Search

This paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning. [ABSTRACT FROM AUTHOR]

The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the fault current contributions of small‐scale single‐phase photovoltaic inverters under grid‐connected operation and their potential impact on the protection of distribution systems. To conduct this analysis, an autotransformer‐based voltage dip generator is proposed as a means to test the photovoltaic inverters' contribution to short‐circuit currents. Laboratory tests are then performed to obtain the short‐circuit current contribution of eight single‐phase photovoltaic inverters. Using the short‐circuit current data obtained, a behaviour model is developed and simulated on Matlab‐Simulink. The model is then applied to a real distribution system and case studies are conducted to simulate the potential impacts on the protection system. Results indicate that while the massive penetration of small‐scale single‐phase photovoltaic inverters can alter the protection system's operating time, the impacts are not significant. Only in very specific scenarios, such as events related to high impedance faults, some impact can be observed. In these situations, the presence of photovoltaic inverters further complicates the already difficult task of identifying high impedance faults through conventional overcurrent protections. This study provides valuable insights into the integration of photovoltaic inverters into distribution systems, and can aid in the development of effective protection measures for future grid designs. [ABSTRACT FROM AUTHOR]

This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid‐supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent‐based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single‐area and multi‐area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation. [ABSTRACT FROM AUTHOR]

This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here. [ABSTRACT FROM AUTHOR]

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system. [ABSTRACT FROM AUTHOR]

The restoration control of the modern alternating current–direct current (AC–DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line‐commutated converter high‐voltage direct current (LCC‐HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC‐HVDC is first analysed from the aspects of start‐up and operation of LCC‐HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC‐HVDC is derived in detail based on the system strength indices of the short‐circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non‐tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39‐bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC–DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC–DC power grid. [ABSTRACT FROM AUTHOR]

The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy. [ABSTRACT FROM AUTHOR]

The commutation failure is the most prevalent fault in line‐commutated converter based high‐voltage direct current systems, which may result in transient overvoltage on the sending‐side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large‐scale wind turbines and implement effective stability control measures. In this paper, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented firstly, along with an analytical expression for the peak value of transient overvoltage. Secondly, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the simulation system of Northwest China local power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to improve the ability of the modular multi‐level converter to quickly clear and isolate faults in the multi‐terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi‐terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid Dynamic Time Warping (DTW) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. Compared with other similarity methods, this method combines Euclidean DTW distance and entropy method to process the signal, realizes the fault pole identification, and can still operate correctly under the interference of 500 Ω transition resistance and 20 dB noise ratio. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid. [ABSTRACT FROM AUTHOR]

With the emergence of renewable energy sources (RESs), the power grid all over the world is going through a paradigm shift. The inverter based RESs are replacing the conventional rotating synchronous generators and this trend is expected to continue in coming years. Consequently, the grid strength is decreasing, which can pose significant challenges on the grid stability, especially during integration of IRESs. This paper presents a thorough analysis on the integration challenges of IRESs in weak grids. The paper also presents a comprehensive investigation on the impacts of factors, such as available fault level at the point of interconnection (POI), feeder length and grid nature on the POI voltage, and maximum allowable power injection by IRES. This is followed by maximum allowable IRES power injection sensitivity analysis to quantify the impacts of the aforementioned factors. The paper concludes with a thorough case study on the IEEE 39‐bus test system to numerically show the impact of diminishing grid strength on the integration of IRESs. [ABSTRACT FROM AUTHOR]

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment. [ABSTRACT FROM AUTHOR]

The occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Cascaded H‐bridge rectifier (CHBR) is widely used in high‐power fields such as STATCOM and power electric traction transformer (PETT) due to its high efficiency and easy expandability. With the high‐reliability operation requirements of power equipment, the fault‐tolerance capability has gradually become a research hotspot nowadays. In order to improve the reliability of the power supply, cascaded H‐bridge rectifier should restore as soon as possible after a fault. However, the traditional fault‐tolerant strategies have a great effect on the grid side and a long recovery time. This paper proposed a quasi‐hot standby fault‐tolerant control strategy. Compared with the traditional strategies, the quasi‐hot standby fault‐tolerant control strategy has a faster recovery speed after the malfunction, and its transient recovery process has less impact on the power grid. Simulation and experiment results show the effectiveness and good recovery performance of the proposed fault‐tolerant control strategy. [ABSTRACT FROM AUTHOR]

Multi‐terminal flexible DC power grid plays an important role in large‐scale new energy integration, which usually requires the installation of multi‐terminal DC circuit breaker (MTCB) at the intersection of multiple DC lines. At present, the research on multi‐terminal DC circuit breaker is mostly limited to theoretical simulation calculation, and there are few experimental studies and engineering applications. In this paper, a four‐terminal DC circuit breaker based on commutating path multiplexing is proposed, which combines with the compact natural commutation scheme of hybrid gap, greatly reducing the cost and volume of the circuit breaker. The commutation characteristics of the four‐terminal hybrid DC circuit breaker equivalent prototype at 10‐kV voltage level were tested. On this basis, the equivalent prototype of the circuit breaker test results shows that under 10‐kV voltage level, the four‐terminal circuit breaker topology can achieve breaking of 11 kA in 2.8 ms by the natural commutation method. [ABSTRACT FROM AUTHOR]

The UK net‐zero target requires the integration of significant renewable energy resources into the electrical grid. This, together with the projected growth in electricity demand imposes practical challenges on the power transfer capability of existing transmission feeders. For environmental and planning reasons, construction of new overhead lines is problematic, and where possible upgrading of existing substations and lines is preferred. This paper investigates the feasibility of transforming an existing three‐phase (3Φ) double‐circuit 400 kV line into a six‐phase (6Φ) feeder for a simulated future GB scenario. Results indicate a 6Φ feeder, operating at a 'phase‐adjacent phase' voltage of 400 kV can effectively solve the constraints of boundaries in transmission networks. When considering the various operating contingencies, associated with an important UK network boundary and its expected near‐future winter‐peak power transfer requirements, the thermal overload issues are mitigated by transforming a feeder from 3Φ to 6Φ. The paper demonstrates upgrading to 6Φ technology is a potential solution to certain future boundary constraints and in these cases avoids the need to build new underground or overhead AC/DC lines. [ABSTRACT FROM AUTHOR]

Economic operation and reliable supply‐demand balance are problems of paramount importance in power grids with a massive share of intermittent renewable energy sources (RESs) of great interest. This paper sought an optimal coordinated generation scheduling for day‐ahead power system operation considering RESs and energy storage units. Renewable power generation, particularly, wind and photovoltaic are uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, a hyperparameter‐optimized long short‐term memory (LSTM) regression model is employed to forecast the day‐ahead weather from the historical time‐series weather data. Eventually, an empirical formula is used to estimate the power conversion from the day‐ahead weather forecasts for a selected PV module and wind turbine. The objective of the scheduling framework is to keep a delicate supply‐demand balance at the lowest possible cost of generation while maintaining the prevailing generation and system constraints. A variance measure uncertainty handling‐based grey wolf optimizer (GWO) technique is used to find the optimal day‐ahead generation schedules and dispatches under RESs forecast uncertainty. The proposed generation scheduling framework is examined on the IEEE 6 and 30‐bus systems. In the studied scenarios, the coordinated operation of generators can decrease the total day‐ahead operating cost for the modified IEEE 6‐bus system by 2.57% compared to supplying electricity generation with conventional generators alone. Likewise, the total operating cost from the coordinated operation of all generation portfolios was reduced by 6.93% from the operating cost of generation during base case simulation (supply only from dispatchable thermal units) on the modified IEEE 30‐bus system. Moreover, the case studies show that coordinated generation scheduling can mitigate the RESs power variability problem, provide secure supply‐demand operation, and minimize the operating cost of electricity generation. [ABSTRACT FROM AUTHOR]

Modern power grid is evolving towards carbon neutrality by deploying increasing amount of renewable energy resources. However, the impact of renewable generation on power system planning and operation is not sufficiently investigated, especially the capability of renewable penetrated power systems to resist and recover from major disturbances, which is a critical concern for system operators. Novel metrics and evaluation methodologies are needed to depict systems' ability in response to events caused by natural disasters, and quantitatively evaluate system performance in various time scales. In this paper, operational resilience metrics are proposed for power systems with penetration of renewable energy resources based on transient stability principles. A systematic methodology is proposed to quantitatively assess the evolution of system performance during various stages of the disaster process. Based on the proposed metrics, a resilience‐oriented disaster management strategy is designed and validated using the modified IEEE 39‐bus test system. The simulation results demonstrate the validity of the proposed metrics and strategy, and show that the system resilience is enhanced during the mitigation of fault conditions. [ABSTRACT FROM AUTHOR]

With the fast development of cyber‐physical power systems, coordinated cyber‐physical attacks have become one of the main threats to the security of modern power grid. Considering that it is difficult for attackers to obtain full information of the entire power grid, the coordinated cyber‐physical attack problem for partitioned power grid is studied in this paper, and a tri‐level defender‐attacker‐dispatcher optimization model with consideration of wind power uncertainty in a specific coordinated cyber‐physical attack mode is proposed. The Karush‐Kuhn‐Tucker (KKT) condition and an improved implicit enumeration algorithm are applied to obtain the solution of the complicated optimization problem effectively. In addition, two risk assessment indices pertinent to the physical lines and load measurements are proposed according to the obtained total amount of optimized load shedding and coordinated attack‐defense strategy under valid attack‐defense scenarios. Taking a partitioned IEEE 39‐bus test system as example, the risks of physical transmission lines and load measurements in different areas under attacks are elaborately studied, and the impact of wind power uncertainty on attack‐defense strategies is discussed as well. Numerical simulation results illustrate the effectiveness of the proposed optimization model and risk assessment indices, and some significant conclusions are drawn. [ABSTRACT FROM AUTHOR]

The present paper introduces an analytical approach for integrating distributed energy resources (DERs) and battery energy storage systems (BESSs) into power grids. The given method aims to simultaneously identify optimal buses and capacities of DERs and BESSs, with regard to the responsive load demand along with the stochastic nature of wind turbine (WT) and photovoltaic (PV) units, and then to minimize energy losses and improve voltage profiles. For this purpose, the responsive load model, the uncertainties in the WT and PV units, and the BESSs are firstly modeled. Secondly, the output curve of BESSs is obtained so that demand and supply can be balanced effectively, so as to utilize the full‐potential output of DERs. Thirdly, a set of formulation is developed for simultaneous integration of the DERs and the BESSs. This formulation reflects on different levels of loads and DERs for each period as well as each segment with their corresponding probability density. Finally, the proposed method is tested on the standard IEEE 33‐bus network. These results verify that sizing and sitting of DERs and BESSs can significantly shape the planning results of DERs and increase their penetration in the power grids at the same time. [ABSTRACT FROM AUTHOR]

Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios. [ABSTRACT FROM AUTHOR]

The transmission line is a fundamental asset in the power grid. The sag condition of the transmission line between two support towers requires accurate real‐time monitoring in order to avoid any health and safety hazards or power failure. In this paper, state‐of‐the‐art methods on transmission line sag monitoring are thoroughly reviewed and compared. Both the direct methods that use the direct video or image of the transmission line and the indirect methods that use the relationships between sag and line parameters are investigated. Sag prediction methods and relevant industry standards are also examined. Based on these investigation and examination, future research challenges are outlined and useful recommendations on the choices of sag monitoring methods in different applications are made. [ABSTRACT FROM AUTHOR]

The fault of the tie line between the photovoltaic (PV) station and the grid is a serious fault for the PV station. It will cause the PV station to operate into an unintentional island. The uncontrolled Island voltage and frequency will inevitably lead to the disconnection of the inverter in the station. This situation will bring great losses to PV operators and impact to the power grid. In order to deal with the tie line fault, this paper analyzes the operation characteristics of PV stations in case of tie line fault firstly. Then a tie line fault ride‐through method based on cooperative strategy of small capacity energy storage (ES), relay protection and PV inverters is proposed. The islanding switching control strategies of PV and ES are designed respectively. The cooperative strategy of protection, PV controller and ES controller is formulated as well. The real‐time digital simulator (RTDS) closed‐loop test platform including line protection device, ES controller and PV controller is built to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Voltage sags are the most frequent and impactful disturbances in industrial power grids, leading to high financial losses for industrial clients. The identification of the cause and its relative location is crucial for the contractual relation between the energy provider and the industrial customers. This paper proposes a methodology to identify the origins of voltage sags based on instantaneous symmetrical components and dynamic time warping. Short‐Time Fourier and Fortescue transform are implemented in the pre‐processing step using the voltage and current waveforms. Then, a distance‐based classification strategy to identify the sources of voltage sags is used. It relies on a four‐dimension time‐series signature used as features. Moreover, a confidence index associated with the classification output is provided. The proposal offers an easy implementation in industrial applications with no previous recorded data. It has the benefit of using a reduced‐size reference database entirely composed of synthetic data. The main advantages of the proposed method are its generalization capabilities and the possibility of raising an alert based on the confidence index. The obtained classification accuracy on synthetic data with seven causes is 100%. The method reaches a classification F1‐score higher than 99% with field measurements representing five classes obtained from three different industrial sites. [ABSTRACT FROM AUTHOR]

In a high proportion of new energy grids or a 100% new energy source of power grid, the dynamic performance of PMSG under fault conditions is very important. However, the current small signal model lacks detailed and deep analysis of a permanent‐magnet synchronous generator (PMSG)‐based wind power generation under LVRT control. Here, a small signal model of a PMSG under pre‐fault, fault and after fault failure recovery stages are established. This paper employs linear analyses to characterize the dynamic performance of a PMSG‐based wind power generation system under LVRT control and practical generator characteristics. In particular, the conflict between frequency regulation and LVRT is discussed based on the constructed model. On this basis, the impact of dc‐link bandwidth on the system performance during failure recovery stage is discussed, and the active damping method is analysed to improve the performance of PMSG during failure and failure recovery stages. Finally, time‐domain simulations are utilized to verify the analyses and models discussed. The research results in this paper are helpful to promote and improve the performance of PMSG during LVRT conditions. [ABSTRACT FROM AUTHOR]

The China Energy Administration has issued policies to encourage energy storage to participate in the electric auxiliary service market, which will provide ideas for electric vehicle charging stations (EVCSs) to collectively participate in peak‐regulation and frequency modulation (FM). Taking into account the benefits of EVs participating in peak‐regulation and FM, this paper studies expansion planning for EVCSs. First, the orderly scheduling strategy of EVs under vehicle‐to‐grid (V2G) technology is established in this paper. And a peak regulation method is proposed to maximize the economic benefits of charging and discharging during peak and valley periods. Second, based on the temporal and spatial prediction model of EV charging demand, the expansion planning model of EVCSs is constructed considering the benefits of peak‐regulation and FM. Aiming at minimizing the comprehensive annual social cost of charging stations, the expansion site selection and capacity scheme of EVCSs are obtained. Finally, taking the 24 nodes in Xiangzhou District of Zhuhai City as an example, the location and capacity of the final annual expansion plan are obtained. And the influence of main control parameters on the results of the whole model and method is analysed in detail, the feasibility and effectiveness of the whole model and method are verified. [ABSTRACT FROM AUTHOR]

The battery swapping mode is a promising way for electric vehicles (EVs) to participate in power grid frequency regulation. However, the operation mechanism and the uncertainty management in the process still lack research. Aiming at this issue, this paper proposes a day‐ahead bidding strategy for the battery swapping and charging system (BSCS) providing regulation reserve capacity in the ancillary service market. Firstly, a new BSCS‐based regulation model is established. Then, an ambiguity set based on Kullback–Leibler (KL) divergence is used to manage the uncertainty of regulation signals and battery swapping demands. Furthermore, a boundary expectation approach is proposed to turn the problem into robust optimization. Finally, after using the extreme scenario method (ESM) to generate extreme scenarios, a duality‐free column‐and‐constraint generation (C&CG) algorithm is employed to solve the problem. The case studies demonstrate that BSCS can provide economical and robust reserve capacity bidding plans in the day‐ahead market with the proposed strategy. At the same time, the BSCS‐based regulation service is simulated with the battery development trend in the future, and suggestions are given. [ABSTRACT FROM AUTHOR]

The power system dynamics have consistently challenged the rapid and persistent proliferation of photovoltaic (PV) systems in power systems. In this paper, the authors propose the generalized discrete‐time equivalent model (GDEM) of PV power generation system using a fourth‐order dynamic equivalent model for representing the physical characteristics of PV power stations in power system dynamic studies. In addition, the authors propose a grid‐connected system model which includes modelling interfaces (MIs) for the GDEM of power grid and the GDEM of PV power generation system in dynamic studies. The proposed equivalent models of the power grid and PV power generation system will improve the simulation accuracy and speed in dynamic power system studies. The authors have used the IEEE 14‐bus system and simulated various types of short‐circuit faults and PV penetration levels in their study to demonstrate the merits of the proposed GDEM of grid‐connected system in power systems. The pertinent simulation results are analyzed and conclusions are presented. [ABSTRACT FROM AUTHOR]

This paper examines how the combined use of hydro governors and a wide‐area damping controller (WADC) can improve the damping of inter‐area oscillations in power systems. Such oscillations can endanger the stability of power grids, thus damping them is a high priority for transmission system operators (TSO). To ensure that low‐frequency oscillations decay as fast as possible, a modal linear quadratic Gaussian (LQG) is proposed as a wide‐area controller approach. Using the 10‐machine New England Test System, the combined application of the WADC in hydropower plants via generator excitation as well as via the hydro governor is examined for their effectiveness in terms of damping capability. In order to minimize the number of essential PMUs in power systems for the model‐based WADC, the residue‐based input/output signal selection method is applied. To increase the robustness against large disturbance faults, the generator voltage as additional signal is utilized for the WADC. For controller performance and robustness, the verification has been done under various operating points, tie‐line failures as well as different PMU delays. The successful damping of the inter‐area oscillation by means of wide‐area controlled hydropower plants has been also demonstrated through a nonlinear time domain simulation. [ABSTRACT FROM AUTHOR]

This paper presents an online protective scheme of estimating critical inter‐area angle for controlling the inter‐area oscillations based on evaluating the rate of changes of rotor angle oscillations as an adaptive tripping index (ATI)‐based protective zone. For this purpose, by using wide area measuring system technology, oscillatory signals Δδ and Δω are measured which based on correlation coefficients criteria, coherent groups are identified which based on evaluating oscillating areas, inter‐area signals ΔδCOI‐AB and ΔωCOI‐AB are provided. In the case of evaluating an inter‐area oscillation, the proposed ATI scheme is activated which the first zero crossing point (ZCP0) of δCOI‐AB is determined as the critical criteria for estimating unstable situation. The proposed ATI approach consists of two adaptive protective zones ATI1 and ATI2 to estimate inter‐area oscillatory and inter‐area transient instabilities, respectively. The proposed ATI is an online and non‐model‐based scheme which evaluates the system dynamic stability through two straight and sinusoidal‐based adaptive zones. The effectiveness of the proposed ATI approach is evaluated on three different test systems IEEE 39‐bus, modified IEEE 39‐bus with converter‐based low inertia sources and a practical Iran nation power grid with presenting positive effects of damping inter‐area oscillations through different operational and topological conditions. [ABSTRACT FROM AUTHOR]

To ensure the stable operation of power systems, critical nodes need to be identified for key protection. The LeaderRank algorithm is a fast and accurate algorithm for identifying key nodes, but it has obvious inappropriateness when targeting the power system. For this reason, this paper considers the scheduling function of the information network and the power flow betweenness of the power grid. A TrendRank algorithm is proposed to identify key nodes in complex power grids. TrendRank values can be computed iteratively by a weighted distribution strategy of internally linked nodes and then ranked. The performance of the TrendRank method has been fully tested and benchmarked on IEEE39 and IEEE118. The comparison of four performance metrics fully validates the effectiveness and superiority of the method. The TrendRank algorithm provides an idea in protecting the power system, which makes the economic cost of protecting the power system lower. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous generators (PMSGs) equipped with back‐to‐back grid‐forming converters have the ability to form a 100% renewable energy power grid. Black‐start is the key capability of grid‐forming converters when restoring the system from a blackout. It is necessary for a grid‐forming converter of the PMSG to operate in black‐start and grid‐connected active support modes. This paper investigates an improved damping adaptive grid‐forming (DA‐GFM) control method for PMSG wind turbines to suppress system frequency fluctuations and achieve a smooth black start. The proposed DA‐GFM control introduces the frequency variations and natural constant function to adaptively adjust its damping coefficient in the dynamic operating progress. The implementations and effects of the proposed DA‐GFM control method on the grid‐forming converter are theoretically evaluated. Additionally, the detailed black‐start operation process is completely studied through a microgrid system with two PMSG wind turbines and energy storages. Compared with the conventional grid‐forming control, the proposed control scheme can not only suppress frequency fluctuations better, but also achieve a smooth forming power grid process. Finally, experiment tests on a controller‐level hardware‐in‐the‐loop simulation platform are carried out to validate the proposed control strategy for achieving a smooth black start. [ABSTRACT FROM AUTHOR]

With the increasing proportion of renewable power generations in the power system, the power grid impedance may fluctuate greatly, and it is difficult for the virtual synchronous generator (VSG) with a single control structure to meet the stability requirements. Thus, the control principles of the two types of VSGs are compared to conclude that there are similarities in the control structure of the two VSGs. And the small signal models of the two types of VSGs are established to analyze the stability boundaries, coming to the conclusion that the stability region is complementary. Based on the conclusions, the paper puts forward the idea of switching operation modes: switch to the U‐VSG mode when the grid strength weakens and switch to the PQ‐VSG mode when greater, so that the inverter can keep stable in a wider range of grid strength. Therefore, the characterization method of the switching boundary with hysteresis properties is proposed. Then the improved recursive least squares (RLS) algorithm is introduced to identify the power grid impedance online without additional injected disturbance, based on which the adaptive multi‐mode smooth switching control strategy is proposed. Finally, the effectiveness of the analysis and the control strategy are verified by simulations. [ABSTRACT FROM AUTHOR]

At present, the MVDC distribution grid is developing rapidly and relay protection is the key technology to ensure stable operation. Therefore, this paper focuses on the fault line selection method. Firstly, the fault characteristics are analysed when a fault occurs in the line, and a novel single‐pole fault line selection method is proposed based on 0‐mode power. The polarity of current fault component measured at the terminal of each line is used to identify the internal and external faults. And the 0‐mode power is used to select the faulty line. Finally, to verify the correctness of the proposed method, a radial MVDC distribution grid is built in PSCAD to output fault data in different fault conditions, and the fault line selection algorithm is implemented in MATLAB. The simulation results show that the proposed method is fast, effective, reliable and it has a certain degree of tolerance to transition resistance with a strong anti‐data delay and anti‐interference ability. Moreover, the fault line selection method proposed in this paper can still correctly select the fault line in the case of serious faults such as multiple line faults, sudden removal of lines, and sudden changes in photovoltaic output power. [ABSTRACT FROM AUTHOR]

Reduced rotational inertia in power grids due to increasing penetration levels of distributed generations (DGs) may lead to degraded performance of the traditional frequency control scheme. In response to this challenge, inertia adequacy has recently emerged as a new research area in modern power systems. Accordingly, an attempt is made in this paper to tackle inertia adequacy in the generation expansion planning (GEP) problem. This in turn helps to realize sustainable economic development. For this purpose, the inertia adequacy constraint is tied to the frequency stability metrics. In other words, minimum permissible inertia that satisfies frequency dynamic standards is defined as an inertia adequacy constraint in the GEP formulation. Further, an experimentally validated model of a cluster of DGs is employed to realize a penetrated power grid to deal with sustainable planning development. Finally, an iterative‐based algorithm is proposed to expand the system with minimum cost and with the highest permissible penetration level of DGs. The effectiveness of the proposed algorithm is examined in the Garver test system. [ABSTRACT FROM AUTHOR]

Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis. [ABSTRACT FROM AUTHOR]

The power outage caused by decay-like fracture of composite insulators significantly impacts the power grid. In this paper, a decay-like fractured insulator of 500 kV transmission line is taken as a research object. The detailed observation of core rod and silicone rubber housings, discharge results, temperature rises, and electric field distribution are obtained. (a) The fractured insulator has abnormal heating on the exposed core rod and sheath surface, and the ambient temperature and humidity significantly influence the temperature rise. (b) It is proposed that one of the most obvious characteristics of decay-like insulator is abnormal temperature and temperature rise (such as T > 50°C, ΔT > 30 K). (c) Under the same conditions, temperature rises, electrical discharge, and electrical field strength of decay-like insulator are higher than those of other insulators, and the discharge at the bare core rod is intense. (d) The electric field strength distribution and discharge distribution of decay-like insulator are different from other aging and new insulators. However, the measurement results are significantly affected by test conditions. Therefore, it is difficult to determine whether the composite insulators are decay-like using electric field distribution and UV measurement on site. [ABSTRACT FROM AUTHOR]

The model of synchronous generator (SG) plays an important role in the simulation of power system and planning of power grid. With the constant parameter practical model (CPPM), it is difficult to consider the time‐varying characteristic of the parameters affected by the complex non‐linear factors, such as the saturation, distortion, skin effect and various spatial harmonics; thus, it cannot guarantee the precision of the simulation. In this paper, a variable parameter practical model (VPPM) of SG is proposed by coupling the CPPM with the three‐dimensional parameter database and the parameter addressing model. The three‐dimensional parameter database containing the steady‐state, transient and sub‐transient reactances and time constants is established with the terminal voltage, active power and reactive power as the variables. The parameters of the VPPM are selected through linear inter‐polation according to the operating states of the generator. In order to verify the precision of the VPPM, a single‐machine infinite system and a real power system with 204 buses and 107 generators are simulated. The result indicates that the VPPM is more accurate than the CPPM, and the stability limit and damping ratio calculated by the VPPM are larger than that by the CPPM. The study can provide important support for the precise simulation of the power system. [ABSTRACT FROM AUTHOR]

The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data‐driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high‐dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi‐level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine‐grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning. [ABSTRACT FROM AUTHOR]

Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework. [ABSTRACT FROM AUTHOR]

Insulator icing endangers the safe operation of overhead power lines. The current measures for image monitoring of icing overhead power lines rely on manual observation. There is an urgent need for image intelligent analysis methods with strong generalisation, accuracy, and efficiency to improve power grid decision‐making. Based on the icing monitoring data from China Southern Power Grid, this paper first proposes rules for insulator image data cleaning, classification, and annotation, establishing a dataset of insulator icing monitoring images during 2014–2018. Then, based on Yolo (You only look once) v5, a weakly supervised and phased transfer learning method is proposed to recognise insulators and ice types such as snow, rime, mixed rime, glaze, and the normal. This method reduces the interference of icing background, uneven illumination, and camera occlusion through multidimensional feature fusion. The precision, recall, and mean average precision of the recognition can reach 86.6%, 91.3%, and 90.1%, respectively, and the recognition speed is as fast as 8 ms/image. Using image pseudo‐labelling based on weakly supervised learning enables the intellectualisation of image annotation, which significantly changes the inefficiency of manual labelling. [ABSTRACT FROM AUTHOR]

The reserve sharing amongst the interconnected power systems will facilitate the improvements on their anti‐disturbance capability and operating economy. Based on the design of reserve sharing mechanism across different areas, a multi‐area generation‐reserve sharing model is constructed. The stochastic fluctuation of load and wind power will cause the power grid to be in an unpredictable random state when the reserve is needed, and the transmission capacity constraint of the power grid may restrict the effective deployment of the reserve. To address these issues, a max‐min bi‐level optimization model is proposed to check the reserve availability. The joint generation and reserve optimization sub‐model, together with the reserve check sub‐model, constitutes the two‐stage robust optimization model in this paper. The column‐and‐constraint generation (C&CG), algorithm is adopted to solve the proposed model. The modified IEEE 118‐node test case is used to verify the proposed model and algorithm. The test results indicate that the proposed method can realize the multi‐area generation‐reserve joint optimization under the condition of guaranteeing the reserve availability, and it possesses the advantage of high efficiency in dealing with stochastic scenarios. [ABSTRACT FROM AUTHOR]

Spacers in the compact power distribution network are essential components for the support, organization, and spacing of conductors. To improve the reliability of these components and have an optimized network design, it is necessary to evaluate the performance of the variation of their geometric parameters. The analysis of these components is fundamental, considering that there are several models available that are validated by the electric power utilities. Due to the various possible design shapes, it is necessary to use an optimized model to reduce the electric potential located in specific sites, improving the reliability in the component, as the higher electrical potential results in a greater chance of failure to occur. The finite element method (FEM) stands out for evaluating the distribution of electrical potential. In this paper, an FEM is used to evaluate variations in vertical and horizontal dimensions in spacers used in the 13.8 kV power grid. The models are analyzed in relation to their behavior regarding the potential distribution on their surface. From the results of these variations, the model is optimized by means of a mixed‐integer linear problem (MILP), replacing the FEM output with a ReLU network substitute model, to obtain a spacer with more efficiency to be used in semi‐insulated distribution networks. [ABSTRACT FROM AUTHOR]

The control of the current injected into the grid with lower harmonics is considered as one of the most important issues for the grid integration of distributed energy resources (DERs). The unbalances and harmonics in the grid voltage usually pollute the current injected into the grid due to the power electronic interfaces, for example, inverters. To address such problems, the present paper proposes a nonlinear higher order sliding mode controller (HOSMC) for grid‐connected three‐phase inverters with LCL$\mathrm{LCL}$ filters in order to control the current injected into grid and improve the power quality. The proposed current controller injects the desired current into the grid with lower values of total harmonic distortions (THDs) under any grid voltage condition as well as it reduces the harmonics in the grid voltage. Apart from these, the proposed scheme is developed to provide robustness against parametric uncertainties where these uncertainties are modeled using the Taylor series expansion method. Finally, the performance of the system is evaluated using processor‐in‐loop (PIL) simulations via MATLAB/Simulink platform through the implementation on a system considering the capacity of the DER as 2 kVA per phase and compared with other existing control strategies. [ABSTRACT FROM AUTHOR]

Efficient bird damage prevention of transmission lines is a long‐term challenge for power grid operation and maintenance. An approach combined lightweight convolutional neural network (CNN), image processing and object detection is presented in this paper to detect typical bird species related to transmission line faults. An image dataset of 20 bird species that threaten transmission line security is constructed. The YOLOv4‐tiny algorithm model is constructed and trained combining stage‐wise training, mosaic data enhancement, cosine annealing, and label smoothing. The mean average precision (mAP) can reach 92.04% on the test set by adjusting the parameters of the training process. Then, the validity of the proposed method is verified according to the test results and performance indexes by comparing with other methods, including Faster RCNN, SSD, YOLOv4 etc. Some image pre‐processing methods such as motion blur, defocus blur, contrast and brightness adjustment are used to simulate the scenarios in practical engineering applications. The proposed method can detect bird species perched around transmission lines with high‐efficiency, which is useful for differential prevention of bird‐related outages of power grids. [ABSTRACT FROM AUTHOR]

Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimisation model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large‐scale, long‐term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernisation of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research‐and‐development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state‐of‐the‐art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions. [ABSTRACT FROM AUTHOR]