The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method.

The Self-Organizing Neural Network Map (SOM) may be considered as good tools for classification and clustering process. Therefore, their performance is investigated in this work to make out the main flaw and propose the suitable improvements to overpass their limitations. These latter were extracted by the use of the SOM to classify a set of values with two different dimensions, the computational time cost and the input space normalization were the main deficiencies faced in this process. It's to note that the SOM was used without any kind of supervising or pre-training. The proposed improvement to overpass these deficiencies relies on enhancing the normalization mechanism with more intelligent techniques that can reduce the computational time and regularize the input space to avoid these limitations.

This paper presents the design, simulation, and test measurements of a microstrip bandstop filter operating at 1.5 GHz, incorporating six split ring resonator (SRR) unit cells. The substrate employed is an FR-4 with a thickness of 1.6 mm and tangent losses of 0.025. In the initial phase, the design is conceptualized, simulated using computer simulation technology (CST) studio and advanced design system (ADS) Agilent simulators, and validated through test measurements. Building upon this foundation, the filter is transformed into a reconfigurable variant by integrating four SMV2019 varactor diodes. These varactors are modeled to ensure the reconfigurability of the bandwidth. The integration of varactors introduces dynamic tuning capabilities to the considered bandstop filter.