An Efficient Approach To Voltage Stability Evaluation Using Tellegen’s Equations

In this paper, the adjoint networks based on Tellegen’s Theorem are used to improve the PV curve assessment. PV curve is the most widely accepted method for determining the margin of the power system state to the voltage collapse point. The repetitive power flow and continuation power flow are used to access PV curves through tracing the power flow solutions for the change of loads. Since the Minimum Jacobi matrix eigenvalue is practically close to zero at neighbor voltage collapse point, there is a restriction in order to access the voltage stability level by repetitive power flow method. This problem solved by appropriate combination of adjoint networks based on Tellegen’s Theorem and conventional equations. The proposed method was tested on SWCC test system. Comparison of three method results show advantages and efficiency of the proposed method.DOI:http://dx.doi.org/10.11591/ijece.v3i2.196

A New Methodology for Allocation of Stabilizers in Uncontrollable poles of multi-machine power systems

One of the main problems in power systems is dynamic stability and damping of Electromechanical  oscillations which for this reason power system stabilizers are being used and the method for determining the location of stabilizer for the purpose of damping critical modes, is using participation factors, in which controllability and observability of modes have influence. The real value or the amplitude of participation factor is usually used as evaluation criterion, while in case that the real values of participation factors are close, the imaginary part of these coefficients are also influential, and if the imaginary part of coefficients are either negative or positive, different results will be obtained. The method introduced in this paper, in modes that the value of participation factors are close to each other, priority for the placement of generator, installation of stabilizer and the optimum value for the stabilizer's gain, is very accurate and appropriate.DOI:http://dx.doi.org/10.11591/ijece.v3i1.183

Development, Validity, and Reliability of a Scale for Exam Preparation Strategies Among Students

Background and Objectives: The aim of the present study was to introduce a valid and reliable scale for the assessment of exam preparation strategies among students at Shahid Bahonar University of Kerman, Iran during the academic year 2015 - 2016. Methods: In this descriptive exploratory research, a 25-item scale was developed based on a Likert scale in accordance with the literature.Face validity of the scale was confirmed, based on the comments of educational sciences experts. Three reliability indices, composite reliability, construct reliability, and internal consistency, were calculated. In addition to confirmatory factor analysis, convergent and divergent validities were determined. Results: The results of exploratory factor analysis indicated 2 underlying constructs: 1) deep exam preparation strategies, including 12 items (coefficient, 0.60 - 0.80; specificity, 12.4); and 2) shallow exam preparation strategies, including 13 items (coefficient, 0.61 - 0.76; specificity, 2.15). Cronbach’s alpha was 0.94 for the first underlying construct and 0.92 for the second construct. In addition, the convergent validity coefficients ranged from 0.50 to 0.57, thus confirming the validity of the constructs. Moreover, the average variance extracted (AVE) of the constructs was higher than the squared correlation of the constructs; therefore, the divergent validity of the scale was confirmed. Conclusions: The present scale for exam preparation strategies consisted of 2 constructs (deep and shallow approaches) and 25 items (deep approach, 12 items; shallow approach, 13 items). According to the analyses, the reliability and validity of the scale were confirmed. Therefore, this scale can be applied by instructors and students to evaluate exam preparation strategies. Keywords: Development, Validity, Reliability, Assessment Tool, Student Exam Preparation Strategie

AAGLMES: an intelligent expert system realization of adaptive autonomy using generalized linear models

Abstract—We earlier introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI). This study presents an expert system for realization of AA, using Support Vector Machine (SVM), referred to as Adaptive Autonomy Support Vector Machine Expert System (AASVMES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts’ judgments. SVM is used as an expert system inference engine. The practical list of PSFs and the judgments of GTEDC’s (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implemented AASVMES in response to GTEDC’s network are evaluated against the GTEDC experts’ judgment. Evaluations show that AASVMES has the ability to predict the proper LOA for GTEDC’s Utility Management Automation (UMA) system, which changes in relevance to the changes in PSFs; thus providing an adaptive LOA scheme for UMA. Keywords-Support Vector Machine (SVM); Adaptive Autonomy (AA); Expert System; Human Automation Interaction (HAI); Experts’ Judgment; Power System; Distribution Automation; Smart Grid

AAHES: A hybrid expert system realization of Adaptive Autonomy for smart grid

Abstract--Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment

Cyber security for smart grid: a human-automation interaction framework

Abstract-- Power grid cyber security is turning into a vital concern, while we are moving from the traditional power grid toward modern Smart Grid (SG). To achieve the smart grid objectives, development of Information Technology (IT) infrastructure and computer based automation is necessary. This development makes the smart grid more prone to the cyber attacks. This paper presents a cyber security strategy for the smart grid based on Human Automation Interaction (HAI) theory and especially Adaptive Autonomy (AA) concept. We scheme an adaptive Level of Automation (LOA) for Supervisory Control and Data Acquisition (SCADA) systems. This level of automation will be adapted to some environmental conditions which are presented in this paper. The paper presents a brief background, methodology (methodology design), implementation and discussions. Index Terms—smart grid, human automation interaction, adaptive autonomy, cyber security, performance shaping facto

An expert system realization of adaptive autonomy in Electric Utility Management Automation

Abstract: Earlier we introduced a novel framework for implementation of Adaptive Autonomy (AA). This study presents an expert system realization of the AA framework, referred to as Adaptive Autonomy Expert System (AAES). The proposed AAES is based on the extracted rules from the Expert’s Judgment on proper Levels of Automation (LOA) for various environmental conditions, modeled as Performance Shaping Factors (PSFs). Decision fusion method is used as expert system inference engine, where eight decision fusion methods are developed as prospective ones. The AAES is realized in the practical case of electric power Utility Management Automation (UMA) for the Greater Tehran Electricity Distribution Company (GTEDC). The practical list of PSFs and the judgments of GTEDC’s experts are used as the expert system rule base in this research. The results of implementing the proposed AAES to GTEDC’s network are evaluated according to two criteria: average error and error margin. Five out of eight decision fusion methods are proven to be suitable inference engines, due to both criteria. Evaluation of the results shows that the proposed AAES can estimate proper LOAs for GTEDC’s UMA system, which change due to the changes in PSFs; thus providing a dynamic (adaptive) LOA scheme for UMA

An approach to deterministic and stochastic evaluation of the uncertainties in distributed generation systems

International audienc