Top PDF Modelling and optimisation of solar voltaic system using fuzzy logic

Modelling and optimisation of solar voltaic system using fuzzy logic

Modelling and optimisation of solar voltaic system using fuzzy logic

Residential Solar connected inverter is single phase and usually smaller ratings (less than or equal to 5kW). For bigger installations it is generally integrated into 3 phase grid. Sometimes 3 single phase inverters is used and integrated into 3 phase grid. In this scenario, it is important to balance the 3 phases equally to grid with same kW ratings. For maximum cost effectiveness and maximum energy transfer of PV to grid it is necessary to select most optimum PV system components. Hence, inverter rated capacity (Pinv) is matched with PV maximum rated capacity (Ppv-pk) for best PV system performance (Faranda et al. 2015). Ideally, the inverter is selected such that the two ratings are matched. However, due to other variables like climate conditions (temperature and irradiance), variations in Standard Test Condition (STC), wind blowing, solar panel tilt angle and so forth, the matching of the two PV components does not produce optimum performance (Faranda et al. 2015). The output inverter nominal AC voltage that can feed the grid without cutting out of ambient temperature of 25 deg C is Pinv-ac. As per explanation in this literature (Earthscan 2013), the following power range can be specified as design range:
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iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimisation

iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimisation

Abstract— Employing effective optimisation strategies in organisations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organisations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilised is a challenging problem as there are many factors that can impact the organisation's overall performance. We have developed a system that optimises to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimisation problems, which means there are many objectives to consider when optimising, and there is much uncertainty in the environment. The latest version of the system uses a multi-objective genetic algorithm as its core optimisation logic, with modifications such as Fuzzy Dominance Rules (FDRs), to help overcome the issues associated with many-objective optimisation. The system also utilises genetically optimised type-2 fuzzy logic systems to better handle the uncertainty in the data and modelling. This paper shows the genetically optimised type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of £1million a year, the reduction of over 2,500 metric tonnes of CO 2
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VHDL AMS based genetic optimisation of fuzzy logic controllers

VHDL AMS based genetic optimisation of fuzzy logic controllers

T.J. Kazmierski has received a Master of Electronic Engineering Degree from Warsaw University of Technology in 1973 and a PhD degree from the Military Academy of Technology in Warsaw in 1976. He is currently a member of the Electronic Systems Design Group at the School of Electronics and Computer Science. His research interests include numerical modelling, simulation and synthesis techniques for computer-aided design of VLSI circuits. He has published over 100 research papers in this area and in recent years he has been working on applications of hardware description languages to high-level system modelling and synthesis, involving modelling of mixed-domain systems, automated analogue and mixed-signal synthesis for ASIC design, including synthesis of artificial, VLSI neural networks.
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A Constant Power Generation of Solar Photo Voltaic Systems using Mppt with Fuzzy Logic Controler

A Constant Power Generation of Solar Photo Voltaic Systems using Mppt with Fuzzy Logic Controler

In layout, increases in sun based brightening produce augments in the short out current, while augments in temperature decay the open circuit voltage, which impacts the yield power of the PV module. This changeability of the yield power suggests that without a coupling device between daylight based PV module and weight; the system doesn't work the most outrageous power point (MPP). the structure and showing a fleecy reason controller perceiving the most outrageous power point in sun based PV module, using the characteristics of fluffy method of reasoning to address an issue through etymological verbalizations. This paper tells interest usage of the numerical sort suggested in for showing of the sun based PV module, which is hate diode ward models, simply use to process the twist fitting parameter. The results were differentiated and the P and O controller, which showed that the proposed technique presents less imperativeness disasters and certifications MPP in all cases surveyed in entertainment. It justifies referencing that this work is a bit of as a great deal of astute control techniques being surveyed in the assessment social affair was use a MPPT controller and CPG of negligible exertion and high profitability.
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Fast Tracking MPPT for Photo Voltaic System Using ANFIS Control Logic Algorithm

Fast Tracking MPPT for Photo Voltaic System Using ANFIS Control Logic Algorithm

ABSTRACT: The main theme of this research is to design and implementation of the MPPT with fuzzy logic & ANFIS control algorithm. Fuzzy logic and ANFIS naturally deals with nonlinearities. This technique overcomes the difficulty in the modelling of the nonlinear systems. In order to accomplish this aim, an SPS model consists of a PV module, a dc-dc converter, and a fuzzy logic controller & ANFIS was developed. An integrated model of the PV module and the identified converter was simulated and the results used to derive the expert knowledge needed to formulate and tune the fuzzy logic controller & ANFIS. The proposed method shows better performance in the mpp and tracking speed to parameter variation. The results represents that a significant amount of additional energy can be extracted from a photovoltaic module by using a fuzzy logic & ANFIS based maximum Power Point tracker.
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Dehydration of Agro Products in a Hybrid Solar Dryer Controlled through a Fuzzy Logic System

Dehydration of Agro Products in a Hybrid Solar Dryer Controlled through a Fuzzy Logic System

Charges of 10 kg plums were dehydrated using only solar energy to heat drying air. The total drying period was 10 hours, coinciding the first 7 hours with the highest solar radiation period, and after that (the last 3 hours) the energy was taken from the solar accumulator. The initial moisture content of plums was 80%, reaching a 71% after 10 hours of drying. Plum skin avoided a higher moisture loss in this period. Since the final moisture is too high for preservation, the drying process should continue in the following days.

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Improve performance of Solar based Hydrogen Tank Pressure System by Fuzzy Logic Controller

Improve performance of Solar based Hydrogen Tank Pressure System by Fuzzy Logic Controller

The sun as the originator of all living creatures needed many millions of years to create the fossil energy materials, but mankind has almost totally used up all this materials within a period of less than two hundred years. Now, the human intelligence has progressed to be capable of utilizing the sun energy directly. The reached sun energy to our global are exceed the world Energy consumption for approximately 6000 fold [2]. According to scientific studies the total of all oil resources of the world will be exhausted within 40 to 50 years [3]. Also the nuclear energy will not cover the world demand of the future in addition to the problems of its waste disposal hazards. The biggest energy source represents the sun which mainly emits its energy by electromagnetic waves onto the earth. Its solar constant amounts 1353W/m2 [4], [5]. The solar constants indicate the performance which reaches a surface of one-square meter, situated externally of the earth atmosphere, which shows vertical to the emission direction. Every day the sun radiates, or sends out an enormous amount of energy. As matter of fact the sun radiates more energy in one second than all the human have used since the beginning of time [1]-[5]. To convert sun energy into electrical energy solar cells have proved to be excellent. Their factor of expenses has considerable decreased through the mass production. Most economical are those cells which possess an efficiency of approximately 25%. Conversion of solar energy to chemical free energy in the form of molecular hydrogen and oxygen is attractive because the products are stable, versatile, and nonpolluting. Hydrogen
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Modelling and Control Strategy of Induction Motor Using Fuzzy Logic Control Technique

Modelling and Control Strategy of Induction Motor Using Fuzzy Logic Control Technique

The defuzzifcation transforms fuzzy set information into numeric data information. This defuzzification process along with the operation of fuzzification is critical to the design of fuzzy systems as both of these operations provide nexus between the fuzzy set domain and the real valued scalar domain. There are so many methods to perform the deffuzification, viz., centre of gravity method, centre of singleton method, maximum methods, the marginal properties of the centroid methods & so on. In our work, we use the centre of gravity method. The output of the defuzzification unit will generate the control commands which in turn is given as input (called as the crisp input) to the plant through the inverter. If there is any deviation in the controlled output (crisp output), this is fed back & compared with the set value & the error signal is generated which is given as input to the TS-fuzzy controller, which in turn brings back the output to the normal value, thus maintaining stability in the system. Finally, the controlled o utput signal, i.e., y is given by Eq. (17) as
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MPPT Based on Fuzzy Logic Controller (FLC) for Photovoltaic (PV) System in Solar Car

MPPT Based on Fuzzy Logic Controller (FLC) for Photovoltaic (PV) System in Solar Car

membership function during fuzzification [10]. In operation of MPPT control, after E and ∆E are calculated, these inputs are converted into linguistic variables and then the output D is generated by looking up a rule-base table. The FLC tracks the MPP based on master rule of “If A and B, Then C” [10]. To determine the output of the fuzzy logic, the inference is used. There are many methods for inference but the popular one is Mamdani [10]. In this paper, fuzzy Figure 6. MPPT curve

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ADMITTANCE BASED CONTROL OF A STANDALONE SOLAR PHOTO-VOLTAIC HYBRID SYSTEM

ADMITTANCE BASED CONTROL OF A STANDALONE SOLAR PHOTO-VOLTAIC HYBRID SYSTEM

This paper presents a Standalone Solar Photovoltaic (PV)-diesel-battery hybrid system with new control algorithm. The proposed system deals with the inconsistent energy generation of solar array and also provide quality power. For obtaining maximum power under varying operating conditions maximum power point tracking (MPPT) algorithm is used. The PV array is integrated with a DC-DC boost converter. Diesel generator set along with battery energy storage system (BESS) coordinated for load management and power flow. The admittance based control algorithm is used for Reactive Power Control, Harmonic Mitigation and Load Balancing for balanced and unbalanced loads. A Voltage source converter with BESS provides neutral current compensation. Simpower systems toolbox of MATLAB/SIMULINK is used for analyzing the proposed system under linear and non linear loads.
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MPPT Based on Fuzzy Logic Controller (FLC) for Photovoltaic (PV) System in Solar Car

MPPT Based on Fuzzy Logic Controller (FLC) for Photovoltaic (PV) System in Solar Car

membership function during fuzzification [10]. In operation of MPPT control, after E and ∆E are calculated, these inputs are converted into linguistic variables and then the output D is generated by looking up a rule-base table. The FLC tracks the MPP based on master rule of “If A and B, Then C” [10]. To determine the output of the fuzzy logic, the inference is used. There are many methods for inference but the popular one is Mamdani [10]. In this paper, fuzzy Figure 6. MPPT curve

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Comparison between IC and Fuzzy Logic MPPT Algorithm Based Solar PV System using Boost Converter

Comparison between IC and Fuzzy Logic MPPT Algorithm Based Solar PV System using Boost Converter

Fuzzy logic is a limb of Artificial Intelligence (AI), a branch of Engineering that deals with development of computer programs based on the study of human intelligence and nature of human thinking. The basic concept understanding fuzzy logic is that of a linguistic variable, that is a variable whose values are words rather than number (such as small and large). Fuzzy logic uses fuzzy sets to related classes of objects with unclearly defined boundaries in which membership is a matter of degree. The fuzzy logic system more is flexible rather than classical and conventional method. Fuzzy logic controller works with imprecise inputs, it does not need an accurate mathematical model. The fuzzy logic rules were first proposed by prof. L. zadeh in 1965 and can be implemented for the complex and unknown system. The IC method is not satisfied for the system especially for non-linear and complex system and cannot obtain the desire results [21, 23]. The structure of the fuzzy controller is based on the changing the control linguistic to form of the if-then in an automatic control system and best knowledge and experience can be more useful instead of understanding a technical behaviour of the system . In this system we use fuzzy logical operator, AND for Intersection, OR for union and NOT for complement [25]. The tracking of the maximum power point will be divided in two phase, the first phase is of tough research, with a significant step to improve the response of the MPPT controller and the second one is the final phase where the step is very small, thus ensuring the system stability and decrease the maximum oscillation around the MPP. The fuzzy controller consists of four functional blocks, fuzzification, fuzzy rule, an inference engine and the defuzzification. The fuzzy controller design contains the three following steps:
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Implementation of Solar PV & Battery Storage System of a Three Level NPC Inverter Using Fuzzy Logic Controller

Implementation of Solar PV & Battery Storage System of a Three Level NPC Inverter Using Fuzzy Logic Controller

ABSTRACT: The effect of linear imbalances and nonlinear loads on the voltage balance of the neutral-point clamped converter is described here. The Neutral-Point-Clamped inverters are used in the multilevel inverters for high power applications. A three level NPC inverter that can accommodate with solar photovoltaic (PV) and battery storage in a grid connected system is described here. This paper presents the Fuzzy Logic controller design philosophy of the proposed configuration and the theoretical framework of the proposed modulation technique. An incipient Fuzzy Logic controller for the proposed system is additionally presented in order to control the power delivery between the solar PV, battery and grid, which simultaneously provides Maximum power point tracking (MPPT) operation for the solar PV. A simulation model for the solar energy system has been developed using MATLAB/SIMULINK. The energy system performances under different scenarios, including battery charging and discharging with different levels of solar irradiation has been verified by carrying out simulation studies.
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Modulation Scheme Identification and Classification System using Fuzzy Logic

Modulation Scheme Identification and Classification System using Fuzzy Logic

logic modulation classifier that works in non-ideal environments in which it is difficult or impossible to use precise probabilistic methods. They first transformed a general pattern classification problem into one of function approximation, so that fuzzy logic systems (FLS’s) can be used to construct a classifier; then, they introduced the concepts of fuzzy modulation type and fuzzy decision and developed a non-singleton fuzzy logic classifier (NSFLC) by using an additive FLS as a core building block. NSFLC uses two-dimensional (2-D) fuzzy sets, whose membership functions are isotropic so that they are well suited for a modulation classifier (MC). They established that NSFLC, although completely based on heuristics, reduced to the maximum-likelihood modulation classifier (ML MC) in ideal conditions. In the application of NSFLC to MC in a mixture of α-stable and Gaussian noises, they demonstrated that NSFLC performs consistently better than the ML MC and it gives the same performance as the ML MC when no impulsive noise is present.
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AUTOMATED NEGOTIATION IN A MARKET BASED SYSTEM USING FUZZY LOGIC

AUTOMATED NEGOTIATION IN A MARKET BASED SYSTEM USING FUZZY LOGIC

Abstract: Negotiation process to get automated a mechanism that allows agent to negotiate automatically to reach mutually beneficial agreement is not a easy task .There may be many inconsistencies in the knowledge of other agents there may be conflicting objective of various agents all these should be properly handled by the mechanism autonomously and coordinate a joint approach to main domain task so that benefit goes to agents. When agents are involved in any of the negotiation process some of the profit percentage is being provided to that agent .This paper, thus, propose a model of negotiation for interaction between the seller and buyer without any intermediate agent. Instead of agent there will be a model of negotiation that will make the negotiation process more cost efficient by eliminating extra percentage give to agents likewise its predecessors this paper will take agent negotiation to another height making it more efficient and effective .We used the Fuzzy logic(Decision making function) which allows dealing with ambiguity and common sense reasoning .To achieve this objective our model will do all the negotiation process using this Fuzzy logic (decision making function).
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Intelligent Control of Photovoltaic System Using Fuzzy Logic for MPPT

Intelligent Control of Photovoltaic System Using Fuzzy Logic for MPPT

In this technique the maximum power of the PV module can be extracted and it is delivered to the load. While extracting the maximum power of the PV module .the temperature and insulation factors are also taken into account. The objective of this is to work is to maintain the voltage of the PV system closer to the voltage at which maximum power is produced. Because of environmental pollution, global warming PV system is mostly preferred nowadays. But the efficiency of the PV should be increase. This can be done with the help of
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An MPPT Control of Photovoltaic system using Fuzzy Logic Controller

An MPPT Control of Photovoltaic system using Fuzzy Logic Controller

ABSTRACT: In a photovoltaic system,the maximum power point varies with insolation and cell temperature . Maximum power point tracking (MPPT) is implemented to identify the maximum power operating point and subsequently system is operated at that particular operating voltage for maximum power gaining.Thisalgorithm is implemented in charge controllers for extracting maximum available power from PV module. Maximum power point tracking in photovoltaic systems using artificial intelligence methods are very popular. Fuzzy systems are very effective than simple conventional MPPT tracking. In the simulation part, a buck-boost converter feeding a permanent magnet dc load is achieved. The accuracy of the overall system depends on the fuzzy rule base and membership functions defined. The performance curves for comparison was obtained using MATLAB/Simulink platform.Simulation results show that fuzzy based tracking has better performance where it can facilitate the solar panel to produce a more stable power. KEYWORDS: GUI-Graphical User Interface; MPPT-Maximum power point tracker; NN- Neural Network; PV- Photovoltaic; P&O- Perturb and Observe; RCC- Ripple correlation control
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Power system stabilisers using fuzzy logic and neural networks

Power system stabilisers using fuzzy logic and neural networks

conventional adaptive confroUers. In tiie adaptive control literamre, k is weU known.. 8.20 The output of the direct FPSS without on-line adaptation for the new operating condition and [r]

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Power Distribution System Event Classification Using Fuzzy Logic

Power Distribution System Event Classification Using Fuzzy Logic

In an effort to identify failing or failed devices, utilities monitor the operation of distribution circuits by monitoring one or more signals, such as current and voltages. However, as outlined in the previous chapter, most methods currently used for monitoring distribution systems are unable to determine cause of a dis- turbance. Hence, they are unable to identify the root-cause of the disturbance, such as devices on a circuit that are failing or that have failed. One reason is that majority of conventional methods rely on voltage measurements alone. Failing apparatus often produce small changes in current waveforms, and virtually no change in voltage waveforms. As such, the majority of methods for detecting power system are not sensitive to the primary electrical evidence generated by most failing apparatus.. Another problem with conventional monitoring methods is that human experts skilled in the art of using measured quantities to identify failed or failing devices must analyze real-time or near real-time data obtained from a power system. These experts may not be available for analysis when needed, and manual analysis can be expensive and prone to error. Yet another problem with conventional methods is that much of the data collected may corre- spond to normal day-to-day operations of the power system, while only a small fraction of collected data corresponds to abnormal operations caused by failing or failed apparatus. It is not feasible to manually analyze such huge amounts of data in a timely and reliable fashion. This research attempts to address these short comings of methods presently used in the utility industry for monitoring the health of distribution systems.
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Operational Risk Management using a Fuzzy Logic Inference System

Operational Risk Management using a Fuzzy Logic Inference System

As Scandizzo (2005) argues, MR and CR managerial decisions affect the resulting risk profile directly and in a manner that measurement models have no problem capturing. Differently, OR measurement managerial decisions may affect the risk profile in a number of different ways, none of which any measurement model can capture in a simple and direct way; statistical approaches in particular will be unable to take into account such changes, as historical data will reflect a risk and control environment which no longer exists. Remarkably, because of the non-linear and unique nature of OR factors, genuine mitigation efforts may even yield undesired outcomes. A firm willing to reduce OR may be tempted to undertake as much mitigation efforts (e.g. implementation of additional controls, new software, etc.) as possible; nevertheless, due to the intricacy of ex-ante evaluation of OR management decisions, the firm may be creating a system complexity build-up 6 , thus fostering the rise of an unnoticed, yet potentially significant, source of OR. 7
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