scholarly journals Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Haowen Wu ◽  
Chen Yang ◽  
Wenwang Xie ◽  
Wei Zhang
Keyword(s):  
Missing Data ◽  
Low Voltage ◽  
Learning Algorithm ◽  
Matrix Decomposition ◽  
Current Data ◽  
Power Grids ◽  
Data Completion ◽  
Completion Effect ◽  
Better Than ◽  
Area Data

In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obtain complete power data, this paper proposes a low-voltage station area missing data complement model based on joint matrix decomposition. First, we analyse the characteristics of the low-pressure station data. Then, a model that comprehensively considers the characteristics of the low-voltage station area data is proposed, which includes three parts: the construction of a low-voltage station area data tensor, the joint matrix decomposition, and the completion of the missing data, and it is named LPZ. After that, the CIM learning algorithm proposed in this paper is used to iteratively solve the model to obtain the completed data. Finally, the method proposed in this paper is used to complement the two situations of random loss and all-day loss of real current data in a low-voltage station area and compared with the traditional complement method. The experimental results show that this method is not only effective but also that the completion effect is better than that of other completion methods.

Adaptive in-situ forecasting for demand-side management in low voltage power grids

2021 ◽  
Author(s):  
Christian Backe ◽  
Miguel Bande ◽  
Stefan Werner ◽  
Christian Wiezorek
Keyword(s):  
Low Voltage ◽  
Demand Side Management ◽  
Power Grids ◽  
Demand Side

Application of Cascade-Correlation Algorithm in Vibration Characteristics of Hydro Turbine

2010 ◽  
Vol 108-111 ◽  
pp. 1070-1074
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Jian Min Hou
Keyword(s):  
Bp Neural Network ◽  
Learning Algorithm ◽  
Optimal Operation ◽  
Vibration Characteristics ◽  
Bp Algorithm ◽  
Practical Significance ◽  
Hydro Turbine ◽  
Correlation Algorithm ◽  
Cascade Correlation ◽  
Better Than

The cascade correlation algorithm that is CC algorithms, CC network structure and CC network weights learning algorithm are introduced, based on the operation data of Wanjiazhai hydropower station, considering the pressure fluctuation, the network model of vibration characteristics is established based on CC algorithm, and the applications of CC and BP algorithm in vibration characteristics of turbine are compared. The results show that the CC algorithm is better than BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance.

scholarly journals Research of transient processes in case of single-phase insulation faults in low-voltage ship power grids, taking into account the resistance at the fault point

2020 ◽  
pp. 49-54
Author(s):  
И.Е. Кажекин
Keyword(s):  
Single Phase ◽  
Low Voltage ◽  
Experimental Studies ◽  
Power Grids ◽  
Transient Processes ◽  
Safety Issues ◽  
Physical Experiments ◽  
Marine Industry ◽  
Calculation Results ◽  
Constant Potential

В работе рассмотрены вопросы безопасности бортовых электросетей объектов морской индустрии, показано влияние перенапряжений на их основные показатели, которыми определяются опасности смертельных электротравм, опасности возникновения пожаров и взрывов. Представлены результаты математического моделирования электрического разряда по уравнению Майра с учетом особенностей переходного процесса при однофазных замыканиях на корпус. Показана роль напряжения смещения нейтрали по постоянному потенциалу, наибольшие значения которого формируются при неустойчивом контакте фазы с корпусом судна. Описаны результаты экспериментальных исследований переходных процессов, сопровождающихся возникновением неустойчивыми искровыми разрядами. Сравнение результатов расчета по предложенной методике с результатами физических экспериментов показало весьма удовлетворительную сходимость. Предложенная модель может быть использована для уточнения показателей, характеризующих безопасность судовых электросетей. The paper deals with the safety issues of on-board power grids of the marine industry facilities, shows the influence of overvoltages on their main indicators, which determine the dangers of fatal electrical injuries, the risk of fires and explosions. The results of mathematical modeling of an electric discharge according to the Mayr equation, taking into account the features of the transient process in single-phase short circuits to the case, are presented. The role of the bias voltage of the neutral at a constant potential is shown, the highest values ​​of which are formed during unstable contact of the phase with the ship's hull. The results of experimental studies of transient processes accompanied by the appearance of unstable spark discharges are described. Comparison of the calculation results by the proposed method with the results of physical experiments showed a very satisfactory convergence. The proposed model can be used to refine the indicators characterizing the safety of ship power grids.

scholarly journals The K Nearest Neighbor Algorithm for Imputation of Missing Longitudinal Prenatal Alcohol Data

2021 ◽  
Author(s):  
Ayesha Sania ◽  
Nicolo Pini ◽  
Morgan Nelson ◽  
Michael Myers ◽  
Lauren Shuffrey ◽  
...  
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Drinking Behavior ◽  
Nearest Neighbors ◽  
First Trimester ◽  
Epidemiologic Studies ◽  
K Nearest Neighbor ◽  
Timeline Followback

Abstract Background — Missing data are a source of bias in epidemiologic studies. This is problematic in alcohol research where data missingness is linked to drinking behavior. Methods — The Safe Passage study was a prospective investigation of prenatal drinking and fetal/infant outcomes (n=11,083). Daily alcohol consumption for last reported drinking day and 30 days prior was recorded using Timeline Followback method. Of 3.2 million person-days, data were missing for 0.36 million. We imputed missing data using a machine learning algorithm; “K Nearest Neighbor” (K-NN). K-NN imputes missing values for a participant using data of participants closest to it. Imputed values were weighted for the distances from nearest neighbors and matched for day of week. Validation was done on randomly deleted data for 5-15 consecutive days. Results — Data from 5 nearest neighbors and segments of 55 days provided imputed values with least imputation error. After deleting data segments from with no missing days first trimester, there was no difference between actual and predicted values for 64% of deleted segments. For 31% of the segments, imputed data were within +/-1 drink/day of the actual. Conclusions — K-NN can be used to impute missing data in longitudinal studies of alcohol use during pregnancy with high accuracy.

scholarly journals Depth-averaged instantaneous currents in a tidally dominated shelf sea from glider observations

2016 ◽  
Author(s):  
Lucas Merckelbach
Keyword(s):  
Real Time ◽  
North Sea ◽  
Current Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Real Time Mode ◽  
The North ◽  
Shelf Sea ◽  
The North Sea ◽  
Better Than

Abstract. Ocean gliders have become ubiquitous observation platforms in the ocean in recent years. They are also increasingly used in coastal environments. The coastal observatory system COSYNA has pioneered the use of gliders in the North Sea, a shallow tidally energetic shelf sea. For operational reasons, the gliders operated in the North Sea are programmed to resurface every 3–5 hours. The glider's deadreckoning algorithm yields depth averaged currents, averaged in time over each subsurface interval. Under operational conditions these averaged currents are a poor approximation of the instantaneous tidal current. In this work an algorithm is developed that estimates the instantaneous current (tidal and residual) from glider observations only. The algorithm uses a second-order Butterworth low-pass filter to estimate the residual current component, and a Kalman filter based on the linear shallow water equations for the tidal component. A comparison of data from a glider experiment with current data from an ADCP deployed nearby shows that the standard deviations for the east and north current components are better than 7 cm s−1 in near-real time mode, and improve to better than 5 cm s−1 in delayed mode, where the filters can be run forward and backward. In the near-real time mode the algorithm provides estimates of the currents that the glider is expected to encounter during its next few dives. Combined with a behavioural and dynamic model of the glider, this yields predicted trajectories, the information of which is incorporated in warning messages issued to ships by the (German) authorities. In delayed mode the algorithm produces useful estimates of the depth averaged currents, which can be used in (process-based) analyses in case no other source of measured current information is available.

scholarly journals Rancang Bangun Alat Deteksi Stabilitas Tegangan Jangka Panjang Pada Jaringan Distribusi Tegangan Rendah

Electrician ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 33
Author(s):  
Osea Zebua ◽  
Noer Soedjarwanto ◽  
Jemi Anggara
Keyword(s):  
Power Distribution ◽  
Voltage Stability ◽  
Low Voltage ◽  
Distribution Network ◽  
Distribution Networks ◽  
Current Data ◽  
Voltage Distribution ◽  
Power Distribution Networks ◽  
Long Time

Intisari — Stabilitas tegangan telah menjadi perhatian yang penting dalam operasi jaringan distribusi tenaga listrik. Ketidakstabilan tegangan dapat menyebabkan kerusakan pada peralatan-peralatan listrik bila terjadi dalam waktu yang lama. Makalah ini bertujuan untuk merancang dan membuat peralatan deteksi stabilitas tegangan jangka panjang pada jaringan tegangan rendah. Sensor tegangan dan sensor arus digunakan untuk memperoleh data tegangan dan arus. Mikrokontroler Arduino digunakan untuk memproses perhitungan deteksi stabilitas tegangan jangka panjang dari data tegangan yang diperoleh dari sensor. Hasil deteksi kondisi stabilitas tegangan ditampilkan dengan indikator lampu led. Hasil pengujian pada jaringan distribusi tegangan rendah tiga fasa menunjukkan bahwa peralatan dapat mendeteksi gangguan stabilitas tegangan jangka panjang secara online dan dinamis.Kata kunci — Deteksi, stabilitas tegangan jangka panjang, jaringan distribusi tegangan rendah. Abstract — Voltage stability has become important concern in the operation of electric power distribution networks. Voltage instability can cause damage to electrical equipments if it occurs for a long time. This paper aims to design and build long-term voltage stability detection equipment on low-voltage network. Voltage sensors and current sensors are used to obtain voltage and current data. The Arduino microcontroller is used to process calculation of long-term voltage stability detection from data obtained from the sensors. The results of detection of voltage stability conditions are displayed with the LED indicators. Test result on three-phase low-voltage distribution network shows that equipment can detect long–term voltage stability disturbance online and dynamically.Keywords— Detection, long-term voltage stability, low-voltage distribution network.

scholarly journals Valid Probabilistic Anomaly Detection Models for System Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chunbo Liu ◽  
Lanlan Pan ◽  
Zhaojun Gu ◽  
Jialiang Wang ◽  
Yitong Ren ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Learning Algorithm ◽  
Recall Rate ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Flexible Tool ◽  
System Logs ◽  
Output Only ◽  
Better Than

System logs can record the system status and important events during system operation in detail. Detecting anomalies in the system logs is a common method for modern large-scale distributed systems. Yet threshold-based classification models used for anomaly detection output only two values: normal or abnormal, which lacks probability of estimating whether the prediction results are correct. In this paper, a statistical learning algorithm Venn-Abers predictor is adopted to evaluate the confidence of prediction results in the field of system log anomaly detection. It is able to calculate the probability distribution of labels for a set of samples and provide a quality assessment of predictive labels to some extent. Two Venn-Abers predictors LR-VA and SVM-VA have been implemented based on Logistic Regression and Support Vector Machine, respectively. Then, the differences among different algorithms are considered so as to build a multimodel fusion algorithm by Stacking. And then a Venn-Abers predictor based on the Stacking algorithm called Stacking-VA is implemented. The performances of four types of algorithms (unimodel, Venn-Abers predictor based on unimodel, multimodel, and Venn-Abers predictor based on multimodel) are compared in terms of validity and accuracy. Experiments are carried out on a log dataset of the Hadoop Distributed File System (HDFS). For the comparative experiments on unimodels, the results show that the validities of LR-VA and SVM-VA are better than those of the two corresponding underlying models. Compared with the underlying model, the accuracy of the SVM-VA predictor is better than that of LR-VA predictor, and more significantly, the recall rate increases from 81% to 94%. In the case of experiments on multiple models, the algorithm based on Stacking multimodel fusion is significantly superior to the underlying classifier. The average accuracy of Stacking-VA is larger than 0.95, which is more stable than the prediction results of LR-VA and SVM-VA. Experimental results show that the Venn-Abers predictor is a flexible tool that can make accurate and valid probability predictions in the field of system log anomaly detection.

scholarly journals Development of Broadband Low Actuation Voltage RF MEM Switches

2002 ◽  
Vol 25 (1) ◽  
pp. 97-111 ◽  
Author(s):  
S. C. Shen ◽  
D. Becher ◽  
Z. Fan ◽  
D. Caruth ◽  
Milton Feng
Keyword(s):  
Insertion Loss ◽  
Integrated Circuit ◽  
Rf Mems ◽  
Low Voltage ◽  
Actuation Voltage ◽  
University Of Illinois ◽  
Mems Switch ◽  
The University ◽  
Better Than ◽  
Millimeter Wave Circuits

Low insertion loss, high isolation RF MEM switches have been thought of as one of the most attractive devices for space-based reconfigurable antenna and integrated circuit applications. Many RF MEMS switch topologies have been reported and they all show superior RF characteristics compared to semiconductor-based counterparts. At the University of Illinois, we developed state-of-the-art broadband low-voltage RF MEM switches using cantilever and hinged topologies. We demonstrated promisingsub-10volts operation for both switch topologies.The switches have an insertion loss of less than 0:1 dB, and an isolation of better than 25 dB over the frequency range from 0.25 to 40 GHz. The RF Model of the MEM switch was also established. The low voltage RF MEM switches will provide a solution for low voltage and highly linear switching methods for the next generation of broadband RF, microwave, and millimeter-wave circuits.

scholarly journals Depth-averaged instantaneous currents in a tidally dominated shelf sea from glider observations

2016 ◽  
Vol 13 (24) ◽  
pp. 6637-6649 ◽  
Author(s):  
Lucas Merckelbach
Keyword(s):  
Real Time ◽  
North Sea ◽  
Current Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Real Time Mode ◽  
The North ◽  
Shelf Sea ◽  
The North Sea ◽  
Better Than

Abstract. Ocean gliders have become ubiquitous observation platforms in the ocean in recent years. They are also increasingly used in coastal environments. The coastal observatory system COSYNA has pioneered the use of gliders in the North Sea, a shallow tidally energetic shelf sea. For operational reasons, the gliders operated in the North Sea are programmed to resurface every 3–5 h. The glider's dead-reckoning algorithm yields depth-averaged currents, averaged in time over each subsurface interval. Under operational conditions these averaged currents are a poor approximation of the instantaneous tidal current. In this work an algorithm is developed that estimates the instantaneous current (tidal and residual) from glider observations only. The algorithm uses a first-order Butterworth low pass filter to estimate the residual current component, and a Kalman filter based on the linear shallow water equations for the tidal component. A comparison of data from a glider experiment with current data from an acoustic Doppler current profilers deployed nearby shows that the standard deviations for the east and north current components are better than 7 cm s−1 in near-real-time mode and improve to better than 6 cm s−1 in delayed mode, where the filters can be run forward and backward. In the near-real-time mode the algorithm provides estimates of the currents that the glider is expected to encounter during its next few dives. Combined with a behavioural and dynamic model of the glider, this yields predicted trajectories, the information of which is incorporated in warning messages issued to ships by the (German) authorities. In delayed mode the algorithm produces useful estimates of the depth-averaged currents, which can be used in (process-based) analyses in case no other source of measured current information is available.

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