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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]

• Self-sensing property of cement-based sensors under flexural load is unsatisfactory. • PVA fibers increase the electrical resistivity of cement-based sensors. • PVA fibers enhance stress–strain sensing of cement-based sensors under flexural load. Cement-based sensors have great potential for structural health monitoring, especially carbon fiber filled functional sensors. In order to improve the sensing efficiency of carbon fiber reinforced cement-based sensors under cyclic flexural loading, polyvinyl alcohol (PVA) fibers were incorporated into cement-based sensors. The PVA fibers, although increasing the electrical resistivity, significantly improved the strain and stress self-sensing properties of carbon fiber reinforced cement-based sensors under cyclic flexural loading. This study provides a new way to accurately measure the parameters of cement-based sensors under cyclic flexural loading. [ABSTRACT FROM AUTHOR]

With the deep coupling between the cyber side and the physical side of power systems, the failure of any link of both sides may lead to power outages, so it is necessary to analyze their vulnerability and vulnerable links for targeted improvement of systems. By dynamically attacking the coupled network nodes, this paper proposes a multilevel model and node-to-edge cyber-physical power system and the corresponding indexes system to analyze the vulnerability of the coupled power grid and its key components. The results showed that in the order of the indexes proposed in this paper, attacking surviving power nodes and cyber nodes results in a network crash rate of 25.0% and 66.7% faster than that in the order of "betweenness" and that attacking surviving cyber nodes results in a network crash rate of 89.4% faster than that in the order of "degree." In terms of attacking power nodes, the index proposed in this paper has the same rate as "degree." Therefore, the proposed model can better describe the vulnerability of the power grid to withstand attacks. [ABSTRACT FROM AUTHOR]

It is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks. [ABSTRACT FROM AUTHOR]

This paper develops a general computationally efficient copula-polynomial chaos expansion (copula-PCE) framework for power system probabilistic power flow that can handle both linear and nonlinear correlations of uncertain power injections, such as wind and PVs. A data-driven copula statistical model is used to capture both nonlinear and linear correlations of uncertain power injections. This allows us to resort to the Rosenblatt transformation to transform correlated variables into independent ones while preserving the dependence structure. It paves the way for leveraging PCE for surrogate modeling and uncertainty quantification of power flow results. To further improve the computational efficiency of the proposed method without sacrificing accuracy, two strategies are developed and unified together. Specifically, the branch flow sensitivity analysis (BFSA) is used to select an appropriate number of outputs that need PCE surrogate modeling while the analysis of variance (ANOVA) scheme enables us to reduce the number of basis functions and coefficients evaluations for each PCE surrogate. Simulations carried out on the IEEE 57-bus and 118-bus and PEGASE 1354-bus systems show that the proposed framework can obtain better accuracy and computational efficiency than the existing PCE-based methods with linear correlation assumption and other Monte Carlo-based methods. The sensitivities to different copula types and parameters are also investigated. • A generalized copula-PCE framework is proposed to handle both nonlinear and linear correlations among uncertain inputs. • To improve the computational efficiency of the proposed framework without loss of accuracy, the branch flow sensitivity analysis (BFSA) and ANOVA method are integrated into a unified manner. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework. [ABSTRACT FROM AUTHOR]

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce drivetrain torsional oscillations of WTGs. This paper addresses this issue and develops a novel nonlinear VIC based on objective holographic feedback theory and the definition of a completely controllable system of Brunovsky type. Simulation results under various scenarios demonstrate that the proposed technique outperforms existing VICs in terms of enhancement of system frequency nadir, suppression of WTG drivetrain torsional oscillations, fast and smooth recovery of WTG rotor speed to the original maximum power point (MPP) before the disturbance as well as preventing secondary frequency dip caused by traditional VIC. The proposed technique is also able to adaptively coordinate multiple WTGs to enhance the frequency support and the dynamic performance of each WTG. [ABSTRACT FROM AUTHOR]

A novel lipolytic gene, estq7, was identified from a fosmid metagenomic library. The recombinant enzyme EstQ7 consists of 370 amino acids with an anticipated molecular mass of 42 kDa. Multiple sequence alignments showed that EstQ7 contained a pentapeptide motif GHSMG, and a putative catalytic triad Ser174–Asp306–His344. Interestingly, EstQ7 was found to have very little similarity to the characterized lipolytic enzymes. Phylogenetic analysis revealed that EstQ7 may be a member of a novel family of lipolytic enzymes. Biochemical characterization of the recombinant enzyme revealed that it constitutes a slightly alkalophilic, moderate thermophilic and highly active carboxylesterase against short-chain fatty acid esters with optimum temperature 50 ℃ and pH 8.2. The Km and kcat values toward p-nitrophenyl acetate were determined to be 0.17 mM and 1910s−1, respectively. Moreover, EstQ7 was demonstrated to have acyltransferase activity by GC–MS analysis. Structural modeling of the three-dimensional structure of this new enzyme showed that it exhibits a typical α/β hydrolase fold, and the catalytic triad residues are spatially close. Molecular docking revealed the interactions between the enzyme and the ligand. The high levels of lipolytic activity of EstQ7, combined with its moderate thermophilic property and acyltransferase activity, render this novel enzyme a promising candidate biocatalyst for food, pharmaceutical and biotechnological applications. [ABSTRACT FROM AUTHOR]

• An analytical relationship between the generator terminal bus voltage and current phasors and the internal rotor speed is derived via the Thevenin equivalent. • A reformulation of inertia estimation is proposed based on a first-principles equation derived from Newton's law applied to rotating masses. Specifically, the measured voltage phasor is the model input, whereas the estimated rotor speed and the measured real and reactive power from PMUs are treated as model outputs.. • The rotor speed estimation in the proposed method is online and model-free, a big difference from existing dynamic state estimation methods using detailed generator models. This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise. [ABSTRACT FROM AUTHOR]

• A comprehensive review of inertia estimation techniques is provided for SGs, including the model-based and measurement-based approaches. • This is the first attempt to propose new potential algorithms for the estimation of the inertia from different VIE-based CIGs according to their various control characteristics. • New insights for future work on quantifying power system inertia considering CIGs and their potential applications are extensively discussed. Understanding and quantifying the inertia of power systems with the integration of converter-interfaced generation (CIG) plays an essential role in the safe transition to a future low-inertia scenario. This paper provides a comprehensive summary of inertia definitions for both synchronous generators and CIGs as well as their corresponding estimation methods. In particular, the estimation methods are categorized as model-based and measurement-based approaches considering both small and large disturbances. The advantages and disadvantages of different methods are carefully discussed. This paper also offers for the first time a framework to quantify the virtual inertia of CIGs at the component and aggregation levels, an open problem in the literature. Finally, future directions for inertia estimation are identified and discussed. This significantly benefits the design of appropriate control and protection schemes in achieving a more reliable, secure, and resilient power system. [ABSTRACT FROM AUTHOR]

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin. [ABSTRACT FROM AUTHOR]