The concept in this paper is to evaluate the performance of hybrid Solar- Pico hydro turbine energy systems. It is achieved by the use of fuzzy logic using MATLAB. It is primarily done to satisfy the load power demand. Its main application is reduction in probability of energy shortage.
Hybridization is the process of combining various renewable resources such as wind, solar and hydropower. Here we are combining the solar and Pico hydro and hybridized output is obtained. These loads can be operated according to the power generated. When the inputs are very high, all the loads will be in operating condition and when they are low, some of the loads will be in on condition and the others will be in off condition. The same process continues for all combinations of inputs Thus according to the generation, loads can be operated. This can be done automatically or manually.
INTRODUCTION OF HYBRID POWER SYSTEMS:
Hybrid power systems (HPS), as the name suggests, incorporate more than one type of power sources. Initially they were designed for powering remotely located telecommunication stations by integrating one or two renewable energy sources with storage devices such as batteries. Modern HPS designed for village/rural electrification may combine, appropriately, almost all kinds of available renewable power sources to augment base load plants such as hydropower, geothermal and biomass or diesel generators.
In order to increase flexibility and reliability in dispatching energy on demand. HPS can also be designed in modular forms ranging in size from individual household power supplies (Pico type < 100 W) to large national power grids (Mega type >100MW). To cope with the short and long term fluctuation in supply and demand, a storage within the system is envisaged so that it can smoothen the system operation and also provide the system more operational flexibility.
HYBRID GENERATION SYSTEM:
Hybrid generation systems usually combine renewable sources like solar power with conventional sources of energy like gas turbines (or micro turbines) to from an equivalent of a miniature (or virtual) grid. Such a configuration addresses two important issues namely, higher emissions because of conventional generation techniques and high cost of energy capture from renewable sources. With modern micro turbine systems, the other generating system in the hybrid generation system, the energy costs are relatively lower and the emissions of undesired gases is also low.
When the systems are connected together they will be having enhanced reliability, higher efficiency, lower emissions and will have acceptable cost.
GENERAL BLOCK DIAGRAM OF HYBRID SYSTEMS:
This is the general block diagram for a hybrid system. The wind generation module is constituted by a windmill, a multipolar permanent-magnet synchronous generator (PMSG), a rectifier, and a dc/dc converter to interface the generator with the dc bus. The converter commands the voltage on the PMSG terminals, indirectly controlling the operation point of the wind turbine and, consequently, its power generation. The solar module comprises several PV panels connected to the dc bus via a dc/dc converter.
Similar to the wind subsystem, the converter controls the operation point of the PV panels. The dc bus collects the energy generated by both modules and delivers it to the load and, if necessary, to the battery bank. Its voltage is imposed by the battery bank, which comprises lead-acid batteries connected in a serial/parallel array Many lead-acid battery manufacturers recommend specific recharge cycles to recover 100% of the charge capacity and also to protect the battery against dehydration.
ADVANTAGES OF HYBRID SYSTEMS:
Higher Efficiency:
Enhanced Reliability:
Lower Emissions:
Acceptable Cost:
SOLAR ENERGY
Solar energy runs the engines of the earth. It heats its atmosphere and its lands, generates its winds, drives the water cycle, warms its oceans.. This energy can be converted into heat and cold, driving force and electricity.
SOLAR RADIATION:
Solar radiation is electromagnetic radiation in the 0.28 - 3.0 µm wavelength range. The visible light which range from 0.38 to 0.78 µm and accounts for around 49% of the spectrum and finally of infrared radiation with long wavelength, which makes up most of the remaining 49% of the solar spectrum.
SOLAR ENERGY UTILISATION:
Solar energy can be converted to useful energy also indirectly, through other energy forms like biomass, wind or hydropower. Solar energy drives the earth’s weather. A large fraction of the incident radiation is absorbed by the oceans and the seas, which are warmed than evaporate and give the power to the rains, which feed hydro power plants. Thus wind, hydropower and biomass are all indirect forms of solar energy.
HYDRO ENERGY
The energy of this water cycle, which is driven by the Sun, is tapped most efficiently with hydropower. The use of water to generate mechanical power is a easiest way of utilization and it is very old practice. A flowing stream can make a paddle turn, but a waterfall can spin a blade fast enough to generate electricity. Another methods of harnessing water’s energy include utilization of the temperature of ocean water in a thermal transfer process, waves and tidal power.
Despite many different ways of harnessing the energy in water the most common way of capturing this energy is hydroelectric power, electricity created by falling water. The principal advantages of using hydropower are its large renewable domestic resource base, the absence of polluting emissions during operation, its capability in some cases to respond quickly to utility load demands, and its very low operating costs. Hydroelectric projects also include beneficial effects such as recreation in reservoirs or in tail water below dams. Disadvantages can include high initial capital cost and potential site-specific and cumulative environmental impacts.
HYDROPOWER POTENTIAL:
There are two main factors that determine the generating potential at any specific site: the amount of water flow per time unit and the vertical height that water can be made to fall (head). Water flow on the other hand is a direct result of the intensity, distribution and duration of rainfall. DETERMINING POWER:
The power is produced at a constant rate according to the amount of water available. Usually the power is generated as electricity and can be eventually stored in batteries.
The power can take other forms: shaft power for a saw, pump, grinder, etc. power = Head x Flow,
the more you have of either, the more power is available. To calculate available power, head losses due to friction of flow in conduits and the conversion efficiency of machines employed must also be considered.
Power (kW) = Head (meters) x Flow (m3/second) x 9.81 W
For small outputs of interest here, and as a first approximation, the formula can be simplified:
Power (kW)= Head (m) x Flow (liters/second)/200
Here the overall efficiency of 50 % is implied.
BLOCK DIAGRAM
EXPLANATION:
The two renewable sources, which are going to be hybridized, are the solar and the Pico hydropower. Solar energy is obtained from the solar panel and the Pico hydropower is obtained from the Pico hydro power plant. The power from the solar panel, which is in dc form, is stored directly in the battery.
On the other side, the power from the Pico hydro turbine, which is in ac form is converted by using suitable rectifiers and is stored in the battery. The suitable inverters invert the hybridized power stored in the battery. This output is given to the load through the switches. The fuzzy logic program is processed in the control unit. The input to this is given in the form of slope diagrams and fuzzy sets. The slope diagram is drawn by considering radiation in terms of flux and the flow rate in terms of lit/sec. The fuzzy program is executed in this unit by getting the input values of the radiation and the flow rate. After execution, the fuzzy unit gives the crisp value. This controls the on or off positions of the load through the corresponding switches.
CONCEPTS OF FUZZY LOGIC CONTROL PROCESS
INTRODUCTION ABOUT FL:
The past few years have witnessed a rapid growth in the number and variety of applications of fuzzy logic. The applications range from consumer products such as cameras, camcorders, washing machines, and microwave ovens to industrial process control, medical instrumentation, decision-support systems, and portfolio selection.
Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalued logic. But in a wider sense, which is in predominant use today, fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory that relates to classes of objects with unsharp boundaries in which membership is a matter of degree. In this perspective, fuzzy logic in its narrow sense is a branch of FL.Most FL control system models can be expressed in two different forms. They are fuzzy rule-based structures and fuzzy relational equations. The principle design elements in a general FL control system are fuzzification strategies, knowledge based, rule based, decision-making logic and defuzzification strategies.
FUZZY LOGIC DEFINITION:
In this context, FL is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems.
ADVANTAGES OF FL FROM CONVENTIONAL CONTROL METHODS:
FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically. The FL model is empirically based, relying on an operator's experience rather than their technical understanding of the system.
WORKING PROCESS OF FUZZY LOGIC: FL requires some numerical parameters in order to operate such as what is considered significant error and significant rate-of-change-of-error, but exact values of these numbers are usually not critical unless very responsive performance is required in which case empirical tuning would determine them ADVANTAGES OF FUZZY LOGIC:
FL offers several unique features that make it a particularly good choice for many control problems.
1) It is inherently robust since it does not require precise, noise-free inputs .
2) the FL controller processes user-defined rules .
3) FL is not limited to a few feedback inputs and one or two control outputs.
4) FL can control nonlinear systems.
5) Fuzzy logic is conceptually easy to understand..
6) Fuzzy logic is flexible.
7) Fuzzy logic is tolerant of imprecise data.
8) Fuzzy logic can be built on top of the experience of experts.
9) Fuzzy logic can be blended with conventional control techniques.
FUZZY RULES:
Human beings make decisions based on rules. Although, we may not be aware of it, all the decisions we make are all based on computer like if-then statements. Rules associate ideas and relate one event to another. Fuzzy rules also operate using a series of if-then statements.
FUZZY CONTROL:
Fuzzy control, which directly uses fuzzy rules, is the most important application in fuzzy theory.
STEP 1. FUZZIFY INPUTS
The first step is to take the inputs and determine the degree to which they belong to each of the appropriate fuzzy sets via membership functions.
STEP 2. APPLY FUZZY OPERATOR
Once the inputs have been fuzzified, we know the degree to which each part of the antecedent has been satisfied for each rule. If the antecedent of a given rule has more than one part, the fuzzy operator is applied to obtain one number that represents the result of the antecedent for that rule. This number will then be applied to the output function.
STEP 3. APPLY IMPLICATION METHOD
Before applying the implication method, we must take care of the rule’s weight. Every rule has a weight, which is applied to the time you may want to weight one rule relative to the others.
STEP 4. AGGREGATE ALL OUTPUTS
Aggregation is the process by which the fuzzy sets that represent the outputs of each rule are combined into a single fuzzy set. Aggregation only occurs once for each output variable.
STEP 5. DEFUZZIFY
The input for the defuzzification process is a fuzzy set and the output is a single number. There are seven built-in methods supported; they are,
1.Max-membership principle
2.Centroid method
3.Weighted average method
4.Mean-max membership
5.Center of sums
6.Center of largest area
7.First (or last) of maxima
Of these seven methods, we are using centroid method because this is the most prevalent and physically appealing of all the defuzzification methods. This is also called center of area and center of gravity.
The controller receives discrete input information; maps these numbers into a series of fuzzy sets which describe the process states of each input variable; applies the Degrees of Belief in these fuzzy terms to a knowledge base that relates input states to output states according to a set of rules; infers the Degrees of Belief (DoBs) in the output fuzzy sets that describe the output variable(s); and assembles these DoBs into a discrete output value through a process known as "Defuzzification".
FUZZY TABLE:
SOLAR
VL
LOW
MOD
MED
NOR
HIGH
VH
EH
PICO HYDRO
VL VL LOW MOD MED NOR HIGH VH EH
LOW LOW LOW MOD MED NOR HIGH VH EH
MOD MOD MOD MOD MED NOR HIGH VH EH
MED MED MED MED MED NOR HIGH VH EH
NOR NOR NOR NOR NOR NOR HIGH VH EH
HIGH HIGH HIGH HIGH HIGH HIGH HIGH VH EH
VH VH VH VH VH VH VH VH EH
EH EH EH EH EH EH EH EH EH
RESULT ANALYSIS:
INPUT 1:
CONCLUSION
In this paper we dealt with the hybridization of two sources such as solar and hydro and obtained the hybridized output. This is mainly achieved through mat lab and fuzzy program. Since the simulink model is considered as theoretical model, its value is found to be different from the other two practical models. The other two models were considered to be practical since we are dealing with the actual formulas and calculations.
Depending upon the value of the hybridized output the loads connected are operated. This is achieved as follows, when both the inputs are very low; the first load will be in operation and when the inputs are extra high all the loads will be in operation. Similar process continues for all combinations of inputs
