finding the severity of the disturbance is also desirable in addition to accuracy for unstable operating scenarios.
For the control operation there is a need to identify the critical machine and non-critical machines which can effectively participate in the generator reschedul- ing during the unstable state of the system to ensure stable system operation. In large power systems, generally the information about coherency of generators can be effectively used to decide the participating generators for rescheduling to improve the transient stability of power systems. Usually methods discussed in literature for coherency identification generally require extensive calculation. Therefore fur- ther investigations are required for ANN applications in finding relative stability of each generator rather than finding the overall system stability as well as coherency identification for stable power system operation and control.
2.3 Power System Stability Enhancement Meth-
dimension of the TSSCOPF problem, it is really a tough exercise to deal with this type of problem. For a given power system configuration, although the number of possible contingencies are numerous, there are a few critical contingencies that may cause instability. Various optimization techniques have been evolved in the last two decades to solve the Transient Stability Constrained OPF (TSCOPF) problem.
An improved genetic algorithm (GA) was proposed by Chan et al. [219] to solve multi-contingency TSCOPF problem where generator rotor angle constraints were additionally considered. An IPM method was introduced by Xia et al. [220] to efficiently perform the TSCOPF.
If the TSA detects that the system is vulnerable to an anticipated contingency, preventive Transient Stability Control (TSC) measures such as generation reschedul- ing should be taken to drive the system to the stable state. TSCOPF is becoming an effective tool for many problems in power systems since it simultaneously considers economy and dynamic stability of system operations. Generation rescheduling is a typical TSCOPF strategy used in [221, 222] to shift power from the most advanced generator to the least advanced generator so as to cause the system to move to a stable operating point. In the past, classical optimization techniques such as in- terior point method [119], and Linear Programming (LP) [120] were employed for TSCOPF solution. These techniques have many limitations. They need an accept- able starting point that should be close to the solution in order not to be stuck in local optimum and have poor convergence. The quality of solution substantially de- teriorate because it depends on the initial conditions and the number of parameters in the problem. Additionally, as they have extremely limited capability to solve real- istic power system problems, the mathematical relationships have to be simplified to obtain the solution of the problem. They are also weak in processing qualitative con- straints. Therefore, many heuristic optimization techniques have recently become more and more attractive in OPF and TSCOPF solution for researchers. Moreover, in the recent past, various other nature-inspired optimization algorithms have been also designated and applied to solve the TSCOPF problem of power system. These includes Particle Swarm Optimization (PSO) [121], Genetic Algorithm (GA) [122], and Differential Evolution (DE) [123], Artificial Bee Colony (ABC) [124], Chaotic Artificial Bee Colony (CABC) [124], Whale Optimization Algorithm (WOA) [125]
and Chaotic Whale Optimization Algorithm (CWOA) [125].
2.3.1 Critical Review
Apart from the assessment part of the power system stability, selection of appropri- ate control strategy is also a major concern to enhance the power system stability.
From the literature survey it is observed that generation rescheduling is one of the control strategy for Power system stability enhancement. Research shows the ap- plication of several meta-heuristic algorithms are available to perform the economic load dispatch. However, the key factor in rescheduling the generators is the fuel cost incurred in a particular approach. The objective of security enhancement by rescheduling the generators with minimum fuel cost can be achieved with the design of an efficient algorithm. This factor motivated to develop an efficient algorithm for contingency constrained economic load dispatch for security enhancement.
From the above research background, it is observed that for security assessment ANN methods have advantage over classical methods. Thus, there is a scope for modeling neural networks for the prediction of severity of a contingency for static security and dynamic security assessment. Similarly, the literature review for the security and stability enhancement & control mechanism shows the use of many meta-heuristic algorithms for the optimal power flow and the generation reschedul- ing. But the important aspect which comes into picture in the control scenario is the cost incurred to perform the task by considering security aspect. This factor mo- tivated to develop an efficient algorithm, which can reschedule the generators with minimum fuel cost, considering its static security and transient stability aspects under contingency scenario.
From the above discussion, it is clear that for the power system static security assessment, dynamic stability assessment and the enhancement, there is a need and scope to develop fast and efficient algorithmic techniques. The application of different algorithmic techniques for solving different aspects of power system stability is the main source of motivation for the present research work.