In practical application, the efficiency of solar and wind energy conversion systems is less, so converters are used to improve efficiency. Implementation of soft computing based solar PV and wind energy conversion systems using MATLAB/SIMULINK.
What Is Energy?
The Energy Scenario
An increase in power generation capacity based on the country's coal reserves, which are in the order of 200 billion tons, is inexorable. India is one of the minority countries that has been successful in using wind turbine technology and today of the total capacity of 1,25,000 MW in the country, about 5% consists of various non-conventional sources of generation, where wind is the biggest contributor. .
Energy Crisis: Global and Indian
On the other hand, in the long run, assuming that we produce environmental contamination results and human health costs, and additionally require an effort to improve or adapt the corruption mode, the initial cost of using renewables for energy recovery may not be too high. In terms of per capita value, India is 145th in the world with an annual emission of 1.25 t of CO2.
Energy Efficiency
Efficient Energy Use
Demand on gas and electricity distribution networks can be reduced, energy efficiency will improve the security and resilience of these networks and reduce dependence on imported fuels. For example, regulations may be introduced that require utilities to encourage customers to use electricity efficiently.
Classification of Energy Sources
To pursue renewable sources of energy, it is frequently important to make an impressive capital financing. To be financially viable, such enterprises must assimilate significant capital outlays and still have the ability to compete on cost with universal sources of energy – something that is challenging to do as a rule.
Solar Photovoltaics
Solar Radiation
Solar radiation shifts during the day from 0 kW/m2 in the evening to a maximum of approximately 1 kW/m2. Solar radiation estimates consist of global or instantaneous radiation estimates taken at intervals throughout the day.
Measurement of Solar Radiation
Wind Energy
Renewable Energy in the 12th Five-Year Plan (2012–2017)(2012–2017)
Significantly, the report prescribes the continuation of the Generation Based Incentive plan throughout the twelfth plan period. In a positive development, the Ministry of New and Renewable Energy (MNRE) has now entered into a Memorandum of Understanding with the Lawrence Berkeley Lab to collaborate on a number of issues identified with the estimation of wind energy potential and the alignment of matrix.
Barriers to Achieving Higher Growth
The basic purpose of developing the wind area was to access AD's profits. An additional obstacle to the development of the wind sector is the high cost of borrowing.
Benefits of Renewable Energy Sources
Solar PV and wind power do not use water to produce electricity and require only small amounts for cleaning purposes. The use of solar PV and wind energy also avoids the thermal pollution and pollution that can be caused by the discharge of cooling water for power plants.
Trends in Energy Consumption
- Annual Energy Consumption
- RES in INDIA
- National Policy Measures Supporting Renewables
- Renewable Energy Law
- Generation Based Incentive (2009–2012)
- Renewable Energy Certificate Scheme
- National Clean Energy Fund
- Other Initiatives: Renewable Regulatory Fund MechanismFund Mechanism
- Land Allocation Policy
- Grid Integration Issues
- Grid Transmission Planning Process
- Interconnection Standards
- Green Energy Corridor
- India Smart Grid Task Force
This is one of the main limitations for the future development of wind energy in the country. This activity if executed effectively can be a significant driver for improving renewable energy sharing in India.
Worldwide Potentials of Renewable Energy Sources
The focus of the analysis will be to determine India's readiness to deploy smart grid technologies. Within this financially managed part of the energy sector, renewable energy sources now provide around 25% of the energy supplied.
Need for New Energy Technologies
IEA or an average energy production equal to 10 % of the installed capacity given in IEA-PVPS (2001). Modification in blade profile and blade trim characteristics are likely to be accompanied by extensive investment in offshore wind. A large demand for heating in winter is little compatible with solar coverage, but if the passive and active efficiency characteristics of the building are optimal, the heat load can be dominated by the use of hot water, which is more suitable for supply with Sun.
Introduction to Matlab and Simulink
Introduction to Soft Computing
Soft Computing Techniques
Artificial neural networks are relatively crude electronic models based on the neural structure of the brain. In this structure some of the neurons are connected to the real world to receive its inputs. The genetic algorithm is implemented by evaluating the fitness of all individuals in the population.
Applications of Soft Computing Techniques in Solar Energyin Solar Energy
Artificial neural networks Prediction of solar radiation Modeling of solar steam generator Estimation of global solar radiation. Prediction of solar radiation and temperature Modeling of photovoltaic power supply Genetic algorithms Photovoltaic solar energy systems. Parameter identification in solar cells Size of a solar thermal electricity plant Size of solar water heaters.
Applications of Soft Computing (AI) Techniques in Wind Energyin Wind Energy
Summary
Kannan R, Leong K, Osman R (2005) “Life Cycle Assessment” study of solar PV systems: an example of a 2.7 kWp distributed solar PV system in Singapore. Importance of MATLAB/SIMULINK model in improving the efficiency of the overall solar PV system. As a result of the MATLAB simulation of the solar PV system components, one can benefit from this model as a photovoltaic generator within the framework of the MATLAB/.
Basics of Solar PV
This is due to the empty spaces between the arrays of solar cells in the module. In the case of inverter, it converts the DC output from the Solar PV module to the AC grid voltage with some degree of efficiency. Hot-spots in the Solar PV module can be avoided by having diodes across each cell, and this is called bypass diodes.
PV Module Performance Measurements
Balance of System and Applicable Standards
The UL standards used for the corresponding BOS components in the PV system are as shown in Table 2.1 above. This can be avoided by carefully sizing the battery according to the 'C-rating' during the system design. The de-rating factor of BOS plays a significant role in increasing the overall efficiency of the solar system.
Photovoltaic Systems Total Costs Overview
The de-rating factor can be calculated by multiplying the de-rating factor of the BOS components. With good selection and installation practices, the total losses from BOS can be limited to 15.5% at STC. With bad practices, the total reduction factor can be 54.7% or even less, which means the losses can be more than 46.
Types of PV Systems
- Grid-Connected Solar PV System
- Stand-Alone Solar PV System
- PV-Hybrid Systems
- Stand-Alone Hybrid AC Solar Power System with Generator and Battery Backupwith Generator and Battery Backup
An example of directly coupled solar PV systems is that in agricultural applications, solar PV modules can be connected directly to run the pump. When designing a solar PV system with battery backup, the designer must determine the proper location for the battery racks and room ventilation. A standalone hybrid solar PV configuration is essentially identical to the DC solar power system.
MATLAB Model of Solar PV
SIMULINK Model of PV Module
Figure 2.13 shows a photovoltaic module with 36 cells connected in series to achieve an increased voltage level. The simulation results for the proposed photovoltaic module based on P-V and I-V characteristics at different solar radiations are shown in Figure 2.14. The simulation results for the proposed photovoltaic module based on P-V and I-V characteristics for different cell temperature values are shown in Figure 2.15.
SIMULINK Model for PV Array
SIMULINK Model to Find Shading Effect
Charge Controller
- Batteries in PV Systems
- Battery Types and Classifications
- Battery Charging
- Battery Discharging
- Battery Gassing and Overcharge Reaction
- Charge Controller Types
- Charge Controller Selection
- Operating Without a Charge Controller
- Using Low-Voltage “Self-Regulating” Modules
- Using a Large Battery or Small Array
The voltage regulation (VR) and the array reconnection voltage (ARV) refer to the voltage setpoints at which the array is connected and disconnected from the battery. The low voltage load disconnect (LVD) and load reconnect voltage (LRV) refer to the voltage setpoints at which the load is disconnected from the battery to prevent overdischarge. The voltage regulation setpoint is the maximum voltage that the charge controller allows the battery to reach, limiting overcharging of the battery.
MATLAB Model of SOC
SIMULINK Model
The simulation result of SOC model for Ni-MH battery is shown in Fig.2.28. Here, the first graph represents the battery terminal voltage under the charging and discharging conditions of the battery. The third graph represents the speed of the DC machine, which changes with respect to the charging and discharging of the battery.
MATLAB Model of Charge Controller
Power electronics circuitry is used in the PV charge controller to achieve maximum efficiency, availability and reliability. The complete circuit including PV array, charge controller and battery is shown in Figure 2.33. The battery voltage and the load voltage are represented as waveforms in Figure 2.34.
Inverter
- Centralized Inverters
- String Inverters
- Multi-string Inverters
- Module Integrated Inverter/Micro-inverters
- Inverter Topology
DC-DC optimizers can be wired serially in strings and they can also be wired in parallel. However, DC-DC optimizers retain an important disadvantage of central converters – a failure of the central converter still results in a complete loss of system output. With additional equipment to purchase and install, DC-DC optimizers add to the initial cost of a PV system.
MATLAB/SIMULINK Model of Inverter
SIMULINK Model
The single phase inverter designed using IGBT, diode and ideal switch is shown in fig. The input voltage was set to 2.7 V based on the design specifications in the DC voltage source block. The internal resistance of the ideal contact was set to 0.001Ω with the initial state 'open'.
Maximum Power Point Tracking
MPPT Techniques
These pilot cells must be carefully selected to accurately represent the characteristics of the PV array. When a PV array is connected to a power converter, the switching action of the power converter places voltage and current ripple on the PV array. Conversely, maximizing the output power of the inverter should maximize the PV array power, assuming a lossless inverter.
MATLAB/SIMULINK Implementation of Perturb and Observe Methodof Perturb and Observe Method
MATLAB/SIMULINK Model of the Incremental Conductance MethodConductance Method
PV Module with MPPT Techniques
It is concluded that the output voltage obtained without MPPT technique showed distortions before obtaining the required output and the output voltage was not at the desired value. The amplitude of the oscillations depends directly on the magnitude of the increase in the reference voltage, AVref. Figure 2.68 shows the voltage versus time curve with P&O MPPT technique and Figure 2.69 shows the voltage versus time curve with In Cond MPPT technique.
Summary
Chowdhury AA, Koval DO (2005) Impact of photovoltaic energy sources on power system performance reliability level. Exide Management and Technology Company (1981) Handbook of Secondary Batteries and Charge Controllers in Photovoltaic Systems - Final Report, for Sandia National Laboratories, SAND81-7135, August 1981. Soft computing techniques applied to the MPPT of a solar PV system and its MATLAB/SIMULINK model .
Introduction
Importance of soft computing techniques like neural networks, fuzzy logic and genetic algorithms in solar PV system. Soft computing techniques are most suitable in dealing with the noise, inaccuracy and uncertainty in the data, and in turn achieve robust, low-cost solutions. Thus, soft computing paradigms and intelligent algorithms are increasingly applied in the study of renewable energy systems.
MPPT Using Fuzzy Logic
Implementation
To beat a percentage of the disadvantages mentioned in previous MPPT strategies, fuzzy logic controller is used for the extreme power following the PV panel. The duty cycle of the beat determines the powerful impedance seen by the solar oriented cell. So basically by modifying the duty cycle of the switch, the current flow to the battery can be controlled.
Description and Design of FLC
The input to the fuzzy controller is change in PV power (ΔPpv) and change in PV show voltage (ΔVpv) related to the two inspection time moments. The two inputs are prepared by the fuzzy controller and the output of the fuzzy controller is the incremental reference voltage (ΔVref), which shifts in magnitude and extreme depending on which region of the IpvVpv curve the framework is operating. The fuzzy-based plan used produces an incremental reference voltage of appropriate limb and variable magnitude.
Simulation and Results
One of the signals is a triangular waveform and the other is a fixed linear signal representing the time equivalent of the trigger voltage. The output of the pulse width modulator is used to supply a train of pulses to the switching MOSFET. The fuzzy logic controller model is simulated in the MATLAB/SIMULINK environment to track the maximum power point, and the value of the maximum power tracked is 59.9 W, as shown in Figure 3.8.
Neural Networks for MPP Tracking
- Background of Neural Networks
- Implementation
- Algorithm for ANN Based MPPT
- Simulation Results
The error BP learning is implemented for updating the weights of the network to minimize the mean square error. This input propagates through the network layer-by-layer and the output voltage is generated. After the network is generated, nine sets of insolation and temperature are given to the network to validate the network.
Neuro-Fuzzy Based MPPT Method
- Fuzzy Neural Network Hybrids
- Theoretical Background of ANFIS
- Architecture of Adaptive Neuro-Fuzzy Inference SystemInference System
- Hybrid Learning Algorithm
- Neuro-Fuzzy Network Model and Calculation Algorithmand Calculation Algorithm
- ANFIS Network Specifications
- Algorithm for Neuro-Fuzzy Based MPPT
In the case of the feature vector T, there are two input predictive models, x1jandx2j, and four membership functions, μA~1. Ok¼sk:Fk ð3:18Þ whereFk(k¼1 to 4) is the value of the function in the subsequent part of the kth fuzzy if-then rule. In the above equations ci and σi are, respectively, the center and spread of the Gaussian membership function μ(1,i) at the layer 1 thnode.
