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The power system blackouts have occurred in recent years all across the world. Therefore, it is necessary to develop more effective methods for preventing such events. In this paper, a novel effective method is proposed to restore the frequency of power system and make the system stable for major disturbances. To do this, for the first time, the adaptive load shedding and emergency demand response algorithms are combined to improve the power quality in addition to system stability. To evaluate the proposed method, the New England 39-bus standard grid is selected and simulated using DIgSILENT software package and its performance is studied for moderate and large power deficit scenarios. The obtained results prove the superiority of the new method in comparison with the pervious methods from points of view lower power interruption and reducing the probability of power system collapse by decreasing the convergence time and minimizing the frequency drop. [ABSTRACT FROM AUTHOR]

The optimal design of Power System Stabilizer (PSS) parameters is a significant optimization problem in power systems. Metaheuristic search (MHS) algorithms are among the most commonly used methods for optimizing PSS parameters. The preferred MHS algorithm must have strong exploration capability and an effective exploitation-exploration balance. The Fitness-Distance Balance (FDB) is a novel method for MHS algorithms, and it is featured by balanced search and effective diversity capabilities. In this paper, the exploration and balanced search capabilities of the Social Network Search (SNS) have been developed by using the FDB method. The FDB method guides the search process in the SNS and enables a more efficient selection of solution candidates in the search space. The performance of FDB-based SNS (FDBSNS) has been tested in the CEC-2014 and CEC-2017 benchmark test suits, and its superiority against SNS has been verified. Moreover, the effect of the FDBSNS was investigated in WSCC 3-machine 9-bus and 10-machine 39-bus New England test systems in the optimal PSS design problem. The obtained results and statistical analyses demonstrated that FDBSNS is a robust MHS algorithm compared to the algorithms in the literature for solving the CEC benchmark suits and optimal PSS design problem. [ABSTRACT FROM AUTHOR]

One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system, and the obtained results indicate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

• We propose a new method for short-term load forecasting (STLF). • The new method is based on artificial neural networks optimized using bat algorithm. • The method has the capability to "shape and fine-tune" the parameters. • We tested the proposed method in Portuguese, New England (USA) and New York city load time-series. • The proposed approach exhibits a better forecasting accuracy. Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more "regularity" to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology. [ABSTRACT FROM AUTHOR]

• A hierarchical framework to accurately detect, classify, and locate the FDEs. • All FDEs (Generator Trip, Line Outage, Load Disconnection, and fault) are evaluated. • A limited number of PMUs are applied to collect few samples of the ROCOF time series. • High-precision RNN and LSTM models are employed online to classify and locate FDEs. Frequency Disturbance Events (FDEs) occur due to various events such as Generator Trip (GT), Line Outage (LO), and Load Disconnection (LD), which affect the stability of power systems. Depending on disturbance severity, it has small to severe effects on an integrated system's performance. Accurate and fast detection and classification of the event and its location are crucial in monitoring resource adequacy, preventing economic losses and blackouts. This paper, focusing on an online cutting-edge hierarchical methodology, first detects the event, then identifies its classification. Eventually, the exact location of FDE is found based on Phasor Measurement Units (PMUs) data. Compared to other counterparts, which focused only on classifying or locating events separately, in this paper, both aims are achieved using the suggested novel hierarchical framework. This research employs Deep Learning (DL) advances to develop a Recurrent Neural Network (RNN) model and a Long Short-Term Memory (LSTM) model to distinguish and locate FDEs with significant accuracy. In the research, only a few time-series of Rate-Of-Change-Of-Frequency (ROCOF) received from a limited number of PMUs is used as the DL algorithm's inputs. This hierarchical methodology is tested in New England 39-bus, IEEE 14 bus systems, and modified IEEE 118-bus system. The evaluation results demonstrate the potential application of the proposed models to detect and classify FDEs compared to conventional algorithms and the frequency-based DL model. The proposed models have achieved significant classification accuracy. Simultaneously, the models locate the events with competitive performance in terms of precision, recall, accuracy, and F1-score. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39-bus New England system. [ABSTRACT FROM AUTHOR]

This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents. Longitudinal observational study. A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities. More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event. Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88–0.91; specificity range = 0.71–0.88). Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population. [ABSTRACT FROM AUTHOR]

This paper proposes an effective computing framework for Short-Term Load Forecasting (STLF). The proposed technique copes with the stochastic variations of the load demand using a stacked generalization approach. This approach combines three models, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Multi-Layer Perceptron (MLP). The inner mechanism of Stacked XGB-LGBM-MLP model consists of generating a meta-data from XGB and LGBM models to compute the final predictions using MLP network. The performance of the proposed Stacked XGB-LGBM-MLP model is validated using two datasets from different locations: Malaysia and New England. The main contributions of this paper are: 1) A novel stacking ensemble-based algorithm is proposed; 2) An effective STLF technique is introduced; 3) A critical multi-study analysis for hyperparameter optimization with five techniques is comprehensively performed; 4) A performance comparative study using two datasets and reference models is conducted. Several case studies have been carried out to prove the performance superiority of the proposed model compared to both existing benchmark techniques and hybrid models. • A novel Stacked XGB-LGBM-MLP model is proposed to improve the overall regression performance. • A novel STLF technique is explored and developed in this study. • A comparative analysis of five hyperparameter optimization algorithms was comprehensively presented for STLF. • An assessment of the proposed technique is conducted using two real datasets. • A comparative study with the recent benchmark techniques is performed. [ABSTRACT FROM AUTHOR]

Background: HMO databases offer an opportunity for community based epidemiologic studies of asthma incidence, etiology and treatment. The incidence of asthma in HMO populations and the utility of HMO data, including use of computerized algorithms and manual review of medical charts for determining etiologic factors has not been fully explored. Methods: We identified adult-onset asthma, using computerized record searches in a New England HMO. Monthly, our software applied exclusion and inclusion criteria to identify an "at-risk" population and "potential cases". Electronic and paper medical records from the past year were then reviewed for each potential case. Persons with other respiratory diseases or insignificant treatment for asthma were excluded. Confirmed adult-onset asthma (AOA) cases were defined as those potential cases with either new-onset asthma or reactivated mild intermittent asthma that had been quiescent for at least one year. We validated the methods by reviewing charts of selected subjects rejected by the algorithm. Results: The algorithm was 93 to 99.3% sensitive and 99.6% specific. Sixty-three percent (n = 469) of potential cases were confirmed as AOA. Two thirds of confirmed cases were women with an average age of 34.8 (SD 11.8), and 45% had no evidence of previous asthma diagnosis. The annualized monthly rate of AOA ranged from 4.1 to 11.4 per 1000 at-risk members. Physicians most commonly attribute asthma to infection (59%) and allergy (14%). New-onset cases were more likely attributed to infection, while reactivated cases were more associated with allergies. Medical charts included a discussion of work exposures in relation to asthma in only 32 (7%) cases. Twenty-three of these (72%) indicated there was an association between asthma and workplace exposures for an overall rate of work-related asthma of 4.9%. Conclusion: Computerized HMO records can be successfully used to identify AOA. Manual review of these records is important to confirm case status and is useful in evaluation of provider consideration of etiologies. We demonstrated that clinicians attribute most AOA to infection and tend to ignore the contribution of environmental and occupational exposures. [ABSTRACT FROM AUTHOR]

In this paper, we consider the frequency synchronization problem in a network of lossless, connected, and network-reduced power system. Frequency synchronization is an important problem in power systems as frequency deviation can lead to degraded power quality, tripping of generators, etc. We present a load-estimator-based consensus algorithm in a network of fourth-order generator models that achieves practical frequency synchronization in the presence of unknown time-varying power demand. We do not assume that the demand is generated by a known model. We follow a multiple time-scale approach for the stability analysis. It is shown that synchronization is achieved for the case of constant power demand, by showing that the system is exponentially stable. For the case of time-varying power demand, we show the error trajectories are ultimately bounded, and the synchronization error is proportional to an upper bound on the derivative of the time-varying power demand. We simulate the controller performance on a four area network which is equivalent to the IEEE New England 39-Bus system. [ABSTRACT FROM AUTHOR]

• An algorithm is designed to perform quantitative analysis for the temporal features. • A model is constructed to obtain optimal observability-constrained feature subsets. • A spatial-temporal ensemble mechanism provides adaptability under missing data. • The method employs spatial-temporal information to reduce effects of PMU loss. Transient stability assessment (TSA) plays an important role in the design and operation of power system. With the widespread deployment of phasor measurement units (PMUs), the machine learning-based method has attracted much attention for its speed and generalization. However, the generalization will deteriorate if some features are missing due to PMU failure. In this paper, a spatial-temporal adaptive TSA method is proposed to handle the missing data issue. By developing an optimal PMU clusters searching model based on temporal feature importance, and by constructing an ensemble mechanism of long short-term memory (LSTM) for the optimal PMU clusters, the spatial-temporal information is utilized adaptively. Therefore, the aim of maintaining the robustness of TSA performance under any possible PMU failure event is achieved. The proposed approach is demonstrated on New England 39-bus power system. Compared with existing methods, the proposed method achieves state-of-art performance in both accuracy and response time under missing data conditions. In addition, the proposed method is more robust in the case of PMU failure than others. [ABSTRACT FROM AUTHOR]

• Static and dynamic performance comparison of synchrophasor estimation algorithms. • Enhancement of Lyapunov exponent approach considering practical scenarios. • Real-time voltage stability assessment with enhanced Lyapunov exponent approach. • Real-time voltage stability evaluation with different synchrophasor estimation algorithms. Power network operators are rapidly embracing the synchrophasor technology for real-time stability assessment due to its superiority over conventional supervisory control and data acquisition (SCADA) systems. Synchrophasor devices (i.e., phasor measurement units (PMUs)) are procured from different vendors to construct the wide-area measurment systems (WAMSs), and hence the PMUs from different vendors may be based on different synchrophasor estimation algorithms (SEAs). Although all synchrophasor devices comply with the IEEE synchrophasor standard, stability assessment using the phasor estimated via different synchrophasor device types may lead to different stability assessment outcomes. This paper investigates the effect of SEAs on real-time voltage stability evaluation. Three prominent synchrophasor algorithms, namely quadrature demodulation (QD), discrete Fourier transform (DFT), and phasor locked loop (PLL) algorithms are considered in this study. Real-time voltage stability was assessed using an enhanced Lyapunov exponent based algorithm. Enhancements to the Lyapunov exponent based real-time voltage stability assessment algorithm are proposed while considering practical implications. The New England (NE) 39 bus system is used as the test system, and PMUs are placed in the test system to achieve optimal observability by using an optimal PMU placement (OPP) algorithm. Simulation results show that each SEA detects the instability condition under different time-frames, while some SEAs could make false alarms under certain dynamic conditions. Therefore, a rigorous benchmark process should be mandated via standards to ensure a consistent stability assessment process from synchrophasor network. [ABSTRACT FROM AUTHOR]

Currently the new state of power system relies on a precise monitoring of electrical quantities such as voltage and current phasors. Occasionally, its operation gets disturbed because of the flicking in load and generation which may result in the interruption of power supply or may cause catastrophic failure. The advanced technology of phasor measurement unit (PMU) is introduced in the late 1990s to measure the behavior of power system more symmetrically, accurately, and precisely. However, the implementation of this device at every busbar in a grid station is not an easy task because of its expensive installation and manufacturing cost. As a result, an optimum placement of PMU is much needed in this case. Therefore, this paper proposes a new symmetry approach of multiple objectives for the optimum placement of PMU problem (OPPP) in order to minimize the installed number of PMUs and maximize the measurement redundancy of the network. To overcome the drawbacks of traditional techniques in the proposed work a reduction and exclusion of pure transit node technique is used in the placement set. In which only the strategic, significant, and the most desirable buses are selected without considering zero injection buses (ZIBs). The fundamental novelty of the proposed work considers most importantly the reduction technique of ZIBs from the optimum PMU locations, as far as the prior approaches concern almost every algorithm have taken ZIBs as their optimal placement sets. Furthermore, a PMUs channel limits and an alternative symmetry location for the PMUs placement are considered when there is an outage or PMUs failure may occur. The performance of the proposed method is verified on different IEEE-standard such as: IEEE-9, IEEE-14, IEEE-24, IEEE-30, IEEE-57, IEEE-118, and a New England-39 bus system. The success of the proposed work was compared with the existing techniques' outcomes from the literature. [ABSTRACT FROM AUTHOR]