Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

Application of coupled analysis methods for prediction of blast-induced dominant vibration frequency

Blast-induced dominant vibration frequency （DVF） involves a complex, nonlinear and small sample system considering rock properties, blasting parameters and topography. In this study, a combination of grey relational analysis and dimensional analysis procedures for prediction of dominant vibration frequency are presented. Six factors are selected from extensive effect factor sequences based on grey relational analysis, and then a novel blast-induced dominant vibration frequency prediction is obtained by dimensional analysis. In addition, the prediction is simplified by sensitivity analysis with 195 experimental blast records. Validation is carried out for the proposed formula based on the site test database of the first- period blasting excavation in the Guangdong Lufeng Nuclear Power Plant （GLNPP）. The results show the proposed approach has a higher fitting degree and smaller mean error when compared with traditional predictions.

Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle

Genomic selection has been demonstrated as a powerful technology to revolutionize animal breeding. However, marker density and minor allele frequency can affect the predictive ability of genomic estimated breeding values （GEBVs）. To investigate the impact of marker density and minor allele frequency on predictive ability, we estimated GEBVs by constructing the different subsets of single nucleotide polymorphisms （SNPs） based on varying markers densities and minor allele frequency （MAF） for average daily gain （ADG）, live weight （LW） and carcass weight （CW） in 1 059 Chinese Simmental beef cattle. Two strategies were proposed for SNP selection to construct different marker densities： 1） select evenly-spaced SNPs （Strategy 1 ）, and 2） select SNPs with large effects estimated from BayesB （Strategy 2）. Furthermore, predictive ability was assessed in terms of the correlation between predicted genomic values and corrected phenotypes from 10-fold cross-validation. Predictive ability for ADG, LW and CW using autosomal SNPs were 0.13＋0.002, 0.21＋0.003 and 0.25＋0.003, respectively. In our study, the predictive ability increased dramatically as more SNPs were included in analysis until 200K for Strategy 1. Under Strategy 2, we found the predictive ability slightly increased when marker densities increased from 5K to 20K, which indicated the predictive ability of 20K （3% of 770K） SNPs with large effects was equal to the predictive ability of using all SNPs. For different MAF bins, we obtained the highest predictive ability for three traits with MAF bin 0.01-0.1. Our result suggested that designing a low-density chip by selecting low frequency markers with large SNP effects sizes should be helpful for commercial application in Chinese Simmental cattle.

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

A PREDICTION TECHNIQUE FOR DYNAMIC ANALYSIS OF FLAT PLATES IN MID-FREQUENCY RANGE

The wave-based method （WBM） has been applied for the prediction of mid-frequency vibrations of fiat plates. The scaling factors, Gauss point selection rule and truncation rule are introduced to insure the wave model to converge. Numerical results show that the prediction tech- nique based on WBM is with higher accuracy and smaller computational effort than the one on FEM, which implies that this new technique on WBM can be applied to higher-frequency range.

A Spectrum Prediction-Based Frequency Band Pre-Selection over Deteriorating HF Electromagnetic Environment

As the earliest invented and utilized communication approach, shortwave, known as high frequency(HF) communication now experience the deterioration of HF electromagnetic environment. Finding quality frequency in efficient manner becomes one of the key challenges in HF communication. Spectrum prediction infers the future spectrum status from history spectrum data by exploring the inherent correlations and regularities. The investigation of HF electromagnetic environment data reveals the correlations and predictability of HF frequency band in both time and frequency domain. To solve this problem, we develop a Spectrum Prediction-based Frequency Band Pre-selection(SP-FBP) for HF communications. The pre-selection of HF frequency band mainly incorporated in prediction of HF spectrum occupancy and prediction of HF usable frequency, which provide the frequency band ranking of spectrum occupancy and alternative frequency for spectrum sensing, respectively. Performance evaluation via real-world HF spectrum data shows that SP-FBP significantly improves the efficiency of finding quality frequency in HF communications.

Numerical Prediction Methods for Clock Deviation Based on Two-Way Satellite Time and Frequency Transfer Data

三个功能的模特儿，多项式，光谱分析，和修改 AR 当模特儿，基于从双向卫星时间和频率转移导出的数据顺序在恰当、预言的钟偏差被学习并且比较。柔韧的当量被使用，它控制无关的观察的重要影响。一些结论证明柔韧的评价的预言精确比 LS 的好。从弄平的观察计算的预言精确比从采样观察计算了高。作为在钟偏差顺序的明显的时期变化的一个计数，多项式模型的预言的价值是难以置信的。光谱分析模型的预言精确是很低的，但是主要时期能是坚定的。6 小时的推测间隔的预言 RMS 是 1 ns 左右，当修改 AR 模型被使用时。

A STATISTICAL MODEL FOR PREDICTION OF INTENSITY AND FREQUENCY OF TROPICAL CYCLONES MAKING LANDFALL ON CHINA

Based on NCEP/NCAR reanalysis data and Yearbook of China landfalling tropical cyclones(hereafter CLTC) from 1949 to 2008,correlation between CLTC frequency/intensity and 500 hPa height field and sea surface temperature(SST) fields are investigated and employed for TC statistical prediction.A prediction model for yearly and monthly intensity and frequency of CLTC is established with binomial curve fitting by choosing the gridpoints with high correlation coefficients as composite factors.Good performance of the model in experiments shows that the model could be used in routine forecast.

A Disturbance Source Location Method on the Low Frequency Oscillation With Time-varying Steady-state Points

The low frequency oscillation is a serious threat to security and stability of a power grid.How to locate the disturbance source accurately is an important issue to low frequency oscillation disposal.Existing methods have poor adaptability to the low frequency oscillation with time-varying steady-state points because of the limitations in the location criterion derivation.A disturbance source location method on a low frequency oscillation with good generality is presented in the paper.Firstly,the reasons why the steady-state points are time-varying on a low frequency oscillation are analyzed.Then,based on the energy function construction form,the branch transmission energy is decomposed into state energy,reciprocating energy and dissipation energy by mathematical derivation.The flow direction of the dissipation energy shows the source and destination of the disturbance energy,and the specific location of a disturbance source can be identified according to its flow direction.Meanwhile,to meet the needs of energy calculation,a recognition method on the electrical quantities steady-state points is also presented by using the cubic spline interpolation.Simulation results show the correctness of the derivation and analysis on energy structure in the paper,and the disturbance source can be located accurately according to the dissipation energy.

TIME-FREQUENCY 2-D LMS BASED LONG-RANGE CHANNEL PREDICTION FOR WIRELESS OFDM SYSTEMS

Adaptive modulation can optimize the spectrum efficiency and system performance with the channel state information achieved by the long-range channel prediction.To avoid re-estimating channel correlation function as the channel statioharity varies and to track the channel adaptively, LMS (Least-Mean-Square) based long-range channel prediction is discussed in the existing literature, but it needs long observation interval to reach the convergence.Given that all OFDM (Orthogonal Frequency Division Multiplexing) subcarriers have the identical time-domain correlation and sta- tionarity during the same time interval,this paper proposed a 2-D LMS based predictor which updates the filter weights in both time and frequency domain.The proposed scheme can effectively decrease the observation intervals and significantly speed up the convergence than the conventional LMS and Parallel LMS (PLMS).Complexity analysis and simulation results prove that the proposed scheme can improve the BER (Bit Error Rate) performance and spectrum efficiency with negligible complexity increase.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control(LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control(DMPC) based on coordination scheme.The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints(GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed–loop performance, and computational burden with the physical constraints.

Atrial Fibrillation during Cerebral Infarction in Brazzaville: Frequency and Predictive Factors

In order to contribute to the improvement of brain infarction management in Brazzaville, a cross-sectional and analytical study with prospective data collection was conducted in the cardiology and neurology departments of the Brazzaville University Hospital, from February 1 to July 31, 2018. It included patients hospitalized for cerebral infarction confirmed with imaging, and having done an etiological assessment with at least one electrocardiogram at rest and one of long duration. Among these 138 patients included, 11 had atrial fibrillation, equaling?a frequency of 7.9%. The mean age of AF patients was 71 ± 8.8 years. The cardiovascular risk factors found were hypertension in eight cases (72.7%), diabetes in five cases (45.5%), abdominal obesity in four cases (36.4%). AF was permanent in 10 cases (91%), and paroxysmal in one case (9%). It was valvular in three cases (27.3%) and non-valvular in eight cases (72.7%). The cardiopathy involved was hypertensive in seven cases (63.6%), ischemic and valvular in two cases each. The CHA2DS2-VASc score, calculated in eight patients, was an average of 2.2, and ≥2 in more than 80% of patients;HAS-BLED score of 2.4 on average was ≥?3 in more than 72% of patients. Digoxin was prescribed in seven cases (63.6%) and an anti-vitamin K in eight cases (72.7%). In multivariate analysis, age (OR = 20.10, p = 0.023), arterial hypertension (OR = 23.82, p = 0.011), and dyslipidemia (OR = 2.03, p = 0.032) were the predictive factors found. AF is infrequent during brain infarction in Brazzaville. This systematic research raises the problem of age in our context.

Steady-state Voltage Security-constrained Optimal Frequency Control for Weak HVDC Sending-end AC Power Systems

Due to the fact that a high share of renewable energy sources(RESs)are connected to high-voltage direct current(HVDC)sending-end AC power systems,the voltage and frequency regulation capabilities of HVDC sending-end AC power systems have diminished.This has resulted in potential system operating problems such as overvoltage and overfrequency,which occur simultaneously when block faults exist in the HVDC link.In this study,a steady-state voltage security-constrained optimal frequency control method for weak HVDC sending-end AC power systems is proposed.The integrated virtual inertia control of RESs is employed for system frequency regulation.Additional dynamic reactive power compensation devices are utilized to control the voltage of all nodes meet voltage security constraints.Then,an optimization model that simultaneously considers the frequency and steady-state voltage security constraints for weak HVDC sending-end AC power systems is established.The optimal control scheme with the minimum total cost of generation tripping and additional dynamic reactive power compensation required is obtained through the optimization solution.Simulations are conducted on a modified IEEE 9-bus test system and practical Qing-Yu line commutated converter based HVDC(LCC-HVDC)sending-end AC power system to verify the effectiveness of the proposed method.

Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System

A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).

Comparison of dominant frequency attenuation of blasting vibration for different charge structures

Dominant frequency attenuation is a significant concern for frequency-based criteria of blasting vibration control.It is necessary to develop a concise and practical prediction equation describing dominant frequency attenuation.In this paper,a prediction equation of dominant frequency that accounts for primary parameters influencing the dominant frequency was proposed based on theoretical and dimensional analyses.Three blasting experiments were carried out in the Chiwan parking lot for collecting blasting vibration data used to conduct regression analysis of the proposed prediction equation.The fitting equations were further adopted to compare the reliability of three different types of dominant frequencies in the proposed equation and to explore the effects of different charge structures on the dominant frequency attenuation.The apparent frequency proved to be more reliable to express the attenuation law of the dominant frequency.The reliability and superiority of the proposed equation employing the apparent frequency were verified by comparison with the other five prediction equations.The smaller blasthole diameter or decoupling ratio leads to the higher initial value and corresponding faster attenuation of the dominant frequency.The blasthole diameter has a greater influence on the dominant frequency attenuation than the decoupling ratio does.Among the charge structures applied in the experiments,the charge structure with decoupling ratio of 1.5 and blasthole diameter of 48 mm results in the greatest initial value and corresponding fastest attenuation of the dominant frequency.

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on shorttime stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

Integrating RFID Technology with Intelligent Classifiers for Meaningful Prediction Knowledge

Radio Frequency Identification (RFID) is wireless technology that has been designed to automatically identify tagged objects using a reader. Several applications of this technology have been introduced in past literature such as pet identification and luggage tracking which have increased the efficiency and effectiveness of each environment into which it was integrated. However, due to the ambiguous nature of the captured information with the existence of missing, wrong and duplicate readings, the wide-scale adoption of the architecture is limited to commercial sectors where the integrity of the observations can tolerate ambiguity. In this work, we propose an application of RFID to take the reporting of class attendance and to integrate a predictive classifier to extract high level meaningful information that can be used in diverse areas such as scheduling and low student retention. We conclude by providing an analysis of the core strengths and opportunities that exist for this concept and how we might extend it in future research.

Notes on Extended-Range Atmospheric Prediction in the Northern Hemisphere Winter

We examined the characteristic feature and predictability of low frequency variability (LFV) of the atmosphere in the Northern Hemisphere winter (January and February) by using the empirical orthogonal functions (EOFs) of the geopotential height at 500 hPa. In the discussion, we used the EOFs for geostrophic zonal wind (Uznl) and the height deviation from the zonal mean (Zeddy). The set of EOFs for Uznl and Zeddy was denoted as Uznl-1, Uznl-2, ..., Zeddy-1, Zeddy-2, ..., respectively. We used the data samples of 396 pentads derived from 33 years of NMC, ECMWF and JMA analyses, from January 1963 to 1995. From the calculated scores for Uznl-1, Uznl-2, Zeddy-1, Zeddy-2 and so on we found that Uznl-1 and Zeddy-1 were statistically stable and their scores were more persistent than those of the other EOFs. A close relationship existed between the scores of Uznl-1 and those of Zeddy-1. 30-day forecast experiments were carried out with the medium resolution version of JMA global spectral model for 20 cases in January and February for the period of 1984-1992. Results showed that Zeddy-1 was more predictable than the other EOFs for Zeddy. Considering these results, we argued that prediction of the Zeddy-1 was to be one of the main target of extended-range forecasting.