Showing 4 results for Ghazizadeh
M. Aghamohammadi, S. S. Hashemi, M. S. Ghazizadeh,
Volume 7, Issue 1 (March 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.
M. R. Aghamohammadi, S. Hashemi, M. S. Ghazizadeh,
Volume 7, Issue 2 (June 2011)
Abstract
Abstract: Voltage instability is a major threat for security of power systems. Preserving voltage security margin at a certain limit is a vital requirement for today’s power systems. Assessment of voltage security margin is a challenging task demanding sophisticated indices. In this paper, for the purpose of on line voltage security assessment a new index based on the correlation characteristic of network voltage profile is proposed. Voltage profile comprising all bus voltages contains the effect of network structure, load-generation patterns and reactive power compensation on the system behaviour and voltage security margin. Therefore, the proposed index is capable to clearly reveal the effect of system characteristics and events on the voltage security margin. The most attractive feature for this index is its fast and easy calculation from synchronously measured voltage profile without any need to system modelling and simulation and without any dependency on network size. At any instant of system operation by merely measuring network voltage profile and no further simulation calculation this index could be evaluated with respect to a specific reference profile. The results show that the behaviour of this index with respect to the change in system security is independent of the selected reference profile. The simplicity and easy calculation make this index very suitable for on line application. The proposed approach has been demonstrated on IEEE 39 bus test system with promising results showing its effectiveness and applicability.
M. Zarif, M. H. Javidi, M. S. Ghazizadeh,
Volume 8, Issue 2 (June 2012)
Abstract
This paper presents a decision making approach for mid-term scheduling of large
industrial consumers based on the recently introduced class of Stochastic Dominance (SD)-
constrained stochastic programming. In this study, the electricity price in the pool as well as
the rate of availability (unavailability) of the generating unit (forced outage rate) is
considered as uncertain parameters. The self-scheduling problem is formulated as a
stochastic programming problem with SSD constraints by generating appropriate scenarios
for pool price and self-generation unit's forced outage rate. Furthermore, while most
approaches optimize the cost subject to an assumed demand profile, our method enforces
the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e.
three months (12 weeks), so that the total production will remain constant.
M. H. Adhami, R. Ghazizadeh,
Volume 19, Issue 1 (March 2023)
Abstract
A novel hybrid method for tracking multiple indistinguishable maneuvering targets using a wireless sensor network is introduced in this paper. The problem of tracking the location of targets is formulated as a Maximum Likelihood Estimation. We propose a hybrid optimization method, which consists of an iterative and a heuristic search method, for finding the location of targets simultaneously. The Levenberg-Marquardt (LM) algorithm is used for iterative search, while the Particle Swarm Optimization (PSO) is used for the heuristic search. We use the maximum sensors separating distance-grouping algorithm (G-MSSD), which was introduced in our previous work, to generate initial guesses for search algorithms. The estimates of both methods are compared and the best one is selected as the final estimation. We demonstrate the accuracy and performance of our new tracking method via simulations and compare our results with the Gauss-Newton (GN) method.
