On the average number of subgroups of the group $${\mathbb {Z}}_m \times {\mathbb {Z}}_n$$ with k-th power sequence

2022 ◽  
Author(s):  
Yankun Sui ◽  
Dan Liu
Keyword(s):  
Power Sequence

A Two-Stage Combination Model for Wind Power Forecasting

2015 ◽  
Vol 737 ◽  
pp. 9-13
Author(s):  
Jun Zhang ◽  
Yuan Hao Wang ◽  
Ying Yi Li ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wind Farm ◽  
General Trend ◽  
Two Stage ◽  
Combination Model ◽  
Second Stage ◽  
Combination Forecasting ◽  
The Mean ◽  
Power Sequence

With the wind farm data from the southeast coast this paper builds a two-stage combination forecasting model of output power based on data preprocessing which include filling up missing data and pre-decomposition. The first stage is a composite prediction of decomposed power sequence in which a time series and optimized BP neural network predict the general trend and the correlation of various factors respectively. The second stage is BP neural network with its input is the results of first stage. The effectiveness and accuracy of the two-stage combination model are verified by comparing the mean square error of the combination model and other models.

Wind Power Forecasting Based on Improved Biased Wavelet Neural Network

2014 ◽  
Vol 915-916 ◽  
pp. 1532-1535
Author(s):  
Yu Han Mao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Forecast Model ◽  
Wavelet Neural Network ◽  
Strong Regularity ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Wind Power System ◽  
Power Sequence

Wind power prediction is the key to grid-connected wind power system. In this paper, first of all, we decompose and reconstruct the power sequence by wavelet analysis, and reduce the noise of the detail signal, to obtain the strong-regularity subsequence. We adapt the biased wavelet neural network rolling forecast model for the processed sequence to obtain seven days of rolling forecast results through several amendments. For the sequence of 5 minutes interval the prediction accuracy is 98.63%, for the sequence of 15 minutes interval the prediction accuracy is 99.88%.

scholarly journals A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chao Yuan ◽  
Yiming Tang ◽  
Rui Mei ◽  
Fei Mo ◽  
Hong Wang
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Time Scale ◽  
Forecast Model ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Wind Speed Prediction ◽  
Wind Power Forecast ◽  
Speed Prediction ◽  
Power Sequence

To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.

Thermal Performance Evaluation and Enhancement for an Automotive Wireless Application Incorporating Multiple Dynamic Heat Sources

2007 ◽  
Author(s):  
Victor Chiriac ◽  
Tien-Yu Tom Lee
Keyword(s):  
Thermal Performance ◽  
Signal Strength ◽  
Optimal Operation ◽  
Operating Conditions ◽  
Temperature Fields ◽  
Heat Sources ◽  
Thermal Budget ◽  
Dynamic Source ◽  
Power Sequence ◽  
Transient Thermal

A detailed transient thermal study for a Remote Keyless Entry System with dynamic heat sources is performed using numerical simulations. The SmartMOS-type device is packaged in a 54 lead SOIC (small outline IC) package with an exposed copper slug. The package is attached to a 4-layer PCB with thermal vias embedded in the board. The challenge resides in the transient thermal interaction between several dynamic heat sources (channels), activated in a sequential fashion following different powering profiles and patterns. The main purpose of the device is to wirelessly provide a communication path between the remote and the receiver placed in the car, so the distance and the signal strength between the two are paramount for an optimal operation. The signal strength is directly associated with the voltage (and associated powering) levels. Several operating scenarios are evaluated by modifying the system design (thermal via pattern) and varying both power dissipation and duration levels. The study starts with just one channel dissipating power, followed by activating the entire dynamic system comprised of six channels dissipating each powers reaching up to 22W at different time intervals. The transient thermal behavior of each source is analyzed during the process. Results indicate that the system dissipating over 14V exceeds the thermal budget (150C) after only 3 powering cycles. Based on the analysis of the complex temperature fields for the multiple dynamic source system, the authors identify alternative power profiles to improve the thermal performance of the overall wireless system, by splitting the power in selective channels and by modifying the power sequence. Several additional cases are further investigated, and the optimized power profiles indicate that they satisfy the thermal budget under various operating conditions and several multiple cycles, while still maintaining the device voltage at 14V levels. A thorough study of the transient patterns and needed system improvements are included.

scholarly journals SOMEn−2TERRACES FROMnPOWER-SEQUENCES,nBEING AN ODD PRIME POWER

2009 ◽  
Vol 52 (1) ◽  
pp. 65-85
Author(s):  
IAN ANDERSON ◽  
D. A. PREECE
Keyword(s):  
Prime Power ◽  
Type A ◽  
Zero Element ◽  
Powers Of 2 ◽  
Power Sequence

AbstractA terrace formis an arrangement (a1,a2, . . . ,am) of themelements ofmsuch that the sets of differencesai+1−aiandai−ai+1(i= 1, 2, . . . ,m− 1) between them contain each element ofm\ {0} exactly twice. Formodd, many procedures are available for constructing power-sequence terraces form; each such terrace may be partitioned into segments, one of which contains merely the zero element ofm, whereas each other segment is either (a) a sequence of successive powers of an element ofmor (b) such a sequence multiplied throughout by a constant. We now adapt this idea by using power-sequences inn, wherenis an odd prime power, to obtain terraces form, wherem=n− 2. We write each element fromnso that they lie in the interval [0,n− 1] and then delete 0 andn− 1 so that they leaven− 2 elements that may be interpreted as the elements ofn−2. A segment of one of the new terraces may be of type (a) or (b), incorporating successive powers of 2, with each entry evaluated modulon. Our constructions providen−2terraces for all odd primesnsatisfying 0 <n< 1,000 except forn= 127, 241, 257, 337, 431, 601, 631, 673, 683, 911, 937 and 953. We also providen−2terraces forn= 3r(r> 1) and for some valuesn=p2, wherepis prime.

A note on the power sequence of a fuzzy matrix

1999 ◽  
Vol 102 (2) ◽  
pp. 281-286 ◽  
Author(s):  
Zhou-Tian Fan
Keyword(s):  
Fuzzy Matrix ◽  
Power Sequence

scholarly journals Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaojiao Chen ◽  
Xiuqing Zhang ◽  
Mi Dong ◽  
Liansheng Huang ◽  
Yan Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Short Term Memory ◽  
Offshore Wind ◽  
Power Prediction ◽  
Modeling Strategy ◽  
Power Sequence

The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time series of wind power, it is present a two-stage modeling strategy, in which a deep neural network combines spatiotemporal correlation to simultaneously predict the power of multiple wind turbines. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). Herein, the LSTM captures the temporal dependence of the historical power sequence, while the CNN extracts the spatial features among the data, thereby achieving the power prediction for multiple wind turbines. The proposed approach is validated by using the wind power data from an offshore wind farm in China, and the results in comparison with other approaches shows the high prediction preciseness achieved by the proposed approach.

scholarly journals Power sequence definition under woodworking milling on contour-milling machines

2017 ◽  
Vol 17 (2) ◽  
pp. 79-87 ◽  
Author(s):  
A. N. Chukarin ◽  
◽  
S. V. Golosnoy ◽  
Keyword(s):  
Milling Machines ◽  
Power Sequence

Feedforward Laser Power Specification for Uniform Cooling of Thin-Walled Parts

2004 ◽  
Author(s):  
Umesh A. Korde ◽  
Michael A. Langerman ◽  
Gregory A. Buck ◽  
Vojislav D. Kalanovic
Keyword(s):  
Laser Power ◽  
Thermal Model ◽  
Deposition Process ◽  
Ongoing Research ◽  
Laser Powder Deposition ◽  
Powder Deposition ◽  
The Matrix ◽  
Thin Walled ◽  
The Time Domain ◽  
Power Sequence

This paper presents results from ongoing research on thermal-model based feedforward specification of laser power in a laser powder deposition process. The goal of this algorithm is to compute, before deposition of a layer, the laser power sequence and distribution that would produce a desired temperature distribution over that layer. This in turn will enable uniform cooling of the layer and avoid build up of residual stresses. In this paper, results based on a simplified thermal model and second-order spatial discretization are presented. Two types of discretization in the time domain are examined. The matrix-exponential-based discretization is expected to be more accurate at lower laser speeds. The desired laser power sequence and the resulting temperature histories for a prescribed laser speed are discussed within the context of a thin-walled part.

Sign in / Sign up

Export Citation Format

Share Document