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For Vehicle Ad-Hoc Network (VANET), the accurate traffic flow information may offer useful guidance to drivers. In the past few years, applications of big data bring about opportunities for intelligent traffic control and management. This paper uses an optimization of support vector regression (SVM) as a way of traffic flow prediction. Particle swarm optimization (PSO) is applied to the parameter optimization of support vector regression (SVR), which improves the performance of the prediction system. The experimental results show that the method has good prediction effect.

Turbine load control instability fault has a great impact on the thermal power unit's primary and secondary frequency modulation performance. A new type of load instability fault was found by checking the actual operation condition of a 660 MW supercritical steam turbine, that is, the actual load rejection amount of the regulating valve is as high as 200 MW under the condition of small action. Through comprehensive analysis, the physical mechanism of the actual fault is found: One of the high-pressure steam control valve servo cards had a problem, which caused the valve to close completely due to abnormal voltage. Feedback monitoring of the valve showed that it was in normal condition, but the valve was found to be completely closed on site. Based on the above fault mechanism, this paper establishes a fault diagnosis model, and realizes the effective identification of such faults based on the fusion of actual running big data. Finally, an effective solution for this fault is proposed, which improves the fast and accurate load-changing capacity of high-power steam turbines, and has certain reference significance for similar units.

The sufficient actual big data analysis shows that wind speed has multi-scale amplitude & frequency modulation characteristics. There is an evident amplitude modulation effect between the average wind speed and the turbulence variance, and the modulation effect is also found with multi-scale characteristic. The turbulence intensity decreases with the wind speed in a certain range. However, when the wind speed is great in a certain degree, the turbulence intensity almost keeps stable. Simultaneously, both the average wind speed and the center frequency also have the multi-scale modulation effect. The center frequency increases with the wind speed in a certain range. However, when the wind speed is great in a certain degree, the center frequency almost keeps stable. The key discussion was given for this multi-scale amplitude and frequency modulation characteristic resulting from the cascade effect of turbulent energy transport. And the instantaneous model of wind speed and wind power is presented for its great significance of power system's real-time dispatch and optimization control.

The performance of the unit is in a state of realtime degradation, and the traditional proprietary test method cannot accurately monitor this performance degradation in time. Aiming at the above problems, a real-time monitoring system and optimization scheme for steam turbine performance based on data driven model is proposed. Firstly, a steam turbine performance prediction model based on pattern recognition and prediction function is established, which can realize medium and long-term prediction of turbine operating economy and provide early warning for performance degradation. Then, performance analysis is performed for units performance degradation, respectively in thermal system and communication. The corresponding optimization scheme is proposed in the flow design. Finally, the 600MW supercritical unit is taken as the research case. The results show that the above method is effective and feasible in practice.

Electricity markets have brought significant benefit to the society. The core of the market clearing system is Security Constrained Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED). This paper first shares the experience from Midcontinent Independent System Operator (MISO) and GE Grid Solution to improve MISO market clearing software performance to meet the needs of market development and business requirement. It then introduces the research and development on next generation market clearing software, including the ARPA-E project to develop high performance computer based optimization engines, to position RTOs/ISOs for future industry evolution.

The impedance matching condition has been a fundamental assumption of many existing studies on voltage stability assessment. In this paper, it has been proven that the Norton current distribution law is equivalent to the impedance matching condition. Based on this law, a new voltage stability index is proposed, which can be further divided into bus voltage stability index and line voltage stability index. These indexes of the focused load bus can be calculated by phasor measurement units data. Then, the voltage stability margin can be assessed by computing the difference between the index value and 1. The effectiveness of the proposed on-line voltage stability index are verified from simulation results perform in a 10-bus system and 39-bus system. Moreover, practical operating constraints such as the generator power limits are taken into account for practical assessment of the proposed method.

This paper proposes a linear ordered successive interference cancellation (OSIC) detection scheme for the oversampled OFDM system to improve the diversity gain and decrease the interference over the doubly selective fading channel. In the proposed scheme, the received OFDM signal is primarily processed by an oversampling module and a FFT module, then the processed data symbols are detected one by one in the order determined by comparing the post-detection signal-to-interference and noise power ratio (SINR) of the symbols, the symbol with the largest SINR is first detected based upon the MMSE and then subtracted from symbol sets, the rest symbols are reconstructed and perform the above operations until all symbols are detected. Simulation results show that the proposed method outperforms the suboptimum Doppler domain block decision feedback scheme by about 2dB for normalized Doppler shift F_d T_o= 0.5, the proposed method is able to effectively reduce the residual interference and error propagation in the low complexity, moreover, it is further suppress ICI and obtain the diversity embedded in fast time-varying channels.

Self-sensing actuation allows combining the actuator and sensor into a single piezoelectric element. In this paper, the Asymmetric Indirect-Driven Self-Sensing Actuation (AIDSSA) circuit for piezoelectric actuators has been proposed. Unlike conventional self-sensing circuits relying on a differential amplifier, the proposed circuit uses active feedback mechanism in op-amps to reject the common-mode interference. The analysis results reveal that the proposed circuit requires simpler matching conditions and is able to sense the mechanical strain in a uniform gain without compromising the actuation stroke. To show the usefulness in piezoelectric-actuated systems, the AIDSSA circuit has been synthesized into a dual-stage hard disk drive. Experimental results show that the position error variance, a measure of disturbance rejection, has been reduced by more than 15% in the track-following mode. The proposed circuit should be of usefulness in a wide range of applications involving piezoelectric actuators.

In recent years, the domestic unit commonly support shaft vibration failure occurred sometimes. Therefore, the stability of the shafting is a key issue concerned with the low load operation and flexible transformation of the turbine. Excessive unit vibration not only caused the movement of the unit, speeding up parts wear, resulting in fatigue damage parts, but also reduce the unit economy, more serious is to damage the unit, causing serious accidents. In order to solve the domestic unit commonly support shaft vibration fault multiple, in this paper, the vibration of the commonly shaft system of the domestic unit is classified, and the distribution ratio of the different types of commonly support shaft faults is calculated, and then, the fault type and its vibration characteristics are analyzed. At the same time, the solution of different types of faults is given with the engineering examples. Finally, the difference of vibration characteristics of commonly support shaft is summarized, and the direction of further research is put forward.

Rotor speed changes with variation of electrical power witch controlled by converter in doubly-fed induction generator(DFIG), compared to the large thermal power unit DFIG is more suitable for adjusting the power fluctuation of fast varying component. In this paper, a primary regulating strategy of DFIG by distinguishing power fluctuating frequency is presented. DFIG is expected to participate in primary frequency regulation as a substitute for thermal unit in fast varying component of frequency fluctuating. Simulink model of a micro power grid with twenty percent wind power penetration is established to verify the advantages of supposed strategy in the end of paper.

MISO faces one of the most challenging Day-ahead Security Constrained Unit Commitment (SCUC) problems in view of its large number of units (including many virtual transactions) and complicated transmission capacity constraints. When combined cycle units with complicated state transitions are present, branch-and-cut shows poor performance in terms of solution quality and solving times. In this paper, a synergistic combination of Surrogate Augmented Lagrangian Relaxation and branch-and-cut is presented. With system coupling constraints relaxed and quadratic penalties on constraint violations added, the augmented Lagrangian is innovatively linearized and decomposed into subproblems to be solved by branch-and-cut. Subproblem solutions are then effectively coordinated based on Surrogate subgradients with much reduced computation requirements and multiplier zigzagging, and accelerated reduction of constraint violations. Numerical testing of a complicated MISO SCUC case demonstrates that our method generates near-optimal solutions in short solving times and significantly outperforms branch-and-cut.

Gas turbine combustion system (GTCS) works in the highly adverse environmental conditions of high temperature and high pressure. Because GTCS initial temperature is too high to be directly measured, exhaust gas temperature (EGT) is an alternative to detect performance of GTCS. However, various interferences, such as working and ambient conditions as well as compressor and combustion efficiencies and so on, have a comprehensive effect on EGT, which causes low detection accuracy to GTCS. So, it's necessary to build a model that can eliminate the interferences on EGT so as to monitor performance of GTCS effectively. Feature extraction and model structure are keys to modeling. In general, it's based on data-driven or mechanism analysis approaches to build the model. However, the former containing a low information density leads to too many input variables and the overcomplicated model structure. In addition, although the latter has a high information density, modeling of a gas turbine is a difficult task in practice, which still limits the application of model-based approaches. For deficiencies of the existing methods, this paper proposes a method based on data and prior knowledge fusion to build a model called Frequent Pattern model for detecting performance of GTCS. Firstly, it's based on prior knowledge to tentatively select the model feature. And it's found that Average Exhaust Gas Temperature (AET) contains all the influences of various factors on EGT. Secondly, it's determined by data analysis that there is a linear relationship between AET and EGT. Therefore, we conclude that a simple linear model can eliminate the interferences on EGT and characterize the structural differences of different flame tubes, by which performance of the GTCS can be monitored accurately and effectively. Thirdly, a contrast test is done to verify the conclusion. A linear model is built based on the proposed method and a nonlinear model is built only based on data-driven method. By comparison, it's concluded that the proposed method can not only simplify the model but also improve the model accuracy.

The performance conditions of steam turbine regenerative system have important influence on the safety and economy of the units. It is of great significance to doing the research on the performance monitoring of the regenerative system to ensure the safe and economical operation of the whole coal-fired power plants. In view of the shortcomings of the complexity of traditional performance monitoring methods, this paper presents a method of performance degradation of steam turbine regenerative system based on Extreme Learning Machine (ELM). The training set is constructed by using the actual running data of steam turbine regenerating system in previous year, besides the input variables and output variables are selected in combination with its operating mechanism analysis to train the ELM model. Then, the model was used to forecast respectively two test set that include previous year's data sample except training set and next year's data sample. The residual line is derived from the discrepancy between the actual values and predicted values of test sets. By comparing the residual line between the previous year's test set and next year's test set, we can see that the heat transfer performance of regenerators has deteriorated. Compared with the actual performance of regenerators, it is verified that the model can effectively monitor the performance of regenerators. In addition, some comparison experiments between ELM, Artificial Neural Networks (ANN) and Elman Regression Neural Networks (ERNN) are taken to compare the performance of each algorithms above mentioned. Through these comparative tests, it is shown that different methods have a certain effect on the performance degradation monitoring of regenerators, but the ELM algorithm is better sensitive and much faster execution than ANN and ERNN.

Due to the investment of new energy such as wind power, the units' regulation system control ability face severe test. In some special load range, the load will be instable, which even results load oscillation fault phenomenon under sequential valve mode. The current studies usually consider that unreasonable overlap settings cause the load oscillation fault. However, the research of the practical case found that this understanding has a certain one-sidedness. In this paper, a practical load oscillation problem of large turbine unit is firstly proposed and then fault is analyzed in theory. On the basis of practical unit's routine data, the model of unit is constructed, by which simulation results are given. The simulation reoccurring the load oscillation fault proves the correctness of the model and the fault mechanism analysis. Therefore, it's proved that the linearity of flow characteristic curve is the core issue of the load oscillation fault. Finally, the optimization of the practical unit is proposed and applied in turbine controlling system. And the long practical check operation effect obviously indicated that the fault is completely eliminated. It's of great practical value in engineering application for safe and stable operation of large turbine participating in one and two power grid frequency modulation.

This paper proposes an advanced technique for Head-on-Media (HOM) detection with PZT self-sensing actuators. Unlike conventional diagnosis approach using X-ray scanning, the proposed method detects HOM events before the drive spins up. Spin-up retries are avoided so that the drives are preserved for failure analysis or data recovery. Firstly, the analytical model of PZT self-sensing actuators is derived to relate the sensing signal to head motion caused by PZT deformation. Next, the technique of Enhanced Indirect-Driven Self-Sensing Actuation (EIDSSA) is applied to retrieve the self-sensing signal with a small probing current (3 mA) injected into VCM driver. Experimental results show that the proposed technique is effective and consistent to detect HOM events without manual involvement.

In recent years, with the economic development of western China, the influence of Xinjiang CPI on national CPI is increasingly becoming great, so accurately predicting Xinjiang CPI has become very significant for the whole country. Considering this, this paper tentatively uses public attention based on Baidu search index to predict Xinjiang CPI. After clarifying the connections among public attention, Baidu search index, Xinjiang CPI from the theoretical aspect, the Baidu search index obtained from Baidu keywords automate recommendation function was synthesized to well measure public attention by stepwise synthetic method. Then this paper empirical analyzes the relationship between Xinjiang CPI and public attention based on Baidu search synthesis index. From the empirical result, the predicted average absolute error of the prediction model is 4.65%, meaning that public attention could well predict the fluctuation of Xinjiang CPI.

Extensive effort has been dedicated to the research and development of Ambient Assisted Living technologies. Specifically, sensor technologies are widely perceived as offering a foundation that enables behavior monitoring, and the categorization of Activities of Daily Living. Meanwhile, in the outdoors, GPS tracking has been harnessed in multiple health related applications. However, research in both domains - indoor and outdoor, tends to be insular. Thus, a critical need exists for a unified approach, both from a technological infrastructure perspective, and in terms of behavior classifier models that encompass indoor and outdoor activities. This paper considers the design of a test bed to enable such an integrated approach.

In the real time operation of a Regional Transmission Organization (RTO), the generation resource commitment plans are constantly revised for the look-ahead time horizon due to changes in system operating conditions. Optimization based resource commitment algorithms can most effectively help to minimize the costs of committing new resources. However, most optimization algorithms lack transparency on how commitment decisions are made internally. Such transparency is necessary for the RTO operators to understand and approve the commitment recommendations. This study provides an effective method for identifying the reliability and economic justifications behind the new resources recommended for commitment by the optimization algorithm. The method has been implemented for the RTO, and has been validated by real-time operational experience.

Acceleration of Audio Video coding Standard (AVS) Jizhun profile decoder has been proposed for ARM Cortex-A with NEON. Data level parallelism is utilized to effectively use the SIMD capability of NEON. Key modules are redesigned to make them SIMD friendly. Our optimized C implementation is set as start point for the acceleration. Thanks to effective use of ARM Cortex-Ax architecture and NEON SIMD engine, AVS decoding has been accelerated considerably. Results are provided for the overall acceleration in decoding and individual acceleration for key modules of AVS decoder.

MISO is in the process of implementing Look Ahead Commitment (LAC) to bridge the gap between Reliability Assessment Commitment (RAC) and Real Time Security Constrained Economic Dispatch (RT-SCED) timeframes. This paper introduces two models in LAC pre-processing logic. The first one is anticipatory startup and shutdown model to account for resource megawatt output during resource startup and shutdown. The second one is an infeasibility identification model to identify data conflicts between state estimation, unit commitment plan and offered parameters. The goal of these two modules is to properly estimate non-dispatchable resource outputs and resolve infeasibility from input data so that LAC can achieve good Mixed Integer Programming (MIP) solutions.

Summary form only given. As a Regional Transmission Organization (RTO), Southwest Power Pool (SPP) is mandated by the Federal Energy Regulatory Commission (FERC) to ensure reliable supplies of power, adequate transmission infrastructure, and competitive wholesale prices of electricity. In order to further improve system reliability and competitive wholesale electricity prices, SPP is moving from its current Energy Imbalance Service (EIS) Market with multiple Balancing Authorities (BA's) to a consolidated BA and an Integrated Marketplace, which includes Energy and Operating Reserve Markets and Transmission Congestion Rights Markets. This paper discusses how system reliability is considered in Day-ahead (DA) to Real-time (RT) business processes of SPP Integrated Marketplace, from both market design and market management system implementation perspectives. These business processes include: (1) Day-Before-Day-Ahead (DBDA) Reliability Unit Commitment (RUC) (2) Reserve Requirements Calculation (3) DA Market (4) DA RUC (5) Intra-Day RUC (6) Real-Time Balancing Market The ultimate goal of SPP Integrated Marketplace is to find the most economic solution to ensure system reliability, which is modeled as resource capacity adequacy and transmission security constraints in market clearing. Similar to other LMP based wholesale electricity markets, SPP adopts Security Constrained Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED) in its new market design, which take into account N-1 grid contingency constraints in linear form for congestion management. In MMS design, Simultaneous Feasibility Test (SFT) is performed to capture transmission security constraints in DA market processes, while Real-time Contingency Analysis (RTCA) of Energy Management System provides real-time security constraints for RTBM clearing and pricing. Market power mitigation is conducted for each market clearing process to prevent from market participants abusing market power when committed or dispatched out-of-merit due to reliability reasons. Resource offer cap decision is made via market behavior test, market structure test, and market impact test that are conducted in DA market, RUC and RTBM. One unique process designed for reliability in the Integrated Marketplace is the Reserve Zone Requirement Calculation. SPP establishes Reserve Zones to ensure the deliverability of cleared Operating Reserve throughout the SPP Balancing Authority Area (BAA). SPP calculates the amount of Operating Reserve required for the Operating Day, on both a system-wide basis and a Reserve Zone basis, to comply with the reliability requirements specified in the SPP Criteria. SPP calculates the hourly Regulation-Up, Regulation-Down and Contingency Reserve requirements on an SPP BAA basis and calculates minimum Operating Reserve requirements and maximum Operating Reserve limitations for each Reserve Zone. Reserve Zone Min and Max are calculated by simulating the outage of the largest generators in each zone and dispatch reserves according to historical reserve deployment patterns. Another feature in the Integrated Marketplace that helps operators to ensure reliable grid operation is the Pre-RTBM process. Pre-RTBM is a multi-interval Look-Ahead (LA) SCED that can provide operators with the recommendations for Out-of-Merit Energy (OOME) to pre-ramp or pre-position resources for future system events, such as load pick-up, wind or interchange fluctuation, etc, in order to avoid potential capacity shortage and reduce the startup of expensive fast-start generators. In summary, SPP Integrated Marketplace design and implementation take system reliability as the first priority. It brings economic benefit to market participants and reduces the “up-lift” payment caused by reliability assurance.

The maturity of pervasive computing and Wireless Sensor Networks (WSNs) enables the development of smart environments in many scenarios, including surveillance and environmental monitoring. Extensive research efforts are being undertaken in sensor perception, data capture, management and interpretation. Such developments are a prerequisite for paradigms such as pervasive sensing and crowd-sourcing services. For mobile users, the issues of dynamic sensor discovery, data interpretation and visualization must be addressed if such services are to be realized in practice. This paper explores the genesis of a generic framework for heterogeneous sensor access and data visualization in remote contexts.

The min-max information (MMI) clustering algorithm was proposed in [8] for robust detection and separation of spherical shells. In current paper, we make efforts to revisit the proposed MMI algorithm theoretically and practically. Firstly, we present the theoretical derivations of the MMI clustering algorithm, i.e., the detailed derivations of the minimization and maximization optimization of the mutual information. Secondly, several insights on the selection of the pruning parameter ¸ are also discussed in this paper.

Creating randomness number mechanism plays an important role in any network security and security information systems from a cryptography viewpoint. In this paper, an exhaustive scheme is proposed to create a series of number sequences between a functional space of logical functions under complementary and permutation operations to show indirectly from the visualization of matrices. Following an example, an exhaustive model has been established to express the functional space of n-ary logical function. A visual structure is represented as a diagrammatic representation for function sequences. The visual results are represented in W, F and C codes respectively, to provide the basic support for the variant logic framework.

A compact flywheel energy storage system assisted by axial-flux partially-self-bearing permanent magnet motor has been proposed by the authors. The proposed machine combines axial magnetic bearing and motoring functionality into a single magnetic actuator, which not only spins the rotor-flywheel but also generates a levitation force to overcome the gravity. The mathematical model of the levitation force and rotational torque are developed. The control system of the position and speed is designed based on PID method. The experimental setup is built and the test results prove the feasibility of the proposed flywheel system concept, the correctness of the mathematical analysis and the validity of the designed control system.

Infeasible, sub-optimal and/or degenerate solutions of the economic dispatch problem cause undesirable effects on the ex-ante and ex-post market price calculations. To determine market prices, this paper explores a scheme that relies on a dual formulation of the economic dispatch problem, stated in terms of prices, i.e. prices become variables. With such a dual perspective, direct control of the prices is achieved, e.g. minimizing the side payments needed to comply with a revenue sufficiency guarantee to market participants. The proposed method combines a primal formulation to find the dispatch (MW), and a dual formulation to determine Locational (nodal) Marginal Prices (LMPs) into a single primal-dual model. Having both, dispatch levels (MW) and nodal prices ($/MWh), as variables, opens possibilities not explored before, e.g. constraints and objective functions that combine both, MW and prices. Computational experiments demonstrate the benefits of the alternative proposal.

The uncertainty of generation required to maintain system balancing has been growing significantly due to the penetration of renewable energy resources such as wind power and the impact of demand response. To deal with such uncertainty, RTO's require not only more accurate demand forecasting for longer-term prediction beyond real-time, but also demand forecasting with confidence intervals. In this paper a new robust dispatch algorithm is proposed to address multiple demand forecast scenarios of different probabilistic uncertainties.

Ambient Assisted Living (AAL) is of particular relevance to those who may suffer from Alzheimer’s Disease or dementia, and, of course, their carers. The slow but progressive nature of the disease, together with its neurological nature, ultimately compromises the behavior and function of people who may be essentially healthy from a physical perspective. An illustration of this is the wandering behavior frequently found in people with dementia. In this paper, a novel AAL solution for caregivers, particularly tailored for Alzheimer’s patients who are in the early stage of the disease and exhibit unpredictable wandering behavior, is briefly described. Salient aspects of a user evaluation are presented, and some issues relevant to the practical design of AAL systems in dementia cases are identified. yes

This paper focuses on a handling system of container port which used the technics of Quay Crane, Container Truck and Yard Crane. Then with the simulation software of ARENA, we build a simulation model for this system, and make an optimization to find the optimal allocation of equipments under the constraints of a certain equipment utilization and a minimum operation time. With the conclusion in our paper which overturns the experience resource utilization value, we can reduce the investment cost by optimizing resource allocation, also under the constraint of the certain equipment utilization.

Spectral data of wood samples gathered by spectrometers are disturbed by a series of noise and interferences, therefore a proper data preprocessing is very important for model establishment and achievement of accurate analytical result. This paper directly applies wavelet transform and first derivative in spectrum preprocessing of Chinese fir. The results show that the combination of first derivative and wavelet transform modulus maximum can eliminate spectral noise and interference but reserve major information. It contributes to increase analysis quality and precision of the near infrared.

In this paper, a screening method called Sequential Bifurcation were introduced and applied. This method plays a very important role in an experiment. This technique is both effective and efficient once the assumption of SB is satisfied; that is, it can find the most important factors in simulation experiment, but it needs relatively few simulation runs. Finding the most important factor can simplify the process of simulation experiment. After using this method in the simulation models of a simple supply chain, it proves that Sequential Bifurcation can correctly and efficiently identify the most important factors in this supply chain.