Power Grid Monitoring Research Articles

Power Grid Dispatching Optimization Assistant Decision-making Model Based on Deep Learning

At present, the power grid dispatching assistant decision-making model relies on the model-driven solution method, which leads to too long a calculation time. Therefore, an auxiliary decision-making model for power grid dispatching optimization based on a deep learning algorithm is proposed. The objective function of power grid dispatching optimization is defined for the power grid connected with multiple renewable energy sources. Relying on cloud computing technology, the power grid dispatching and monitoring data are obtained and saved as different knowledge systems. The long and short-term memory network is selected from the deep learning field, and the Bi-LSTM network structure unit is designed. The scheduling decision model is constructed using the bi-layer Bi-LSTM neural network, and the optimal scheduling decision is obtained by deep learning of the historical scheduling data. The calculation results show that the longest time of the model is 0.04s. The shortest time is only 0.02s, which improves the efficiency of auxiliary decision-making and enhances the stability of power grid operation to a certain extent.

On Biased Harmonic Signal Estimation: Application to Electric Power Grid Monitoring

Parametric estimation of a biased harmonic signal is a significant technical challenge for many engineering applications. Such a problem is particularly important for electric utility grid-connected power electronic converters. This article utilizes a linear regression model of the signal to solve this interesting practical problem. A continuous-time dynamic regressor extension and mixing (DREM) based approach is then applied for parameter estimation. For practical implementation, continuous-time estimators are discretized using implicit and explicit Euler methods. We then prove that the implicit discretization can achieve fixed-time convergence for the unknown frequencies estimation. Thanks to the estimated frequencies, another DREM-based linear regression problem is solved for the parameter estimation purpose. The overall order of the proposed technique is the same as the number of unknown parameters, making the estimator suitable for real-time implementation in embedded devices. Theoretical results are validated through extensive comparative experimental studies.

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

Objective. In order to control the vibration of the beam structure more effectively and improve the safety and availability of the beam structure, an application study of the Lagrange equation for vibration control of smart sensors for power grid monitoring is proposed. The vibration of the beam structure, the displacement of the beam structure under the excitation of seismic acceleration, the response analytical electrical formula, and the displacement response formula of the beam structure under the action of the kinematic force are deduced. The optimal parameters of the beam-TMDI system are given, and the parameter sensitivity analysis is carried out. Then, the control effect of the TMDI system is studied by numerical analysis, and the vibration reduction effect of the TMDI system and the tuned mass damper (TMD) system is compared. Experimental results show that when the mass ratio μ of the TMDI system and the TMD system are both set to a fixed value of 0.005, and the parameter β of the TMDI system is set to 0, namely β = b = 0 , at this time, the TMDI system degenerates into a TMD system. The TMD natural frequency is 14.179 rad/s and the damping ratio is 0.0432 by the DH optimization method, while the TMD natural frequency is 14.1812 rad/s, and the damping ratio is 0.0436 by the augmented Lagrangian optimization algorithm. Conclusion. The vibration displacement response spectrum of a beam structure obtained by the frequency domain method can effectively reflect electricity in the displacement response of a beam structure. The parameters that minimize the vibration response of the beam structure can be accurately obtained by using the augmented Lagrangian parameter optimization method. The sensitivity of the TMDI system is controlled by the inertial device, and the inertial device has a significant impact on its robustness. The vibration reduction performance of the TMDI system is obviously better than in the conventional TMD systems.

Multiple Delayed Signal Cancellation Filter-Based Enhanced Frequency-Locked Loop Under Adverse Grid Conditions

An accurate and prompt estimation of phase angles, frequency, and magnitudes of the grid voltage is extremely important in paving a good platform for a fast and reliable operation of power grid monitoring, protection, and control. The phase-locked loop (PLL) served this purpose for decades, while the concept of the frequency-locked loop (FLL) has been proposed more recently. Even though the FLL and the PLL share some similarities, some differences still exist. Further research on FLLs can boost their capabilities and open up new windows in designing devices for grid synchronization. Multiple delayed signal cancellation (MDSC) filters are a peculiar system of filters. Their applications to FLLs can enrich FLL’s performance. This article focuses on applying the stationary reference frame MDSC filters at in-loop and prefiltering stages of the FLL and compares its effectiveness with widely used cascaded delayed signal cancellation (CDSC) and complex bandpass filter (CBF) based filtering techniques. The application of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -factor-methodology-based controller design to FLLs has also been explored for dynamic performance enhancements. A new cascaded multiple delayed signal cancellation (CMDSC) filter is proposed and is compared with its CDSC counterpart proposed in the literature to address the effects of dc offsets and imbalances under adverse grid conditions. The effectiveness of the proposed method is verified through OPAL-RT real-time simulations and dSPACE ds1104 and TMS320F28335 microcontroller experimental test bench.

Simulation test of transformer short-circuit impedance based on equivalent model at different frequencies

A distribution transformer is one of the most important equipments in the power grid, and its operational status is directly related to the security and stability of the entire power system. The performance of transformer windings determines the service life of the transformer. It is of great significance to evaluate the performance of transformer windings accurately, reliably, and effectively after the short-circuit test of the transformer. Aiming at the problem that the natural vibration frequency of the transformer measured before and after the short circuit may not be the same under the impact of the short circuit of the transformer, this paper puts forward the evaluation method for the performance evaluation of the winding of the transformer after the short circuit test and adopts the method of combining the analytic hierarchy process with the fuzzy evaluation method to quantitatively obtain the score for the performance evaluation of the winding. And that evaluation language is given according to the score. Finally, the evaluation of the transformer winding performance after the short-circuit test is realized. The evaluation results show that this method can effectively get the performance index of the transformer winding, without the need for hanging cover inspection, and solve the subjectivity of human eye observation to objectively, accurately, and quantitatively describe the performance state of the transformer winding. The method is favorable for power grid monitoring and maintenance personnel to accurately evaluate the operation stability of the transformer and has very positive significance for improving the short-circuit impact resistance of the transformer and ensuring the safety and stability of the operation of a power system.

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models.

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 , being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 , showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.

Power Grid Monitoring Event Recognition Method Integrating Knowledge Graph and Deep Learning

“Eventization” of power grid monitoring is an effective way to deal with massive alarm information. The existing event recognition method adopts the method of text information mining, and the overall recognition accuracy is not high. Therefore, this paper proposes a power grid monitoring event recognition method integrating knowledge graph and deep learning. First, the method constructs the knowledge graph of monitoring equipment and uses the improved GraphSAGE (graph sample and aggregate) algorithm to perform representation learning on the graph, and integrate the structural characteristics of monitoring equipment into the generated alarm vectors. Then, the GRU (Gated Recurrent Unit) neural network trains the alarm vectors and related events. In addition, this paper combines the proposed method with the existing monitoring expert system, and puts forward a monitoring event recognition strategy. Finally, through the case analysis and comparison of the actual data of the power grid, the effectiveness of the proposed method and strategy is verified, which further improves the accuracy of monitoring event recognition.

A Load-Independent Self-Oscillating Control of Domino Wireless Power Transfer Systems for High-Voltage Power Grid Monitoring Equipment

Real-time monitoring devices are popularly utilized in modern power grids. To ensure long-term operations of the monitoring systems under complex outdoor conditions, a reliable and stable power supply is essential. In this paper, a general analysis of domino wireless power transfer (WPT) systems with load-independent outputs is proposed to realize a constant power supply for the monitoring equipment. In addition, the methodology of analyzing the self-oscillating points of the proposed domino WPT systems is deduced. The availability and feasibility of the proposed analysis and control method are verified by both simulation and experiment results based on a four-coil WPT system.

Security Assessment of Coal Mine Power Grid Voltage Based on an Improved AHP-FCE

The voltage level of a coal mine power grid directly affects the starting and normal operation of the electric motor, but the current grid monitoring system cannot use real-time grid structure parameters and power flow distribution data to conduct early warning research on the running status and voltage level of the power grid, so it cannot eliminate hidden danger in the operation of the power grid. Therefore, it is necessary to accurately and effectively evaluate the voltage safety level of the coal mine power grid. In this paper, an improved AHP-FCE method is proposed to evaluate the voltage safety level of a coal mine power grid. By replacing the comparison judgment matrix with the interval judgment matrix, the objectivity of the weight vector is improved while retaining expert subjectivity. According to the real-time power flow distribution data of the power grid monitoring system, the current voltage safety level of the power grid is evaluated. The results show that the improved AHP-FCE method can accurately evaluate the current grid voltage level, analyze the voltage development trend, give early warnings regarding coal mine grid voltage operation, and improve the monitoring function of the power monitoring system.

A knowledge representation based recognition method on power grid alarm monitoring data

The identification of power grid monitoring and alarm information is an essential guarantee for the stable operation of the power grid. Identifying current power grid alarms is based on an expert rule system. This method has the problems of cumbersome rule matching, difficulty in maintaining the rule base, and low matching efficiency. In order to solve the above problems, this paper proposes a method of identifying power grid monitoring and warning information based on knowledge representation. The method first uses grid topology information to construct a knowledge graph. It then uses knowledge representation learning algorithms to represent the power knowledge graph which digitally represents the textual warning information, and finally uses a series of deep learning models to identify ”alarm-event.” This paper uses six algorithms, such as TransE for knowledge representation, combined with three deep learning models such as ConvLSTM for identification. The verification shows that this method has specific ”alarm-event” identification capabilities based on actual power grid monitoring data.

Intelligent power grid monitoring and management strategy using 3D model visual computation with deep learning

The legacy grid is developing into an intelligently rooted grid system that can enhance and adapt its performance to learning experiences in its architectural components. Due to the evolution of artificial intelligence and in-depth learning techniques, decision-making and execution can become autonomous activities in intelligent power grids throughout the years. 3D visualization plays a vital role in simplifying monitoring, analysis, and reaction to events in the smart grid. The visualization of the 3D models for various energy system applications is first summarized, including the smart grid, power vehicles, and energy consumption of buildings. Accordingly, design principles are provided for wide-screen, personal computer, or mobile interfaces. Visualization technology for geographical information systems is described. 3D technology, animations, and AR&VR to visualize the energy system is discussed. In this article, a coherent 3D visualization approach for the control and monitoring of intelligent power grids (P.G.) via the deep learning (DL) method is examined in industry literature (3D-PGDL). Data visualization is gaining significance with improved measurement systems and Big Data Analytics in intelligent grids and low carbon energy systems. No data design study, accompanying technology, or visualization tools have been conducted. The analysis and practice of data viewing on energy and power systems are thoroughly addressed here. Because of the intrinsic properties of intelligent grids, all collected and processed data are heterogeneous to provide a more comprehensive, multifaceted control viewpoint for the energy sector by creating new ICT instruments and platforms. This section’s experiment resulted in a 95.3 % prediction rate and improved data management ratio of 95.7 %, a lower energy consumption ratio of 18.2%, a lower probability of 18.4 %, a lower CO2 emission level of 15.6 % and a humidity rate of 93.1 %.

Authenticating source information of distribution synchrophasors at intra-state locations for cyber-physical resilient power networks

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information and communication technologies, they become increasingly reliant upon more accurate monitoring and fast control. Phasor Measurement Units (PMUs) collect high-precision Distribution Synchrophasors (DS) data regarding the system dynamics and provide real-time situational awareness for better monitoring and control of large-scale power grids. The accuracy and generalizability of the PMUs heavily rely upon the data quality of DS measurements, which is very susceptible to newly emerging “Source ID Mix” data spoofing attacks. Such attacks could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby jeopardizing most DS-based applications and even causing catastrophic power interruptions. This paper proposes a novel data-driven source authentication method to automatically identify the source information of DS collected from multiple intra-state locations and thus enhance the reliability and cybersecurity of power systems. The proposed method integrates Mathematical Morphological Decomposition (MMD) and Multi-Weighted Deep Stacking Forest (MLW-DSF) for providing accurate source authentication with a low computational cost which requires neither system’s models nor parameters. The effectiveness of the proposed method is corroborated through seven “state-of-the-art” data-driven models by using real-life DS measurements of power networks in Queensland state.© 2017 Elsevier Inc. All rights reserved.

PMU-Based Estimation of Systematic Measurement Errors, Line Parameters, and Tap Changer Ratios in Three-Phase Power Systems

The availability of accurate data is fundamental for several monitoring and control applications of modern power grids. Nevertheless, the knowledge of critical data, such as transmission line and transformer parameters, is often affected by uncertainty. This can lead to important problems in the correct management of the power systems. In spite of a monitoring infrastructure that is being renewed due to new generation devices providing synchronized measurements, the actual values of line parameters and tap changer ratios are still affected by uncertainty sources that need to be properly considered. The behavior of all the elements involved in the measurement chain must be duly modeled. This article proposes an improved method to carry out the simultaneous estimation of line parameters, tap changer ratios, and systematic measurement errors for a three-phase power system. The proposed method is based on the suitable modeling of the measurement chain and three-phase constraint equations (voltage drop and current balance) of all the components involved. Its effectiveness is confirmed by tests performed on an IEEE 14-bus test system reproduced as a three-phase system under different operative conditions.

Power domination in splitting and degree splitting graph

A vertex set S is called a power dominating set of a graph G if every vertex within the system is monitored by the set S following a collection of rules for power grid monitoring. The power domination number of G is the order of a minimal power dominating set of G. In this paper, we solve the power domination number for splitting and degree splitting graph.

Dynamic Multicast Routing Management for Robust Wide Area Power Grid Communication

High-volume synchrophasor data generated by the wide area measurement system (WAMS) present new challenges for the design of scalable and robust communication architectures suitable for the large-scale deployment in power grid monitoring and control applications. Features of information and communication technology (ICT) can be harnessed to devise a more flexible infrastructure than the conventional unicast-based hierarchical architecture. Multicast has been suggested as a potential solution for scalability issues in the current WAMS architecture. This article proposes a decentralized dynamic multicast routing management algorithm that manages network traffic efficiently, improves scalability, and makes the system resilient to component failures. The algorithm models the creation of alternate links as a stochastic process using the random-graph theory and manages connections between end hosts at the session layer. This protocol can be implemented using existing ICT protocols at a minimal cost. Numerical simulations and study of frequency control in an IEEE 39-bus system testbed show that the proposed algorithm improves the topological efficiency and resilience of the network.

Design and Implementation of Real-time Power Grid WebGIS Visualization Framework Based on New Generation Dispatching and Control System

With the strengthening of the integrated characteristics of power grid and the construction of the New Generation Dispatching and Control System with “physical distribution, logical integration”, the demand for global monitoring and analysis of power grid has gradually increased. On the basis of understanding of design of the new generation real-time dispatching and control data platform system, with the principles of componentization and servitization, the real-time power grid WebGIS visualization framework is designed and implemented. And this paper further introduces the design of the front-end secondary development interface and examples, as well as the cartographic generalization of the power grid WebGIS visual map. This framework has successfully supported the construction and online operation of several real-time power grid WebGIS visualization applications.

Algorithm analysis based on machine learning in alarm information of metering system

Due to the huge scale of the metering warning system and the wide variety of warning information, manual identification can no longer meet the real-time monitoring needs of modern power grids. Through the use of machine learning, data mining technology, linear regression method, naive Bayes method, automatic classification analysis of alarm information from the massive alarm information. Construct an SVM prediction model to improve the accuracy of alarm information analysis and improve the effectiveness of power grid monitoring and management. The experimental results show that it can provide support for the normal operation of the grid metering system.

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large-scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra-high density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication.

EPS-Ledger: Blockchain Hyperledger Sawtooth-Enabled Distributed Power Systems Chain of Operation and Control Node Privacy and Security

A distributed power system operation and control node privacy and security are attractive research questions that deliver electrical energy systems to the participating stakeholders without being physically connected to the grid system. The increased use of renewable energy in the power grid environment creates serious issues, for example, connectivity, transmission, distribution, control, balancing, and monitoring volatility on both sides. This poses extreme challenges to tackle the entire bidirectional power flow throughout the system. To build distributed monitoring and a secure control operation of node transactions in the real-time system that can manage and execute power exchanging and utilizing, balancing, and maintaining energy power failure. This paper proposed a blockchain Hyperledger Sawtooth enabling a novel and secure distributed energy transmission node in the EPS-ledger network architecture with a robust renewable power infiltration. The paper focuses on a cyber-physical power grid control and monitoring system of renewable energy and protects this distributed network transaction on the blockchain and stores a transparent digital ledger of power. The Hyperledger Sawtooth-enabled architecture allows stakeholders to exchange information related to power operations and control monitoring in a private ledger network architecture and investigate the different activities, preserved in the interplanetary file systems. Furthermore, we design, create, and deploy digital contracts of the cyber–physical energy monitoring system, which allows interaction between participating stakeholders and registration and presents the overall working operations of the proposed architecture through a sequence diagram. The proposed solution delivers integrity, confidentiality, transparency, availability, and control access of the distribution of the power system and maintains an immutable operations and control monitoring ledger by secure blockchain technology.

Intelligent Monitoring Analysis of Power Grid Monitoring Information Based on Big Data Mining

With the rapid economic progress of various regions in China, the demand for electricity is also gradually increasing. In order to effectively solve this problem, we increase the coverage of the grid and the upper limit of power consumption. This has not only led to the economic development of some regions. Big data analysis technology has also been widely used [1]. However, the increase of power consumption also increases the burden of power grid staff. The working pressure of intelligent monitoring system of power grid monitoring information is also increased. In order to simplify the process of power grid information monitoring, data mining technology will be applied to the construction of intelligent monitoring system. According to the theory of power grid inspection, big data information monitoring technology can monitor the operation of power grid in real time. This paper briefly analyzes the current power grid information monitoring problems. This paper puts forward the structure design of power grid monitoring information system based on data mining. Finally, this paper proposes an effective data mining information monitoring algorithm.