Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems

Abstract

Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.

Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations

“…This concept grants a compact inference process oriented toward control. • Applicability in neuro-fuzzy supervised systems using the structure in Figure 7, and Equations (16) and (17). Training parameters in a supervised form are achieved when using continuous membership functions, and fuzzy operators as both, the t-norm product and the algebraic sum to t-conorm.…”

“…For more complex applications related to the FIS-BBR, can be considered [15] where a neuro-fuzzy control system for the Control of a Permanent Magnet Synchronous Generator is presented. Additionally, [16] presents an adaptive scheme to the Control Flow Power in Distributed Generation Systems. These works use a compact fuzzy scheme equivalent to an FIS-BBR, which is determined in a heuristic way taking a discrete linear time system which is converted to a compact fuzzy system.…”

“…After this work, where the design principles are presented, the development of more complex applications would be expected, however, in a heuristic way, making an analogy with the operation of a discrete time dynamic system, a structure of an FIS-BBR could be obtained such as those shown in [15,16]; particularly, in [15] there is a neuro-fuzzy control system where plant identification and system control is performed using an equivalent FIS-BBR scheme, in [16], further progress is made by identifying the plant and optimizing the controller in an iterative way to have an adaptive control system. In these works, the similar FIS-BBR scheme is determined in a heuristic way; however, it could be formulated through the methodology proposed in this document, this aspect is expected to be addressed in future works.…”

An Improved Backward/Forward Sweep Power Flow Method Based on a New Network Information Organization for Radial Distribution Systems

“…Avoiding those investments is possible, thanks to a tool named "load flow analysis" [7], which is able to calculate the needed network information only from the values of load consumed or injected of each node. Load flow analysis is a widely used tool transmission system in several applications, as power generating scheduling [8], and could also be very useful for many applications in distribution networks, such as network analysis [9], load control [10,11], network reconfiguration [12], integration of generation [13], optimal allocation of UPQC [14], and integration of electric vehicle [15]. Several methods had been developed and deployed in the transmission system such as Gauss-Seidel [16], Newton-Raphson [17] and fast-decoupled methods [18].…”

Steady‐State Analysis and Output Voltage Minimization Based Control Strategy for Electric Springs in the Smart Grid with Multiple Renewable Energy Sources

“…In recent years, due to the energy crisis and the increasingly severe environmental issues, renewable energy sources are attracting more and more attention. Nowadays, wind [1][2][3] and solar energies are the two most widely used renewable energy sources for distributed generation [4][5][6]. However, due to the highly intermittent and unpredictable natures, renewable energy power generation brings a series of challenges to the power grid.…”