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Showing 6 results for Sensitivity
Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network
M. R. Aghamohammadi,
Volume 4, Issue 3 (10-2008)
Abstract

This paper proposes a novel approach for generation scheduling using sensitivity

characteristic of a Security Analyzer Neural Network (SANN) for improving static security

of power system. In this paper, the potential overloading at the post contingency steadystate

associated with each line outage is proposed as a security index which is used for

evaluation and enhancement of system static security. A multilayer feed forward neural

network is trained as SANN for both evaluation and enhancement of system security. The

input of SANN is load/generation pattern. By using sensitivity characteristic of SANN,

sensitivity of security indices with respect to generation pattern is used as a guide line for

generation rescheduling aimed to enhance security. Economic characteristic of generation

pattern is also considered in the process of rescheduling to find an optimum generation

pattern satisfying both security and economic aspects of power system. One interesting

feature of the proposed approach is its ability for flexible handling of system security into

generation rescheduling and compromising with the economic feature with any degree of

coordination. By using SANN, several generation patterns with different level of security

and cost could be evaluated which constitute the Pareto solution of the multi-objective

problem. A compromised generation pattern could be found from Pareto solution with any

degree of coordination between security and cost. The effectiveness of the proposed

approach is studied on the IEEE 30 bus system with promising results.


Improving Voltage Stability Margin Using Voltage Profile and Sensitivity Analysis by Neural Network
M. Aghamohammadi, S. S. Hashemi, M. S. Ghazizadeh,
Volume 7, Issue 1 (3-2011)
Abstract
This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profile of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Bus voltage profile contains useful information about system stability margin including the effect of load-generation, line outage and reactive power compensation so, it is adopted as input pattern for VSANN. In fact, VSANN establishes a functionality for VSM with respect to voltage profile. Sensitivity analysis of VSM with respect to voltage profile and reactive power compensation extracted from information stored in the weighting factor of VSANN, is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select most effective buses for reactive power compensation aimed enhancing VSM. The proposed approach has been applied on IEEE 39-bus test system which demonstrated applicability of the proposed approach.
Residential Load Manageability Factor Analyses by Load Sensitivity Affected by Temperature
N. Eskandari, S. Jalilzadeh,
Volume 12, Issue 4 (12-2016)
Abstract

Load side management is the basic and significant principle to keeping the balance between generation side and consumption side of electrical power energy. Load side management on typical medium voltage feeder is the power energy consumption control of connected loads with variation of essential parameters that loads do reaction to their variation. Knowing amount of load's reaction to each parameters variation in typical medium voltage feeder during the day, leads to gain Load Manageability Factor (LMF) for that specific feeder that helps power utilities to manage their connected loads. Calculating this LMF needs to find out each types of load with unique inherent features behavior to each parameters variation. This paper results and future work results will help us to catch mentioned LMF. In this paper analysis of residential load behavior due to temperature variation with training artificial neural network will be done. Load behavior due to other essential parameters variations like energy pricing variation, major event happening, and power utility announcing to the customers, and etc will study in future works. Collecting all related works results in a unit mathematical equation or an artificial neural network will gain LMF.


Multi-level Thrust Ripples Minimization of Linear Flux Switching Motors With Segmented Secondary by Combined Genetic Algorithm and Response Surface Methodology
M. S. Hosseini, H. Javadi, S. Vaez-Zadeh,
Volume 16, Issue 1 (3-2020)
Abstract
Linear flux switching motors with simple passive segmented secondary, referred as Segmented Secondary Linear Flux Switching Motors (SSLFSMs), have low cost secondary and therefore are applicable to transportation systems like Maglev. However, it is shown that the SSLFSMs suffer from high thrust ripples. In this paper, minimizing SSLFSM thrust ripples besides maximizing its developed thrust are performed by considering the motor dimensions as design variables. Since the optimization of the motor is a high dimensional problem, a multi-level optimization method is employed to improve the machine performances and efficiency. According to the effects of the design variables on the optimization objectives, a sensitivity analysis is carried out to divide the design variables into two levels: mild-sensitive level and strong-sensitive level. Then, the two levels of design variables are optimized based on a mathematical model. Two different optimization methods as the Design of Experiment (DOE) and the Response Surface Method (RSM) are used in mild-sensitive level and the Genetic Algorithm (GA) is also used in strong-sensitive level. Based on FEM analysis, electromagnetic performance of the original motor and the optimal one are compared and the validity of the proposed optimization method is verified. Also, the effectiveness of the mathematical model used in thrust and thrust ripples calculations is evaluated and verified.

New Computational Method for Optimal Allocation of Fault Current Limiters in Power Systems
M. Ghotbi-Maleki, R. Mohammadi Chabanloo,
Volume 17, Issue 4 (12-2021)
Abstract
Expansion of power system causes short-circuit currents (SCC) of networks to exceed the tolerable SCCs of equipment. The utilization of fault current limiter (FCL) in such networks is needed to address this issue. This paper presents a new method for optimal allocation of FCLs to restrain the SCCs under permissible value. In this method, it is suggested to select a line as FCL location where the addition of FCL to this line will have the greatest impact on reducing the SCC of buses which their SCCs exceed the permissible value (known as exceeded buses). Since the optimization algorithms are not capable for optimal allocation of FCL especially in large networks, therefore, the proposed FCL allocation method is presented in the form of a computational process. In this computational process, the candidate lines for FCL location are firstly prioritized by a new index based on the effect of location of FCL on the reduction of SCCs. Then, the FCL size is determined by solving a quadratic equation firstly presented in this paper. The proposed method is implemented on networks with different sizes, and the obtained results show the performance of the proposed method over previous FCL allocation methods.

Sensitivity Analysis Based Comparative Assessment of Resource Mix Using MCDM Technique: A Case Study of Thar Desert, India
N. Thakkar, P. Paliwal,
Volume 18, Issue 4 (12-2022)
Abstract
In the last decade, there has been a lot of focus on sustainable development in the electrical power industry to meet the growing energy demand. This has led to an increase in the integration of renewable energy sources (RES). In addition to being abundantly available, the RES offers advantages such as low environmental impact and increased social development of rural communities which are imperative for a sustainable society.  However, the selection of a particular generating resource or resource mix (RM) for an autonomous micro-grid is a complex problem that involves multiple conflicting factors. In this paper, a planning strategy for selecting an appropriate RM has been proposed. Seven RMs comprising different combinations of four generation/storage technologies such as solar photovoltaic array (SPVA), wind turbine (WT), diesel generator (DG) and battery storage (BS) have been considered. The planning is initiated with the determination of optimal component sizing for all seven RMs. The RMs are then analyzed with respect to four primary sustainability parameters i.e. economic, social, technical and environmental. The analysis is further enhanced by investigation of 13 sub-parameters as well. Thereafter, prioritization of RMs is carried out using two MCDM methods: Best worst method (BWM) and PROMETHEE II. Finally, to assert the importance of weight assignment on RM ranking, sensitivity analysis is performed. In order to impart the practical aspect to analysis, the planning formulation is applied to a case study of the Thar desert, India. The results suggest that a combination of SPVA and BS provides the most optimum RM solution.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.