Based on the land size we can predict how much does each solar panel has to generate power, in the market we have different types of solar panels that produce power like 250,300,320 watts respectively if the size of the land is more, then 250 watts likewise if land size less, then 300 or 320 watts opted for best performance. PV(Photo Voltaic) models the solar panels are mounted in a right position that is based on the rays of sunlight that falls on the panels are accordingly set to generate electricity, which in turn sent to AJB (Array Junction Box) through DC cables, the stored electricity will be passed to invertors here the DC converts into AC and sent to transformers this will perform step up and step down operations to supply for power grids. Through all these solar power plant components we monitor the data and sent it to the server for every minute that will be in the form of ASCII code
Controlling and monitoring
It is very important to note that all the components of solar power plants are to be periodically monitored for smooth process, in order to do this a communication must be established between each component.
Here in every component (PV model, AJB, Inverters, transformers) there are certain parameters that should be determined for the future enhancement this can be performed.
- Key Result Areas:
- So based on the requirement the parameters are tabulated and estimated the performance measures.
- After the performance measure, the synchronization of power from solar power plant to grids is done.
- There are some techniques that we are going to adopt to determine the components parameter in each module.
- Data logging Techniques:
- Through the data logging techniques solar energy panels are equipped with IOT devices which will fetch the data and sent to the server.
- With the help of MQTT protocol server handling will be much more easier.
- In order to determine the parameters in every component of solar power plant these data loggers (IOT devices) are fixed in all the components like AJB, whether Monitoring System, power Grid, inverters.
- In solar power plant every component has Mode Bus Access with which it will fetch the data from data loggers i.e. IOT devices and send to server.
- The data obtained from IOT devices, every minute these data are being sent to server that will be in the form of ASCII code.
- These ASCII codes will be converted hexadecimal which in turn stored into database.
The converted hexadecimal data present in server will be stored in the form of raw data, these data’s which is present in database exist in two forms namely real time and historical data. These data’s will be very use full for monitoring and controlling the solar power plant.
With the help of these raw data’s we perform analytics, which helps in providing the decisions for critical views like cable fault, environment effects etc. Operation and Maintenance (O & M) that are very important to avoid entire solar power plant(ecosystem) breakdowns.
If more information is gathered from various components we can improve the performance of solar power plant to a greater extinct.
Controlling and Monitoring mechanism can be achieved by IOT devices.
Operation and Maintenance (O&M):
Maximum yield from minimum operation, this is where the major things are carried out for performing the various tasks.
As in any power plant, a solar power plant in operation requires maintenance. Also, as the solar power plant becomes older, operation and maintenance (O&M) becomes more and more important for improving or keeping the performance of the plant. Another aspect to be taken into account is that usually the solar power plants are in remote locations with unreliable communication infrastructure. Most of the remote monitoring systems need an Internet connection, and in the absence of a reliable connection, there could be problems of lack of data logging for long periods of time. This makes it very difficult to diagnose and rectify problems in a timely manner. For unreliable communication breakdowns in the solar power plants and network problems planning for developing a product that works for no network and with network.
In a solar power plant it requires the following type of maintenance:
Preventive maintenance that includes periodic inspection and servicing of equipment which helps prevent breakdowns and reduces losses, it is basically a scheduled activity.
Corrective Maintenance includes the servicing of breakdown equipment and usually reactive.
Predictive maintenance also called condition based monitoring which involves monitoring of equipments based on the condition and operations on a real time basis and addressing the potential problem at a very early stage to prevent the downtime, this requires a robust platform to monitor the system.
The above three will help to improve the prediction and analytics.
It is the measure of the quality of a PV plant that is independent of the location and stated as percent and describes the relationship between the actual and theoretical output of the PV plant. The performance parameters detect the operational problems and facilitate the comparison of systems provided by the performance ratio and final yield factor.
Extremely large data sets may be analyzed computationally to reveal patterns, trends, and associations. Data analytics enables to shape of the huge volume of unstructured data generated from sensors. Performance can capitalize on data by forecasting trends and patterns to make informed decisions and for fuelling exponential growth – Magnitude (Massive data exploding), Speed (decision making) & Multiplicity (Analyzing various forms of data).
Considering the huge data sets let us take up a small scenario:
Here every solar power plant component contains different parameters, in day every minute the data or records are sent to the server by taking this if we consider a single parameter from a single component then.
|1 minute||1 record/data|
|1 hour (60 minutes)||60 records|
|1 day (1440 minutes)||1440 records|
|1 year (1440 * 365)||525600 records|
|5 years (1440*365*5)||2628000 records|
|To generate 1Mega Watt(MW) |
we must have 3600 to 3800 panels
|Likewise, if we consider 200 |
parameters in the components
of the solar power plan then
With this enormous amount of data, it is really difficult to do analytics, in order to overcome this drawback, big data methodologies are used for accurate results.
The average lifespan of the solar power plant and its components is estimated to be 25 years major challenge is every year the Performance Ratio decreases, in order to meet the requirement proper servicing must be done periodically. The above-estimated result is for a single component with a single parameter if we increase this by 5 times, then those many records will be stored in the database, for computation purposes, big data tools and methods have to adopted for obtaining the best result or outcome.
The predictive data analysis is very much useful to improve the performance along with other applications like real time monitoring and Failure detection can improve the performance of solar power plants as compared to the normal performance ratio of existing power plants.
With the formulation of a standard data schema it would be easy to make more devices that communicate with cloud services without worrying about any proprietary protocol. The cloud servers care less about what format the data is going to come in and dedicate more towards the analysis and data and learn from the data. Cloud specific AI and Data algorithms like the MapReduce help in analyzing the huge amount of data with ease and at a very high speed. Furthermore and more algorithms can be formulated to make sense of the data so collected and help in increasing the efficiency of the solar energy system. If the good analytic system is set up then a very efficient Solar analytics system could be built at a very low cost and at a very high efficiency rate.