## 1 Introduction
The proposal includes moth flame optimizer variants to mitigate the frequency constraint issue. In addition, the interconnection and damping assignment method is presented in [21] where the SG model is represented as a Port Controlled Hamiltonian system. The applicability of that proposed scheme is illustrated by the simulation results for efficient reference voltage tracking and attenuation of electromechanical oscillations when large disturbances are presented. The electric power system is one of the most complex systems that engineers have created. Moreover, its relation demand/generation is changing continuously. It has to respond in a fast way when adverse events (some of them severe) take place and ensure its good performance with the integration of new components [22]. However, dealing with this wide range of variables in the control design stage is not always possible. Thus, adaptive control algorithms could overcome this drawback by updating the control response to the new circumstances of the power system and for a particular steady state condition. These dynamic requirements must be largely guaranteed by auxiliary control elements of SG with a suitable regulation strategy. Therefore, new control strategies must interact in a positive way with the current controllers with less dependency of model and parameters of components in the power system [23]. The automatic voltage regulator in SG plays a fundamental role in the power system dynamic performance. However, the model dependency of the control strategies could degrade the performance of the system due to the continuous changes in electrical grid operation. This dependency could be minimized with an adaptive performance included in the regulation strategy when large scale power systems are evaluated. With the proposed strategy it is possible to include uncertainties in the formulation, avoiding higher complexity by using a scheme based on B-spline Neural Networks. There are several ways to model an SG. The appropriate model selection depends on the time-scales and phenomena under study [2,3]. For small system analysis, detailed models are often used [1]. Considering the transient response, a simplified model is commonly used, which includes the electromechanical variables along with the internal voltage. This model presents a compromise between the description of the dynamics of interest and a reduction in the computational complexity to attain a solution for large scale power systems. This paper deals with the problem of transient stabilization and tracking of the reference profile of the power-angle for synchronous generators in interconnected electric power systems. A new adaptive neural stabilization strategy within the context of a reference trajectory tracking problem in interconnected power systems is proposed. In the presented approach, differential flatness and B-spline Neural Networks are synergically combined to compensate significant parametric uncertainty and reduce dependency in detailed nonlinear mathematical models of synchronous generators in interconnected power systems. Traditionally, two main approaches have been used in this sense, the first, based on the mathematical model and the last, based on measurements. Moreover, the problem significantly increases when more synchronous machines are included in the formulation, as occurs in real power systems. With our proposed strategy, the advantages of both approaches are extracted. First a systematic design procedure for the controller of each SG is obtained where the number of included machines is not a restriction and then simultaneous fine tuning is developed for each controller without additional requirements in design stage or knowledge of current steady state condition. The last task is carried out with the B-spline Neural Network scheme, which replies for changes in the system to attain the desired dynamic performance, without the need of the system model.