Sensor optimum location algorithm for estimating harmonic sources injection in electrical networks

2014 ◽  
pp. 291-295
Author(s):  
Luis F. Beites ◽  
Manuel Alvarez ◽  
Agustin Díaz
Keyword(s):  
Electrical Networks ◽  
Optimum Location ◽  
Location Algorithm

Study on Cuff-Less Blood Pressure Measurement Based on Electrocardiography and Photoplethysmography Signal

2019 ◽  
Vol 2 (3) ◽  
pp. 206-214
Author(s):  
Putri Indes Oktabriani ◽  
Fuad Ughi ◽  
Aulia Arif Iskandar
Keyword(s):  
Blood Pressure ◽  
Pressure Measurement ◽  
Blood Pressure Monitoring ◽  
Blood Pressure Measurement ◽  
Ecg Signal ◽  
Optimum Location ◽  
Blood Pressure Monitor ◽  
Pressure Monitor ◽  
Left Hand ◽  
Measure Blood Pressure

The continuous blood pressure measurement research is widely known for helpingthe development of ambulatory blood pressure monitoring where it measures blood pressureevery 15 to 30 minutes throughout the day. The cuff is a problem for the patient withAmbulatory Blood Pressure Monitor. It can make a person feel uncomfortable and must staystill when the cuff starts to inflate. It is limiting and disturbing their daily activity when thedevice is starting to measure the blood pressure. Blood pressure measurement without cuff isbeing proposed in this research, called cuff-less blood pressure measurement. It will be based onPhotoplethysmography (PPG) and Electrocardiography (ECG) signal analysis. ECG (Lead 1,Lead 2, and Lead 3) with PPG signal produced from index finger on the left hand are comparedand analyzed. Then the relation of PPG and ECG signal and the optimum location for daily usecan be obtained. The optimum location will be based on the electrode’s position that producedthe optimum ECG lead Signal to measure blood pressure. Based on the result, PPG and ECGsignal have a linear relation with Blood Pressure Measurement and Lead 1 is more stable inproducing the ECG signal. The equation from Lead 1 appeared as one of the optimum equationsfor measuring Systolic Blood Pressure (SBP) or Diastolic Blood Pressure (DBP).

Modeling of Strain Energy Harvesting in Pneumatic Tires Using Piezoelectric Transducer

2014 ◽  
Vol 42 (1) ◽  
pp. 16-34 ◽  
Author(s):  
Ali E. Kubba ◽  
Mohammad Behroozi ◽  
Oluremi A. Olatunbosun ◽  
Carl Anthony ◽  
Kyle Jiang
Keyword(s):  
Energy Harvesting ◽  
Strain Energy ◽  
Fiber Composite ◽  
Experimental Tests ◽  
Evaluation Study ◽  
Monitoring Systems ◽  
Monitoring Device ◽  
Optimum Location ◽  
Piezoelectric Fiber Composite ◽  
Piezoelectric Fiber

ABSTRACT This paper presents an evaluation study of the feasibility of harvesting energy from rolling tire deformation and using it to supply a tire monitoring device installed within the tire cavity. The developed technique is simulated by using a flexible piezoelectric fiber composite transducer (PFC) adhered onto the tire inner liner acting as the energy harvesting element for tire monitoring systems. The PFC element generates electric charge when strain is applied to it. Tire cyclic deformation, particularly at the contact patch surface due to rolling conditions, can be exploited to harvest energy. Finite element simulations, using Abaqus package, were employed to estimate the available strain energy within the tire structure in order to select the optimum location for the PFC element. Experimental tests were carried out by using an evaluation kit for the energy harvesting element installed within the tire cavity to examine the PFC performance under controlled speed and loading conditions.

Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
D. Ostrenko ◽  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy System ◽  
Electrical Networks ◽  
Electric Networks ◽  
Electric Network ◽  
Time Dynamics ◽  
Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

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