Monitoring and evaluation system of solar power generation

Abstract

The monitoring and evaluation system of solar power generation is a system capable of real-time monitoring, checking, evaluating, processing, and analyzing the efficiency of solar power generation. This system enhances operational effectiveness by analyzing datasets through a structured and sequential in-depth analysis process, ensuring the accuracy and precision of the system, based on real-time circumstances. Additionally, the system features an automatic calibration process with virtual standard values, enabling data resets over time to obtain new datasets, thus ensuring greater accuracy and precision of the data collected. Furthermore, the system further comprises a condition-based maintenance system and an analysis function of the value of system losses referred to as the Loss Analysis, to predict potential future system losses. The purpose of this invention is to invent a real-time monitoring and evaluation system for solar power generation that works collaboratively with devices and utilizes artificial intelligence, employing various methods for monitoring and evaluating performance.

Description

MONITORING AND EVALUATION SYSTEM OF SOLAR POWER GENERATION

[0001] Technical Field

[0002] The present invention relates to engineering, especially, to a monitoring and evaluation system of solar power generation.Background Art

[0003] Solar energy has long been studied and developed as an alternative energy source. Solar power is converted into electricity using electronic devices known as "solar cells" or “photovoltaic (PV) cells.” These cells transform solar energy into direct current (DC) electricity. In a solar power generation system, solar panels capture sunlight and convert it into DC electricity, which is then transferred to an inverter. The inverter transforms DC electricity into alternating current (AC) to be distributed to the internal electrical control units, providing power to various electronic devices and acting as a replacement for electricity supplied by public utilities.

[0004] In the process of generating electricity through solar power generation systems, although sunlight is generally accessible for approximately twelve hours each day. However, the effectiveness of these systems is contingent upon the intensity of sunlight to facilitate electricity generation. On average, the systems can produce electricity effectively for about five hours daily, contingent upon the installed capacity of the solar power system (measured in direct current electricity). Solar panels function by converting the intensity of sunlight into electrical energy. Consequently, even in the absence of direct sunlight, or when the sun's rays are partially obstructed by clouds, as long as there is adequate light intensity, the solar power system is capable of continuing to generate electricity in a manner consistent with usual operations. Nonetheless, the rate of electricity generation may diminish depending on the existing light conditions time.

[0005] The invention described herein aims to develop a monitoring and evaluation system of solar power generation which is capable of monitoring, evaluating, processing, and analyzing the efficiency of solar power generation. Thereby making it possible to analyze various types of electricity generation. On the worldwide patent database, relevant inventions have been found as follows.

[0006] The US Patent application No. US2011 / 0153095 A1 , titled “Solar power plant with scalable field control system”, discloses a system for controlling a solar field, including a field control server that communicates with a plurality of solar energy collector controllersinstalled in the solar field. The field control server can incorporate one or more processors capable of deploying user interface modules, which can provide a solar field user interface with a plurality of user interface control elements. Each user interface control element can represent one or more solar energy collector assemblies. A user selecting a specific user interface control element may cause the field control server to send commands to one or more solar energy collector controllers linked to the selected solar energy collector assembly or assemblies. Additionally, the system can include a customization module that provides functionality for users to modify the solar field user interface to accommodate changes in the number of solar energy collector controllers installed in the solar field.

[0007] The US Patent publication No. US201 1 / 0153087 A1 , titled “Solar power plant with virtual sun tracking”, discloses a system enabling solar energy collectors to track the sun. The system further comprises a collector controller capable of controlling one or more solar collectors in the solar field. The collector controller includes one or more processors, wherein the collector controller can estimate the sun’s position, determine the angle of one or more solar receptors relative to the sun based at least in part on the tilt angle of one or more solar receptors, and compare the estimated sun position with the angle of one or more solar receptors to determine the necessary movement of one or more solar energy collectors. Additionally, the collector controller can command motors to execute the required movement until the angle of one or more solar energy collectors is within the hysteresis value of the virtual sun tracker’s deadband.

[0008] The US Patent publication No. US2014 / 0278332 A1 , titled “System and Method for Performance Monitoring and Evaluation of Solar Plants”, discloses a system configured to monitor a solar power plant. The monitoring system comprises sensors configured to generate data based on the operational characteristics of components. The monitoring system also further comprises: a data logger configured to receive data from the sensors; and a server configured to receive data from the data logger and generate a simulated power output for components based on the data.

[0009] The US Patent publication No. US2016 / 0019323 A1 , titled “Solar power generation system, abnormality determination processing device, abnormality determination processing method, and program”, discloses a model for estimating power output used to estimate the expected amount of electricity to be generated by solar power generation modules. This model is built on the daily correlation between the amount of electricity generated by the solar power generation modules exposed to sunlight and the amount of solar energy emitted to the solar power generation modules. Furthermore, the expected amount of electricity generated is evaluated based on the power generation estimate model to detect whether there are anomalies by comparing the expected power outputwith the actual power generated by the solar power generation modules. This technique can be applied to any other solar power generation system, for example.

[0010] From a search of global patent databases indicates the existence of numerous inventions pertaining to solar power generation, specifically focused on the monitoring and evaluation of the performance of solar power plants. Notable among these inventions is the Solar Power Plant with a Scalable Field Control System, which details a method for managing a solar field. This system includes a field control server that facilitates communication with multiple solar energy collector controllers deployed within the solar field. The field control server is equipped with one or more processors capable of implementing user interface modules, thus enabling the scalability of the field control system. Additionally, another relevant invention is the Solar Power Plant with Virtual Sun Tracking, which outlines a mechanism that allows solar energy collectors to accurately track the sun's position. This system further comprises a collector controller responsible for regulating one or more solar collectors within the solar field; the collector controller, which integrates one or more processors, is designed to estimate the sun’s position and ascertain the angle of the solar receptor relative to the sun, based at least partially on the tilt angle of one or more solar receptors equipped with Virtual Sun Tracking capabilities.

[0011] Based on the aforementioned disclosures, it is evident that the inventions are related to solar power plants that have undergone improvements and developments aimed at enhancing the efficiency of electricity generation. This includes facilitating scalability and incorporating components designed for solar tracking.

[0012] In previously related patents concerning solar power plant monitoring and performance evaluation, systems and methods for monitoring and evaluating solar power plant performance were disclosed. These include a monitoring system configured to monitor solar power plants, as the monitoring system incorporates sensors configured to generate data based on the operational characteristics of components, wherein the monitoring system includes the data logger configured to receive data from the sensors. Another invention, the Solar power generation system, abnormality determination processing device, abnormality determination processing method, and program, was disclosed. This patent literature reveals a model created for estimating the expected amount of electricity to be generated by solar power generation modules, wherein the model is built on the daily correlation between the actual amount of electricity generated by the modules and the solar energy absorbed.

[0013] It can be observed the aforementioned inventions involve data processing from the data logger received from sensors and model creation. When compared to the currentinvention, there are significant differences in the components used for monitoring and evaluating solar power generation performance. This invention is a system for monitoring and evaluating system solar power generation’s performance, comprising five key components: the Solar Power Plant; the Electrical Signal Receiving Box (ESMA Board); the environmental monitoring devices (Pyranometer, Sun Tracking Camera, Sky Camera); the data storage on a database and processing through the internet (Cloud); and the analysis of solar panel data by Artificial Intelligence (Al), all working collaboratively in the system.

[0014] Characteristics and Objectives of the invention

[0015] The present invention relates to the monitoring and evaluation system of solar power generation according to this invention is a system capable of real-time monitoring, checking, evaluating, processing, and analyzing the efficiency of solar power generation. This system allows the operating system to perform effectively by analyzing datasets through a structured and sequential in-depth analysis process, ensuring the accuracy and precision of the system, based on real-time circumstances. The system also includes an automatic calibration process with virtual standard values, enabling data resets over time to obtain new datasets, thus ensuring greater accuracy and precision of the data. Furthermore, the system further comprises a condition-based maintenance system and an analysis function of Loss analysis, to predict potential future system losses.

[0016] The purpose of this invention is to invent the real-time monitoring and evaluation system of solar power generation working collaboratively with devices and artificial intelligence, employing various methods for monitoring and evaluating performance.Brief Description of Drawings

[0017] The invention is herein described, by way of example only, with reference to the accompanying drawings.

[0018] FIG. 1 shows the installation and connection of devices in the solar power generation system, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0019] FIG. 2 shows the installation of the light-level measuring device (ESMA Sensor) and the transmission of the string dataset, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0020] FIG. 3 shows the installation of the electrical signal receiving box (ESMA Board) and the transmission of the panel dataset, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0021] FIG. 4 shows the internal connections of the electrical signal receiving box (ESMA Board), of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0022] FIG. 5 shows the installation of a sun tracking camera and the transmission of the string dataset, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0023] FIG. 6 shows the installation of a sky camera and the transmission of the string dataset, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0024] FIG. 7 shows the steps for managing the low cost-effective string performance monitoring system, of one embodiment of monitoring and evaluating solar power production efficiency.

[0025] FIG. 8 shows the steps for managing the panel dataset of the cost-effective solar panel performance monitoring, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0026] FIG. 9 shows the steps for managing the string dataset in the cost-effective string performance monitoring, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0027] FIG. 10 shows the steps and methods for analyzing, calculating, and predicting total value of losses as the Loss Analysis, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0028] FIG. 11 shows the analysis of the panel dataset, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0029] FIG. 12 shows the analysis of the string dataset, one embodiment of the monitoring and evaluation system of solar power generation according to this invention.

[0030] FIG. 13 shows an example of the analysis of failures in each PV panel or each PV string, of one embodiment of the monitoring and evaluation system of solar power generation according to this invention.Description of Embodiments

[0031] The invention to be disclosed herein are described in detail with reference to the illustrations. Nonetheless, this invention may be provided in different aspects and ought not to be limited to the disclosed figure. The figure has been made so that this disclosureis absolutely clear so that those who are skilled in the art can fully understand the figure. Regardless, the thickness and scale of the figure may be exaggerated for clarity.

[0032] FIG. 1 , FIG. 2, FIG. 3, FIG. 4, FIG.5, and FIG. 6 illustrate the installation and connection of devices for a solar power generation efficiency monitoring and evaluation system, of one embodiment of the system according to this invention. This system comprises a solar power plant (1), wherein the system is configured to operate in an energy-saving mode (Sleep Mode) and an active mode (Wake Up Mode) based on sunlight intensity.

[0033] Within the plant (1), the solar power plant (1) installs at least one position of photovoltaic (PV) panels (2) connected in series and arranged as a set or row called a string (PV String) (3),

[0034] wherein each row of PV strings (3) is connected in parallel to combine the electric current and power values to meet the specified values.

[0035] Then, the electric current and power values generated from each PV string (3) are transmitted and collected in a DC combiner (4),

[0036] wherein, inside the DC combiner (4), a specific type of circuit card is installed capable of supporting the aggregation of the electric current and power values from each predetermined PV string (3) row,

[0037] wherein the electric current and power values produced are then transmit to an inverter (5), transformer (6), and grid (7), respectively.

[0038] At least four light-level measuring devices (ESMA Sensor) (15) determined to be installed cover the area of the solar power plant (1),

[0039] wherein the number of light-level measuring devices (ESMA Sensors) (15) for installation within the solar power plant (1) area depends on the data reception radius, which is correlated with the physical and geographical analysis of the entire area of the solar power plant (1).

[0040] The light-level measurement devices (ESMA Sensors) (15) have specific characteristics that allow them to measure the sunlight levels hitting the PV panels (2) in each PV string (3), wherein the light-level measuring devices (ESMA Sensor) (15) can measure each PV string (3) and convert the values into irradiance (IRR),

[0041] wherein the irradiance (IRR) is used to reference the irradiance of the PV panels (2) in each PV string (3) within the data reception radius of the light-level measuring devices (ESMA Sensors) (15) installed around the area,

[0042] Furthermore, by the light-level measuring devices (ESMA Sensors) (15), all the obtained data, which is measured or read from the devices, is stored and gathered via the gateway (9), and subsequently stored on a database and processing system via the internet (Cloud) (10) for further analysis.

[0043] At least one electrical signal receiving box (ESMA Board) (8) connected to each PV panel (2), with the installation and connection characteristics pre-determined,

[0044] wherein one ESMA Board (8) has the capability to support the connection to more than or equal to one unit of PV panel (2), and

[0045] wherein the database and processing system through the Internet (Cloud) (10) stores data received from the electrical signal receiving box (ESMA Board) (8), or lightlevel measuring devices (ESMA Sensor) (15), or master pyranometer sensor (16), or sun tracking camera (17), or sky camera (18), or DC combiner (4), or the inverter (5).

[0046] Further, the Cost-Effective String Performance Monitoring and the Cost-Effective Solar Panel Performance Monitoring comprises the following steps:

[0047] analyzing panel dataset, or string dataset;

[0048] calculating performance ratio calculation system;

[0049] analyzing the panel dataset by artificial intelligence (Al), or the string dataset by Al;

[0050] calibrating the data by the panel auto calibration system, or the string auto calibration system;

[0051] operating a monitoring system;

[0052] confirming the data analysis result;

[0053] storing data management; and analyzing, calculating, and predicting the total value of losses as the Loss Analysis collectively.

[0054] Additionally, the Electric Signal Measurement and Acquisition Board (ESMA Board) 8 functions to measure the quantity of solar irradiance, voltage, and solar temperature of each solar panel (PV Panel) 2, with a high degree of accuracy and precision in measurement, wherein the values thus measured are stored and gathered via the gateway(9), and subsequently stored on a database and processing system via the internet (Cloud)(10) for further analysis.

[0055] The electrical signal receiving box (ESMA Board) (8) is designed with electrical circuits that can handle PV panel (2) voltages up to 400 volts during an open-circuit condition of the PV string (3) containing at least ten PV panels (2). Moreover, it supports Wi-Fi datatransmission, is also equipped with a backup battery that lasts at least 2 days, uses at least 3 watts of power, and operates within a temperature range of 0-80 °C,

[0056] wherein, its inside, the electrical signal receiving box (ESMA Board) (8) comprises: a light intensity sensor (12), a voltage sensor (13), and a temperature sensor (14) on the PV panels.

[0057] The light intensity sensor (12) functions to measure the sunlight levels hitting the PV panels (2), wherein the light intensity sensor (12) converts sunlight levels into irradiance (IRR) for calculating the performance ratio (PR Ratio) of the solar system or detecting failures in the PV panels.

[0058] The voltage sensor (13) measures the voltage of the PV panels (2), wherein the voltage by the voltage sensor (13) is used to calculate the energy production or detect failures in the PV panels (2) individually or in a combination thereof.

[0059] The temperature sensor (14) is a device measuring the temperature on the PV panels (2), used for evaluating failures in the PV panels (2).

[0060] The electrical signal receiving box (ESMA Board) (8) has an accuracy of 60% when measuring the sunlight levels hitting the PV panels (2) via the light intensity sensor (12),

[0061] preferably, when combined with measuring voltage of the PV panels (2) using the voltage sensor (13), the accuracy of the electrical signal receiving box (ESMA Board) (8) is increased to 71-80%, and

[0062] more preferably, when additionally combined with measuring temperature of the PV panels (2) via the temperature sensor (14), the accuracy of the electrical signal receiving box (ESMA Board) (8) exceeds 80%, wherein the voltage sensor (13) and the temperature sensor (14) on the PV panels (2) are separately operable.

[0063] at least one master pyranometer sensor (16) determined to be installed at a specific area within the solar power plant (1) to measure solar radiation covering the entire solar power plant (1) or to measure the solar radiation hitting each PV string (3) resulting in irradiance (IRR), wherein the irradiance (IRR), readable from the master pyranometer sensor (16), is used:

[0064] to reference the irradiance (IRR) of the PV panels (2) in each PV string (3); and

[0065] to automatically self-calibrate the light-level measuring devices (ESMA Sensor) (15) and / or the electrical signal receiving box (ESMA Board) (8) when being under the clear and steady light conditions, throughout the solar power plant (1) including the powerprofile, at the time of data measurement and reading, to allow accurate data measurement and reading during constantly changing conditions,

[0066] wherein the sensor is determined to be subject to the light conditions, throughout the solar power plant (1) at the time of data measurement and reading, which are clear and steady.

[0067] At least one sun tracking camera (17) installed within the solar power plant (1) at a specific area within the plant (1),

[0068] wherein the camera (17) is a device to track the movement of the sun as it passes over the solar power plant (1) at any given time.

[0069] Furthermore, the sun tracking camera (17) analyzes the correlation of the position and angle of sun moving across the solar power plant (1), relative to the surface of the solar power plant (1), to obtain the images where the sun is always centered, wherein

[0070] an image processing system is used to distinguish conditions where cloud cover over the sun and / or cloud shadows hitting the PV panels (2), and

[0071] the irradiance (IRR) of the PV panels (2) in each PV string (3) can be compared to that obtained from the master pyranometer sensor (16) or other output values measured from similar output devices.

[0072] The sun tracking camera (17) is configured to track the movement angle of the sun (17), and record images of the sun’s movement across the solar power plant (1), and transmits it to the gateway (9) for storage and processing in the cloud system (10).

[0073] At least one sky camera (18) installed within the solar power plant (1), wherein the installation of the Sky Camera (18) employs the image processing system:

[0074] to detect or identify the position and angle of the sun; and

[0075] to distinguish conditions where cloud cover the sun and / or cloud shadows hitting the solar power plant (1)

[0076] wherein the irradiance (IRR) of the PV panels (2) in each PV string (3) can be compared to that obtained from the master pyranometer sensor (16) or other output values measured from similar output devices.

[0077] The sky camera (18) detects the movement of the sun and clouds passing over the solar power plant (1), converts the images into data, and then transmits the data via the gateway (9), and subsequently stored on a database and processing system via the internet (Cloud) (10) for further analysis,

[0078] wherein the camera (18) is installed at a central or any pre-determined area within the solar power plant (1).

[0079] Furthermore, the solar power plant (1), of the monitoring and evaluation system of solar power generation according to this invention, also includes the renewable energy power plant, or the location generating energy from various energy sources with similar characteristics, individually or in a combination thereof.

[0080] FIG. 7, FIG. 8, FIG. 9, FIG. 10, FIG. 1 1 , FIG. 12, and FIG. 13 show the steps for analyzing the dataset from the monitoring and evaluation system of solar power generation according to this invention is determined to perform data analysis, as three types, of monitoring and evaluation methods including the Low Cost-Effective String Performance Monitoring, the Cost-Effective String Performance Monitoring, and the Cost-Effective Solar Panel Performance Monitoring,

[0081] The Low Cost-Effective String Performance Monitoring installs at least one master pyranometer sensor (16) at a specific area within the solar power plant (1 ) to measure the irradiance (IRR) from the solar radiation covering the entire solar power plant (1) or to measure the solar radiation hitting the PV panels (2) in each PV string (3),

[0082] wherein the system is able to analyze, calculate, and predict the total value of losses as a Loss Analysis generated.

[0083] Alternatively, the Low Cost-Effective String Performance Monitoring involves:

[0084] comparing a power profile of the PV panels (2) in each PV string (3), under the clear and steady light conditions, throughout the solar power plant (1) at the time of data measurement and reading, to be used as a reference for IRR and the power profile of the PV panels (2) in each PV string (3) throughout the solar power plant (1), to allow accurate data measurement and reading during constantly changing conditions, and

[0085] comparing a performance ratio (PR Ratio) in each PV string (3).

[0086] The Cost-Effective String Performance Monitoring installs:

[0087] - at least four light-level measuring devices (ESMA Sensor) (15) to cover the area of the solar power plant (1), wherein the number of light-level measurement devices (ESMA Sensors) (15), to be installed within the solar power plant (1) area, depends on the data reception radius, which is correlated with the physical and geographical analysis of the entire area of the solar power plant (1), and / or

[0088] - at least one sun tracking camera (17) within, at any designated point, the solar power plant (1), together with at least one master pyranometer sensor (16) or

[0089] - at least one sky camera (18) within the solar power plant (1), together with at least one master pyranometer sensor (16).

[0090] The evaluation and analysis of the Cost-Effective Solar Panel Performance Monitoring installs an electrical signal receiving box (ESMA Board) (8) connected to the PV panels (2).

[0091] The data stored in the database and processed via the Internet (Cloud) (10) is analyzed by the solar power generation monitoring and evaluation system according to this invention. The evaluation and analysis of both string-level and panel-level performance are performed in the following sequential steps:

[0092] The system retrieves the panel dataset or string dataset stored in the database and processed via the Internet (Cloud) (10) for management, organization, and separation. Then, the system analyzes the panel dataset (Analyze Panel Dataset) or the string dataset using the Performance Ratio Calculation System, and further analyzes the panel dataset with artificial intelligence (Analyze Panel Dataset by Al) or the string dataset with artificial intelligence (Analyze String Dataset by Al). This analysis is combined with Mathematical Model to determine accurate and precise data for evaluating the performance of the solar power plant (1). The system also includes an automatic calibration system for panel data (Panel Auto Calibration System) or an automatic calibration system for string data (String Auto Calibration System).

[0093] The automatic calibration system for panel data (Panel Auto Calibration System) or string data (String Auto Calibration System) determines all devices connected to the system to perform the automatic self-calibration in order to allow the data measurement and reading accurately during changing conditions,

[0094] wherein the automatic self-calibration is activated under the clear and steady light conditions, throughout the solar power plant (1) at the time of data measurement and reading,

[0095] The results of the analysis of the panel dataset for each solar panel (PV Panel) (2), or the analysis results of the string dataset for each string (PV String) (3), using the Performance Ratio Calculation System and the analysis of the solar panel dataset by artificial intelligence (Analyze Panel Dataset by Al) or the analysis of the string dataset by artificial intelligence (Analyze String Dataset by Al) at any given time will be processed by the monitoring system. This system checks and evaluates any failures in each solar panel (PV Panel) (2) or each string (PV String) (3). The results of checking these failures in each solar panel (PV Panel) (2) or each string (PV String) (3) are then entered into the Confirm Result step as the data analysis result, by the Low Cost-Effective String PerformanceMonitoring, the Cost-Effective Solar Panel Performance Monitoring, and the Cost-Effective String Performance Monitoring confirm the data analysis result, to validate the analysis data. When it is found that the performance ratio of each PV panel (2) or each PV string (3) at any given time matches the standard or is close to the performance ratio of the solar panel system (Performance Ratio) of each solar panel (PV Panel) (2) or each string (PV String) (3) in the same location as previously recorded, the system will store the data (Store data Management) in the database and processed via the Internet (Cloud) (10) for future use.

[0096] The data storage system (Store data Management) stores the data in the system’s database as a reference for comparing the performance ratio of each solar panel (PV Panel) (2) or each string (PV String) (3) over different periods,

[0097] when the performance ratio of each PV panel (2) or each PV string (3), at any time, matches the virtual standard values or has similar values in the same location, and aligns with the results of the dataset analysis by artificial intelligence (Al), thereby considering each PV panel (2) or each PV string (3) normal, and

[0098] when it is found that the performance ratio of each PV panel (2) or each PV string (3), at any time, does not match the virtual standard values or does not have similar values of the solar panel system (Performance Ratio) of each solar panel (PV Panel) (2) or each string (PV String) (3) in the same location, and does not align with results of the dataset analysis by artificial intelligence (Al), thereby considering each PV panel (2) or each PV string (3) failure or a warning of potential failure. The system will then proceed with Loss Analysis to calculate and predict the total value of the losses.

[0099] The evaluation and analysis of the Cost-Effective String Performance Monitoring with the installation of the sun tracking camera (17) and / or the sky camera (18), retrieves the string dataset stored in the database and processed via the Internet (Cloud) (10). This data is managed, organized, and separated before being analyzed by the Image Processing System to assess and distinguish conditions wherein clouds obscure the sun or shadows from clouds fall over the solar power plant (1), and to compare the power profile of the solar panels (PV Panel) (2) in each string (PV String) (3)

[0100] wherein the Cost-Effective String Performance Monitoring with the installation of the sun tracking camera (17) and / or the sky camera (18) comprises the following steps:

[0101] performing an analysis of image processing system;

[0102] calculating performance ratio calculation system;

[0103] analyzing the panel dataset by artificial intelligence (Al), or the string dataset by Al;

[0104] calibrating the data by the panel auto calibration system, or the string auto calibration system;

[0105] operating a monitoring system;

[0106] confirming the data analysis result;

[0107] storing data management; and

[0108] analyzing, calculating, and predicting the total value of losses as the Loss Analysis collectively.

[0109] wherein the Cost-Effective String Performance Monitoring installs at least four lightlevel measuring devices (ESMA Sensor) (15) to cover the area of the solar power plant (1), and / or at least one sun tracking camera (17), and / or at least one sky camera (18) within the solar power plant (1), togetherwith at least one master pyranometer sensor (16).

[0110] The analysis of image processing system is:

[0111] to determine the boundaries of reference areas in various times changing over time based on the movement of the sun; or

[0112] to determine the areas where solar radiation hits the entire area of the solar power plant (1),

[0113] Furthermore, the analysis of image processing system is performed in conjunction with analyzing the correlation of the geographical locations of the solar power plant (1) to analyze the image data obtained from the sun tracking camera (17) and the sky camera (18). This process defines the reference area over different periods, which changes based on the movement of the sun or determines the area wherein solar radiation hits the entire solar power plant (1). It also includes analyzing the geographical characteristics of the solar power plant (1), and / or performing spatial analysis of the data or images obtained from the Sky Camera (18). This involves defining the reference area over different periods, which changes according to the movement of the sun overthe entire solar power plant (1), along with analyzing the correlation between the sun’s angles as it moves across the solar power plant (1) at different times.

[0114] When the results of the light condition settings for the entire solar power plant (1) at the time of data measurement and reading show a clear and stable condition, the system will retrieve the irradiance (IRR) from the solar radiation covering the entire solar power plant (1) or the solar radiation hitting the solar panels (PV Panel) (2) in each string (PV String) (3), and / or compare the power profile from the solar panels (PV Panel) (2) in each string (PV String) (3). Additionally, the system will automatically calibrate the data of thestrings (String Auto Calibration System), which involves setting all connected devices to perform automatic self-calibration. This will allow for the calculation of the performance ratio of each string (String Performance Ratio Calculation), based on the principles of calculating the performance ratio (PR Ratio) of the solar power system, using the irradiance (IRR) measured from at least one Master Pyranometer Sensor (16) installed at any point within the solar power plant (1) to calculate the performance ratio (PR Ratio) of the solar power system.

[0115] The Low Cost-Effective String Performance Monitoring, the Cost-Effective Solar Panel Performance Monitoring, and the Cost-Effective String Performance Monitoring includes the monitoring system to assess the failures, in each PV panel (2) or each string (PV String) (3), based on the results of the dataset analysis by artificial intelligence (Al).

[0116] The results of the solar power system performance ratio (PR Ratio), of the Low Cost- Effective String Performance Monitoring, calculations are then fed into the monitoring system to assess the failures of the PV panels (2) in each PV string (3). This is done by by comparing the performance ratio to other PV strings (3) with other strings (PV String) (3) installed throughout the solar power plant (1),

[0117] wherein the performance ratio (PR Ratio) of the solar cell system is calculated by using the irradiance (IRR), of the panel or string, received from the electrical signal receiving box (ESMA Board) (8), orthe light-level measuring devices (ESMA Sensor) (15), orthe master pyranometer sensor (16), or the sun tracking camera (17), and / or with the installation of the sky camera (18).

[0118] The Low Cost-Effective String Performance Monitoring confirms the data analysis result by comparing the performance ratio of each PV string (3) with other PV strings (3) installed throughout the solar power plant (1),

[0119] when the performance ratio falls within the range of the specified virtual standard value, thereby considering the PV string (3) normal, and

[0120] when the performance ratio does not fall within the range of the specified virtual standard value, thereby considering the PV string (3) failure.

[0121] The comparison includes the comparison of the performance ratio (PR Ratio) of the PV panels (2) in each PV string (3) through basic statistical comparison methods, such as the mean and data distribution, to detect failures in each PV panel (2) and PV string (3) also.

[0122] The system will store the data (Store Data Management) to create a database for reference, allowing for future comparison of the performance ratio (PR Ratio) of each string(PV String) (3). If the performance ratio (PR Ratio) of the solar panels (PV Panel) (2) of each string (PV String) (3) at any given time is not within the predetermined standard range, it indicates that the solar panels (PV Panel) (2) of each string (PV String) (3) are malfunctioning.

[0123] Furthermore, the data analysis result by the Low Cost-Effective String Performance Monitoring, in the event that the performance ratio of each PV string (3), at any time, matches the virtual standard values with still high deviation, the statistical hypothesis testing methods is to be applied to assess the deviation. This is done to verify whether the performance ratio (PR Ratio) of each string (PV String) (3) at that time is within the standard range. The system will then proceed to analyze, calculate, and predict total value of losses as the Loss Analysis as described above.

[0124] The analysis, calculation, and prediction of total value of losses as the Loss Analysis consists of the following steps:

[0125] performing Loss Detection and Loss Prediction by collecting the number of solar panels (PV Panel) (2) in each panel or string (PV String) (3) from the monitoring system’s analysis results, indicating that the solar panels (PV Panel) (2) in each panel or string (PV String) (3) are malfunctioning. The number of losses and the predicted power loss of each solar panel (PV Panel) (2) or each string (PV String) (3), then calculating Loss Value and notifying an Issuing Ticket collectively.

[0126] The calculation of the Loss Value is conducted in two ways: Power Loss Calculation and AC Loss Value Calculation, as detailed below:

[0127] The Power Loss Calculation is based on the electric energy produced in a past year divided by the installed direct current power (kWh / kWp / year), or

[0128] by determining the specific production (kWh / kWp / year) of the solar power generation system of the solar power plant) (1) based on the location of the solar power plant (1), using the equation below:

[0129] Power Loss=Lossed Power (kWp) x specific production (kWp^ear)

[0130] The AC Loss Value Calculation is to determine the unit rate for electricity sales to be referenced from the agreed price between the buyer and the seller, using the following equation below:

[0132] The Issue Ticket, notified by the results of the loss value calculation, serves to inform the customer or relevant parties of the loss value calculated through the AC Loss ValueCalculation. This notification is issued via an invoice and / or financial document to notify the relevant stakeholders. The system will then confirm the payment (Receive Payment) or any payment-related details after receiving the payment document or proof from the customer or relevant parties. Following the payment confirmation, the system will provide failure information to the customer (Provide Failure Information to the customer). The Issuing Ticket specifies the location of each PV panel (2) or each PV string (3) with a failure, then sends this information to an operating section at the solar power plant (1) to inspect and resolve failure with the PV panels (2). The failure and loss information will be provided to the customer or recipient (Provide Failure Information to customer) either in an electronic format via online channels or as a physical document.

[0133] The analysis of the solar panel datasets for the Cost-Effective Solar Panel Performance Monitoring and the string dataset for the Cost-Effective String Performance Monitoring, in accordance with the invention, includes the calculation of the performance ratio for each solar panel (Panel Performance Ratio Calculation) or for each string (String Performance Ratio Calculation), and the analysis of the panel dataset by artificial intelligence (Analyze Panel Dataset by Al) or the analysis of the string dataset by artificial intelligence (Analyze String Dataset by Al).

[0134] The calculation of the performance ratio for each solar panel (Panel Performance Ratio Calculation) or string (String Performance Ratio Calculation) is based on the solar cell performance ratio calculation principles (Performance Ratio; PR Ratio). The system calculates the performance of each solar panel (PV Panel) 2 or each string (PV String) 3 at any given time, by using the irradiance (IRR) of the solar panel (PV Panel) 2, measured or obtained from the irradiance sensor within the electrical signal reception box (ESMA Board) 8, or the irradiance (IRR) of the string (PV String) 3, measured or obtained from the irradiance sensor (ESMA Sensor) 15.

[0135] The dataset analysis by artificial intelligence (Al) of the panel dataset, or the string dataset follows a sequential process, comprising the following algorithms:

[0136] 1) Clear-Sky-Condition Filtering (CSF) Algorithm

[0137] 2) Fully Connected Comparison (FCC) Algorithm

[0138] 3) Sequential Derivative Comparison (SDC) Algorithm

[0139] The Clear-Sky-Condition Filtering (CSF) Algorithm uses irradiance (IRR) as a reference for the sunlight hitting each PV panel (2) or each PV string (3) during clear and steady light conditions at any given time,

[0140] wherein the Clear-Sky-Condition Filtering (CSF) Algorithm determines the irradiance, measured from the electrical signal receiving box (ESMA Board) (8) or the light-level measuring devices (ESMA Sensor) (15), to be in a specified range to detect failures in the solar panel.

[0141] The analysis results of the irradiance (IRR), hitting each PV panel (2) or each PV string (3) during clear and steady light conditions at any given time, must all remain within the same range, and when the irradiance hitting each PV panel (2) or PV string (3) falls outside the specified range, it is considered the failure of the solar panel, or

[0142] a Warning Solar Panel indicating a risk of failure, or String Failure, or Warning String indicating a risk of failure in the string, based on the percentage of data variation.

[0143] The irradiance (IRR) is analyzed to determine the position or area of cloud covering on the PV panels (2) or each string (PV String) 3 at any given time to detect failures in the solar panels. This indicates that the irradiance (IRR) hitting each solar panel (PV Panel) 2 or each string (PV String) 3 under cloud cover will decrease. Nonetheless, the irradiance (IRR) measured on each solar panel (PV Panel) 2 or each string (PV String) 3 in such conditions should ideally be consistent across panels or strings in nearby areas. If the irradiance (IRR) does not meet these set conditions, it will be categorized as a Failure Solar Panel or Warning Solar Panel or String Failure or Warning String as defined. This status, as the results of the analysis using the Clear-Sky-Condition Filtering (CSF) Algorithm are determined to be expressed as the first failure state (y1). The system will then perform further analysis to identify Failure Solar Panel or String Failure more precisely using the Fully Connected Comparison (FCC) Algorithm and Sequential Derivative Comparison (SDC) Algorithm.

[0144] The Fully Connected Comparison (FCC) Algorithm, developed from the Smart Spider Algorithm, analyzes by comparing the characteristics of failures in each PV panel (2) or each PV string (3) with neighboring PV panels (2).

[0145] The system with algorithm retrieves a Panel Temperature or a String Temperature, a Panel Voltage or a String Voltage, a Panel Power or a String Power, and a Panel Electric Current or a String Electric Current retrieved from the database and processing system through the Internet (Cloud) (10) to determine Hyper Parameters,

[0146] wherein the Fully Connected Comparison (FCC) Algorithm determines the Hyper Parameters are defined as the allowable error (%Error Allowance) and the size of the Cropped Matrix, then converting the Hyper Parameters into a matrix, through the following steps:

[0147] The definition of the data matrix is established by using the following equation:

[0148] The selection of the data range, based on the hyperparameters, defines the matrix that will be resized into a smaller dimension. This is achieved by cropping the matrix according to predetermined dimensions while considering edge areas and any extra parts beyond the specified boundaries. In this case, the matrix is set to a size MxNM \times NMxN. The comparison of the matrix will involve looping through the matrix (Loop Matrix) to generate a cropped matrix with a size MxNM \times NMxN, as expressed in the following equation:M,NeZ+, m e M, n e N

[0149] For defining the matrix, which is selected at a reduced size according to predetermined dimensions, in the edge areas and / or exceeding areas with a size of MxN can be defined as follows in the equation:

[0151] And the size of the newly selected matrix (Cropped Matrix) will be generated as a new matrix component (Matrix).

[0153] Wherein the loop matrix of both aforementioned cases will be defined to operate from the first matrix to the last matrix of each solar panel (PV Panel) 2 or each string (PV String) 3, using the equation.

[0154] Number of Loop Iteration = n(l) = MxN

[0155] Then, the obtained matrix will be converted into the form of a vector and transformed into a column vector, resulting in a new matrix through the process of transposed vectors, as per the equation.

[0156] l / (xm,n)=vec(xm,n)

[0157] And the matrix will be transformed into a symmetric matrix by using absolute values (Absolute), as per the equation.

[0158] Symmetric Matrix of the Matrix UJ = = | a>v

[0159] Thus, <;_ojfollows that the matrix is a symmetric matrix, which consists of identical components of the matrix across both columns and rows from the same diagonal crossing position. Additionally, the components of the matrix in each row or column will be compared at each position of the components in sequence, resulting in an equation that represents the outcome of the comparison process using the position of the components in sequence, with reference variables for the operation as per the equation.

[0161] Then, the obtained values are compared with the%age of allowable error (% Error Allowance) that has been predetermined. The determination ofthe%age of allowable error (% Error Allowance) is as per the equation.

[0162] Error Allowance = ^_all

[0164] And the number of failures in each solar panel (PV Panel) 2 or each string (PV String) 3 is counted by calculating the error variation between matrix elements for the position of (m, n), resulting in a new matrix consisting of ^m,n, thereby obtaining the number of failures in the matrix as shown in the equation.

[0166] Failure Counter = ycm n= 0

[0167] Then, the number of failures between the rows of the matrix is counted, with the failure criterion defined as ycrj, as shown in the equation.

[0169] And the status of failure for each position is defined by referencing the number of failures counted between the rows of the matrix, which is represented as follows:

[0171] The result is a matrix that displays the failure state (y) for each solar panel (PV Panel) 2 or each string (PV String) 3 selected at sequence T, which is calculated only from the initially selected matrix. Afterward, the calculation is performed by shifting from the previous position to the next, with the selection moving from left to right and top to bottom. This calculation method is repeated by shifting the selected matrix of size equal to M and N under the specified conditions.

[0172]

[0173]

[0174] Nonetheless, it is necessary to define the position that indicates the failure state (y) with a value greater than T, due to the overlap of the selected matrix shifting by T times. This process yields the true result of the failure state (y), as shown in the equation.

[0176] Then, integrate it with the aforementioned equation, wherein the result of the matrix represents the condition of the failure state (y) under the condition as shown in the equation.71 , ym,n 0.75

[0177] Yo= 0.5 , 0.5 < ym n< 0.75 (° , ym,n < 0.5

[0178] The end results indicate the failure state (y) for each position of all components in the Matrix, wherein a value of 1 indicates a normal status, 0.5 indicates a warning of potential failure, and 0 indicates failure.

[0179] This will be used to determine the failure state (y) and compared with the results of each solar panel (PV Panel) 2 or each string (PV String) 3 that are adjacent or nearby. The result, as the results of the analysis using the Fully Connected Comparison (FCC) Algorithm, will be determined to be expressed as the second failure state (y2).

[0180] The Sequential Derivative Comparison (SDC) Algorithm, which is the application of the Kalman Filter, to analyze and compare data sequences used to establish reference data (Threshold) for each sequence,

[0181] wherein the Sequential Derivative Comparison (SDC) Algorithm uses the Panel Electric Current or the String Electric Current retrieved from the database and processing system through the Internet (Cloud) (10) to determine the Hyper Parameters and

[0182] wherein the Sequential Derivative Comparison (SDC) algorithm is used to identify sequences that show failures, and determines the Hyper Parameters to be defined as the allowable error (%Error Allowance), the number of stored data, and the number of Continuous Failure Times).

[0183] The method for analyzing the failures of each solar panel (PV Panel) 2 or each string (PV String) 3 using the Sequential Derivative Comparison (SDC) Algorithm involves setting up data sequences and managing each stored sequence of data in the form of a matrix. The stored data is defined as a data sequence, and the reference data is moved for each sequence. The pattern of data changes for each adjacent sequence is calculated by defining a set of sequences of data and a set of sequence variations. The initial state of the set of sequence variations is set as "Normal" or 0. The error allowance is defined, along with the range of allowable errors. The values are combined to establish a set of allowable errors, and an empty set is designated to store the data used to calculate the changing reference data (Self-adjustment Threshold), including calculating the self- adjusting reference data (Self-adjustment Threshold).

[0184] Then, excess data is removed from the set of data sequences, and the initial state of the failure state of the dataset is set to the normal state (Normal). New data is stored at new time intervals, and the sequence of data sequences is re-ordered according to the set of data sequences. The difference in the change in sequence values used in the calculation of reference data (Threshold) for each component of the next set of data sequences is defined in succession, and the data changes in the final sequence of the data sequence for each position are calculated. A new set of data changes is stored, and the state of the set of data changes for the set of data sequences is defined. The status of the new sequence of data changes is stored by determining the initial count of failures for each component position. The number of continuous failures is then counted, and the number of continuous failures is set. The failure state of each position in the dataset is determined. Subsequently, the changing reference data (Self-adjustment Threshold) is calculated, and the excess data is removed from the set of data sequences based on the failure state of each position.

[0185] The process of self-adjustment and prediction (Self-Adjustment & Prediction (SAP)) is repeated and recalculated until the end of the matrix sequence is reached. The result of this process is a matrix showing the failure state (y) of each solar panel (PV Panel) 2 or each string (PV String) 3. The end results of the Sequential Derivative Comparison (SDC) Algorithm indicate the failure state (y) for each position of all components in the matrix,

[0186] wherein a value of 0 indicates failure status, 0.5 indicates a warning of potential failure status, and 1 indicates a normal status.

[0187] Furthermore, the end results of the Sequential Derivative Comparison (SDC) Algorithm are used to determine the failure state (y) and are compared with the results of each PV panel (2) or each PV string (3) in the subsequent sequence,

[0188] wherein the end results are determined to be expressed as the third failure state (y3).

[0189] In otherwords, the results of the dataset analysis by artificial intelligence (Al) establish a correlation between the constant weights obtained from experiments, as well as the first failure state (y1), second failure state (y2), and third failure state (y3).

[0190] The results of the data analysis of the solar panels through artificial intelligence (Analyze Panel Dataset by Al) or the string data analysis through artificial intelligence (Analyze String Dataset by Al) from the analysis using the three algorithms mentioned earlier lead to the following equation:

[0192] Wherein co, ci, and C2 are constant weight values derived from experimental results, and \( y \) represents the failure state (y) of each solar panel (PV Panel) 2 or each string (PV String) 3, which can be expressed in the following equation:

[0194] The results for each position of the PV panels (2) or PV strings (3) are defined as follows: a value greater than 0.75 indicates a failure status, a value between 0.5 and 0.75 indicates a warning of potential failure status, and a value below 0.5 indicates normal status.

[0195] Any change made to this invention may be vividly understood and can be done by a person skilled in the field. The change may be within the scope and intent of this invention as shown in the claim attached.

Claims

Claims

1. A monitoring and evaluation system of solar power generation according to this invention comprises: a solar power plant (1) installing, within the solar power plant (1), at least one position of photovoltaic (PV) panels (2) connected in series and arranged as a set or row called a string (PV String) (3), wherein each row of PV strings (3) is connected in parallel to combine the electric current and power values to meet the specified values; at least four light-level measuring devices (ESMA Sensor) (15) determined to be installed cover the area of the solar power plant (1), wherein the number of light-level measuring devices (ESMA Sensors) (15) for installation within the solar power plant (1) area depends on the data reception radius, which is correlated with the physical and geographical analysis of the entire area of the solar power plant (1); at least one electrical signal receiving box (ESMA Board) (8) connected to each PV panel (2), with the installation and connection characteristics pre-determined, wherein one ESMA Board (8) has the capability to support the connection to more than or equal to one unit of PV panel (2); at least one master pyranometer sensor (16) determined to be installed at a specific area within the solar power plant (1) to measure solar radiation covering the entire solar power plant (1) or to measure the solar radiation hitting each PV string (3), wherein the sensor is determined to be subject to the light conditions, throughout the solar power plant (1) at the time of data measurement and reading, which are clear and steady; at least one sun tracking camera (17) installed within the solar power plant (1) at a specific area within the plant (1), wherein the camera (17) is a device to track the movement of the sun as it passes over the solar power plant (1) at any given time; at least one sky camera (18) installed within the solar power plant (1) to detect the movement of the sun and clouds passing over the solar power plant (1); andthen, the electric current and power values generated from each PV string (3) are transmitted and collected in a DC combiner (4), wherein, inside the DC combiner (4), a specific type of circuit card is installed capable of supporting the aggregation of the electric current and power values from each predetermined PV string (3) row, wherein the electric current and power values produced are then transmit to an inverter (5), transformer (6), and grid (7), wherein all the obtained data, measured or read from the devices, is stored and gathered via the gateway (9), and subsequently stored on a database and processing system via the internet (Cloud) (10) for further analysis, and wherein the analysis of the dataset from the monitoring and evaluation system of solar power generation according to this invention is determined to perform data analysis, as three types, of monitoring and evaluation methods including the Low Cost-Effective String Performance Monitoring, the Cost-Effective String Performance Monitoring, and the Cost-Effective Solar Panel Performance Monitoring, wherein the system is able to analyze, calculate, and predict the total value of losses as a Loss Analysis generated.

2. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring installs at least one master pyranometer sensor (16) at a specific area within the solar power plant (1) to measure the irradiance (IRR) from the solar radiation covering the entire solar power plant (1) or to measure the solar radiation hitting the PV panels (2) in each PV string (3).

3. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring involves: comparing a power profile of the PV panels (2) in each PV string (3), under the clear and steady light conditions, throughout the solar power plant (1) at the time of data measurement and reading, to be used as a reference for IRR and the power profile of the PV panels (2) in each PV string (3) throughout the solar power plant (1), to allow accurate data measurement and reading during constantly changing conditions, and comparing a performance ratio (PR Ratio) in each PV string (3),wherein the comparison includes the comparison of the performance ratio (PR Ratio) of the PV panels (2) in each PV string (3) through basic statistical comparison methods, such as the mean and data distribution, to detect failures in each PV panel (2) and PV string (3) also.

4. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Cost-Effective String Performance Monitoring installs at least four light-level measuring devices (ESMA Sensor) (15) to cover the area of the solar power plant (1), and / or at least one sun tracking camera (17), and / or at least one sky camera (18) within the solar power plant (1), together with at least one master pyranometer sensor (16).

5. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Cost-Effective Solar Panel Performance Monitoring installs an electrical signal receiving box (ESMA Board) (8) connected to the PV panels (2).

6. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the electrical signal receiving box (ESMA Board) (8) according to this invention comprises: a light intensity sensor (12), a voltage sensor (13), and a temperature sensor (14) on the PV panels.

7. The monitoring and evaluation system of solar power generation according to claim 1 or 6, wherein the light intensity sensor (12) converts sunlight levels into irradiance (IRR) for calculating the performance ratio (PR Ratio) of the solar system or detecting failures in the PV panels.

8. The monitoring and evaluation system of solar power generation according to claim 1 or 6, wherein the voltage sensor (13) is used to calculate the energy production or detect failures in the PV panels (2) individually or in a combination thereof.

9. The monitoring and evaluation system of solar power generation according to claim 1 or 6, wherein the temperature sensor (14) on the PV panels (2) is used to measure the temperature for evaluating failures in the PV panels (2).

10. The monitoring and evaluation system of solar power generation according to claim 1 or 6, wherein the electrical signal receiving box (ESMA Board) (8) has an accuracy of 60% when measuring the sunlight levels hitting the PV panels (2) via the light intensity sensor (12),preferably, when combined with measuring voltage of the PV panels (2) using the voltage sensor (13), the accuracy of the electrical signal receiving box (ESMA Board) (8) is increased to 71-80%, and more preferably, when additionally combined with measuring temperature of the PV panels (2) via the temperature sensor (14), the accuracy of the electrical signal receiving box (ESMA Board) (8) exceeds 80%.

11. The monitoring and evaluation system of solar power generation according to claim 1 or 10, wherein the voltage sensor (13) and the temperature sensor (14) on the PV panels (2) are separately operable.

12. The monitoring and evaluation system of solar power generation according to claim 1 , wherein at least four light-level measuring devices (ESMA Sensor) (15) are determined to be installed to coverthe area of the solar power plant (1), wherein the number of light-level measurement devices (ESMA Sensors) (15), to be installed within the solar power plant (1) area, depends on the data reception radius, which is correlated with the physical and geographical analysis of the entire area of the solar power plant (1).

13. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the light-level measuring devices (ESMA Sensor) (15) can measure each PV string (3) and convert the values into irradiance (IRR), wherein the irradiance (IRR) is used to reference the irradiance of the PV panels (2) in each PV string (3) within the data reception radius of the lightlevel measuring devices (ESMA Sensors) (15).

14. The monitoring and evaluation system of solar power generation according to claim 1 or 2, wherein the master pyranometer sensor (16) is used: to reference the irradiance (IRR) of the PV panels (2) in each PV string (3); and to automatically self-calibrate the light-level measuring devices (ESMA Sensor) (15) and / or the electrical signal receiving box (ESMA Board) (8) when being under the clear and steady light conditions, throughout the solar power plant (1) including the power profile, at the time of data measurement and reading, to allow accurate data measurement and reading during constantly changing conditions.

15. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the sun tracking camera (17) is configured to track the movementangle of the sun (17), and record images of the sun’s movement across the solar power plant (1).

16. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the sky camera (18) detects the movement of the sun and clouds passing over the solar power plant (1), wherein the camera (18) is installed at a central or any pre-determined area within the solar power plant (1).

17. The monitoring and evaluation system of solar power generation according to claim 1 or 16, wherein the sun tracking camera (17) analyzes the correlation of the position and angle of sun moving across the solar power plant (1), relative to the surface of the solar power plant (1), to obtain the images where the sun is always centered, wherein an image processing system is used to distinguish conditions where cloud cover over the sun and / or cloud shadows hitting the PV panels (2), and the irradiance (IRR) of the PV panels (2) in each PV string (3) can be compared to that obtained from the master pyranometer sensor (16) or other output values measured from similar output devices.

18. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the database and processing system through the Internet (Cloud) (10) stores data received from the electrical signal receiving box (ESMA Board) (8), or light-level measuring devices (ESMA Sensor) (15), or master pyranometer sensor (16), or sun tracking camera (17), or sky camera (18), or DC combiner (4), or the inverter (5).

19. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Cost-Effective String Performance Monitoring and the Cost- Effective Solar Panel Performance Monitoring comprises the following steps: analyzing panel dataset, or string dataset; calculating performance ratio calculation system; analyzing the panel dataset by artificial intelligence (Al), or the string dataset by Al; calibrating the data by the panel auto calibration system, or the string auto calibration system; operating a monitoring system;confirming the data analysis result; storing data management; and analyzing, calculating, and predicting the total value of losses as the Loss Analysis collectively.

20. The monitoring and evaluation system of solar power generation according to claim 1 or 19, wherein the Cost-Effective String Performance Monitoring with the installation of the sun tracking camera (17) and / or the sky camera (18) comprises the following steps: performing an analysis of image processing system; calculating performance ratio calculation system; analyzing the panel dataset by artificial intelligence (Al), or the string dataset by Al; calibrating the data by the panel auto calibration system, or the string auto calibration system; operating a monitoring system; confirming the data analysis result; storing data management; and analyzing, calculating, and predicting the total value of losses as the Loss Analysis collectively.

21. The monitoring and evaluation system of solar power generation according to claim 1 , 19, or 20, wherein the performance ratio (PR Ratio) of the solar cell system is calculated by using the irradiance (IRR), of the panel or string, received from the electrical signal receiving box (ESMA Board) (8), or the light-level measuring devices (ESMA Sensor) (15), or the master pyranometer sensor (16), or the sun tracking camera (17), and / or with the installation of the sky camera (18),

22. The monitoring and evaluation system of solar power generation according to claim 1 , 19, or 20, wherein the dataset analysis by artificial intelligence (Al) comprises algorithmic analysis including Clear-Sky-Condition Filtering (CSF) Algorithm, Fully Connected Comparison (FCC) Algorithm, or Sequential Derivative Comparison (SDC) Algorithm, individually or in a combination thereof.

23. The monitoring and evaluation system of solar power generation according to claim 1 or 22, wherein the Clear-Sky-Condition Filtering (CSF) Algorithm usesirradiance (IRR) as a reference for the sunlight hitting each PV panel (2) or each PV string (3) during clear and steady light conditions at any given time, wherein the Clear-Sky-Condition Filtering (CSF) Algorithm determines the irradiance, measured from the electrical signal receiving box (ESMA Board) (8) or the light-level measuring devices (ESMA Sensor) (15), to be in a specified range to detect failures in the solar panel.

24. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 23, wherein the irradiance (IRR) must all remain within the same range, and when the irradiance hitting each PV panel (2) or PV string (3) falls outside the specified range, it is considered the failure of the solar panel.

25. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 23, wherein the irradiance (IRR) is analyzed to determine the position or area of cloud covering on the PV panels (2) to detect failures in the solar panels.

26. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 23, wherein the results of the analysis using the Clear-Sky- Condition Filtering (CSF) Algorithm are determined to be expressed as the first failure state (y1).

27. The monitoring and evaluation system of solar power generation according to claim 1 or 22, wherein the system retrieves a Panel Temperature or a String Temperature, a Panel Voltage or a String Voltage, a Panel Power or a String Power, and a Panel Electric Current) or a String Electric Current retrieved from the database and processing system through the Internet (Cloud) (10) to determine Hyper Parameters.

28. The monitoring and evaluation system of solar power generation according to claim 1 or 22, wherein the Fully Connected Comparison (FCC) Algorithm compares the characteristics of failures in each PV panel (2) or each PV string (3) with neighboring PV panels (2), wherein the Fully Connected Comparison (FCC) Algorithm determines the Hyper Parameters are defined as the allowable error (%Error Allowance) and the size of the Cropped Matrix, then converting the Hyper Parameters into a matrix, which is selected at a reduced size according to predetermined dimensions, including the edge areas and / or exceeding areas.

29. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 27, wherein the end results indicate the failure state (y) for each position of all components in the Matrix, wherein a value of 1 indicates a normal status, 0.5 indicates a warning of potential failure, and 0 indicates failure.

30. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 27, wherein the results of the analysis using the Fully Connected Comparison (FCC) Algorithm are determined to be expressed as the second failure state (y2).

31. The monitoring and evaluation system of solar power generation according to claim 1 or 22, wherein the Sequential Derivative Comparison (SDC) Algorithm analyzes and compares data sequences used to establish reference data (Threshold) for each sequence, wherein the Sequential Derivative Comparison (SDC) algorithm is used to identify sequences that show failures, and determines the Hyper Parameters to be defined as the allowable error (%Error Allowance), the number of stored data, and the number of Continuous Failure Times).

32. The monitoring and evaluation system of solar power generation according to claim 1 or 22, wherein the Sequential Derivative Comparison (SDC) Algorithm uses the Panel Electric Current or the String Electric Current retrieved from the database and processing system through the Internet (Cloud) (10) to determine the Hyper Parameters.

33. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 31 , wherein the end results of the Sequential Derivative Comparison (SDC) Algorithm indicate the failure state (y) for each position of all components in the matrix, wherein a value of 0 indicates failure status, 0.5 indicates a warning of potential failure status, and 1 indicates a normal status.

34. The monitoring and evaluation system of solar power generation according to claim 1 , 22, or 31 , wherein the end results of the Sequential Derivative Comparison (SDC) Algorithm are used to determine the failure state (y) and are compared with the results of each PV panel (2) or each PV string (3) in the subsequent sequence, wherein the end results are determined to be expressed as the third failure state (y3).

35. The monitoring and evaluation system of solar power generation according to claim 1 or 31 , wherein the results of the dataset analysis by artificial intelligence (Al) establish a correlation between the constant weights obtained from experiments, as well as the first failure state (y1), second failure state (y2), and third failure state (y3).

36. The monitoring and evaluation system of solar power generation according to claim 1 or 35, wherein the results for each position of the PV panels (2) or PV strings (3) are defined as follows: a value greater than 0.75 indicates a failure status, a value between 0.5 and 0.75 indicates a warning of potential failure status, and a value below 0.5 indicates normal status.

37. The monitoring and evaluation system of solar power generation according to claim 1 or 20, wherein the analysis of image processing system is: to determine the boundaries of reference areas in various times changing over time based on the movement of the sun; or to determine the areas where solar radiation hits the entire area of the solar power plant (1), wherein the analysis of image processing system is performed in conjunction with analyzing the correlation of the geographical locations of the solar power plant (1) to analyze the image data obtained from the sun tracking camera (17) and the sky camera (18).

38. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the automatic self-calibration is activated under the clear and steady light conditions, throughout the solar power plant (1) at the time of data measurement and reading, to determine all devices connected to the system to perform the automatic self-calibration in order to allow the data measurement and reading accurately during changing conditions.

39. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring, the Cost- Effective Solar Panel Performance Monitoring, and the Cost-Effective String Performance Monitoring includes the monitoring system to assess the failures, in each PV panel (2) or each string (PV String) (3), based on the results of the dataset analysis by artificial intelligence (Al).

40. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring includesthe monitoring system to assess the failures of the PV panels (2) in each PV string (3) by comparing the performance ratio to other PV strings (3) installed throughout the solar power plant (1).

41. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring, the Cost- Effective Solar Panel Performance Monitoring, and the Cost-Effective String Performance Monitoring confirm the data analysis result, when the performance ratio of each PV panel (2) or each PV string (3), at any time, matches the virtual standard values or has similar values in the same location, and aligns with the results of the dataset analysis by artificial intelligence (Al), thereby considering each PV panel (2) or each PV string (3) normal, and when the performance ratio of each PV panel (2) or each PV string (3), at any time, does not match the virtual standard values or does not have similar values in the same location, and does not align with results of the dataset analysis by artificial intelligence (Al), thereby considering each PV panel (2) or each PV string (3) failure or a warning of potential failure.

42. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring confirms the data analysis result by comparing the performance ratio of each PV string (3) with other PV strings (3) installed throughout the solar power plant (1), when the performance ratio falls within the range of the specified virtual standard value, thereby considering the PV string (3) normal, and when the performance ratio does not fall within the range of the specified virtual standard value, thereby considering the PV string (3) failure.

43. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the Low Cost-Effective String Performance Monitoring confirms the data analysis result when the performance ratio of each PV string (3), at any time, matches the virtual standard values with still high deviation, the statistical hypothesis testing methods is to be applied to assess the deviation.

44. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the analysis, calculation, and prediction of total value of losses as the Loss Analysis consists of the following steps: performing Loss Detection and Loss Prediction, calculating Loss Value, and notifying an Issuing Ticket collectively.

45. The monitoring and evaluation system of solar power generation according to claim 1 or 44, wherein the calculation of the Loss Value is conducted in two ways: Power Loss Calculation and AC Loss Value Calculation.

46. The monitoring and evaluation system of solar power generation according to claim 1 , 44, or 45, wherein the Power Loss Calculation is based on the electric energy produced in a past year divided by the installed direct current power (kWh / kWp / year), or by determining the specific production (kWh / kWp / year) of the solar power generation system of the solar power plant) (1) based on the location of the solar power plant (1).

47. The monitoring and evaluation system of solar power generation according to claim 1 , 44, or 45, wherein the AC Loss Value Calculation is to determine the unit rate for electricity sales to be referenced from the agreed price between the buyer and the seller.

48. The monitoring and evaluation system of solar power generation according to claim 1 or 45, wherein the results of the loss value calculation notify the Issuing Ticket that specifies the location of each PV panel (2) or each PV string (3) with a failure, then sending this information to an operating section at the solar power plant (1) to inspect and resolve failure with the PV panels (2).

49. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the system is configured to operate in an energy-saving mode (Sleep Mode) and an active mode (Wake Up Mode) based on sunlight intensity.

50. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the solar power plant (1) includes the renewable energy power plant, or the location generating energy from various energy sources with similar characteristics, individually or in a combination thereof.

51. The monitoring and evaluation system of solar power generation according to claim 1 , wherein the installation of the Sky Camera (18) employs the image processing system: to detect or identify the position and angle of the sun; and to distinguish conditions where cloud cover the sun and / or cloud shadows hitting the solar power plant (1)wherein the irradiance (IRR) of the PV panels (2) in each PV string (3) can be compared to that obtained from the master pyranometer sensor (16) or other output values measured from similar output devices, j

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