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M.M. Hosseini-Bioki

Bio: M.M. Hosseini-Bioki is an academic researcher from Graduate University of Advanced Technology. The author has contributed to research in topics: Optimization problem & Particle swarm optimization. The author has an hindex of 1, co-authored 1 publications receiving 17 citations.

Papers
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Journal ArticleDOI
TL;DR: The obtained results show the effectiveness of the proposed methodology in implementing the optimal load shedding satisfying social welfare by maintaining voltage stability margin (VSM) through technoeconomic analyses.

20 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, an optimal unified power flow controller (UPFC) placement and load shedding coordination approach for voltage collapse prevention in N−−K (K = 1, 2 and 3) contingency condition using Hybrid Imperialist Competitive Algorithm-Pattern Search (HICA-PS).

39 citations

Journal ArticleDOI
TL;DR: This review summarizes and updates the important aspects ofUVLS and provides principle understanding of UVLS, which are critical in planning such defense schemes and will serve as one‐stop information for power system engineers, designers, and researches.
Abstract: A blackout is usually the result of increasing load beyond the transmission capacity of the power system. One of the main reasons for power blackouts is voltage collapse. To avoid this problem, the proper corrective measures called load shedding is required. In critical and extreme emergencies, under voltage load shedding (UVLS) is performed as a final remedy to avoid a larger scale voltage collapse. Therefore, UVLS is considered state of the art to achieve voltage stability. This review summarizes and updates the important aspects of UVLS; it also provides principle understanding of UVLS, which are critical in planning such defense schemes. Moreover, this article provides a discussion on recent state-of-art UVLS schemes applied in various power industries. Additionally, the pros and cons of the conventional and computational intelligence techniques are discussed. It is envisioned that this work will serve as one-stop information for power system engineers, designers, and researches.

32 citations

Journal ArticleDOI
10 Jul 2018-Energies
TL;DR: In this paper, a hybrid algorithm based on GA and particle swarm optimization (PSO) is proposed to find the optimal amount of load shed for systems under stress (overloaded) in smart grids.
Abstract: A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.

31 citations

Journal ArticleDOI
TL;DR: In this article, an integer-value modeling for optimal under voltage load shedding (UVLS) problem is presented, which considers the load feeders of load buses in the modeling as well as assigning interruption penalty factors and participation penalty factors to feeders and buses respectively.

27 citations

Journal ArticleDOI
01 Mar 2016
TL;DR: Results of testing the proposed multi-objective market clearing method on the IEEE 24-bus Reliability Test System (IEEE24-bus RTS) are presented and compared with the direction scalarization, the varepsilon, weighted sum and weighted sum methods from efficiency, diversity and computational burden requirement points of view.
Abstract: In this paper, market clearing of joint energy and reserves auctions is framed as a multi-objective mathematical programming (MMP) to simultaneously consider the economic and security objectives. Social welfare maximization, the minimization of lines overload and voltage deviation as well as loadability limit maximization are competitive objectives of the proposed market clearing framework. Traditional MMP methods such as direction scalarization and $$\varepsilon $$?-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, a fuzzy-based non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to find the optimal schedule of the units energy and reserves. In the proposed method, to improve the performance of NSGA-II, a fuzzy inference system is employed to dynamically set the parameters of NSGA-II ($$P_\mathrm{c}$$Pc and $$P_\mathrm{m})$$Pm). Results of testing the proposed multi-objective market clearing method on the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) are presented and compared with the direction scalarization, the $$\varepsilon $$?-constraint and weighted sum methods from efficiency, diversity and computational burden requirement points of view. These comparisons confirm the efficiency of the developed method.

11 citations