The dynamic behavior of many industrial plants is heavily influenced by disturbances
and, in particular, by changes in the operating point. This is typically the case of
interconnected power system. The large scale power systems are normally divided into control
areas based on the principle of coherency. The coherent areas are interconnected through
tie lines, which are used for contractual energy exchange between areas, providing
inter-area support during abnormal operations. Many investigations in the area of
Load Frequency Control (LFC)—also referred as Automatic Generation Control (AGC)— of interconnected power system have been reported in the past (Fosha and Elgerd,
1970; Elgerd, 1982; Mahalanabis et al., 1988; Kundur, 1994; Nagrath and Kothari, 1994;
Saadat, 1999; and Ibraheem et al., 2005). Several control strategies such as classical
control, optimal control, suboptimal control, adaptive control, variable structure control, etc.,
have been employed in the past to explore an optimum controller for LFC. Because of
the inherent characteristics of changing loads, the operating point of a power system
may change very much during a daily cycle. Thus, fixed gain controllers designed at
nominal operation point may fail to provide best control
performance over a wide range of operating conditions. Adaptive controllers with self-adjusting gain settings have been
proposed. Despite the promising results achieved by these adaptive controllers, the control
adjustments are complicated and require online system model identification. Centralized
information structure and knowledge of all system parameters are technically difficult and
economically unjustifiable.
AGC/LFC determines the active power such that overall system generation
meets the system load. Further, it controls the frequency and the tie-line flows between
different power system areas. An example of fuzzy rule presented in Ha (1998), for the
calculation of Area Control Error (ACE) is: "If ACE is positive but is returning to zero at a slow
rate, don't do anything". It is very difficult to express `slow' mathematically, which is
qualitative in nature. However, this can be easily handled by the fuzzy logic approach. |
