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Go to Editorial ManagerWith the aim of enhancing the small signal stability of electric power systems, the present paper evaluated and compared some power system stabilizers (PSSs). The dilemma of small signal instability is avoided by equipping the generator’s automatic voltage regulator (AVR) with a backup controller known as a PSS. Conventional PSS operates with acceptable efficiency when designed to suit specific operating conditions, but there are limitations and drawbacks that arise when disturbances lead to fluctuation in system parameters. Strengthening the design methodology for PSS in the face of these limitations is achieved by adopting artificial intelligence. This research presents a fuzzy, neural system-based approach to the development of PSS. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to design the Fuzzy Neural Power Systems stabilizer (FNPSS) . ANFIS eliminates the disadvantages of using fuzzy logic and neural networks independently in PSS design. The single machine infinite bus (SMIB) power system was used as a case study to evaluate the effectiveness of the proposed methodology. Additionally, the study includes root locus scheme for loop of voltage regulation by utilizing proportional Integral controller, P-I controller, a widely used traditional linear design technique, for comparison. The simulation results confirm the effectiveness of the method, demonstrating the superiority of the ANFIS design method over other PSS designs. MATLAB, along with Control System Toolbox and SIMULINK, is used for simulation and design.
The development of Fuzzy Logic Controllers (FLC) with low error rates and cost effectiveness has been the subject of numerous studies. This paper study goals to the investigation and then implementation an FLC using the readily accessible and reasonably priced Raspberry Pi technology. The FLC used in this work has two inputs, one output, and five Membership Functions (MFs) for each input and output. The FLC goes through two processes, tweaking the MF parameters and tuning input/ output Scaling Factors. The tuning technique makes use of the Genetic Algorithm (GA). The whole set of the FLC probabilities is taken into account as the tuned FLC controller, and then transformed into a lookup table. The Center of Gravity (COG) approach is used to determine the output for the tuned FLC controller. The resulting table is converted into values of digital binary using a specific type of encoder, and then extraction of the set of Boolean functions to apply this tuned circuit. Finally, the Python 3 programming language is used to define the resultant Boolean functions on the Raspberry Pi platform, and then a decoder extracted the appropriate control action from the output. The Benefit of this method is the use of a digital numbering system to define the FLC, which is implemented on Raspberry Pi technology and allows for fuzzified high processing speed output per second. The controller speed has not been unaffected by the quantity for these fuzzy rules.