Fuzzy Interference System Research Articles

AI-based modeling studies for dye removal using mixed biomass composites from algae and plant seeds: Isotherm, kinetics, and mechanistic insights

Biomass based remediation have emerged as a promising solution for the economic and sustainable pollutant removal from environment. In this study, novel mixed biomass derived from algae and manila tamarind seeds was synthesized and extensively characterized for application in dye adsorption. Mesoporous structure with pore diameter of 4.006 nm was observed from Brunauer-Emmett-Teller (BET) characterization. Best fit for isotherm was identified with Langmuir model indicating the monolayer nature of dye removal. Artificial intelligence (AI) based models – artificial neural network (ANN) and adaptive neuro fuzzy interference (ANFIS) system were employed for the prediction of dye removal under different conditions, a unique approach which has not been explored in previous studies based on mixed biomass adsorption. In addition, embedding particle swarm optimization (PSO) had a significant improvement in prediction of dye removal. For both Eriochrome black (EB) dye and Brilliant Orange (BO) dye adsorption employing mixed biomass, the ANN-PSO model yielded the best correlation of 0.9999 and 0.9994. For eriochrome black and brilliant orange dye, the maximum adsorption of 79.95 mg/g and 102.4 mg/g for the mixed biomass composite was noted, highlighting the adsorption efficiency of biomass. The uniqueness of the study lies in the integration of mixed biomass adsorption with AI driven modeling, providing a dual approach to enhance dye removal and adsorption efficiency. In addition, the regenerated mixed biomass can be reused for five consecutive cycles, highlighting its practical application, distinguishing from existing research that focused on single use biomass systems.

Adaptive Neuro-Fuzzy Interference System (ANFIS) Control of a Dynamic, Two-Region Nuclear Power Plant Pressurizer Model

Abstract Designing modern control systems is a critical factor in producing rigid control and effective performance of non-linear and complex systems—particularly employing intelligence-based controllers, derived from soft computing algorithms. It is known to be well capable of exploiting tolerances for uncertainty and nonlinearity. Fuzzy logic, which utilizes soft computing techniques, deals with vague information and approximate reasoning, yet it lacks effective learning capability, while ANN is known for exceptional learning and adaptation of training data. Adaptive neuro-fuzzy interference system (ANFIS), a hybrid of these two algorithms, is a fuzzy interference system optimized by neural networks. Most nuclear generating stations still use PID controllers, which does not show good controlling capabilities, resulting in large coolant level overshoot and longer settling time. This paper bridges this gap by testing an ANFIS approach that yields high predictive and control capability. In validating the configured control loop model, three loss-of-load transients from a Shippingport reactor were tested, trained and demonstrated using the simulations in the MATLAB/Simulink environment. The minimal training RMSE for the 51 MW(e), 74 MW(e), and 105 MW(e) loss-of-load transients are 18.85%, 19.37%, and 24.36%. The pressure response showed ideal output, with all three steady-state pressures close at setpoint (14 MPa), proving that the ANFIS controller is well capable of rejecting disturbances with lesser overshoot and faster settling times in maintaining level setpoint. Validating solely the pressurizer model was executed in a turbine leading power trip from a 100% to 75% reduction range in PCTRAN, a pressurized water reactor (PWR) simulator made available by IAEA. The comparative performance exhibited a good fit between the reported simulation data and model data. Both results against simulation and experimental transients captured the effective predictive and control capabilities of the pressurizer unit and control system.

Fault classification and mitigation in grid-connected solar PV system using hybrid whale optimization algorithm with ANFIS

ABSTRACT Generally, fault mitigation and classification technique has become an important issue in grid-connected PV (GCPV) systems. This study intends a control topology for fault grouping and mitigation scheme in GCPV systems. Whale optimization algorithm (WOA)-ANFIS controllers are applied to uphold the DC-link voltage in UPQC as a perpetual limit. The ANFIS algorithm is employed to attain the apprising procedure of the WOA. The ANFIS controller is working based on the ANN (Artificial Neural Network) and FIS (Fuzzy Interference system). Additionally, the fault classification is proposed in this paper. The fault classification is attained through the use of DWT and ANFIS. Initially, the normal signals are analyzed; the fault is created and extracted in the consumption of DWT. Then the extracted characteristics are fed to the ANFIS for classification. The ANFIS classified faults in the consumption of fuzzy rules. The goals of the projected model are to classify and mitigate the faults in GCPV systems. The projected system is executed using MATLAB/Simulink. So as to investigate the efficacy of the projected technique, the several categories of concerns are examined and verified with the prevailing tactics for instance, UPQC with PSO-ANFIS, UPQC with WOA-ANN method.

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

AbstractThe increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

Utilizing ANFIS for strength characteristics forecasting in variable heat-cured geopolymer composites

Reducing cement usage through geopolymer concrete (GPC) can be crucial for resolving environmental concerns. The byproduct of the coal and steel industry, such as fly ash (FA) and ground granulated blast furnace slag (GGBS), is mainly used as a precursor in the production of GPC due to the availability of aluminosilicate products in it. In this work, nano silica is added to the GPC in different percentages and GPC is cured at different temperatures to study the change in the strength of GPC due to change in curing temperature. The compressive strength, flexural strength, and split tensile strength up to 68.99 MPa, 5.89 MPa, 6.82 MPa, respectively was achieved after 28 days of heat curing. However, testing GPC experimentally to anticipate its strength is time-consuming and expensive. Therefore, the ability to precisely forecast concrete strength can be achieved through machine learning. In this study, the correlation for both nano silica (%) and curing temperature towards the strength of concrete is also investigated using adaptive neuro fuzzy interference system (ANFIS) models. ANFIS results showed better compressive, split tensile, and flexural strength prediction with the highest R2 0.9801, 0.9943, and 0.9877, respectively. The performance efficiency of the developed ANFIS model is more significant minimum error for compressive strength prediction (MAE = 0.21375, RMSE = 0.02574, MAPE = 0.42), split tensile strength prediction (MAE = 0.07375, RMSE = 0.0923, MAPE = 1.46) and flexural strength prediction (MAE = 0.04, RMSE = 0.0728, MAPE = 0.78) of GPC cured under different temperature conditions. The results from experiments show that the prediction efficiency of the model is also good, as indicated by the correlation coefficient (R). This model is helpful for further designing the proportion of material for achieving good strength of GPC.

A public static agreement key based cryptography for secure data transmission in WSN based smart environment application

Wireless Sensor Networks (WSN) is an emerging networking technology that allows for the low-cost, unattended monitoring of a variety of environments, thus WSN technologies have become increasingly important. Simultaneously, lack of security and poor energy efficiency is considered the major concerns in many routing protocols in various existing WSN protocols. Security and energy efficiency concerns are mainly due to the emergence of various kinds of malicious attacks because of advancements in networking technology. To mitigate these issues secured transmission is essential in any routing protocol. In order to provide safe data transfer in WSN-based smart environment applications, the TABEESR protocol (Trust Analysis Based Energy Efficient and Safe Routing) was developed. The proposed routing protocol encompasses four phases such as network creation and node deployment, grading for trust evaluation, route identification process and data transmission. The trust evaluation is performed with the aid of sensor packet features to categorize the positive nodes and negative nodes. The path identification process includes two processes such as cluster head selection through red fox optimization and relay node selection using a fuzzy interference system for energy efficient and secure path selection. A public static agreement key based cryptography technique is introduced for secure data transmission. The proposed TABEESR is tested with MATLAB software to validate its performance. SMEER, SHER, SRPA and AFSA are the prior routing protocols in WSN that are considered to compare the performance of the proposed routing protocol. The proposed TABEESR algorithm attained 95(%) of Average Residual Energy, 91 (%) of packet delivery ratio, 1350 (sec) network lifetime, 0.131 (Mbps) of throughput and 7(%) Packet Loss. Moreover, the proposed PSAKC algorithm attained 5.8 (%) information loss, 3.8 (sec) encryption time, 2.3 (sec) decryption time, 6 (sec) execution time, and 6.5 (J) energy consumption which is better than the prior methods. The proposed routing algorithm is applicable for providing security in both server to device and device to device in various smart applications.

Sensorless speed and position control of permanent magnet BLDC motor using particle swarm optimization and ANFIS

This paper describes the operation of a Permanent Magnet Brushless Direct Current (PMBLDC) motor without a position sensor. In this case, the sensorless operation is enhanced by an effective hybrid technique that detects the back electromotive force (Back EMF) of the zero crossing point (ZCP) from the terminal voltages. The Adaptive Neuro Fuzzy Interference System (ANFIS) controller, which is based on Particle Swarm Optimization (PSO) and uses PSO to train its operation, is combined in the proposed hybrid analysis. The PMBLDC motor's ANFIS controller receives the line voltages as input, and it uses this information to estimate the sample signals that are then sent to the ZCP detection circuit. Appropriate commutation control of the inverter is generated by the ZCP detecting circuit. By varying the ANFIS consequent parameters, the PSO algorithm iterates until the error between the sample output and the real training data reaches a low value. The MATLAB/Simulink platform is utilized to implement the suggested sensorless controller action. To verify the controller's performance, a comparison with the other soft computing methods is also carried out.

Suitability mapping and management monitoring in Castilian organic and conventional wheat fields with Sentinel-2 and spatial data

The Mediterranean region is a climate change hotspot due to projected precipitation decreases, soil erosion, and rising temperatures in the coming decades. In such a context, wheat productivity is expected to decrease; thus, all available croplands will need to be farmed to meet food demands. It is therefore crucial to have adequate management to help balance wheat production and environmental protection in specific target environments. With this aim, we present a study in Castile and Leon (Spain) in which we assess conventional and certified organic wheat grain yield and quality. Moreover, we spatially define wheat cultivation suitability categories and monitor crop rotation practices. For wheat suitability mapping, a fuzzy interference system was used to standardize topographic (height and slope), edaphic (pH, soil erosion, organic matter, and texture), and climatic (rainfall and temperature) variables and create marginal, suitable, and very suitable wheat cultivation suitability categories. We calculated the area of wheat suitability categories with confidence intervals and discussed factors affecting productivity. Additionally, we assessed the performance of certified organic and conventionally managed fields with organic and chemical fertilization. We also examined crop rotations using Sentinel-2 data over two seasons (2020−21). Certified organic management yielded 50% less than conventional management in similar environments while maintaining grain quality. Besides management, the main causes of the yield gap are the growing environment and variety. In conventional wheat management, organic fertilization achieves comparable yields to chemical fertilization. Crop rotation practices are uncommon, and over 50% of fields rotate wheat-to-barley or wheat-to-wheat, indicating poor soil management. The region has 25% of cropland classified as marginal wheat croplands (163,503 ± 33,796 ha) producing <2500 kg/ha. Wheat organic agriculture has decreased yield gaps in marginal croplands compared to more productive areas. By farming organic wheat in marginal croplands, the best croplands might be used for more productive farming.

Radial basis function neural network controller for power control of molten salt breeder reactor of nuclear power plant

The molten salt breeder reactor (MSBR) basically uses molten salt as fuel. Due to molten salt, it is difficult to control power of the MSBR core. To solve this problem, a neural network control scheme is adopted for the same system. Firstly, a nonlinear MSBR model is taken and after linearizing a single input single output (SISO) system is constructed out of it. Radial basis function neural network (RBFNN) has strong tolerance to input noise, online learning ability and good generalization. Therefore, RBFNN controller is used for controlling the core power of the MSBR. The conventional RBFNN dominates in the neural active region. Compact RBF networks has been designed by using newly developed algorithm. By using Lyapunov stability criterion, it has proved that the variation of core power is bounded and it may be comparable to any traditional neural networks or fuzzy interference system. Results of the study reflect the efficiency of RBFNN controller for controlling power of MSBR core.

Software Reliability Growth Model for N-Version Fault Tolerant Software with Common and Independent Faults

Research and development teams have become increasingly focused on developing highly reliable software for safety-critical systems. It is a major challenge for real-time control systems to achieve high reliability software to meet safety standards. A reliability evaluation focuses primarily on analytical and modeling techniques for fault prediction. In safety-critical systems like nuclear plant controls, aircraft controls and railroad signalization systems, N-version programming (NVP) is an effective technique for raising software’s reliability, particularly in areas with high-risk ratios because small errors can result in hazardous incidents. It allows the software to be fault-tolerant, aiding it to produce accurate results even when the software has faults. We present an analytical method for assessing the reliability of N-version software systems. Analysis of the system’s reliability and other performance metrics is provided with closed-form expressions. As an additional extension, we conduct numerical analyses of two cases, the 2VP system and 3VP system, in which suitable parameters are used. We conduct numerical simulations using MATLAB to generate the analytical results and compare the analytical results by using numerical results and neuro-fuzzy results using fuzzy interference systems.

An Efficient PV - Integrated UPQC System for Power Quality Enhancement Using Improved Chicken Swarm Optimization

In today’s world, the power distribution system's problems with Power Quality (PQ) are not unfamiliar. However, customer awareness of these issues has also risen. Likewise, there are many conventional solutions to improve the power quality. However, these traditional solutions rely on passive elements and frequently fail to respond effectively as the nature of the power system changes. Custom power devices provide adaptable solutions for many issues. Therefore, a Unified Power Quality Conditioner (UPQC) Coupled with a PV system is proposed in this paper to improve the PQ problems, including fluctuations, voltage sag and swell etc. The deployment of an Interleaved Zeta converter aids in maintaining the constant DC link voltage. In order to derive the maximum desirable power from PV system, STATCOM with Adaptive Neuro- Fuzzy Interference System (ANFIS) is used, which is fine-tuned with the help of the Improved Chicken Swarm (ICS) algorithm. In addition, UPQC with series and shunt converter is employed to decrease the current and voltage harmonics, thereby enhancing the power factor. For controlling the working of UPQC, the d-q theory-based Proportional Integral (PI) is proposed. The suggested design of PV-UPQC is evaluated and the simulation results are obtained in the MATLAB platform with a reduced THD of 2.01%.

ANFIS Control for Solar PV fed Modular Multilevel Inverter for Marine Water Pumping Applications

In this paper, we show our work developing and implementing a Modular Multilevel Inverter (MMI) for intelligent Induction Motor (IM) drive control. Pumping water from the sea is the primary focus of these methods. It has been suggested that an eleven-stage inverter could be used to regulate the speed of a photovoltaic solar-powered IM drive. It is estimated that pumps account for roughly half of a ship's total energy consumption. Therefore, the purpose of this study is to investigate and assess the proposed control design for an induction motor (IM) drive and MMI-based marine water pumping system. The primary motivation behind this design was to reduce the complexity of the controller. Controllers based on Proportional-Integral (PI), Fuzzy Logic (FL), and Adaptive Network-based Fuzzy Interference System (ANFIS) are tested alongside the inverter to see how well they improve performance. The Total Harmonic Distortion (THD) of inverters, controller settling times, and peak overshoot have all been compared to identify the most robust option. In order to establish which of these elements is superior, this comparison was performed. The suggested control scheme's novel feature is the design and integration of a marine-specific MMI, IM drive, and intelligent controller for use in marine water pumping applications.

Novel template free synthesis of high surface area mesoporous ceria and adaptive neuro fuzzy interference system (ANFIS) modelling for Cr(VI) adsorption

The search for sustainable and ecofriendly mesoporous materials with high surface area is intensively desired for catalytic applications. Herein, we explore a template-free hydrothermal method for synthesizing mesoporous Cerium Oxide (meso-CeO2) nanoparticles as an adsorbent, for the removal of toxic hexavalent chromium Cr(VI) from water. The morphology and structure of synthesized nanomaterials were characterized using various techniques such as X-ray diffraction (XRD), scanning electron microscope (SEM), UV–visible spectrophotometer (UV–Vis), Fourier transform infrared spectroscopy (FTIR), and Brunauer, Emmett, and Teller(BET) analysis. The BET analysis confirmed CeO2 possess a high surface area of 270 m2/g, a 4.5 nm pore diameter, 0.22 cm3/g pore volume, and a stable mesoporous structure. The synthesized meso-CeO2, exhibited efficient removal (99.8% at pH 5) of Cr (VI) from water within 60 min at room temperature. The meso-CeO2 material demonstrated reusability for up to 5 cycles. The removal of Cr (VI) using meso-CeO2 followed Pseudo-second order kinetics (R2 > 0.99) and showed a good fit with the Freundlich isotherm model. Moreover, adaptive neuro fuzzy inference system (ANFIS) models were employed on 320 experimental data sets to predict the adsorption process accurately. Overall, this study highlights the potential of meso-CeO2 nanoparticles as a promising adsorbent for effective water remediation of heavy metal contaminants like Cr (VI).

Speed Control of Brushless DC Electric Motor (BLDC) Motor Using Hybrid Takagi Sugeno Fuzzy Logic and Enhanced Gravitational Search Algorithm with PSO

The brushless direct current motors have gained popularity among the emerging technologies due to their increased efficiency, speed of operation, and density of flux. This paper presents a novel technique for controlling the speed of the brushless direct current motor by using hybrid algorithms. The Particle Swarm Optimization (PSO) algorithm is combined with the Takagi Sugeno adaptive fuzzy interference system and gravitational search algorithms to analyze the speed control of the brushless DC electric motor (BLDC) motors. The proposed model has designed multiple outputs prototype for BLDC motor with Takagi Sugeno fuzzy logic. Since the adaptive fuzzy logic system has three outputs, the final output is evaluated as the average value of all the outputs using the LMS algorithm. The speed control of the Brushless DC motor has also been analyzed using a combination of the gravitational search algorithm and the PSO algorithm. The motor parameters taken under consideration for the analysis have been tabulated. The different results obtained at the end of this study have been demonstrated through graphs and tables.

Backstepping-Based Artificial Intelligence Algorithm for Photovoltaic-Quadratic Boost Converter System

With energy demands rising, fossil fuel resources depleting, and global warming caused by carbon emissions, there’s an urgent need for efficient, renewable, and clean energy. Solar energy, especially photovoltaic systems, is gaining popularity due to its simple structure, low energy production costs, and eco-friendliness. However, these systems suffer from insufficient efficiency due to environmental factors. To address this issue, a Maximum Power Point Tracking interface is required to extract the full power from the photovoltaic panel and increase its efficiency. This study proposes a Backstepping Maximum Power Point Tracking algorithm for photovoltaic systems based on an artificial intelligence algorithm that involves a Quadratic Boost Converter as a power electronics adaptation stage. The control scheme comprises two steps, where an Artificial Neuro Fuzzy Interference System generates photovoltaic reference voltages during the first loop, which serves as an input for the backstepping controller in the second loop. To validate the effectiveness of the suggested algorithm, simulations are performed using MATLAB/Simulink tools, and the results are compared with those of the conventional Perturb and Observe and Perturb and Observe-based Backstepping algorithms. As a result, the suggested Artificial Neuro Fuzzy Interference System-based Backstepping controller exhibits excellent dynamic behavior, indicating that the system is stable, fast, and has accurate tracking.

Assessment of tribological behaviour of laser textured brake disc/composite brake pad system using adaptive neuro fuzzy interference system technique

This paper examines the impact of Laser Surface Texturing (LST) on the tribological performance of a commercial automotive brake system consisting of a metallic brake disc and composite brake pads. The study focuses on achieving an optimal combination of wear and friction, aligning with the goals of green technology. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology is used to rank the characteristics, while the Adaptive Neuro Fuzzy Interference System (ANFIS) methodology is employed to model the friction and wear behavior of the brake pad-disc system under varying normal loads and sliding speeds. At a normal load of 100 N and a sliding speed of 0.73 m s−1 under dry conditions, the brake system exhibited a minimum coefficient of friction of 0.2066 and a specific wear rate of 7.93 × 10−6 mm3 N−1-m−1. Although, there was no specific correlation found between frictional power and specific wear rate, a higher load of 100 N and sliding speed of 2.71 m s−1 resulted in decrease in specific wear rate by 83% when compared to that of untextured brake discs. By applying brake oil to the laser-textured boundary, the coefficient of friction experienced a 31% reduction.

A hybrid approach for machining optimization of the WEDM using grey-fuzzy with Metaphor-Less algorithms for Ti-3Al-2.5V

Purpose This paper aims to investigate the influence of “BroncoCut-X” (copper core-ZnCu50 coating) electrode on the machining of Ti-3Al-2.5V in view of its extensive use in aerospace and medical applications. The machining parameters are selected as Spark-off Time (SToff), Spark-on Time (STon), Wire-speed (Sw), Wire-Tension (WT) and Servo-Voltage (Sv) to explore the machining outcomes. The response characteristics are measured in terms of material removal rate (MRR), average kerf width (KW) and average-surface roughness (SA). Design/methodology/approach Taguchi’s approach is used to design the experiment. The “AC Progress V2 high precision CNC-WEDM” is used to conduct the experiments with ϕ 0.25 mm diameter wire electrode. The machining performance characteristics are examined using main effect plots and analysis of variance. The grey-relation analysis and fuzzy interference system techniques have been developed to combine (called grey-fuzzy reasoning grade) the experimental response while Rao-Algorithm is used to calculate the optimal performance. Findings The hybrid optimization result is obtained as SToff = 50µs, STon = 105µs, Sw = 7 m/min, WT = 12N and Sv=20V. Additionally, the result is compared with the firefly algorithm and improved gray-wolf optimizer to check the efficacy of the intended approach. The confirmatory test has been further conducted to verify optimization results and recorded 8.14% overall machinability enhancement. Moreover, the scanning electron microscopy analysis further demonstrated effectiveness in the WEDMed surface with a maximum 4.32 µm recast layer. Originality/value The adopted methodology helped to attain the highest machinability level. To the best of the authors’ knowledge, this work is the first investigation within the considered parametric range and adopted optimization technique for Ti-3Al-2.5V using the wire-electro discharge machining.

A review of solar and solar-assisted drying of fresh produce: state of the art, drying kinetics, and product qualities.

Global demand exists for high-quality fresh produce. Nevertheless, the quality of fresh produce is severely impacted by its perishability due to its high moisture content. Therefore, fresh produces are preserved using artificial dryers (hot-air dryers, catalytic infrared dryers, etc.) driven by electricity or natural fuels. Nonetheless, the exorbitant cost of power has heightened the need for sustainable resources, notably solar energy, for drying. Hence, this article ris a review of how solar dryers and solar-assisted dryers affected fresh produce's drying kinetics and quality in the last 5 years. The review showed that solar drying modeling technology (thin-layer modeling, computational fluid dynamics, adaptive-network-based fuzzy interference system, artificial neural network) helps examine fresh produce drying characteristics using various simulation tools before developing any procedure. Solar-assisted drying shortens drying times and increases drying rates. Besides, the quality of the dried fresh produce (color, aroma, appearance, rehydration, etc.) should always be considered. Hybrid solar drying produces higher drying rates and product quality than other solar dryers. However, energy analysis needs to be done as several studies have recognized energy efficiency and product quality. In addition, fresh produce must be pre-treated before solar drying to maintain the final product quality. Therefore, future studies should focus on creating other pretreatment techniques to produce the needed chemical and physical changes and enhance mass and heat transfer. Finally, the influence of solar drying on the final products' nutrient retention or loss, functionalities, or sensory characteristics needs further investigation and comparison to other non-solar drying technologies. This article is protected by copyright. All rights reserved.

An intelligent mitigation of disturbances in electrical power system using distribution static synchronous compensator

The power quality of an electrical system is critical for industrial, commercial, and housing applications, and with the increasing use of sensitive loads, customers and utilities are beginning to pay more attention to it. A distribution static synchronous compensator (D-STATCOM) represents one of the best custom power devices (CPDs) for improving the power quality of a distribution system. The performance of this device relies upon the algorithm and strategy used for its control. Artificial intelligence was utilized to overcome these shortcomings, while a response optimizer tool was used for the tuning process. An adaptive controller design was also proposed, based on the integration of fuzzy logic with traditional proportional-integral (PI) controller. The fuzzy logic controller system was designed using the adaptive neuro fuzzy interference system (ANFIS) editor. In this work, a D-STATCOM controller was used to mitigate sag and swell problems, while the ANFIS together with the optimization method was used to improve the system response. This study was carried out using MATLAB/Simulink, and the results showed a superior and adaptive performance in mitigating voltage sag and swell problems at different loading conditions compared to the traditional PI.

Power Quality Enhancement in a Wind Farm Connected Grid with a Fuzzy-based STATCOM

Integrating wind farms with electricity transmission networks presents several problems, and power quality plays a vital role among these. This study proposed a novel fuzzy logic controller to reduce the effect of power quality issues in such applications and investigated two different FACTS (Flexible AC Transmission System) devices, the Static Var Compensator (SVC) and the Static Synchronous Compensator (STATCOM). The fuzzy logic controller was designed as a voltage controller to improve power quality. Detailed analysis was carried out to investigate the successful mitigation of voltage sag/swell, active and reactive power improvement, and voltage flickers control by two controllers in a Multi-Terminal Load (MTL) system consisting of wind generators. The results were verified in MATLAB simulations, and a comparison was performed between the proposed shunt controllers. Furthermore, the design of a fuzzy interference system based on Vref (reference voltage) and the voltage measured at STATCOM location signals was investigated and compared with the SVC in terms of voltage sag, voltage swell, voltage flickers, and Total Harmonic Distortions (THD). The proposed fuzzy system was compared with a PI-based STATCOM and SVC. The power quality issues were exacerbated when using Multi-Terminal Load (MTL) in the transmission network.