Comparison of IRPT, ANN and ANFIS based Control Algorithm for Shunt Compensation in Grid Connected Systems

This paper presents the hysteresis controller, based scheme for distribution static compensator (DSTATCOM) for analysis of faults, line-line fault, line to ground fault, single line to ground fault for power quality improvement. DSTATCOM is used for power quality improvement. It provides the deficient reactive power in the distribution system. Conventionally hysteresis controller uses proportional and integral (PI) controller. In this paper, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) based DSTATCOM controllers are implemented for the analysis of different faults. This paper also provides the comparison of different faults for ANN and ANFIS based DSTATCOM with the help sequence analyzer. Sequence analyzer is used for the positive, negative and zero sequence analysis of three phase signal. Per unit study of the output voltage is carried out to compare the results of the different faults based on ANN and ANFIS controller. The simulation results show the improved results for the voltage profiles with ANFIS controller. The simulation of the proposed model is done with the help of MATLAB/Simulink software.

The decay of power quality is one of the principal issues of electrical distribution systems. One of the main reasons for this is the use of non-linear devices like the switching devices and the power electronics converters. Non-linearity leads to detrimental effects on the utility of the power supply, system efficiency, reduction in power factor, etc. The reactive power is increased due to the reduction in power factor and it does not have any contribution in the transfer of energy, hence its compensation is needed. In this paper, the Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) based control methods for a Three-Phase Distribution Static Compensator (DSTATCOM) are proposed. This is used for compensation of the reactive power along with the current related power quality issues that include harmonics. These control methods are simulated in the MATLAB environment with the help of the SIMULINK toolbox. The simulation results suitably demonstrate the performance of these methods and it is found that source current waveforms with lesser distortion are achieved with ANFIS as compared to ANN.

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures.

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

Due to the increasing need for intelligent systems, the adaptive neuro-fuzzy inference system (ANFIS) has recently attracted the attention of researchers in various scientific and engineering areas. The purpose of this work is to present the modeling of a photovoltaic power supply (PVPS) system using an ANFIS. For the modeling of the PVPS system, it is required to find suitable models for its different components (ANFIS PV-generator, ANFIS battery and ANFIS regulator) under variable climatic conditions. A database of measured weather data (global radiation, temperature and humidity) and electrical signals (photovoltaic, battery and regulator voltage and current) of a PVPS system installed in Tahifet (south of Algeria) has been recorded for the period from 1992 to 1997 using a data acquisition system. These data have been used for the modeling and simulation of the PVPS system. The ANFIS for the PV-generator, battery and regulator have been trained by using 10 signals recorded from the different components of the PVPS system. Each signal is represented by 365*5 values (complete 5-years). A set of data for 4-years have been used for the training of the ANFIS and data for 1-year has been used for the testing of the ANFIS. In this way, the ANFIS was trained to accept and handle a number of unusual cases. The comparison between actual and estimated values obtained from the ANFIS gave satisfactory results. The correlation coefficient between measured values and those estimated by the ANFIS gave good prediction accuracy of 98%. In addition, test results show that the ANFIS performed better than the artificial neural networks (ANN). Predicted electrical signals by the ANFIS can be used for several applications in PV systems.

Glass fiber reinforced polyester (GFRP) composite materials are widely used in various applications. The prediction of wear values for composite materials is very complex and nonlinear phenomena. Artificial intelligence methods (AI) and expert systems such as artificial neural networks (ANNs) and fuzzy inference systems (FIS) have a series of properties on modeling nonlinear systems. In some situations, ANNs are insufficient under abrupt changes in input variables. Adaptive Neuro Fuzzy Inference System (ANFIS) is capable of integrating the linguistic expressions of FIS with the adaptation and learning skills of the ANNs. The aim of this study is to determine the optimum material content and working conditions in terms of wear resistance. This study proposes an ANFIS sub-clustering based prediction model for estimation of wear behavior of GFRP composites within various concentrations of materials and under diverse loads and speeds. Proposed ANFIS model extracted optimum concentrations and operating parameters to obtain the minimum wear rate. Due to the wear rate estimation model, optimum wear rate value is reached to 25.0013 (mm3/Nm)*10−6 at CaCO3, polystyrene, glass fiber, glass bead, alumina, load and speed values of 49%, 0%, 11%, 10%, 0.8%, 10 N and 100 rpm respectively. A high estimation capability (R2 = 0.964) has been achieved using ANFIS Model.

Process control strategies for a steel making furnace using ANN with bayesian regularization and ANFIS

RSM, ANN and ANFIS applications in modeling fermentable sugar production from enzymatic hydrolysis of Colocynthis Vulgaris Shrad seeds shell

Integrating Solid Oxide Fuel Cells (SOFCs) with the electrical grid and diesel generators at the point of common coupling forms a hybrid power system. However, power converters and nonlinear loads contribute to voltage drops and current distortions, especially under dynamic and imbalanced load conditions. Transient mismatches between supply and demand can lead to voltage instability, while the electrochemical response of SOFCs causes oxidant flow disturbances, accelerating H2 starvation, O2 imbalance, thermal stress, and efficiency loss. This study proposes a novel Adaptive Neuro-Fuzzy Inference System (ANFIS)-controlled Adaptive Tunable Shunt Active Power Filter (AT-SHAPF) to enhance power quality in Solid Oxide Fuel Cell (SOFC)-driven hybrid systems under unbalanced supply and nonlinear load conditions. The core contribution lies in employing ANFIS for intelligent real-time compensation of harmonics and reactive power, outperforming conventional Proportional-Integral (PI) and Fuzzy Logic Controllers (FLC). The system is evaluated using MATLAB/Simulink to analyze key performance metrics: Total Harmonic Distortion (THD), Current Distortion Factor (CDF), and true power factor. Without compensation, THD reaches 29.31% and 28.68% under steady and dynamic load conditions, respectively. With compensation, PI-based AT-SHAPF reduces THD to 5.88% and 5.46%, FLC-based to 3.22% and 4.03%, and ANFIS-based to 1.63% and 1.98%. Additionally, the Instantaneous Reactive Power Theory (IRPT) is applied for real-time reference current generation, enabling precise compensation. The proposed ANFIS-controlled AT-SHAPF demonstrates superior adaptability, power factor correction, and harmonic suppression compared to traditional methods.

This study examined the modelling and optimisation of the electrocoagulation-flocculation (ECF) recovery of aquaculture effluent (AQE) using aluminium electrodes. The response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were used for the modelling, while the optimisation tools were the numerical RSM and genetic algorithm (GA). Furthermore, the kinetics of the ECF process was studied to provide insight into the mechanism governing the ECF of AQE. The experimental design was performed using the central composite design (CCD) of the RSM. The ANFIS modelling was accomplished via the Grid Partition (GP) of the data set, while the ANN used the multi-layer perceptron (MLP) based feed-forward system. Statistically, the prediction accuracy of the models followed the order: ANFIS (R2: 0.9990), ANN (R2: 0.9807), and RSM (R2: 0.9790). The process optimisation gave optimal turbidity (TD) removal efficiencies of 98.98, 97.81, and 96.01% for ANFIS-GA, ANN-GA, and RSM optimisation techniques, respectively. The ANFIS-GA gave the best optimization result at optimum conditions of pH 4, current intensity (3 A), electrolysis time (7.2min), settling time (23min), and temperature (43.8°C). In the kinetics study, the experimental data was analysed using pseudo-first-order (0.8787), pseudo-second-order (0.9395), and Elovich (R2: 0.9979) kinetic models; the Elovich model gave the best correlation with the experimental data showing that the process is governed by electrostatic interaction mechanism. This study effectively demonstrated that ECF recovery of AQE can effectively be modelled using RSM, ANN, and ANFIS and be optimised using RSM, ANN-GA, and ANFIS-GA techniques, and the order of performance is ANFIS > ANN > RSM and ANFIS-GA > ANN-GA > RSM, respectively.

In recent years, power electronics devices have evolved, and used for varies applications. Also, this device caused the problem of power quality in electrical system. In the utility side arc furnaces, variable frequency drives (VFD), personal computer, fluorescent lamp such non-linear load produces current harmonics. For consumer and distributed side the power quality is an important issue. The active power filter which is capable for improving the power quality and reactive power compensation. In this paper the harmonics problem which is created by non-linear load is discussed. Shunt active filter is used for eliminating the harmonics from non-linear load and in the retrieval technique for controlling the current hysteresis current controller is used. In this paper for producing the reference current instantaneous reactive power (PQ) theory is used. The simulation result of shunt active filter using PQ theory is carried out using Matlab-Simulink toolbox.

This paper proposes an intelligent control strategy based on the adaptive neuro-fuzzy inference system (ANFIS) to enhance power quality in wind energy systems connected to weak grids. Weak grids, characterized by high impedance and low short-circuit ratios, suffer from voltage fluctuations, harmonic distortions, and reactive power imbalances when integrating wind energy. Conventional control methods, such as proportional-integral and fuzzy logic controllers, lack real-time adaptability, limiting their effectiveness in weak grid scenarios. The proposed ANFIS-based synchronous reference frame (SRF) control for a distribution static compensator (DSTATCOM) introduces an intelligent learning mechanism that dynamically adjusts reactive power compensation, harmonic mitigation, and voltage stabilization based on grid conditions. Unlike traditional approaches, the ANFIS-SRF controller leverages self-adaptive tuning and non-linear decision-making capabilities, ensuring superior system performance. The obtained simulations validate the effectiveness of the proposed method, demonstrating that grid voltage total harmonic distortion is reduced from 11.26 to 9.83% under non-linear load conditions and from 4.97 to 2.64% in mixed-load scenarios, maintaining compliance with IEEE 1547 and IEEE 519-2014 standards. Additionally, the power factor is significantly improved, reaching values above 0.98, while the proposed controller successfully maintains grid voltage and current stability under varying wind conditions. These numerical findings assures that the ANFIS-SRF-controlled DSTATCOM outperforms traditional control methods in ensuring reliable and efficient wind energy integration into weak grids. This study contributes to intelligent grid control applications by providing a self-learning, real-time adaptive solution that enhances grid stability, power quality, and renewable energy penetration.

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP