Implementation of Line Stability Index for Contingency Analysis and Screening in Power Systems

Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

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Pipeline multiprocessor algorithm for contingency analysis in power systems

2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262) ◽

10.1109/pess.2001.970290 ◽

2001 ◽

Author(s):

M. Kacarska ◽

D. Andonov ◽

V. Glamocanin ◽

B. Stojkovska

Keyword(s):

Power Systems ◽

Contingency Analysis

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Power systems contingency analysis using bus impedance matrix

2018 Simposio Brasileiro de Sistemas Eletricos (SBSE) ◽

10.1109/sbse.2018.8395533 ◽

2018 ◽

Author(s):

Othon F. Avila ◽

Rafaella M. B. Prado ◽

Carine M. B. Silva ◽

Vander M. Costa

Keyword(s):

Power Systems ◽

Impedance Matrix ◽

Contingency Analysis

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Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

Archives of Electrical Engineering ◽

10.2478/aee-2014-0027 ◽

2014 ◽

Vol 63 (3) ◽

pp. 367-384 ◽

Cited By ~ 5

Author(s):

K. Pandiarajan ◽

C.K. Babulal

Keyword(s):

Genetic Algorithm ◽

Power Systems ◽

Optimization Problem ◽

Pareto Optimal ◽

Contingency Analysis ◽

Nsga Ii ◽

Generation Cost ◽

Fuzzy Ranking ◽

Network Overload ◽

Line Overload

Abstract This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

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Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation

Energies ◽

10.3390/en11092268 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2268 ◽

Cited By ~ 3

Author(s):

Dong-Hee Yoon ◽

Sang-Kyun Kang ◽

Minseong Kim ◽

Youngsun Han

Keyword(s):

Cloud Computing ◽

Power Systems ◽

Power System ◽

Power Flow ◽

Computation Time ◽

Coarse Grained ◽

Contingency Analysis ◽

Flow Computation ◽

Cloud Computing System ◽

The Impact

We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of power system components; thus, extensive contingency analysis is required to ensure that power systems operate safely and reliably. Since many calculations are required to analyze possible contingencies under various conditions, the computation time of contingency analysis increases tremendously if either the power system is large or cascading outage analysis is needed. We also introduce a task management optimization to minimize load imbalances between computing resources while reducing communication and synchronization overheads. Our experiment shows that the proposed architecture exhibits a performance improvement of up to 35.32× on 256 cores in the contingency analysis of a real power system, i.e., KEPCO2015 (the Korean power system), by using a cloud computing system. According to our analysis of the task execution behaviors, we confirmed that the performance can be enhanced further by employing additional computing resources.

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