Efficient maximum power tracking technique in grid connected PV-wind system

This paper presents an adaptive neuro fuzzy interference system (ANFIS) trained voltage response for a wind-diesel based isolated hybrid power system (IHPS). The system is studied by introducing 1% random variables as probabilistic pattern with 10% increase in reactive power load demand and wind input power simultaneously. STATCOM is used as dynamic reactive power compensator. The proportional integral gain constants in Static Compensators (STATCOM) are tuned with genetic algorithm (GA) technique, voltage response of GA tuned system is used for ANFIS training. The system performance is compared on the basis of system transient parameters.

The proposed work clarifies the functioning of hybrid system with the voltage compensation in a micro grid. The hybrid system under discussion is the combination of a wind turbine and solar PV system. The wind turbine is governed by adaptive Neuro-Fuzzy Interference System (ANFIS) and the PV system is managed by MPPT. The ANFIS comes into picture along the rotor side converter of the doubly fed induction generator (DFIG). This proposed work is to maintain the voltage regulation in the wind energy conversion system. The important issue to be resolved is the reduction of torque ripples. The response of the hybrid system controlled by ANFIS is analyzed using Matlab-Simulink by designing an unbalanced micro grid.

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

It is very difficult for the adaptive neuro-fuzzy interference system (ANFIS) using conventional training methods to converge while the samples space distribution is more complex, the desired results for that couldn't be achieved. To change the situation and improve the learning behavior of ANFIS, in this paper we propose a new self-adaptive learning algorithm for ANFIS differently from conventional training methods. The method firstly adopts the EM algorithm to learning fuzzy parameters of the ANFIS, and then applies emotion learning to learn the Takagi-Sugeno-Kang (TSK) parameters of the linear TSK functions of the ANFIS. The relevant researches indicate that the proposed learning method possesses faster training speed and better adaptability, and is more ubiquitous. In the end, a simulation example shows the availability of the proposed method.

Hybrid Energy Storage System (HESS) is formed by combining the ESD and the benefits of various ESDs are utilized. In the microgrid, every storage device’s highest operation is ensured by algorithms of efficient control, which is essential for sharing power among numerous ESDs and also ESD’s power output is decided by Conventional Fuzzy Logic Controllers (FLCs) with the consideration of fuzzy controlled ESD’s State of Charge (SoC) and entire demand of power. Futhermore, in photovoltaic (PV) generating system, efficiency of output and quality of electrical energy is improved by the method of Maximum Power Point Tracking (MPPT). Because of the PV system’s characteristics of time-varying output and its non-linearity, accomplishing MPPT is extremely difficult. Modified Firefly Algorithm (MF) is assisted for the utilization of Hybrid Optimized Bidirectional based Controller (HOBC). This predictive structure’s control parameters are optimized by using Adaptive Neuro Fuzzy Interference System (ANFIS). The rate of faster conversion is achieved by standard algorithm’s position formula and it is modified by this proposed algorithm. Perturbation and Observation Algorithm (P&O) and Adaptive Fuzzy Logic Controller (AFLC) are the two state-of-art method compared with various parameters in this simulation.

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.

Machines have served the humanity starting from a simple ceiling fan to higher industrial applications such as lathe drives and conveyor belts. This research work aims at providing an appropriate software based control system because it provides computer featured applications, prevents rapid signal loss, reduces noise while also significantly improves the steady state and dynamic response of the motor. In this research paper, we have worked on DC motors due to its significant advantages over other types of machine drives. We have first individually studied Fuzzy and ANFIS (Adaptive Neuro-Fuzzy Interference System) controller in controlling speed for a separately excited DC motor. Afterwards, we have analyzed both results to conclude that which technique is better to be adopted for precisely controlling the speed of DC motor. Outcomes from MATLAB fuzzy logic toolbox for simulation of our schematic has been provided in this research work. Our study parameters include input voltage of DC motor, its speed, percentage overshoot and rising time of the output signal. Our proposed research has interpreted the outcomes that ANFIS controller is better than Fuzzy controller because it produces less percentage overshoot and causes less distortion of the output signal as the overshoot percentage of ANFIS controller is 8.2% while that of Fuzzy controller is 14.4%.

Despite their significant contribution to the country's economy, Malaysian SMEs have not been given adequate attention by researchers. The researchers have been mostly biased towards larger and listed firms. Moreover, they also have put more focus on financial factors, whereas, in case of SMEs, financial factors will not show appreciable figures unless non-financial factors are considered. Utilizing these non-financial factors, this research proposes a strength prediction model for Malaysian SMEs using Adaptive Neuro Fuzzy Interference System (ANFIS). This paper concentrates on optimizing ANFIS by choosing the best rule-base, training antecedent and consequent parameters by Accelerated Particle Swarm Optimization (APSO). For accuracy validation, results of the proposed model are compared with SCORE; a system developed by SME Corporation Malaysia for ranking SMEs. The model will also help reduce financial losses by providing pre-warning to investors and creditors.

Renewable energy is an attractive solution for water pumping systems particularly in isolated regions where the utility grid is unavailable. An attempt is made to improve the performance of solar photovoltaic water pumping system (SPVWPS) under partial shading condition. Under this condition, the power versus voltage curve has more than one maximum power point (MPP), which makes the tracking of global MPP not an easy task. Two MPP tracking (MPPT) strategies are proposed and compared for tracking MPP of SPVWPS under shading condition. The first method is based on the classical perturb and observe (P&O) and the other method is based on a Salp Swarm Algorithm (SSA). Based on extensive MATLAB simulation, it is found that the SSA method can provide higher photovoltaic (PV) generated power than the P&O method under shading condition. Consequently, the pump flowrate is increased. But, under normal distribution of solar radiation, both MPPT techniques can extract the maximum power but SSA is considered a time-consuming approach. Moreover, SSA is compared with particle swarm optimization (PSO) and genetic algorithm (GA). The obtained results ensure the superiority of SSA compared with PSO and GA. SSA has high successful rate of reaching true global MPP.

To get the most power out of photovoltaic (PV) panels, PV systems must utilize a maximum power point tracking (MPPT) controller. In partial shading conditions (PSC), the power-voltage (P-V) characteristic of the PV network may show a single global maximum power point (GMPP) and two or more local maximum power points (LMPP). This indicates that the PV cells and panels do not get uniform illumination. As they converge on the maximum power point (MPP) that makes contact first, which is often one of the LMPPs in this scenario, common MPPT approaches like incremental conductance (InC) and perturb and observe (P&O) are unable to distinguish between a GMPP and LMPPs. In this paper, the extraction of the GMPP of the PV system under PSC based on a suggested particle swarm optimization (PSO) approaches is presented. The particularity of the suggested approach is that it takes into account the calculation of the position of each particle as a function of the duty cycle and the global maximum power. Results on the performance of the suggested PSO method show an advantage over the conventional PSO and the commonly used traditional P&O method. The suggested PSO technique offer better performance in terms of global power extracted, ripple rate of the power and efficiency.

This dissertation proposes an elite converter for the optimal utilization of hybrid renewable energy sources in the microgrid-associated systems using hybrid technique. Microgrid (MG)-associated systems are photovoltaic, wind turbine and battery. The proposed elite converter is the high conversion ratio (HCR) DC–DC converter with a maximum power point tracking (MPPT) controller. The main advantage of the converter is to eliminate the problem of high switch voltage stress. The proposed hybrid technique is the joint execution of both the adaptive whale optimization algorithm (AWOA) and adaptive neuro-fuzzy interference system (ANFIS), and hence, it is named as AWO-ANFIS technique. Here, AWOA technique is utilized to establish the exact control signals based on the power variation between the source side (renewable energy sources and battery) and load side. ANFIS technique is utilized for error minimization by using the achieved dataset from AWOA technique. Here, the reference currents of the controller are managed in view of the MPPT for solar and wind energy. In the proposed technique, the objective function is defined by the system data subject to equality and inequality constraints. Finally, the proposed technique is executed in the MATLAB/Simulink working platform and the execution is compared with the existing techniques. To test the performance of the proposed method, different case studies are conducted such as variation of irradiance under constant wind speed, variation of PV power under constant irradiance and load variation under constant irradiance and wind speed. For all the cases, the response of current, voltage and power of all the sources is analyzed and compared with that of the existing techniques such as GA, PSO and WOA. The statistical analysis of the proposed technique is also analyzed. Likewise, the computation time using various trails such as 100, 250, 500 and 1000 is also analyzed. The comparison results affirm that the proposed technique has less computational time than the exiting techniques. Moreover, the proposed method is cost-effective power production of MG and effective utilization of renewable energy source without wasting the available energy.

The major objective of this task is a control technique for power flow management of a network associated hybrid PV-wind-battery based system with multi-input transformer coupled bidirectional DC-DC converter using ANFIS controller is introduced. The proposed system intends to fulfill the demand of load, deal with the power flow from the distinctive sources, injects surplus power into the network and charge the battery from framework as and when required. A transformer coupled boost half-bridge converter is used to tackle power from wind, while bidirectional buck-boost converter is used to outfit control from PV alongside battery charging/releasing control. A single stage full-bridge bidirectional converter is used for ac loads and collaboration with grid. The proposed converter has reduced number of intensity change stages. This enhances the effectiveness and unwavering quality of the framework. In this article Adaptive Neuro Fuzzy interference System (ANFIS) is proposed for better performance of the system. Neural system has many inputs and also has multiple outputs but the fuzzy logic has multiple inputs and single output, so the combination of this two is known as ANFIS which is utilized for nonlinear applications. The proposed paper of simulation results acquired using MATLAB/Simulink demonstrate the execution of the proposed control procedure for power stream the executives under different methods of activity.

This research study proposed a novel approach utilising AI models to predict the metallurgical performance of complex sulfide ore flotation. Five machine learning and artificial intelligence models were employed in this study, that is, Random Forest (RF), Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Interference System (ANFIS), Mamdani Fuzzy Logic (MFL) and Hybrid Neural Fuzzy Interference System (HyFIS). Sixty-two flotation tests were conducted on samples containing galena, chalcopyrite and sphalerite as the main valuable minerals, and pyrite as the main gangue mineral. Different variables were used as inputs in the AI studies including physiochemical and operational parameters. The flotation recovery of lead and copper and their corresponding grades in the bulk concentrate were the primary dependent variables (outputs). The input variables included the dosages of sodium cyanide (pyrite's depressant), sodium isopropyl xanthate (collector), zinc sulfate (sphalerite's depressant) and Methyl isobutyl carbinol (MIBC, frother); air flow rate; flotation time; and the speed of the impeller in the flotation cell, which is indicative of the energy input. For the purpose of AI model development, datasets were divided into two subsets. The first subset was primarily used for the training phase, and it comprised 80% of the total data. The second subset, consisting of 20% of the total data, was used for testing. The models’ performance was assessed using two main indicators: R-squared (R 2 ) for the proportion of explained variation and RMSE for the average prediction error. The Hybrid Neural Fuzzy Interference System demonstrated superior performance in predicting the recovery and grade of copper and lead, with R² and RMSE of 0.9895 and 1.069 for the training phase, respectively, whereas for the testing step the respective values were 0.9128 and 2.859.

Experimental investigation on wave transmission, reflection and dissipation characteristics of rubble mound breakwater models are time consuming and expensive. However, such studies are required for designing the rubble mound breakwaters for marine structures in an optimal condition. In order to overcome such problems many researchers used various soft computing techniques such as Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Interference System (ANFIS), Genetic Programming (GP), Support Vector Machine (SVM) etc, in order to predict the design factors in the field of coastal engineering. The current work proposes Genetic Programming (GP) as a modeling tool to evolve mathematical models for the behavior of single and double breakwaters. Based on the detailed experimental data, GP models were performed to predict the reflected wave height (H r ), wave height on the breakwater (H 5 ) and transmitted wave height (H t ) by considering with and without trigonometric effects of those breakwaters. The quality of predictability of the present model is measured by the statistical parameter, RMSE (Root Mean Square Error). Since the waves were more complex in nature, it is very essential in considering the trigonometric function’s effect in the modeling aspects. It is evident that, the GP model accurately described the non linear complex effects.

Wear mechanism of a cutting tool is highly complex in that the processes of tool wear results from interacting effect of machining configurations. Various output generated by the study and analysis of each tool is extremely useful in analyzing the tool characteristics in general and to make efforts to obtain the estimated tool life in particular. The gradual process of tool wear has adverse influence on the quality of the surface generated and on the design specifications in the work piece dimensions and geometry, and causes, at the worst case, machine breakdown. Advanced manufacturing demands proper use of the right tool and emphasizes the need to check the wear rate. A scientific method of obtaining conditions for an optimal machining process with proper tools and control of machining parameters is essential in the present day manufacturing processes. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the indirect methods of tool wear analysis and monitoring is based on the acoustic emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process and provides a means of evaluating the wear of cutting tools. Wear parameters obtained in the process are analyzed with the output generated by using Multi Layer Perceptron (MLP) based back propagation technique and Adaptive Neuro Fuzzy Interference System (ANFIS). The results obtained from these methods are correlated for the actual and predicted wear. Experiments have been conducted on EN8 and, EN24 using Uncoated Carbide, Coated carbide and Ceramic inserts (Kennametal, India make) on a high speed lathe for the most appropriate cutting conditions. The AE signal analysis (considering signal parameters such as, ring down count (RDC), rise time (RTT), event duration (ED) and energy (EG). Flank wear in tools and corresponding cutting forces for each of the trials are measured and are correlated for various combinations of tools and materials of work piece.