this study.

Smart-Grid Topology Identification Using Sparse Recovery

Smart grid (SG) technology reshapes the traditional power grid into a dynamical network with a layer of information that flows along the energy system. Recorded data from a variety of parameters in SGs can improve the analysis of different supervisory problems, but an important issue is their cost and power efficiency in data analysis procedures. This paper develops an efficient solution for power network topology identification and monitoring activities in SG. The basic idea combines optimization-based sparse-recovery techniques with a graph theory foundation. The power network (PN) is modeled as a large interconnected graph, which can be evaluated with the dc power-flow model. It has been shown that topology identification for such a system can mathematically be reformulated as a sparse-recovery problem (SRP), and the corresponding SRP can efficiently be solved using SRP solvers. In this study, we especially exploit the concentration of nonzero elements in the corresponding sparse vectors around the main diagonal in the nodal admittance or structure matrix of the PN to improve the results. The network models have been generated with the MATPOWER toolbox, and MATLAB-based simulation results have indicated the promising performance of the proposed method for real-time topology identification (TI) in SGs.

Distributed Approach for SmartGrids Reconfiguration Based on the OSPF Routing Protocol

Smart grids (SG) are essential for efficient management and monitoring of electric power networks. One of the most important tasks in SG focuses on fault detection and automatic network reconfiguration. This process allows minimizing power losses and load balancing in distribution networks. In this paper, an adaptation of the open shortest path first (OSPF) routing protocol to accomplish the network reconfiguration task is proposed. The algorithm is intended to run in secondary substation nodes over an agent-based distributed architecture. The proposed algorithm has been tested on the IEEE 123 modified node test feeder and on an actual grid deployed by an electrical distribution company. Moreover, a performance comparison with a typical centralized reconfiguration algorithm is carried out.

Applications of Czech PMU / WAMS Systems in Distribution Systems

Abstract: The paper presents several advanced applications of the wide area monitoring system (WAMS),which are implemented in the WAMS designed by the AIS company, for online power network monitoring, such as on-line ampacity monitoring and frequency oscillations monitoring. It is also shown, that there are significant systematic errors in measured current phasors negatively influencing the calculation of actual line parameters. Hence, the method for calibration of the current phasor measurements is proposed. Finally, two case studies are devoted to the on-line ampacity monitoring and frequency oscillations detection, respectively.

Wide Area Monitoring and State Estimation for a Given Phasor Network

In this paper, the modeling for a complete scenario of a proposed wide area measurement system (WAMS) based on phasor measurement units (PMUs) technology is presented. The purpose is to increase the overall system efficiency and reliability for all power stages with improved monitoring, protection, and control capabilities of power networks. The developed system is simulated using the Matlab/Simulink program. The power system layer consists of a 500 kW generation station, two transformers, four circuit breakers, two short transmission lines, and two one MW loads. The communication layer consists of four PMUs, located at generation and load buses. The proposed system is tested under two possible cases; normal operation and fault state. It was found that power system status can be easily monitored and controlled in real time by using the measured values online which improves the overall system reliability and avoids cascaded blackout during fault occurrence. The simulation results confirm the validity of the proposed WAMS technology for smart grid applications.

Impact of the Model on the Accuracy of Synchrophasor Measurement

Phasor measurement units (PMUs) are becoming one of the key issues of power network monitoring. They have to be able to perform accurate estimations of quantities of interest either under steady-state or transient conditions. Among all the sources which may contribute to the uncertainty introduced by PMUs, this paper analyzes the impact of the phasor estimation models on the accuracy of these devices, focuses on algorithms proposed in the literature for the estimation of dynamic phasors, and studies their performances under several different conditions.

Distributed estimation via iterative projections with application to power network monitoring

This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine the occurrence of threatening situations. State estimation has been recognized to be a fundamental task for network control centers to operate safely and reliably a power grid. We consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltage phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state, given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is used, whereas, in the second method, a diffusive interaction is implemented. Our procedures, which require each control center to know only the measurements and the structure of a subpart of the whole network, are computationally efficient and scalable with respect to the network dimension, provided that the number of control centers also increases with the network cardinality. Additionally, a finite-memory approximation of our diffusive algorithm is proposed, and its accuracy is characterized. Finally, our estimation methods are exploited to develop a distributed algorithm to detect corrupted network measurements.

Wide area measurement system for smart grid applications involving hybrid energy sources

This paper presents a model and experimental verification for a complete scenario of a proposed wide area measurement system (WAMS) based on synchronized phasor measurement units (PMUs). The proposed system is feasible for hybrid smart ac/dc power networks; such as grid-connected PV-power plants. The purpose is to increase the overall system reliability for all power stages via significant dependence on WAMS as distributed intelligence agents with improved monitoring, protection, and control capabilities of the power networks. The developed system is simulated in the Matlab/Simulink environment. The system was tested under two different cases; normal operation and fault state. Furthermore, the proposed WAMS was experimentally validated with results obtained from a reduced scale setup which built and tested in the laboratory based on the Hardware-in-the-loop concept. It was verified that the power system status can be easily monitored and controlled in real time by using the measured bus data in real time. This improves the overall system reliability and avoids cascaded blackout during fault occurrence. The simulation and experimental results confirm the validity of the proposed WAMS technology for smart grid applications.

Development of a Wide Area Measurement System for Smart Grid Applications

AbstractIn this paper, the modeling for a complete scenario of a proposed wide area measurement system (WAMS) based on synchronized phasor measurement units (PMUs) technology with the access of a broadband communication capability is presented. The purpose is to increase the overall system efficiency and reliability for all power stages via significant dependence on WAMS as distributed intelligence agents with improved monitoring, protection, and control capabilities of power networks. The developed system is simulated using the Matlab/Simulink program. The power system layer consists of a 50 kW generation station, 20 kW wind turbine, three transformers, four circuit breakers, four buses, two short transmission lines, and two 30 kW loads. The communication layer consists of three PMUs, located at generation and load buses, and one phasor data concentrator (PDC), that will collect the data received from remote PMUs and send it to the control center for analysis and control actions. The proposed system is tested under two possible cases; normal operation and fault state. It was found that power system status can be easily monitored and controlled in real time by using the measured bus values online which improves the overall system reliability and avoids cascaded blackout during fault occurrence. The simulation results confirm the validity of the proposed WAMS technology for smart grid applications.