Grid Monitoring Research Articles

Study on The Applicability of Drones in The Process of Forming Towers of 500KV Transmission and Substation Lines in Highland Areas

With the continuous development of science and technology in China, China's aviation field has also been expanding, the rapid development of aviation technology, China has gradually achieved leapfrog development in UAV equipment, and gradually tends to civilization, practicality and commercialization. At present, more and more UAVs are not only opening up in the domestic market, but also have flowed to overseas markets, and the modern advanced technology represented by UAVs has gradually become the current trend development trend. In the field of electric power engineering construction, the use of drone technology is short and wide, and there are drones in both high-altitude areas and plain areas. In terms of the engineering field, the UAV technology involves the construction, monitoring and management of power grids, fault and equipment maintenance of power grids and transmission line inspection, etc.

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

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

A Data-Driven Convolutional Neural Network Approach for Power Quality Disturbance Signal Classification (DeepPQDS-FKTNet)

Power quality disturbance (PQD) signal classification is crucial for the real-time monitoring of modern power grids, assuring safe and reliable operation and user safety. Traditional power quality disturbance signal classification approaches are sensitive to noise, feature selection, etc. This study introduces a novel approach utilizing a data-driven convolutional neural network (CNN) to improve the effectiveness of power quality disturbance signal classification. Deep learning has been successfully used in various fields of recognition, yielding promising outcomes. Deep learning is often characterized as a complex system, with its filters and layers being determined through empirical investigations. A deep learning model was developed for the purpose of classifying PQDs, with the aim of narrowing down the search for unidentified PQDs to a specific problem domain. This approach demonstrates a high level of efficiency in accelerating the process of recognizing PQDs among a vast database of PQDs. In order to automatically identify the number of filters and the number of layers in the model in a PQD dataset, the proposed model uses pyramidal clustering, the Fukunaga–Koontz transform, and the ratio of the between-class scatter to the within-class scatter. The suggested model was assessed using the synthetic dataset generated, with and without the presence of noise. The proposed models outperformed both well-known pre-trained models and state-of-the-art PQD classification techniques in terms of classification accuracy.

Operational status monitoring of smart grid and power communication network coupling and collaboration based on multi-head attention mechanism

Smart grid is an organic combination of traditional power grid and advanced digital communication technology, and power communication network plays a vital role in ensuring its steady operation. However, as the coupling correlation between the smart grid and the power communication network increases, the propagation of coupled network failures may constitute a great threat to the safe and stable operation of the power system. To address this phenomenon, based on the algorithm of the multi-head attention mechanism (MHA) and convolutional neural network (CNN), this paper proposes a framework that integrates the multi-head attention mechanism with the convolutional neural network (MHA-CNN) for the monitoring of the coupling network’s operation status. In the proposed algorithm, Hilbert–Huang transform (HHT) is used to extract the intrinsic features of the operational data, and MHA employs multi-subspace analysis and extracts the key feature information of the fault data to maximize the fault information extraction and help the system to discover the fault as soon as possible. Simulation results demonstrate that the proposed algorithm can effectively realize a better detection speed and higher detection probability compared with CNN and ResNet-50, and maintain certain robustness under poor transmission conditions, ensuring the steady operation of the coupled network.

Power grid monitoring based on Machine Learning and Deep Learning techniques

<p><strong>Background:</strong> In this work, some application examples of machine learning and deep learning techniques in the monitoring of electricity distribution services and infrastructures are proposed. Three different fields of application are considered to highlight the use of techniques based on neural networks: detection and classification of power quality disturbances, monitoring of underground cables in medium voltage lines and diagnosis of joints in high voltage overhead power lines. <strong>Methods:</strong> In the field of power grid monitoring, this work proposes a classification method based on a complex valued neural network to assess working conditions of junction regions in high-voltage overhead lines and insulating materials in medium voltage underground networks. The purpose of this method is to prevent the rupture of joint structures and the abnormal degradation of underground cables via frequency response measurements. This approach allows the direct processing of complex measurements and to reduce the computational effort compared to other methods available in the literature. <strong>Results:</strong> The results obtained in the monitoring of underground cables and joints of high voltage lines guarantee an overall classification rate higher than 90%. In the field of power quality, several deep learning and machine learning methods are proposed to detect the most common voltage disturbances. <strong>Conclusions:</strong> In this paper, an innovative use of widespread algorithms such as convolutional neural networks is proposed with excellent results. Furthermore, the use of a complex-valued neural network in electrical infrastructure monitoring is presented, introducing a minimally invasive classification method that could be instrumental in the transition from corrective to predictive maintenance in the near future.</p>

Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid

AbstractThis article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.

IoT-based monitoring of smart grid using high-gain converter with optimized maximum power point tracking

IoT-based monitoring of smart grid using high-gain converter with optimized maximum power point tracking

A cyber-layer based on weighted average consensus in blockchain environment for accurate sharing of power systems’ dynamic states

Sharing the dynamic states among power system generators is crucial for wide-area monitoring, decentralized control, and protection of the prospective smart grid. However, this sharing process is subject to cyber–physical contingency conditions, which limits its accuracy. This paper proposes a framework that incorporates a cyber-layer with the power system to ensure accurate sharing of the generators’ dynamic states. The proposed framework utilizes a weighted average consensus mechanism within a blockchain environment. The consensus ensures that the communication links/data among generator buses are reliable in both directions and that any link/data failure is detected to guarantee the validity, and hence accuracy of the shared generators’ states. Moreover, a smart contract at each generator applies the proposed consensus mechanism and credits the validator generator bus that creates the next block in the states’ blockchain after achieving the consensus. Finally, the proposed framework is tested on the standard IEEE 3-generator 9-bus power system under data contingency conditions. The generators’ speed states are tested, while the other states can be verified in a similar manner. Simulation results show the ability of the proposed framework to ensure accurate sharing of the generators’ speed states and to detect possible cyber-attacks, compared to the case without using the proposed framework.

Monitoring-Based Localization of Unbalances and Root Cause Analysis in Low-Voltage Distribution Systems

The dynamic nature of power systems operation, lack of sufficient monitoring initiatives, and the continuous nature of the power quality phenomena are the primary reasons behind a distribution system operator's inability to localize and understand the root cause behind unbalance. Additionally, there is a deficiency of algorithms that can leverage the grid monitoring data, even if limited, to present a sufficiently useful overview of unbalance propagation which can lead to appropriate corrective measures. This article presents: a new unbalance index derived from feeder active power measurements, an algorithm for localization of unbalanced contributions, and an algorithm for root cause analysis that can distinguish whether the unbalance problem is loading, asymmetry, and/ or fault-oriented. The end goals of this work are twofold: to present a holistic view of the grid unbalanced situation using EN50160 type monitoring, and to assist the distribution system operators in unbalance reduction. The usability of the proposed algorithms and indices is shown using a hybrid European MV-LV model simulated in OpenDSS and on two actual LV distribution systems with real-time power quality monitoring.

Performance of PMU in an electric distribution grid during transients

AbstractPMUs and micro PMUs are an emerging technology that is gaining increasing importance in the monitoring and control of electric distribution grids. Their ability to provide accurate, real-time information on power quality, along with their ability to quickly detect and isolate any problems on the grid, make them a key tool for increasing grid safety and reliability. In terms of research and development, there is a growing interest in developing more advanced and reliable Micro PMUs that can provide more detailed and accurate information about the grid. In addition, there is a growing focus on integrating Micro PMUs with other grid control and monitoring systems, such as frequency and voltage control systems, to achieve greater efficiency in grid management. In this research, a commercial PMU is tested during transient conditions typical of distribution grids such as load insertion and de-insertion and distributed generation. The system involves simulating a grid in a Simulink environment and transforming digital signals into analog signals to be supplied to the PMU. Some indices such as TVE, ROCOF are then used for performance analysis.

IoT-based monitoring and control of substations and smart grids with renewables and electric vehicles integration

The integrated renewable energy resources (RERs) based smart grid in the power distribution network (PDN) has financial and ecological benefits. However, the emergence of RER-based microgrids and substations without real-time monitoring of their power parameters leads to various challenges in the PDN, such as suboptimal resource allocation, poor load management, grid instability, and lack of real-time decision-making capabilities. To mitigate these challenges, increase the system's visibility, and make efficient decisions, intelligent monitoring and control system is required for both smart grid and power substation. This proposed study develops IoT-based monitoring and control of power substations and associated distributed smart grids to make effective decisions of integration/segregation into the PDN. The proposed IoT-based integration/segregation of smart grids and load management can mitigate the stated challenges effectively. Using the HOMER Grid®, the research also investigates the annualized power production pattern of smart grids and the power consumption pattern of integrated loads to enable proactive decisions about energy management. The proposed study implements IoT technology for power parameters monitoring of substations and smart grids for their effective use, as it considers four types of load management, including industrial, domestic, commercial, and electric vehicles, with the aid of IoT technology to avoid power fluctuations and contingencies. Effective load management has been adopted based on annualized energy consumption, load patterns and real-time monitoring, and active decisions making. The results highlight that the proposed IoT-based approach helps advance smart grid integration into the PDN and enhance energy load management, consequently reducing energy costs and suppressing carbon emissions. The validation of the proposed model is verified by the constructed prototype, where the achieved real-time monitoring and control of power substations and smart grids into PDN is performed to make effective decisions related to energy and load management. Moreover, it effectively allows power distribution companies to manage loads during high demand or crises and enhances grid stability and energy efficiency.

A systematic approach to modeling synchronous generator using Markov parameters and Takagi–Sugeno fuzzy systems

The problem of precise modeling of Synchronous Generator (SG) devices is demanding and crucial for monitoring, stability, and control of electric grids and microgrids. SGs are known to exhibit nonlinear behavior, and because they may work in a wide range of operating points due to different electric network phenomena like load changes, current intermittent renewable energy resources, temperature variations, topological changes, etc., the modeling task of SG can be a challenging problem. This paper proposes a new method to find a global model for a synchronous generator from the input and output measurement data using state-of-the-art methods in artificial intelligence (AI) like fuzzy clustering, subspace identification, and Takagi–Sugeno (T–S) fuzzy modeling. The fuzzy C-means method is utilized to cluster the measurement data, and then using a subspace identification method based on Markov parameters concepts, the dynamic model of SG for each cluster is obtained in the form of a state space model. A Takagi–Sugeno fuzzy model is applied to determine the corresponding outputs based on the measured inputs and the available state space models. The effectiveness of the proposed approach is illustrated by applying it to the data obtained from simulations on a known fourth-order SG nonlinear model and the data obtained from real-world experimental tests.

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

To face the impact of climate change in all dimensions of our society in the near future, the European Union (EU) has established an ambitious target. Until 2050, the share of renewable power shall increase up to 75% of all power injected into nowadays’ power grids. While being clean and having become significantly cheaper, renewable energy sources (RES) still present an important disadvantage compared to conventional sources. They show strong fluctuations, which introduce significant uncertainties when predicting the global power outcome and confound the causes and mechanisms underlying the phenomena in the grid, such as blackouts, extreme events, and amplitude death. To properly understand the nature of these fluctuations and model them is one of the key challenges in future energy research worldwide. This review collects some of the most important and recent approaches to model and assess the behavior of power grids driven by renewable energy sources. The goal of this survey is to draw a map to facilitate the different stakeholders and power grid researchers to navigate through some of the most recent advances in this field. We present some of the main research questions underlying power grid functioning and monitoring, as well as the main modeling approaches. These models can be classified as AI- or mathematically inspired models and include dynamical systems, Bayesian inference, stochastic differential equations, machine learning methods, deep learning, reinforcement learning, and reservoir computing. The content is aimed at the broad audience potentially interested in this topic, including academic researchers, engineers, public policy, and decision-makers. Additionally, we also provide an overview of the main repositories and open sources of power grid data and related data sets, including wind speed measurements and other geophysical data.

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

Smart Micro Grid Architecture for Realtime Monitoring of Solar Photovoltaic Based on Internet of Things

The use of PV solar energy as an alternative renewable energy source has increased worldwide. The smart grid monitoring system is applied on a micro scale for the fulfilment of household electricity needs or Smart Home Micro Grid (SHMG) using Artificial Intelligent (AI) and Internet of Things (IoT) approaches. Problems in solar PV systems include uncertainty and non-linearity which have an impact on the increasingly complex structure of the energy data generated and accelerate control and decision making. The forecasting process carried out using the AI approach is indispensable for the utilization of PV systems, especially for distributed residential PV that is operated and maintained independently. Furthermore, the use of IoT technology is also important to accelerate information processing and improve performance in the monitoring system. The purpose of this research is to develop a solar PV monitoring system framework at SHMG to optimize the performance of the electricity supply sourced from solar PV basen on IoT.

Intensive Data-Driven Model for Real-Time Observability in Low-Voltage Radial DSO Grids

Increasing levels of distributed generation (DG), as well as changes in electricity consumption behavior, are reshaping power distribution systems. These changes might place particular stress on the secondary low-voltage (LV) distribution systems not originally designed for bi-directional power flows. Voltage violations, reverse power flow, and congestion are the main arising concerns for distribution system operators (DSOs), while observability in these grids is typically nonexistent or very low. The present paper addresses this issue by developing a method for nodal voltage estimation in unbalanced radial LV grids (at 0.4 kV). The workflow of the proposed method combines a data-driven grey-box modeling approach with generalized additive models (GAMs). Furthermore, the proposed method relies on experimental data from a real-world LV grid in Denmark and uses data input from only one measuring device per feeder. Predictions are evaluated by using a test data set of 31 days, which is more than twice the size of the training data set of 13 days. The prediction results show high accuracy at root mean squared errors (RMSEs) of 0.002–0.0004 p.u. The method also requires a short computation time (14 s for the first stage and 2 s for the second stage) that meets requirements for the practical, real-time monitoring of DSO grids.

Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units

The emerging smart-grid and microgrid concept implementation into the conventional power system brings complexity due to the incorporation of various renewable energy sources and non-linear inverter-based devices. The occurrence of frequent power outages may have a significant negative impact on a nation’s economic, societal, and fiscal standing. As a result, it is essential to employ sophisticated monitoring and measuring technology. Implementing phasor measurement units (PMUs) in modern power systems brings about substantial improvement and beneficial solutions, mainly to protection issues and challenges. PMU-assisted state estimation, phase angle monitoring, power oscillation monitoring, voltage stability monitoring, fault detection, and cyberattack identification are a few prominent applications. Although substantial research has been carried out on the aspects of PMU applications to power system protection, it can be evolved from its current infancy stage and become an open domain of research to achieve further improvements and novel approaches. The three principal objectives are emphasized in this review. The first objective is to present all the methods on the synchro-phasor-based PMU application to estimate the power system states and dynamic phenomena in frequent time intervals to observe centrally, which helps to make appropriate decisions for better protection. The second is to discuss and analyze the post-disturbance scenarios adopted through better protection schemes based on accurate and synchronized measurements through GPS synchronization. Thirdly, this review summarizes current research on PMU applications for power system protection, showcasing innovative breakthroughs, addressing existing challenges, and highlighting areas for future research to enhance system resilience against catastrophic events.

IoT based secured data monitoring system for renewable energy fed micro grid system

Recently, the Wireless Sensor Network (WSN) plays a prominent role in many applications for efficiently monitoring the performance of the system. As the conventional power grid faces multiple issues in the process of power transmission, a smart grid based Internet of Things (IoT), which uses integrated electrical and communication infrastructure is employed in this study. The micro grid using Renewable Energy (RE) resources like wind and solar power is preferred in this work. The major objective of this study is to have perfect monitoring and control of the overall power grid using WSN. By connecting a smart micro grid with IoT, the issue of voltage violation and grid instability caused by power insufficiency is resolved. The IoT-based network integrates grid with the distributed source through WSN, which senses the data and forwards the gathered data to the cloud while taking a backup for future monitoring. Before forwarding, the data is highly encrypted using a hybrid approach known as Advanced Encryption Standard (AES) and Rivest Cipher 4 (RC4).The hybrid encryption generates improved ratio of transaction success with respect to malicious ratio indicating the secured data transmission preventing the intrusion of hackers. Simultaneously, the secured data is transmitted at high speed by making use of Pulse Coupled Neural Network (PCNN) along with Remora Optimization algorithm. The inclusion of optimization for the effective selection of the hyperparameters of neural network ensures the generation of global optimal solution which in turn enables the efficient identification of shortest path. The hardware in this system uses Arduino controller and the proposed approach aids in efficient monitoring of smart grid parameters facilitating uninterrupted power availability.

A Cross-Domain Optimization Framework of PMU and Communication Placement for Multidomain Resiliency and Cost Reduction

Phasor measurement units (PMUs) play a crucial role in real-time monitoring and control of power grids. They rely on a communication network to transfer measurement data to the phasor data concentrator (PDC) for further processing and analysis. In this paper, a resilient cross-domain PMU and communication link placement method for minimizing the overall installation cost of the wide-area measurement system (WAMS) is proposed. The main idea is to break down the barrier between the power grid domain and the communication domain, and consider the impact of one when design the other. The PMU placement in the power grid domain takes into account the cost of communication links by generating multiple solutions with equally minimum PMU costs for communication link placement evaluation. On the other hand, the communication link placement problem reduces the cost by customizing the routing policies based on the different roles of PMUs in grid observability. The proposed WAMS design is capable of withstanding any single component failure in the power domain (PMU failure or power branch failure) or in the communication domain (communication link failure or PDC failure). Numerical study on the IEEE 57-bus system reveals that the developed cross-domain optimization framework can significantly reduce the overall installation cost of WAMS while attaining multi-domain resiliency.

Grid‐aware provision and activation of fast and slow power flexibilities from distributed resources in low and medium voltage grids

AbstractThe intermittencies of renewable generations give rise to situations, which require both slow‐ and fast‐ramping flexibility capability from a variety of resources connected at the medium voltage (MV) andlow voltage (LV) distribution grids. Moreover, this may increase the grid reinforcement costs. To defer this reinforcement, the grid needs to be operated optimally. This paper proposes—(a) such an optimal operational methodology forthe MV and LV grids; and (b) an aggregated flexibility estimation methodology separately for fast and slow services at the primary substation (transmission interface). The methodologies based on model‐based MV grids and sensitivity coefficients‐based LV grids are suitable for LV grids where an up‐to‐date, accurate model and topology are not always available. Besides, the costs and resources associated to create full LV grid model and visibility should be prevented. The approaches, here, use the synchronised and accurate measurements from grid monitoring devices located at the LV distribution grids. They have been validated on a real MV and LV networks of a Swiss distribution grid operator equipped with such devices. The results, in terms of reducing technical losses, reducing grid violation costs,and estimating flexibility capability show the efficacy of the proposed methodologies and therefore, can be easily deployed.