Power system network sensitivity to Voltage collapse

Abstract

Preventing voltage collapse occurrences in power systems are a challenging task worldwide. An adequate reactive power supply along with voltage limit violation may lead to blackout. The ability to sense such dynamic change and accurately determine voltage stability indications allows system control to take the necessary action to prevent such incidents. This paper presents efficient indices to calculate voltage stability analysis and predict system voltage collapse. Voltage stability margins can be calculated indicating how far the power system is from its severely loading condition. The proposed method was demonstrated on the 5-bus and IEEE 180-bus systems and compared with existing methods to show its validation and efficiency.

Correction Control Model of L-Index Based on VSC-OPF and BLS Method

“…If the minimum singular value is zero, the voltage is close to collapse. Althowibi and Mustafa proposed a method and index based on network sensitivity to determine the voltage stability and stability margin [11].…”

Defining Power System Health - Framework and Process Towards a System Health Index

“…Authors in [78] give a comprehensive list of voltage stability indices that are in general divided into line, bus and overall voltage stability indices. Since it is essential to look at the power system as a whole, we have included an overall stability indicator, that is simple, accurate and fast in conducting voltage stability analysis -Network sensitivity approach (SG) [69]. The calculation of SG is based on total active power generation, total active power demand and total reactive power demand.…”

“…The calculation of SG is based on total active power generation, total active power demand and total reactive power demand. The system approaches to its collapse point when SG increments gradually, causing a sharp rise to infinite values [78,69].…”

Procedimento para avaliação em tempo real da estabilidade de tensão de médio e longo prazo

“…Todas essas abordagens são treinadas com dados resultantes de medições PMU online ou de FCCs obtidos de maneira offline. Por outro lado, em Althowibi et al (2012), é obtido um VSI para monitoramento online do sistema a partir das medições PMU (Phasor Measurement Unit), o qual também apresenta uma abordagem para a estimação da MET considerando a sensibilidade do sistema a variações na geração de potência ativa em relação às cargas ativa e reativa. Essa abordagem pode ser aplicada para operações de tempo real, utilizando medidas PMU online ou offline, ou simulações por FCC.…”

“…A maioria destes trabalhos prevê dinâmicas de médio e longo prazo do sistema. Outras propostas, com bastante impacto na literatura são (GLAVIC;CUTSEM, 2009a; CUTSEM, 2009b;ZHAO;YANG;GAO, 2010;ALTHOWIBI;MUSTAFA, 2012;…”