Automatic controller for steam generator water level during low power operation

Robust estimator-based optimal control designs for U-tube steam generators

“…Relying only on the water level measurement has proven insufficient, as water level response can become unstable when single-element control is used during start-up and low power operation mode (Irving et al, 1979;Menon and Parlos, 1992;Tong, 1988). This frequently forces operators to control the water level manually at the low power mode, which has been found to be unreliable, resulting in poor overall system performance and causing numerous plant shutdowns (Choi et al, 1989;Irving et al, 1979;Menon and Parlos, 1992;Na, 2001;Tong, 1988). The transfer of the modern technology into UTSG control systems has improved the control performance (Doran et al, 1995), but the overall performance is still poor because of the same traditional control strategy.…”

“…Several studies have shown that the UTSG control system is a major contributor to nuclear power plant unavailability, about 25% of the causes, mainly due to the poor control of the UTSG water level (Irving et al, 1979;Menon and Parlos, 1992;Tong, 1988). Hence, the UTSG control designs have expanded over the last 30 years in the literature, including adaptive control (Irving et al, 1979), PID-type controllers (Choi et al, 1989;Dong et al, 2008;Futao et al, 1996;Liu et al, 2010;Na, 2001;Suh and No, 1985;Thomas et al, 1988;Zhao et al, 2000), model predictive controllers (Hu and Yuan, 2008;Kothare et al, 2000;Lee et al, 2012;Na and Lee, 2003), H N controllers (Parlos and Rais, 2000;Sohn and Seong, 2010), L 2 controllers (Kim et al, 1999), the linear quadratic regulator (LQR) method (Basher and March-Leuba, 2001;Forrest et al, 1992), fuzzy and neurofuzzy controllers (Marseguerra et al, 2007;Munasinghe et al, 2005), output feedback control (Dong et al, 2009), data driven-based methods (Ahmad, 2010), adaptive backsteppingbased control (Wei et al, 2014), the adaptive observer-based method (Na and No, 1992) and sliding mode control (Ablay, 2012;Ansarifar et al, 2012;Moradi et al, 2012). The above methods have considered either local operating conditions for low power mode or piece-wise linear regions to have a UTSG control scheme.…”

Optimal Tracking Control for a Discrete Time Nonlinear Nuclear Power System

“…Since the last century, one mature control strategy called PID control policy has deeply a ected the power control in the nuclear industry [1,2]. With the advancement in control technology, some model predictive control (MPC) and multimodel adaptive control theories focused on local linearization to approximate the nonlinearity of nuclear power systems and have also been applied in this area [3][4][5].…”

A robust estimator-based optimal algebraic approach to steam generator feedwater control system

“…Relying only on the water level signal has proven inadequate because the water level response can become unstable when single-element control is used during start-up and low power operating mode [1][2][3]. This frequently forces operators to manually control the water level in the low power mode, which has been found to be unreliable, resulting in poor overall system performance and causing numerous plant shutdowns [1][2][3]5,9]. With the usage of modern technology in the feedwater control systems, the control performance is improved 36], but still the overall performance is poor due to the same traditional control strategy.…”

“…Several studies have shown that the steam generator feedwater control system is a major contributor to plant unavailability, about 25% of the causes, mainly due to poor control of the steam generator water level [1][2][3]. For this reason, the steam generator feedwater control designs have expanded over the last thirty years in the literature, including adaptive control [1], PID type controllers [4][5][6][7][8][9][10], H ∞ controller [11,12], L 2 controller [13], model predictive controller [14][15][16][17], output feedback control [18], linear quadratic regulator method [19], adaptive observer based method [20], fuzzy and neurofuzzy controllers [21,22], data driven-based methods [23], adaptive backstepping-based control [24], and sliding mode control [25][26][27]. Most of these methods considered either local operating conditions (specifically for a low power mode) or piece-wise linear regions to provide a feedwater control mechanism.…”