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The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC). In this paper, a novel data-driven sparse polynomial chaos expansion (DDSPCE) method is proposed for estimating the probabilistic characteristics (e.g., mean, variance, probability distribution) of probabilistic TTC (PTTC). Specifically, the proposed method, requiring no pre-assumed probabilistic distributions of random inputs, exploits data sets directly in estimating the PTTC. Besides, a sparse scheme is integrated to improve the computational efficiency. Numerical studies on the modified IEEE 118-bus system demonstrate that the proposed DDSPCE method can achieve accurate estimation for the probabilistic characteristics of PTTC with a high efficiency. Moreover, numerical results reveal the great significance of incorporating discrete random inputs in PTTC and ATC assessment, which nevertheless was not given sufficient attention., Comment: Accepted by IEEE Transactions on Power Systems for future publication

This paper shows that the August 10, 1996-WECC blackout originated from negative damping arising from sin6 nonlinearity. The proof is based on keeping the nonlinearity in the dynamic equation of a turbine-generator swinging against an infinite bus. Small signal perturbation linearization can predict the occurrence of negative damping. But the linear approach cannot produce the negatively damped waveform of the WECC blackout. Therefore, the graphical Phase Plane method is resorted to. As MatLab, MATHEMATICA, Wolfram MathWorld, etc. offer software plotters, the phase plane method is not difficult to use. Phase portraits show that the “Swing Equation” has stable and unstable regions. The WECC blackout happened because the initial states fell in the unstable region. Although dismissed as being limited to two-dimensions, the paper makes a contribution by showing that phase plane can be used to study the impact of 3 independent controllers. A worked-example shows when the independent controllers (turbine torque, the generator counter-torque and the damping torque) cannot meet the Transient Stability Limit after a line short circuit fault, local blackout occurs. The global WECC blackout is considered to be a scaled up local blackout. Instead of a turbine-generator swinging against an infinite bus, the last stage of the August 10, 1996-WECC blackout is assumed to be the Seattle-area swinging against the California-area. A phase portrait is found in the unstable region whose trajectory unravels in several cycles. Time-domain simulation shows a waveform oscillating with same number of cycles, which qualitatively matches the waveform of the August 10, 1996-WECC blackout.

In this paper, a wide-area measurement system (WAMS)-based method is proposed to estimate the system state matrix for AC system with integrated voltage source converters (VSCs) and identify the electromechanical modes. The proposed method is purely model-free, requiring no knowledge of accurate network topology and system parameters. Numerical studies in the IEEE 68-bus system with integrated VSCs show that the proposed measurementbased method can accurately identify the electromechanical modes and estimate the damping ratios, the mode shapes, and the participation factors. The work may serve as a basis for developing WAMS-based damping control using VSCs in the future., Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

In this paper, the impact of stochastic load and renewable generation uncertainty on the dynamic voltage stability margin is studied. Stochastic trajectories describing the uncertainty of load, wind and solar generation have been incorporated in the power system model as a set of Stochastic Differential-Algebraic Equations (SDAEs). A systematic study of Monte Carlo dynamic simulations on the IEEE 39-Bus system has been conducted to compute the stochastic load margin with all dynamic components active. Numerical results show that the uncertainty of both demand and generation may lead to a decrease on the size of the dynamic voltage stability margin, yet the variability of renewable generators may play a more significant role. Given that the integration of renewable energy will continue growing, it is of paramount importance to apply stochastic and dynamic approaches in the voltage stability study., Comment: Accepted in 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

In this paper, the cascaded H-bridge STATCOM parallel system is taken as the research object to analyze the instability of STATCOM system caused by the interaction between parallel STATCOMs in wind farm afflux station. Firstly, the inherent relation of harmonic circulation and low-frequency disturbance will be revealed by CPS-PWM theory. Then, the output impedance and transfer function of STATCOM give an insight into the interaction between STATCOMs and low-frequency resonance, and the virtual impedance method is proposed to shape the resonant peak. Finally, the correctness of theoretical analysis is verified by real-time simulation.

The detailed physics model of the modular multilevel converters (MMCs) consumes excessive computational resources because of massive submodules (SMs). To deal with this problem, several simplified modeling methods have been proposed. However, the startup process modeling techniques attract little attention. In this paper, two common startup processes of MMC are analyzed, including the ac-side and dc-side pre-charge. Firstly, a charging index is defined to represent the charging state of the arm capacitors. Based on the charging index, the ordinary differential equations of MMC under startup process are derived. Furthermore, an average value model (AVM) of MMC is proposed based on the ordinary differential equations. The proposed model is compared and validated by the IGBT-based MMC model in PSCAD/EMTDC.

In the future, household electricity consumption is used as part of the smart grid. It is of great significance to study the optimal operation of household intelligent electrical equipment. Firstly, the mathematical model of typical household intelligent electrical equipment in the future is established, and then based on the time-of-use electricity price, an optimized operation model of intelligent electrical equipment in the future home with the aim of economic and user comfort is proposed, which is convenient for users to formulate a power consumption plan that meets their own needs. Finally, taking a typical home user as an example, the genetic algorithm is used to optimize the operation of the home intelligent electrical equipment by establishing a typical daily simulation scenario. The simulation results show the effectiveness of the established optimization model.

This paper studies the impacts of stochastic load fluctuations, namely the fluctuation intensity and the changing speed of load power, on the size of the voltage stability margin. To this end, Stochastic Differential-Algebraic Equations (SDAEs) are used to model the stochastic load variation; bifurcation analysis is carried out to explain the influence of stochasticity. Numerical study and Monte Carlo simulations on the IEEE 14-bus system demonstrate that a larger fluctuation intensity or a slower load power changing speed may lead to a smaller voltage stability margin. Particularly, this work may represent the first attempt to reveal the influence of the time evolution property of the driving parameters on the voltage stability margin in power systems.

Power distribution systems require continuous monitoring as the integration of renewable energy resources is increasing and the load demand is growing. The implementation of synchrophasors in distribution systems enhances the situational awareness of the system and provides a unique opportunity for developing new monitoring algorithms. This paper proposes a voltage monitoring algorithm based on the synchrophasor-based linear state estimation method. Particularly, the voltage monitoring algorithm combines a set of early warning indicators and the BDS independence test which can detect the voltage instability in a timely manner while avoiding false alarms when the system is still away from the stability boundary. Numerical study has been conducted in the Quebec test feeder to show the effectiveness of the method.

To enhance the computational efficiency of dynamic stability analysis, the Quasi Steady-State (QSS) model was proposed, yet it was later shown that the QSS model might fail to capture instability of the complete dynamic model. To deal with the issue, a hybrid QSS model has been proposed to ensure both the accuracy and efficiency of dynamic stability analysis. In this paper, we show that the failure of the QSS model provides important insights on designing control such that an intuitive yet effective emergency control is designed and integrated into the hybrid QS model to delay the occurrence of Hopf bifurcation and hence make the system oscillation-free.

Consider of influences of noise in sampling signals comprehensively, a method of fault diagnosis which combines matching pursuit (MP) and biomimetic pattern recognition (BPR) is put forward in this paper. Firstly, the matching pursuit (MP) algorithm is used to select optimum wavelets in different SNR situations from the Laplace wavelet dictionary. Then the feature vector is extracted according to the operation result of waveform MP and super high dimensional detection feature spaces of biomimetic pattern recognition (BPR) are constructed. After that, the real-time detected partial discharge (PD) signals are cut, and the feature for each discharge pulse is extracted respectively, which realized the multiple fault recognition revolutionarily. Simulations show that the robustness and accuracy of fault pattern recognition is improved.

A human face recognition algorithm based on greedy kernel principal component analysis (GKPCA) is presented to meet the requirement of quick face recognition on line. In the algorithm, typical human face are decomposed by fast wavelet transform(FWT), then the greedy algorithm is used to reduce training set and the features of the low frequency sub-images are extracted by kernel principal component analysis(KPCA). Consequently, the features extracted are recognized by support vector machine (SVM). Simulations of the algorithm proposed on the basis of ORL (Olivetti Research Lab) face database and NORL face databases show that the algorithm is capable of reducing training time with high recognition rate.

According to the characteristics of partial discharge (PD) signals of gas insulated switchgear (GIS), a fault type recognition method based on wavelet packet transform is put forward. First the wavelet packet decomposition tree of PD signals is constructed. Then in feature extraction of PD signals, different data fusion algorithms are used in feature vector dimension reduction to enhance sensitivity of feature vector to PD fault signals characteristics. A fault recognizer based on back propagation neural network (BPNN) is designed. The results of simulation show that the wavelet transform based GIS fault signal recognition method is effective.

As a synthetic training procedure and method, science and technology research project can directly affect the training quality of our university students. Always aimed at resolving the real problem, research projects can implement valid summing-up and improvement of the knowledge learned in class. In this paper, we analyzed the phenomenon and status of university students. Then, the requirement and expectation of the students are listed in details. With the actual conditions, we propose some actions and solution such as team work, sharing supervision, strict middle-stage checking and report. We extend the coverage of physical platforms and introduce internet into enhancing the communication between teachers and students. To strength the supervision and guidance, some experienced doctor candidates and master candidates are selected to engage in the guidance of research projects. Rooted in the experience of the practice, all the established rules and guidance are proved feasible and useful through the actual results.

Different from the traditional layer protocols, layering optimization can improve QoS of the whole wireless sensor network (WSN). Therefore, a novel power-aware layering optimization scheme is proposed to balance network lifetime and communication reliability. In the scheme, a directed spanning tree routing algorithm is discussed to resolve unnecessary energy consumption during data transmission. Regarded as a key factor, transmission power of node should be controlled to avoid occurrence of communication contention in some hot sub-area. And a Lagrange dual function is designed and iterated in order to find the optimal transmission power of every sensor node. These cross-layer schemes can lead WSN to get energy efficiency and smooth communication mode. With the simulations, the proposed scheme is evaluated in comparison with other related algorithm from energy consumption of nodes to number of received packets. The results shows that the scheme proposed in this paper have much better performance than others.

The trajectory of moving people is always random and flexible, which requires the motion model should be more complicated than general motion model, and a dynamical model suitable for maneuvering object is introduced in this paper. The dynamic model named maneuvering model is expressed in terms of the intrinsic coordinate frame for the target and is a good example of a nonlinear model, so we apply particle filter which has good performance in target tracking to track mobile people in WSN (wireless sensor network). According to our computer simulation, we consider that our tracking approach using maneuvering model is more robust and performs more consistently than that with general motion model.

In the paper, we will introduce a new approach of pattern recognition theory. In the control of power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be detected quickly and defined exactly. In our researches, global information will be introduced into the backup protection system, basing on linear discriminant analysis of pattern recognition theory, we are going to seek after for the data characteristics of electrical quantities' marked changes by analyzing and computing real-time PMU measurements, so that we carry out fast and exact discrimination of fault components and fault sections.

Nonexpansive mappings and inverse-strongly monotone mappings are two kinds of nonlinear operators, nonlinear functional analysis Operator theory is a very important theory, which has some valuable applications to operations research and economics. It has recently been applied to Engineering, particularly in the application of automatic control. In this paper, we introduce an iterative method for finding a common element of the set of fixed points of a nonexpansive mapping and the set of solutions of the variational inequality problem for an alpha inverse-strongly monotone mapping in Hilbert space. We obtain a strong convergence theorem for a sequence generated by this progress.

Learning is the process of constructing a model from complex world. And machine learning is concerned with constructing computer programs that automatically improve with experience. Machine learning draws on concepts and results from many fields. Obviously, no matter what we adopt new analytical method or technical means, we must have a distinct recognition of system itself and its complexity, and increase continuously analysis, operation and control level. The forecasting analysis of nonlinear complex system will be discussed carefully in this paper. We are mainly using grey forecasting theory to forecast data sequences, and the usual forecast precision has exceeded 90%. In the symbolic forecast of 2-letters nonlinear complex system, the precision of grey forecasting certainly will decrease as the length of symbolic sequence is increasing. But in this place we have found a generating rule that may realize chaotic synchronization at least in short and medium term, and we can analysis and forecast in this way.

Starting with two famous recreational problems, Euler's problem of the Thirty six officers and Kirkman's schoolgirl problem, and stimulated by the needs of computer science, digital communication, modern business management, the subject of design theory has grown into a large branch of combinatorial mathematics. Large set of design is an important topic of design theory, which has some valuable applications to experimental design, coding theory and so on. Though the topic has acquired numerous great advances in recent years, due to its difficulty, the progress is not more than expected. An extended directed triple system is one of oriented designs, It was studied first by Huang and Yaon, By now the question about the large sets of extended directed triple system has not been discussed. In this paper, We give the concept of large sets of extended directed triple system and a recursive construction for large sets of extended directed triple system.

The forecasting using neural networks in unimodal surjective map chaotic dynamic system will be studied carefully in this paper. And most of the forecasting precision has exceeded 90%. Because of the intrinsic property of chaos, the forecasting precision will decrease as the length of symbolic sequence is increasing. But in this place we have found a generating rule that may realize chaotic synchronization at least in short and medium term, and we can analysis and forecast in this way. Nonlinear dynamics maintain manifold links with biologic information system. We also hope to offer an effective prediction method to study certain properties of DNA base sequences, 20 amino acids symbolic sequences of proteid structure, and the time series that can be symbolic in finance market et al.

Wireless communication channel and limited communication resource lead MAC protocol to be one of pivotal issue in wireless sensor networks. Focus on energy efficiency and avoidance of packets collision, a power controlled MAC that is named cross-layer power alternative MAC (CLPA-MAC) is proposed. In CLPA-MAC, nodes send packets with tow different transmission power levels instead of one fixed value. A power alternative scenario is discussed to help nodes choose appropriate power level to send packets. For saving energy, avoiding collision and reducing time delay, routing table based transmission mechanism is proposed. This cross-layer mechanism leads nodes to discern the available data and utilize the channel reasonably. Relatively, the frame of RTS/CTS/DATAACK is designed to adapt the power alternative scenario and cross-layer mechanism. With the simulations, the proposed protocol is evaluated in comparison with S-MAC and PCSMAC from transmission power of nodes to time delay. The performance of CLPA-MAC is verified to be efficient and competent.

This paper proposes an approach called dasiastructure-based spectral clusteringpsila to identify clusters in motion time series for sequential pattern discovery. The proposed approach deploys a dasiastatistical feature-based distance computationpsila for spectral clustering algorithm. Compared to traditional spectral clustering approaches, in which the similarity matrix is constructed from the original data points by applying some similarity functions, the proposed approach builds the matrix based on a finite set of feature vectors. When the proposed approach uses less data points and simpler similarity function to computing the similarity matrix input for spectral clustering, it can improve the computational efficiency in constructing the similarity graph in spectral clustering compared to conventional approach. Promising experimental results with high accuracy on real world data sets demonstrate the capability and effectiveness of the proposed approach for pattern discovery in motion video sequences.

Statistical topic models such as the Latent Dirichlet Al- location (LDA) have emerged as an attractive framework to model, visualize and summarize large document collec- tions in a completely unsupervised fashion. Considering the enormous sizes of the modern electronic document col- lections, it is very important that these models are fast and scalable. In this work, we build parallel implementations of the variational EM algorithm for LDA in a multiproces- sor architecture as well as a distributed setting. Our ex- periments on various sized document collections indicate that while both the implementations achieve speed-ups, the distributed version achieves dramatic improvements in both speed and scalability. We also analyze the costs associated with various stages of the EM algorithm and suggest ways to further improve the performance.

We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. Such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. We demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. The proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. The success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.

Discovering knowledge from video data has recently at- tracted growing interest from vision researchers. In this pa- per, we describe a tensor space representation for analyzing human activity patterns in monocular videos. Given a set of moving silhouettes derived from raw video data, the pro- posed methodology first learns a tensor subspace model to embed the silhouettes into low-dimensional projection tra- jectories with preserved temporal order. Symmetric mean Hausdorff distance is then used to measure dissimilarity be- tween the embedded motion trajectories in the tensor sub- space, as the basis for supervised or unsupervised learn- ing. The experimental results on two recent video data sets have shown that the proposed method can effectively ana- lyze human activities with intra- and inter-person variations on both temporal and spatial scales.

In this paper the typical interference sources for wireless communication in medium voltage (MV) switchgear are first studied and summarized, and the gap breakdown is then chosen as the primary interference source for IEEE 802.11b communication. The immunity tests to the interference of gap breakdown and narrow-band signals are carried out. The results show that both SF6 and vacuum gap breakdown have remarkable impacts on IEEE 802.11b communication. It is found that when the strength of radio signal is strong enough, it will also cause 802.11b working abnormally. According to the test results, the reliability performance of 802.11b devices is assessed preliminary.

With the wide spread use of wireless data transmission technologies in offices and houses, and also due to the flaws of wired communication in LV wiring system in switchgear, the prospect of the application of existing wireless data transmission technologies to replace LV wiring in switchgear is attracting more and more researchers and manufacturers. The cost of using readily available wireless data transmission technologies to replace some or all of the low voltage wiring in switchgear is currently thought to be very low. The wireless communication doesn’t need transmitting media, so it can prevent the damage caused by conducted interference, and will not take up more space when the number of monitoring parameters increase. So it is easier to install and maintain. However, currently very little research has been carried out about its possible application in substation and MV switchgear, where electromagnetic interferences are rather complicated and of uncertain amplitude. There is still a scope to investigate the possibility of using such a low cost wireless data transmission technology in switchgear. One of the remaining challenges is to choose a proper wireless technology and prove its reliability in switchgears. This paper studies the feasibility of applying the Bluetooth and 802.11b wireless data transmission technologies for the purpose of Medium Voltage (MV) switchgear monitoring and control. In particular, a series of experiments are carried out to test the reliability of both Bluetooth and 802.11b devices for applications in switchgear under various interference conditions, and the immunity property of the two candidates is obtained preliminarily from experimental results. All results are presented and discussed in the paper.

The design of a multiprocessor digital signal processing system for applications in power converters such as those that would be used in the next generation of high-voltage DC (HVDC) and static VAr compensator (SVC) systems is described. The system incorporates three high-speed digital signal processors operating independently in parallel, but communicating with one another. The fully programmable system is a powerful device which can implement sophisticated control algorithms in real time for converters. A prototype operating at a clock frequency of 40 MHz has been built for testing and evaluation of control strategies with a laboratory model of an experimental HVDC system based on pulsewidth modulation (PWM). The results from an application demonstrate that the design is well suited for real-time digital control of converters and the system is a useful tool for research and development. >