جستجوی مقالات مرتبط با کلیدواژه "اندازه گیری" در نشریات گروه "برق"

Recently, the frequency and severity of natural and man-made disasters (extreme events), which have a high-impact low-frequency (HILF) property, are increased. These disasters can lead to extensive outages, damages, and costs in electric power systems. A power system must be built with “resilience” against disasters, which means its ability to withstand disasters efficiently while ensuring the least possible interruption in the supply of electricity, sustaining critical social services, and enabling a quick recovery and restoration to the normal operation state. Quantifying the power system resilience is a complicated and controversial problem. However, this is necessary for the evaluation and comparison of different resilience enhancement strategies. The resilience metrics are mathematical tools to measure the resilience level of a power system, which are normally employed for resilience cost-benefit in the planning and operation domains. Numerous resilience metrics have been presented in the power system literature. However, there is a lack of a comprehensive conceptual framework regarding the different types of resilience metrics in electric power systems, and existing frameworks have essential shortcomings. In this paper, after introducing and criticizing the existing frameworks, a conceptual framework is suggested to classify different types of resilience metrics in the power system literature. In this conceptual framework, power system resilience metrics are divided into “non-performance-based” and “performance-based” groups. The “performance-based” resilience metrics are also divided into “performance” and “consequence (outcome)” groups. The “performance” resilience metrics consist of five groups including “power”, “duration”, “frequency”, “probability” and “curve”. The “consequence (outcome)” resilience metrics consist of four groups including “economic”, “social”, “geographic” and “safety and health”. In addition, both of the “performance” and “consequence (outcome)” groups have a distinct group naming “general”. In order to verify and validate the comprehensiveness and inclusivity of the proposed conceptual framework, two actions are accomplished. Firstly, the existing power system resilience metrics are allocated to the framework’s groups. Secondly, the proposed conceptual framework is compared with the existing frameworks. These actions show that the proposed conceptual framework can cover and classify different types of power system resilience metrics in the literature, is more comprehensive comparing the existing frameworks, and lacks the essential shortcoming of those frameworks. Thus, the proposed conceptual framework is comprehensive and useful. The proposed conceptual framework can be used by academic and industrial researchers. Academic researchers can concentrate on groups that need further research to propose new resilience metrics, whereas industrial researchers can choose the appropriate resilience metric according to their needs.

In this paper, a new image processing technique has been proposed to measure the contact angle of symmetric and asymmetric drops. In our proposed method, the contact angle is calculated by analyzing the side view image of the drop without needing any liquid parameters. The procedure of the technique is according to the secant technique coupled with a Gaussian weighted function, as follows: at the beginning the exact location of the contact points is obtained by Harris corner detector function, then a series of points are selected on the drop boundary near the contact points. In the third step, a set of contact angles are estimated by passing secant lines through the contact point and each of the boundary points. In the final step, a gaussian weighted average function is applied to the calculated angles to estimate the main contact angle. The contact angle measurement algorithm is verified by comparing its results with the obtained measurements of a recent standard study. High accuracy, usability for all ranges of contact angles, applicability to both symmetric and asymmetric drops and being fully automatic are other advantages of the presented method.

Today, most energy consumption in industry is related to induction motors. Evaluation of induction motor’s efficiency is an important issue for life estimation, extend the life and energy saving managements. Using the estimated efficiency of the induction motor, its performance can be judged and replacing the existing low efficiency motor by a high efficiency motor could be decided. In this paper, a novel and efficient method based on Modified Artificial Bee Colony (MABC) Algorithm is presented for efficiency estimation in the induction motors. The main advantage of the proposed method is efficiency evaluation of induction motor without any intrusive test. In order to demonstrate the capabilities of the proposed method, a comparison with other intelligent optimization algorithms is performed. Then, one of the important applications of efficiency estimation, which replaces the high efficiency induction motors instead of conventional motors, is discussed. The results of the calculation of energy savings show that if a standard motor is replaced with a high efficiency motor, energy savings will be significant.

Nowadays, the optimal application of phasor measurement units (PMUs) in power systems is increasing because of their advantages such as the capability for dynamical state estimation and improvements in the speed of control and protection systems. In this paper, a new method is presented using binary integer linear programming for the optimal placement of PMUs to guarantee full observability of a power system. Moreover, the problem of the optimal placement of these units in the case of a single line outage is investigated. In all of the investigations, the effect of zero-injection buses in the power system was considered. The ability of the proposed method in solving any of these problems was demonstrated by applying to the 14-, 30-, 39-, 57- and 118-bus IEEE standard systems, 42-bus transmission network of Esfahan Regional Electric and also two very large-scale systems with 2383 and 2746 buses. The simulation results verified the effective and appropriate performance of the proposed method in comparison with other methods.

In this work the inverse transfer function of the measurement room is obtained using a linear and RLS (recursive least-squares algorithm) adaptive filter. At first, an antenna with known pattern (reference antenna) is placed in the test zone to obtain the optimal tap weights of the adaptive filter. Once the filter converges, the learning stage finishes. After that the reference antenna is replaced with the test antenna and its pattern is measured and the data of the measurement is filtered by the optimal tap weights adaptive filter. The filter cancels the stray signals (reflected and scattered signal) and produces the radiation pattern of the test antenna. This technique is superior to that of others because it has better accuracy and it is less costly.