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Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems., 9 pages, 6 figures

Multi-view representation learning (MvRL) aims to learn a consensus representation from diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and classification. Due to the limited representative capacity of the adopted shallow models, most existing MvRL methods may yield unsatisfactory results, especially when the labels of data are unavailable. To enjoy the representative capacity of deep learning, this paper proposes a novel multi-view unsupervised representation learning method, termed as Multi-view Laplacian Network (MvLNet), which could be the first deep version of the multi-view spectral representation learning method. Note that, such an attempt is nontrivial because simply combining Laplacian embedding (i.e., spectral representation) with neural networks will lead to trivial solutions. To solve this problem, MvLNet enforces an orthogonal constraint and reformulates it as a layer with the help of Cholesky decomposition. The orthogonal layer is stacked on the embedding network so that a common space could be learned for consensus representation. Compared with numerous recent-proposed approaches, extensive experiments on seven challenging datasets demonstrate the effectiveness of our method in three multi-view tasks including clustering, recognition, and retrieval. The source code could be found at www.pengxi.me .

In this paper, a new method to analyze Boolean functions is proposed. By this method, one can analyze the balancedness, the nonlinearity, and the input-output correlation of vectorial Boolean functions. The basic idea of this method is to compute the refined covers of some parametric Boolean polynomial systems which are equivalent to these problems. By a refined cover, the parameter space is divided into several disjoint components, and on each component, the parametric Boolean polynomial system has a fixed number of solutions. An efficient algorithm based on the characteristic set method to compute refined covers of parametric Boolean polynomial systems is presented. The experimental results about some instances generated from cryptanalysis show that this new method is efficient and can solve some instances which can not be solved in reasonable time by other methods.

Power systems worldwide are evolving toward a new mix of generating resources, transmission networks, and consumption devices, driven by economic development, environmental sustainability, and deep electrification. The resource mix has been changing in recent years with rapidly growing inverterbased resources such as wind, solar generation, and battery storage. The electricity infrastructure is also evolving, being augmented with dc power lines, flexible alternating current transmission systems (FACTS), and solid-state transformers. Electric loads are being transformed by the electrification of end uses such as electric vehicles (EVs) as well as the adoption of new grid interfaces such as variable frequency drives and digital power supplies. All these evolutions have one thing in common-power electronics. They all need power electronics to connect to the rest of the power system (Figure 1). As a result, a high percentage of electricity will be generated, transmitted, and consumed by power electronics in the future power system.

This paper proposes a correlation-aided robust adaptive unscented Kalman filter (UKF) for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKF-UI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that, thanks to the use of hidden system temporal and spatial correlations, the proposed robust AUKF-UI has lower requirement of the number of measurements for dynamic state estimation while achieving better robustness against bad data as compared to the existing UKF methods with unknown inputs.

Economic dispatch (ED) is an important part in the economic operation of power systems. It is an NP-hard problem with multiple practical constraints. This paper proposes a novel approach that combines a swarm intelligence algorithm with a constraint-handling mechanism to solve the ED problem. First, we design a comprehensive learning cuckoo search algorithm with two strengthen strategies. A comprehensive learning strategy is designed to give the algorithm advanced learning ability in high-dimensional and multi-modal environment and thus enhance the search ability. A duplicate elimination strategy is utilized as an elite strategy to improve the evolving efficiency of the algorithm. Then, we propose a constraint-based population generation method named chaos-lambda method to reduce the searching complexity, and a solution repair method to repair unfeasible solutions that violate the constraints. The proposed approach is tested on 5 systems with different benchmarks and compared with the state-of-the-art algorithms. Our approach achieves the best performance on every test.

The objective of traditional phasor measurement unit (PMU) placement approaches is to find the minimal number of PMUs to achieve full system observability. Very few consider the probability and the consequence severity of observability loss. For example, following cascading failures, lines are tripped, and the topology of the grid changes, which may result in observability loss for parts of the grid. With limited number of PMUs, it is desired that the PMUs should still sustain observability for the power grid after top-severe cascading failures, other than after those cascading failures of low severity. To address the above issues related with the consequence severity of observability loss, this study proposes a risk-oriented PMU placement approach. The proposed approach uses two indices, namely the probability of observability loss and the severity of its corresponding consequence to design the optimal PMU placement strategy. The authors show that the proposed approach is able to capture the critical events, while traditional approaches fail to do so. Extensive simulation results carried out on the IEEE 39-bus and 118-bus systems demonstrate the effectiveness of the proposed risk-oriented approach.

In this paper, detectability analysis is studied and extended to guide selection of measurements for the dynamic state estimation (DSE) of a synchronous machine. To make sure that the DSE converges, past studies suggested that enough measurements must be available to make the system observable. In this paper, the convergence condition is relaxed via detectability analysis to reduce the number of required measurements. Because the objective of the DSE is to estimate the current states as time progresses, it is shown that a DSE observer can converge not only for an observable system but also for an unobservable system if the eigenvalues corresponding to the unobservable states are stable. Simulation results using the IEEE 10-machine, 39-bus system show that the unscented-Kalman-filter-based DSE can converge when measurements are chosen such that the unobservable states have stable eigenvalues. In comparison with observability analysis, the proposed application of detectability analysis can reduce the number of measurements required for the existence of a DSE observer.

Script event prediction is a significant task to extract event evolution rules from text. It requires to capture the intra-rationality and inter-relationship of events. The previous methods have made great progress, but they can not fully capture the inner features of events. This paper proposes an event representation layer with argument-level attention to capture the relationship between predicate and arguments, which benefit to assess the rationality of the event. Besides, we integrate the enhanced event representation with the multi-level event similarity scores to obtain more robust performance. The multi-level similarity consists of scene similarity and semantic consistency. We utilize the similarity between candidates and individual events to evaluate the scene similarity. Semantic consistency is evaluated by the hidden state of Gate Recurrent Unit (GRU). Unlike the previous work, we only use the last hidden state of GRU instead of all hidden states to reduce training time. In addition, we adopt adversarial training to improve the robustness and generalization of the model. The experimental results verify the effectiveness of our model compared to these baseline methods. This paper also demonstrates what the attention mechanism has learned to enhance the interpretability of our model.

In real-world applications, it is common that only a portion of data is aligned across views due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called Partially View-aligned Problem (PVP). To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss. In brief, for each sample from one view, our method aims to identify its within-category counterparts from other views, and thus the cross-view correspondence could be established. As the contrastive learning needs data pairs as input, we construct positive pairs using the known correspondences and negative pairs using random sampling. To alleviate or even eliminate the influence of the false negatives caused by random sampling, we propose a noise-robust contrastive loss that could adaptively prevent the false negatives from dominating the network optimization. To the best of our knowledge, this could be the first successful attempt of enabling contrastive learning robust to noisy labels. In fact, this work might remarkably enrich the learning paradigm with noisy labels. More specifically, the traditional noisy labels are defined as incorrect annotations for the supervised tasks such as classification. In contrast, this work proposes that the view correspondence might be false, which is remarkably different from the widely-accepted definition of noisy label. Extensive experiments show the promising performance of our method comparing with 10 state-of-the-art multi-view approaches in the clustering and classification tasks. The code will be publicly released at https://pengxi.me.

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this study proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system show that the proposed approach can improve estimation accuracy and increase robustness against model errors.

The GVW algorithm is an efficient signature-based algorithm for computing Grobner bases. In this paper, the authors consider the implementation of the GVW algorithm by using linear algebra, and speed up GVW via a substituting method. As it is well known that, most of the computing time of a Grobner basis is spent on reductions of polynomials. Thus, linear algebraic techniques, such as matrix operations, have been used extensively to speed up the implementations. Particularly, one-direction (also called signature-safe) reduction is used in signature-based algorithms, because polynomials (or rows in matrices) with larger signatures can only be reduced by polynomials (rows) with smaller signatures. The authors propose a new method to construct sparser matrices for signature-based algorithms via a substituting method. Specifically, instead of only storing the original polynomials in GVW, the authors also record many equivalent but sparser polynomials at the same time. In matrix construction, denser polynomials are substituted by sparser equivalent ones. As the matrices get sparser, they can be eliminated more efficiently. Two specifical algorithms, Block-GVW and LMGVW, are presented, and their combination is the Sub-GVW algorithm. The correctness of the new proposed method is proved, and the experimental results demonstrate the efficiency of this new method.

Combined heat and power economic dispatch (CHPED) problem aims to optimally schedule the output of generating units with minimum fuel cost, which is a highly non-linear, non-convex and large-scale global optimization problem with many practical constraints. The complexity of the problem demands solution methods with powerful search ability, robustness, and computational efficiency. This paper proposes a heterogeneous evolving cuckoo search (HECS) algorithm with a novel constraint-handling mechanism to solve the large-scale CHPED problem considering valve-point effect. Based on the cuckoo search algorithm, we apply a comprehensive learning strategy to enhance the search ability in the high-dimensional environment, and a heterogeneous evolving strategy to improve the robustness of the algorithm. Moreover, we develop a novel constraint-handling mechanism that uses strict mathematical methods to repair unfeasible solutions and avoid redundant calculation. 5 tests are conducted on 24-unit, 48-unit, 84-unit, 96-unit, and 192-unit systems and the results are compared with the state-of-the-art algorithms published in the year 2015-2019. The comparisons show that the HECS could annually save millions of dollars in some large-scale systems, which verify its effectiveness.

As an important metric to tell whether a nonlinear dynamic system has a singular attractor or divergent trajectory, the maximal Lyapunov exponent (MLE) can be calculated from either system models or time series of state variable measurement. However, in the real world, due to inaccurate models, measurement noise, and the fact that sometimes state variables cannot be measured directly, it is very difficult to get an accurate MLE, which limits its application in, for example, in prediction of a nonlinear physical system (e. g. power systems) behavior. To overcome these factors, this paper proposed a trajectory estimation-based MLE calculation approach. The proposed approach addressed how to calculate the MLE when state variables cannot be accessed directly, and uncertainties in system models, as well as noise in measurements. The simulation results show that the proposed approach is able to handle well the nonlinear measurement functions between state variables and measurements, and get better results than pure model-based approaches or measurement-based approaches in front of measurement noise and model uncertainties.

Gaussian phasor measurement unit (PMU) measurement error has been assumed for many power system applications, such as state estimation, oscillatory modes monitoring, voltage stability analysis, to cite a few. This letter proposes a simple yet effective approach to assess this assumption by using the stability property of a probability distribution and the concept of redundant measurement. Extensive results using field PMU data from WECC system reveal that the Gaussian assumption is questionable.

Outlier detection is an important task in many domains and is intensively studied in the past decade. Further, how to explain outliers, i.e., outlier interpretation, is more significant, which can provide valuable insights for analysts to better understand, solve, and prevent these detected outliers. However, only limited studies consider this problem. Most of the existing methods are based on the score-and-search manner. They select a feature subspace as interpretation per queried outlier by estimating outlying scores of the outlier in searched subspaces. Due to the tremendous searching space, they have to utilize pruning strategies and set a maximum subspace length, often resulting in suboptimal interpretation results. Accordingly, this paper proposes a novel Attention-guided Triplet deviation network for Outlier interpretatioN (ATON). Instead of searching a subspace, ATON directly learns an embedding space and learns how to attach attention to each embedding dimension (i.e., capturing the contribution of each dimension to the outlierness of the queried outlier). Specifically, ATON consists of a feature embedding module and a customized self-attention learning module, which are optimized by a triplet deviation-based loss function. We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded. ATON finally distills a subspace of original features from the embedding module and the attention coefficient. With the good generality, ATON can be employed as an additional step of any black-box outlier detector. A comprehensive suite of experiments is conducted to evaluate the effectiveness and efficiency of ATON. The proposed ATON significantly outperforms state-of-the-art competitors on 12 real-world datasets and obtains good scalability w.r.t. both data dimensionality and data size.

This paper proposes a robust adaptive decentralized dynamic state estimation method for power system with unknown inputs of the highly detailed synchronous machine model. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive model. The latter is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. Thanks to the consideration of implicit cross-correlations between the states and the unknown inputs, only generator terminal voltage and current phasors are needed. Test results on a realistic hydro power plant using the field PMU measurements show that the proposed method is able to track both the system dynamic states and unknown controller inputs. These information could significantly benefit the validation and calibration of generator controller parameters.

Understanding the interaction of relays with dynamics of a cascade is critical to the design of resilient power systems. Requirements include models of signal processing devices running complex protection algorithms and models of nonlinear power system dynamics, plus various uncertainties. This paper provides a brief overview of power system dynamics and protection, highlights recent cascading failures, covers existing modeling techniques, reviews typical simulation frameworks, and identifies open questions and gaps for future research and development for dynamics and protection in cascading outages.

Dynamic state estimation (DSE) accurately tracks the dynamics of a power system and provides the evolution of the system state in real-time. This paper focuses on the control and protection applications of DSE, comprehensively presenting different facets of control and protection challenges arising in modern power systems. It is demonstrated how these challenges are effectively addressed with DSE-enabled solutions. As precursors to these solutions, reformulation of DSE considering both synchrophasor and sampled value measurements and comprehensive comparisons of DSE and observers have been presented. The usefulness and necessity of DSE based solutions in ensuring system stability, reliable protection and security, and resilience by revamping of control and protection methods are shown through examples, practical applications, and suggestions for further development., Comment: 10 pages, 10 figures, submitted to IEEE Transactions on Power Systems

This paper develops a robust power system state estimation framework that accounts for correlations and imperfect time synchronization of the measurements. In this framework, correlations of the measurements obtained from the supervisory control and data acquisition (SCADA) system and the phasor measurement units (PMUs) are separately calculated through the unscented transformation and a vector auto-regression (VAR) model. Specifically, the PMU measurements during the waiting period of two successive SCADA measurement scans are buffered via a VAR model whose parameters are robustly estimated using the projection statistics. The latter take into account their temporal and spatial correlations and provide the needed measurement redundancy to suppress bad data and mitigate imperfect time synchronization. In the case where the SCADA and the PMU measurements do not arrive simultaneously at the control center, yielding imperfect measurement time synchronization, either the forecasted PMU measurements or the prior SCADA measurements from the latest state estimation run are leveraged to restore system observability. Finally, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate the measurement error correlations and to handle the outliers, also known as bad data. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.

Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

Every year, transmission congestion costs billions of dollars for electricity customers. This clearly identifies the critical need for more transmission capacity and also poses big challenges for power grid reliability in stressed conditions due to heavy loading and in uncertain situations due to variable renewable resources and responsive smart loads. However, it becomes increasingly difficult to build new transmission lines, which typically involve both economic and environmental constraints. In this paper, advanced computing techniques are developed to enable a non-wire solution that realizes unused transfer capabilities of existing transmission facilities. An integrated software prototype powered by high-performance computing (HPC) is developed to calculate ratings of key transmission paths in real time for relieving transmission congestion and facilitating renewable integration, while complying with the North American Electric Reliability Corporation (NERC) standards on assessing total transfer capabilities. The innovative algorithms include: (1) massive contingency analysis enabled by dynamic load balancing, (2) parallel transient simulation to speed up single dynamic simulation, (3) a non-iterative method for calculating voltage security boundary and (4) an integrated package considering all NERC required limits. This tool has been tested on realistic power system models in the Western Interconnection of North America and demonstrates satisfactory computational speed using parallel computers. Various benefits of real-time path rating are investigated at Bonneville Power Administration using realtime EMS snapshots, demonstrating a significant increase in path limits. These technologies would change the traditional goals of path rating studies, fundamentally transforming how the grid is operated, and maximizing the utilization of national transmission assets, as well as facilitating integration of renewable energy and smart loads.

High voltage direct current (HVDC) transmission lines are being constructed throughout the world, aided by advancements in power electronics and the potential value to transfer power between distant areas and off-shore locations. Multiple HVDC lines within and across large AC interconnections could bring about economic benefits such as interregional capacity exchange and transfer of low-cost, distant electric energy directly to load centers. In addition, network configuration of HVDC lines could result in additional benefits that have not been deeply studied. This paper describes the modeling process for continentallevel power system interconnections with the addition of multiple HVDC lines configured as a macrogrid. The models used for study are based on industry-accepted power-flow and dynamic system models for the North American Eastern and Western Interconnections. The model provides insight on feasibility and initial steady-state and stability tests of the HVDC macrogrid and its interactions with the existing electricity infrastructure, opening the door to analysis of the technical value of such a macrogrid.

Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or offline analysis is not adequate to capture dynamic behaviors of the power system, especially with the new mix of intermittent generation and intelligent consumption, making the power system more dynamic and nonlinear. Real-time dynamic model reduction has emerged to fill this important need. This paper explores using clustering techniques to analyze real-time phasor measurements to identify groups of generators with similar behavior, as well as a representative generator from each group for dynamic model reduction. Two clustering techniques—graph clustering and k-means—are considered. These techniques are compared with a previously developed dynamic model reduction approach using singular value decomposition. Two sample power grid datasets are used to test these different model reduction techniques. Based on the algorithms' relative performance, recommendations are provided for practical use.

Time-domain simulations are heavily used in today's planning and operation practices to assess power system transient stability, posttransient voltage/frequency profiles as well as oscillatory behavior following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is many times slower than real time for state-of-the-art commercial software packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents an innovative parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model library in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored and implemented such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a Western Electricity Coordinating Council (WECC) planning base case with detailed synchronous generator models and it exhibits outstanding scalable performance with sufficient accuracy.

Dynamic simulation for transient stability assessment is one of the most important, but intensive, computational tasks for power system planning and operation. Several commercial software tools provide functionality for performing multiple dynamic simulations such as those in contingency analysis simultaneously on parallel computers. Nevertheless, a single dynamic simulation is still a time consuming process performed sequentially on one single computing core as the tools were originally designed. Modern high performance computing (HPC) holds the promise to accelerate a single dynamic simulation by parallelizing its kernel algorithms without compromising computational accuracy. Parallelizing a single dynamic simulation is a much more challenging problem than the contingency-type parallel computing. It requires a good match between simulation algorithms and computing hardware. This paper provides guidance for such a match so as to design and implement parallel dynamic simulation to maximize the utilization of computing hardware and the performance of the simulation. The guidance is derived through comparative implementation of four parallel dynamic simulation schemes in two state-of-the-art HPC environments: 1) message passing interface and 2) open multi-processing. The scalability and speedup performance of parallelized dynamic simulation are thoroughly studied to determine the impact of simulation algorithms and computing hardware configurations. Several testing cases are presented to illustrate the derived guidance.

The need for accelerating power grid simulation through high performance computing (HPC) has long been recognized, and prior efforts have been devoted to developing one-off parallel computing applications for particular power grid functions. Non-transferable software codes and duplicated implementations in these prior efforts are a major barrier to more widespread HPC adoption in power grid applications. Modern HPC hardware and architecture require significant computing expertise for application development. The GridPACK™ software framework described in this paper provides an HPC-compatible software structure to access modern parallel solvers and HPC-ready modules for common components in power grid simulation applications. GridPACK hides the HPC details and enables power system developers to focus on applications instead of computational details. Several example applications of GridPACK are presented to demonstrate the capabilities of GridPACK and the performance of HPC simulations with large power grid networks. Examples discussed include: a dynamic simulation application capable of running a 17,156-bus Western Electricity Coordinating Council (WECC) system in a computational speed faster than real time (e.g., under 30 s for a 30-s simulation), a static contingency analysis application using a task manager, and a dynamic contingency analysis application utilizing two levels of parallelism. These example applications illustrate GridPACK's capabilities to support different types of simulations within a unified framework and to support reuse of transferable software codes across power grid applications. The computational results indicate strong performance improvements for power grid simulations with GridPACK.

With (enlargement of network operation scale, there is a tremendous growth of information network devices and sharply increasing difficulty in fault handling of operation and maintenance management system (OMMS). This poses a challenge to traditional operation and maintenance methods such as manual logs. Considering unstructured characteristics of artificial log data, this paper adopts convolutional neural network to extract text features from the log data, using random forest to construct classification decision tree to realize automatic decision and classification in fault handling. The operation and maintenance data set of 7 years test results demonstrate that the method can achieve a classification accuracy of no less than 80% on average.

Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii) probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. The two-area four-machine system is used to evaluate the effectiveness of the proposed MMAKF approach. It is shown through the Monte-Carlo method that the estimation accuracy and robustness of the proposed approach is better than those from any individual filtering algorithm.

Multi-view clustering aims to cluster data from diverse sources or domains, which has drawn considerable attention in recent years. In this paper, we propose a novel multi-view clustering method named multi-view spectral clustering network (MvSCN) which could be the first deep version of multi-view spectral clustering to the best of our knowledge. To deeply cluster multi-view data, MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function, where the local invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches. In addition, we enforce and reformulate an orthogonal constraint as a novel layer stacked on an embedding network for two advantages, i.e. jointly optimizing the neural network and performing matrix decomposition and avoiding trivial solutions. Extensive experiments on four challenging datasets demonstrate the effectiveness of our method compared with 10 state-of-the-art approaches in terms of three evaluation metrics.

Accurate system state information under various operation conditions is a prerequisite for power grid monitoring and efficient control. To achieve that goal, a new multi-scale state estimation framework is proposed, paving the way for the development of next generation of energy management system (EMS). The developed framework consists of three key components, namely the static state estimation (SSE) module, the dynamic state estimation (DSE) module, the interfaces and switching logics between the two modules. Specifically, the singular spectrum analysis (SSA)-based change point detection approach is developed to monitor the system continuously. If no event is detected by the SSA, the robust SSE using both SCADA and PMU measurements is executed. Otherwise, the event is declared and the results from SSE are used to derive the initial condition for DSE. During the transient process, only PMU-based DSE is executed for system monitoring and it will be terminated when SSA does not detect any change point of the system. After that, the DSE results are forwarded for SSE initialization and bus voltage magnitude and angle estimations. Simulation results carried out on the IEEE 39-bus system demonstrate the effectiveness and benefits of the proposed framework.

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes., Comment: 12 pages

Long-span spatial steel structures are affected by unfavourable factors during its service life, which can cause damage accumulation, and consequently hazards. The monitoring data of structural response of spatial steel is the basis for understanding structural behaviour and assessing structural safety, but limited number and location of sensors lead to incomplete response acquisition. Because structural members are connected to each other by joints, their structural responses are correlated to a certain degree. Based on this fact, in this paper, a structural response estimation method based on the mapping relationship and a correlation analysis is proposed. The mapping relationship between the measured response and the estimated response is determined by support vector machine with particle swarm optimisation under any load. From this, the method to obtain the optimal mapping relationship is established to realize the response estimation under different loads. In other words, through the correlation analysis of structural response under different loads, the measured point and its corresponding estimated points are determined according to the law of variation of the correlation, and parameters of the optimal mapping model are obtained. Based on the stress and displacement responses of the steel roof above the Shenzhen bay stadium under different wind loads, the effectiveness and feasibility of the proposed method are verified.

This study builds a large database from an extensive survey of existing single-lap shear tests on fiber-reinforced polymer (FRP)-concrete interfacial bonds, comprising 969 test results. Twenty shear-bond strength models published over the past 20 years have been collected and analyzed. These models take into account the effects of the concrete compressive strength, concrete width, FRP elastic modulus, FRP thickness, FRP width and FRP bond length on the ultimate bond strength of the FRP-concrete interface. This paper evaluates the predictive accuracy of the 20 collected models and finds that these models have limited accuracy. To accurately predict the bond strength of the FRP-concrete interface, this paper employs the back propagation neural networks (BPNN) method to train and test the database and builds an artificial neural networks (ANN) model that consists of weighted values, biases and transfer functions. The ANN model test conducts 84 training iterations and selects the optimal combination of input nodes. The accuracy of the developed ANN model is higher (i.e., lower predictive error) than that of the existing bond strength models in the literature. Furthermore, this paper develops an explicit user-friendly formula based on the trained ANN model. The proposed formula estimates and validates the 969 bond strength results, and the predictions using the explicit equation fit the test data very well with small error. As such, the formula can be easily applied during practical designs instead of the implicit processes in the ANN model.

Dynamic security assessment (DSA) is a critical function to evaluate power grids' capability to survive the transition caused by a set of disturbances to an acceptable steady-state condition. Its computational burden is heavy. With the challenges brought by renewable energy and new smart grid technologies, DSA under uncertainty has to be considered to study the impact of forecast errors on DSA simulation, which further increases the computational burden. To address this challenge, this paper presents a high performance computational framework to support DSA simulation user uncertainty. The computational framework provides a seamless work flow that links data from high performance computing, statistical analysis, to visualization so that a problem can be easily expressed in a way compatible with different functions. It also enables software compatibility such that application development can be more efficient. Case study results of the ESCA60 system and a western U.S. system show the advantages and efficiency of the computational framework.

A smart monitoring system based on fiber-optic interferometers using multi-core fiber (MCF) is presented in this paper. Interference among cores in the seven-core fiber (SCF) was introduced by using multi-mode fiber (MMF) coupling and experimental setup was designed to collect the data of user on bed for processing. With optimized algorithm, three body activities, including on the bed, off the bed and body movement, can be identified successfully. For further processing on collected data, respiration of the user can also be obtained. The smart monitor is contactless and non-invasive which is user-friendly and secure.

Solving Boolean polynomial systems as an important aspect of symbolic computation, plays a fundamental role in various real applications. Although there exist many efficient sequential algorithms for solving Boolean polynomial systems, they are inefficient or even unavailable when the problem scale becomes large, due to the computational complexity of the problem and the limited processing capability of a single node. In this paper we propose an efficient parallel characteristic set method called PBCS for solving Boolean polynomial systems under the high-performance computing environment. Specifically, PBCS takes full advantage of the state-of-the-art characteristic set method and achieves load balancing by dynamically reallocating tasks. Moreover, the performance is further improved by optimizing the parameter setting. Extensive experiments are conducted to demonstrate that PBCS is efficient and scalable for solving Boolean equations, especially for the equations rasing from stream ciphers that have block triangular structure. In addition, the algorithm has good scalability and can be extended to the size of thousands CPU cores with a stable speedup.

Massive Open Online Courses (MOOCs), usually developed by some famous universities and well-designed by some experienced instructors, provide excellent online resources to support the self-learning of anybody at anytime and anywhere. In order to identify research trends and investigate useful evaluation and analysis, this paper aims to provide a comprehensive review of MOOCs literature in recent years, including recent studies on completion rate, peer assessments and blended learning. Also, the implication for MOOCs research and practice is proposed in this paper.

Transmission and distribution (T&D) dynamic co-simulation is a practical and effective approach to leverage existing simulation tools for transmission and distribution systems to simulate dynamic stability and performance of T&D systems in a systematic manner. Given that these tools are developed as stand-alone programs and there are inherent differences between them, interface techniques become critical to "bridge" them. Two important unsolved questions are: 1) which interface technique is better and should be used, and 2) how the modeling and simulation capabilities in these tools that are available and can be exploited for co-simulation should be considered when selecting an interface technique. To address these questions, this paper presents a comparative study for different interface techniques that can be employed for T&D dynamic co-simulation. The study provides insights into the pros and cons of each interface technique, and helps researchers make informed decisions on choosing the interface techniques.

This paper describes the GridPACKTM framework, which is designed to help power grid engineers develop software capable of running on high-performance computers. The framework makes extensive use of software templates to provide high-level functionality while still providing flexibility to easily implement a broad range of models and algorithms. GridPACKTM contains modules for setting up distributed power grid networks, supporting application-specific bus and branch models, creating distributed matrices and vectors and using parallel linear and non-linear solvers. It also provides mappers to create matrices and vectors based on properties of the network and functionality to support Input/Output (IO) and to manage errors. The goal of GridPACKTM is to substantially reduce the complexity of writing software for parallel computers while still providing efficient and scalable software solutions. The use of GridPACKTM is illustrated for a simple powerflow example and performance results for the powerflow and dynamic simulations are discussed.

Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods.

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Message Passing Interface (MPI) on distributed-memory clusters. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.

Power system simulation tools are traditionally developed in sequential mode and codes are optimized for single core computing only. However, the increasing complexity in the power grid models requires more intensive computation. The traditional simulation tools will soon not be able to meet the grid operation requirements. Therefore, power system simulation tools need to evolve accordingly to provide faster and better results for grid operations. This paper presents an integrated state estimation and contingency analysis software implementation using high performance computing techniques. The software is able to solve large size state estimation problems within 1 second and achieve a near-linear speedup of 9,800 with 10,000 cores for contingency analysis application. The performance evaluation is presented to show its effectiveness.

Maintaining good quality of power plant stability models is of critical importance to ensure the secure and economic operation and planning of today's power grid with its increasing stochastic and dynamic behavior. According to North American Electric Reliability (NERC) standards, all generators in North America with capacities larger than 10 MVA are required to validate their models every five years. Validation is quite costly and can significantly affect the revenue of generator owners, because the traditional staged testing requires generators to be taken offline. Over the past few years, validating and calibrating parameters using online measurements including phasor measurement units (PMUs) and digital fault recorders (DFRs) has been proven to be a cost-effective approach. In this paper, an innovative open-source tool suite is presented for validating power plant models. The tool suit includes a Power Plant Model Validation (PPMV) tool to check model performance, trajectory sensitivity analysis tool for assessing the effect of parameter variation, and an ensemble Kalman filter (EnKF) based algorithm tool for parameter calibration. In this paper, the architectural design of the tool suite is described; in addition, the detailed procedures to run the tool suite are presented. Results of tests on a realistic hydro power plant using PMU measurements for 12 different events are documented. The calibrated parameters of the generator, exciter, governor and PSS models demonstrate much better performance than the original models for all the events considered and show the robustness of the proposed calibration algorithm.