Overload alleviation in electric power system using fuzzy logic

Abstract

The electric power system is a complex system, whose operating condition may not remain at a constant value. This necessitates the power system operator be alert to keep the system performance under normal condition. The various contingencies like, large load variations, outage of components (transmission lines, transformers, generators, etc.) are more common. Any of these conditions causes overloads and system parameters to exceed the limits thus resulting in an insecure system. Hence the operator has to maintain the security level by proper analysis and reschedule the system accordingly. The most practiced techniques for overload alleviation is generator rescheduling and/or load shedding. The conventional method is to solve an optimal power flow problem to find out the rescheduling for overload alleviation. But this will not give the desired speed of solution. This paper considers the application of fuzzy logic to dispatch the generator to relieve the overload in power system. A fuzzy rule based controller is developed which reschedules the real power generation. The system parameters such as overload factor (OF), generation shift sensitivity factor (GSSF) and sensitivity of vulnerability index of generation system (SVIGS) are given to a fuzzy inference system (FIS) as inputs. The output from the FIS gives the quantity of generation to be rescheduled. The proposed approach is illustrated with 39 bus New England system. The validity of the proposed method is done using MATPOWER.Index Terms-fuzzy logic control, generator rescheduling, overload alleviation, sensitivity factors and vulnerability index.

An Online Optimization Algorithm for Alleviating Contingencies in Transmission Networks

“…References [19] and [20] propose a model predictive control algorithm to mitigate line overloads. In recent years fuzzy logic is also used to address this problems (e.g., [21] and [22]).…”

Online Economic Re-dispatch to Mitigate Line Overloads after Line and Generation Contingencies

“…Herein, the optimization model does not consider the calculation of power demand rationing. Therefore, the optimization model defined in P1 uses the objective function of Equation (10) and the constraints of Equations (11), (12), and (13). P1 also corresponds to the main subprocess of the algorithm, because the solution of the economic re-dispatch is obtained from it, which is refined through the iterations with the AC simulator.…”

“…However, as stated above, the evaluated line outages create critical contingencies N−1. Thus, before the outage of any line (05-08 and [13][14], there is no totally secure economic re-dispatch; that is, under this condition there will be at least one N−1contingency that causes an overload above the maximum limit of the overloaded element. In Table 3, we present in detail the values of the economic re-dispatch to consider, before the outage of lines 05-08 or 13-14.…”

Reconfiguração de Redes de Distribuição de energia elétrica utilizando unineuron e nullneuron

“…Entre os métodos Heurístico e Metaheurísticos mais aplicados a problemas de otimização para reconfiguração de redes de distribuição, destacam-se, Branch and Bound (Merlin and Back, 1975;Romano et al, 2013) e Branch Exchange (Civanlar et al, 1988; Baran and Wu, 1989;Peponis and Papadopoulos, 1997;Bernardon et al, 2009;Junior, 2014) como métodos Heurísticos e PSO (Particle Swarm Optmization) (Tofis et al, 2017;Macedo, 2012;Atteya et al, 2017), Inteligência Artificial (Pandiarajan and Babulal, 2011;Bernardon et al, 2009;Berredo et al, 2011;Neto and Vellasco, 2016) e Colônia de formigas (Amin Heidari, 2017;Silva, 2010;Ghorbani et al, 2008) como métodos Metaheuríscos.…”