This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.
This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID) controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the inputoutput space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm. 25Function-based FPID controllers (F-FPID) [Tang et. al., 2001]. In this paper, the controller functionally performs fuzzy derivative and fuzzy integral functions, so that no calculations are required outside the FLC. The proposed controller employs only two inputs (present and previous errors), so that the design procedure is simpler. Additionally, most fuzzy logic based PID controllers in literature adapt the triangular membership function shape for simplicity of implementation, despite it's linear nature. In this paper, other types of membership functions, such as Gaussian, and sigmoid membership functions are utilized in the design of the controller to allow tuning for the controller membership functions as well as online adaptation of the controller based on the Back-propagation with momentum (BP) learning algorithm.Since the early 1990s, FNN have attracted a great deal of interest because such systems are more flexible and transparent than either NN or FLS alone. Different types of FNN have been presented in the literature [Shing and Jang, 1993]. For tracking control where the reference and dynamics is always changing, the inverse dynamic control is the best to be utilized, despite very difficult to be implemented mathematically. Consequently, researcher tends to use neural network and neuro-fuzzy systems to avoid complex mathematical formulation [Sinthipsomboon et. al., 2011]. In [Anh and Pham, 2010] a gain-scheduling neural PID controller is utilized with 2-axis robotic structure for varying the parameters of the neural PID controller to include information from the robot dynamics. The FNN types can be identified based on the structure of the FNN, the fuzzy model employed and the learning algorithm adopted [Ahn and Anh, 2009]. On the other hand, the most commonly used and successful approach is the feed-forward and recurrent structure model, while using the BP as the learning algorithm [Yuan et. al., 1992, Nauck andKruse 1993]. On the other hand, accord...
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