WO2020202184A1

WO2020202184A1 - A system employing electrical digital twin for solar photovoltaic power plant

Info

Publication number: WO2020202184A1
Application number: PCT/IN2020/050281
Authority: WO
Inventors: Keyur Kishorkumar GANDHI
Original assignee: Helios Iot Systems Private Limited
Priority date: 2019-03-29
Filing date: 2020-03-26
Publication date: 2020-10-08
Also published as: IN201921012539A; IN332353B

Abstract

: An automated system and method, which implements an electrical digital twin 5 (100), for efficient operation & maintenance and asset management of a solar pv power plant is disclosed herein. The said system comprises plurality of weather sensors (400) for collecting "time-stamped" operational data of geo-location specific solar pv power plant in real-time or on-demand; a "plant build" module (300) to configure an as-built and as-operated solar PV power plant; a "component" 10 module (200) to configure OEM device specific data related to the solar pv power plant; an "electrical digital twin engine" (100) for making performance and losses predictions by utilizing weather data generated by the weather sensors and plant configuration data generated by the "plant build" and the "component" module; and a remote server to store, execute and deliver the predictions data to consumers.

Description

A SYSTEM EMPLOYING ELECTRICAL DIGITAL TWIN FOR SOLAR PHOTOVOLTAIC POWER PLANT

FIELD OF INVENTION:

The present invention generally relates to a system and method for efficient and automated operation & maintenance and asset management of a solar PV power plant. More particularly, the invention relates to an electrical digital twin and method thereof for automated operation & maintenance and asset management via performance benchmark and enhancing operational status of the solar PV power plant leading to reduced losses and downtime.

BACKGROUND & PRIOR ART:

In solar photovoltaic (PV) power plant, the solar energy (light) is converted into the electricity by means of Solar panels aka PV Modules. Generally, there are three types of solar power plants, viz. - Ground mounted (Utility), Rooftop mounted (Distributed) and hybrid (combination of ground mounted and roof top mounted and other energy generation stations such as wind turbines). A ground mounted Solar power plant is composed of PV modules as power generation units, connected via a combination of series and parallel topology into strings and arrays respectively. The strings or arrays are connected in parallel into the combiner boxes or directly into inverters. Each inverter typically can accommodate around 10 strings or arrays or combiner boxes and each string can hold dozens of PV Modules. With only a handful of inverters, the number of PV modules drastically rises to several thousands. The combiner boxes collect the DC electricity from the PV modules. Thereafter, the inverters convert the collected DC power to the AC power. The inverters are connected to the transformer to step up the voltage in order to inject AC power desired by the grid, i.e. the electricity network to which the solar plant is connected. The AC power from all the transformers are combined through High Transmission (HT) panels which contain electrical protection related switches before the power is injected into grid via a DP panel and switch yard. In the roof top mounted system, the general topology remains the same as that of the ground mounted. Typically, string inverters are being used instead of central inverters and can have no transformers connected to the power grid. If the roof top plant is larger or requires voltage step-up then a transformer is installed. The operation & maintenance (O&M) and asset management of solar pv power plant is a time and labour-intensive job. The solar pv power plant running at an optimum level has better efficiency, compared to the one which is not up to its capacity. Optimisation of solar PV power plant performance requires insights in finding and resolving technical O&M problems. A typical O&M and asset management of the solar pv power plant is expected to run for 25 years from the date of commission.

Accurate and realistic performance prediction of an as-built and as-operated solar PV power plant is a key step towards performing aforementioned steps.

Using weather forecast, accurate power forecast can be achieved under various environmental and geolocation variations, such as variation in irradiation, temperature, humidity, wind speed as well as latitude, longitude and altitude of a specific solar pv power plant. Heterogeneous plant engineering in electrical components leads to inaccurate performance predictions due to variations in Original Equipment Manufacturers (OEMs). Further, due to abnormal ageing and degradation of OEM components, it becomes difficult to gauge the operating capacity of the solar power plant. Current solutions, that are being implemented for O&M of solar plant, include use of SCADA (Supervisory Control and Data Acquisition) system, and a remote monitoring & control software tool. In a last decade, a new technology called “digital twin” has emerged that effectively collects the data to make predictions and optimisation of system. A digital twin is a digital replica of physical assets, such as system, processes, in order to assess and predict the outcome of a system for the purpose of optimisation. In other words, the digital twin simulates the real/physical system under observation on a digital platform in order to make predictions and gauge the operational status and health of the system.

The applications of the digital twin can be found in aircraft industry, HVAC, wind turbines etc. Some of the documents utilising digital twins are listed below.

US20160247129 relates to system, method and apparatus for creating and utilizing digital twins for energy efficient asset maintenance. The said system is particularly applicable to maintenance of HVAC like machines. The system claimed in US’ 129 comprises of a pair of digital twin and a simulation platform. Each of the digital twins corresponds to each remotely located physical machines. Each respective digital twin comprises product nameplate data corresponding to a unique physical machine, one or more simulation models, and a database data collected from sensors associated with the unique physical machine.

US9995278 discloses a use of digital twin interface for operating wind farms. The said interface comprises of a graphical user interface (GET) displaying a digital equivalent to wind farms. This digital equivalent may include information, such as environment information, arrangement of the wind turbines etc. One or more Control icons present on the digital interface include optimum operating conditions of the wind turbines. The environment information may be collected by various sophisticated sensors and direct the same to SCADA to stream back to cloud system. Further, the SCADA system is configured to provide on-site communications for operators to be able to retrieve data from turbines and/or to interact with the control system.

US20170286572 discloses an apparatus to implement a digital twin of a twinned physical system such that one or more sensors sense values of one or more designated parameters of the twinned physical system. A computer processor receives the data associated with the sensors and monitors a condition of the selected portion of the twinned physical system. Further, it also assess a remaining useful life of the selected portion based at least in part on the sensed values of the one or more designated parameters. A communication port may transmit information associated with a result generated by the computer processor. The sensors may sense values of the one or more designated parameters, and the computer processor may perform the monitoring and/or assessing, when the twinned physical system is not operating.

US9881430 relates to a cooling system in a vehicle, which comprises of plurality of sensors and a digital twin system. The sensors gather data indicative of the cooling condition of the vehicle, whereas the digital twin system simulates the operation of the cooling system. Further, the digital twin system is configured to change or control actual operation of the cooling system of the vehicle based on the health score or the operation of the cooling system that is simulated.

It may be noted that the aforesaid documents need, upto a certain extent, the involvement of the user to generate performance and health insights. Further, most of the known digital twin systems employ machine learning and artificial intelligence (AI) techniques. Furthermore, none of the known digital twin systems are dedicated to a realistic solar PV power plant systems and process, in terms of predicting energy loss at each relevant level in the plant.

SUMMARY OF INVENTION:

Accordingly, the present invention provides an automated system and method which implements an electrical digital twin for efficient O&M and asset management of a solar pv power plant.

The said system comprises plurality of weather sensors for collecting“time- stamped” operational data of geo-location specific solar pv power plant in real-time or on-demand; a“plant build” module to configure an as-built and as-operated solar PV power plant; a“component” module to configure OEM device specific data related to the solar pv power plant; an“electrical digital twin engine” for making performance and losses predictions by utilizing weather data generated by the weather sensors and plant configuration data generated by the“plant build” and the “component” module; and a remote server to store, execute and deliver the predictions data to consumers.

The data required for the system to function are plant configuration data and operational data. The data acquired from the “plant build” module and the “component module” forms the plant configuration data. The“plant build” module acquires design engineering data of as-built and as-operated solar power plant, whereas the component module accepts datasheet specification of specific device type, provided by the manufacturer, and integrates with the plant configured data from the“plant build” module.

The“time-stamped” operational data is acquired by the weather station sensors, such as a pyranometer and pyrheliometer for irradiation, a RTD or thermocouple sensor for ambient & PV module back surface temperature, a hygrometer for relative humidity, a precipitation sensor for rainfall, an anemometer for wind speed and direction. Alternatively, the“time stamped” operational data may also be acquired from the weather forecast agencies or database.

The electrical digital twin engine of the present invention distinguishes itself from the prior art, by not relying solely upon SCAD A, data loggers etc for acquisition of data. Further, the electrical digital twin engine of the present invention is not based on machine learning or artificial intelligence systems, as in the case of the state of the art. Instead, it relies on the information related to the functioning of electrical/electronic devices commissioned on the solar power plant, in addition to the geo-location specific weather data.

The invention further discloses an automated method to generate performance and loss predictions for efficient O&M and asset management of a solar PV power plant. The said method employs the electrical digital twin engine, wherein the electrical digital twin engine is configured to perform following steps in automated manner:

(i) Initialise the system by reading and validating solar power plant’s configuration and operational requirements;

(ii) Characterise and predict weather & irradiation losses based on operational data received from plurality of weather station sensors and plant configuration;

(iii) Characterise and predict PV cells performance and losses based on steps (i) and (ii);

(iv) Characterise and predict inverter performance and loss based on steps (i) and (iii);

(v) Characterise and predict transformer performance and losses based on steps (i) and (iv);

(vi) Characterise and predict HT panel performance and losses based on steps (i) and (v);

(vii) Characterise and predict DP panel performance and losses based on steps (i) and (vi);

(viii) Characterise and predict cable losses based on actual cable length and step (i), steps (iii) to steps (viii);

(ix) Recalibrate PV cells performance under inverter due to losses in steps (iv) & (v);

(x) Calculating voltage, current, power (Performance) and energy losses from steps (ii) to (viii); and

(xi) Storing and exporting all performance and losses predictions values from steps (i) to (ix).

The invention further discloses an electrical digital twin for generating performance and losses predictions in efficient operation & maintenance and asset management of a solar PV power plant by utilizing weather data of plurality of weather sensors and configuration data generated by a plant build module and a component module. BRIEF DESCRIPTION OF DRAWINGS:

Fig. 1 illustrates block diagram of the system of the present invention. Configuration data from plant build module (300) & component module (200) as well as operation data (400) from different weather sensors serve as an input to the electrical digital twin (100).

Fig. 2 depicts a typical devices topology and the ohmic losses calculated at each level of solar power plant by the system of the present invention. Typically, the solar PV power plant includes a DC side, AC low transmission (ACLT) and AC high transmission (ACHT). The electrical digital twin (100) disclosed in the present invention calculates ohmic losses at each of these levels. There are multiple weather related gains and losses based on devices topology.

Fig. 3 illustrates schematics of DC side topology of the solar PV power plant, where the ohmic losses are calculated by the electric digital twin (100).

Fig. 4 illustrates schematics of AC -Low Transmission side (ACLT) topology of solar PV power plant, where the ohmic losses are calculated by the electric digital twin (100).

Fig. 5 illustrates schematics of AC -High Transmission side (ACHT) topology of solar PV power plant, where the ohmic losses are calculated by the electric digital twin (100).

Fig. 6 illustrates a flowchart demonstrating steps (601-611) for the purpose of predictions by the electrical digital twin (100) of the present invention.

Fig. 7 illustrates a graphical representation of time-stamped performance data of solar pv power plant

Fig. 8 illustrates feasibility of the electrical digital twin (100) of the present invention for automated operation & maintenance and asset management of large ‘n’ number of power plants.

DETAILED DESCRIPTION OF INVENTION:

The present invention provides an automated system and method which implements an electrical digital twin to generate performance and loss predictions for efficient O&M and asset management of a solar PV power plant. The invention may be comprehended with reference to figure 1-8 appended at the end of the description. The figures include best / preferred mode of working. However, it may be noted that the figures demonstrate the construction and working of invention and, by no means, intend to restrict the scope of the invention. Any variation in the present invention may be envisaged as within the ambit of the present invention.

Further, the technical and scientific terms used in the specification have their usual meaning associated with it. Some of the terms, which are specifically used for the purpose of the invention, have been elaborated at appropriate places.

The present invention provides an automated system and method which implements an electrical digital twin to generate performance and loss predictions for efficient operation & maintenance and asset management of a solar PV power plant.

Referring to Fig. 1, which represents preferred embodiment, the said system comprises plurality of weather sensors (400) for collecting “time-stamped” operational data of geo-location specific solar power plant in real-time or on- demand; a“plant build” module (300) to configure an as-built or as-operated solar power PV plant; a“component” module (200) to configure specific device related to the solar power plant; an electrical digital twin engine (100) for generating performance and losses predictions by utilizing configuration data, and a remote server to store, execute and deliver the data to consumers.

The configuration data, in the context of the present invention, means combination of the data generated as a result of the deployment of devices in an as-build or as- operated solar PV power plant. Such configuration includes, but not limited to, make, model, rated capacity, technology, physical dimensions cables material and length, electrical & mechanical property of the OEM devices. For this purpose, the datasheet along with the test results provided by the equipment manufacturer is required, specifically for“component” module. The said data may take the form of computer generated/readable files and stored in a repository which can be referred by the electrical digital twin engine (100). Further to this, the“plant build” module (300) captures the as-built or as-operated solar power PV plant’ s configuration. The said configuration may be stored in computer-generated repository files, which can be accessed by the electrical digital twin engine upon request or demand. Hence, the configuration data is generated based on combination of data provided by the OEM and data provided with as-build or as-operated solar power plant architecture. The“plant build” module (300), as described in the present invention, configures the data from the as-built or as-operated solar power plant. This data is a complete plant engineering data, which includes DC & AC single line diagram (SLD), array layout, cable schedules, OEM component count sheet, PV module flash test report. Such an engineering data is saved into the plant built module.

The“component module” takes into account the technical and engineering data related to the devices (OEM) and products. The said technical and engineering data includes data sheet parameters, such as operations and engineering related technical data along with performance and product warranty Serve Level Agreements (SLA’s), ageing / degradation test report, Guaranteed Technical Particulars (GTP). The electrical digital twin (100) executes sequence of function based on plant configuration and device configuration. Further, the modules proposed in the present invention are processing units where associated data are received and processed for the stated purpose. These modules may be computer-based or any other smart device based. However, these modules cannot function without various sensors and other hardware parts.

The operational data is received from various weather sensors (400) deployed at the site of the solar power plant. The operational data is received by means that includes, but not restricted to, SCADA server, data logger server, remote monitoring systems, cloud storage database, data lake, data dump upload or a weather service provider API service. The weather sensors include, but are not limited to, pyranometer, pyrheliometer, RTD, thermocouple, hygrometer, precipitation, anemometer etc. The operational data is further processed to the electrical digital twin (100).

The said sensors are well-known devices and extract the relevant information for which they are deployed. For example, pyranometer measures solar irradiation at several instances of time or season. The pyrheliometer measures the direct beam solar irradiance and is mounted on a solar tracker device, in order to measure irradiance. All other sensors used therein have their usual meaning as understood by the person ordinarily skilled in the art.

In another embodiment, the operational data may be obtained from weather monitoring agencies.

The“time-stamped” data in the context of the present invention has a specific meaning. The aforementioned data, for the purpose of the present invention, is acquired per minute resolution, which may be termed as a data point. Therefore, there are 1440 data points available in 24 hours for further assessment. The system is able to accept multiple time resolutions, but not restricted to, minute, seconds, milli-second & hours.

In an optional embodiment, if a particular data point is not received due to some unavoidable circumstances, that particular data point is imputed. This provision allows the present invention to fill the gap by determining general time series pattern of the data records via statistical imputation process.

The remote server, employed in the present invention, communicates with the third party systems when the said third party requests the data related to a specific solar pv power plant.

Fig. 2 illustrates schematic representation of the ohmic losses calculated at each level of solar pv power plant topology by the system of the present invention. Typically, the solar PV power plant has following phases: weather conditions, a DC side, AC low transmission (ACLT) and AC high transmission (ACHT). Referring Fig. 2, it may be clear the phases at which ohmic losses occur, which are further calculated by the system of the present invention. The electronic digital twin (100) detects ohmic losses occurring at each of these phases, enhancing prediction ability of the present invention. The weather condition phase includes geo-location (latitude, longitude and altitude) specific solar irradiation, ambient temperature, wind seed, wind direction, relative humidity, rainfall parameters. There are several types weather related gains and losses on the performance of the solar pv power plant. Some of the weather related gains and losses includes, but not restricted to, are sun tracker irradiation, airmass, spectral gain and thermal. The DC side phase includes solar panels, which are comprised of PV cells, as the basic power generating block. The solar panels are mount-in either fixed tilt or sun tracking on to strings and arrays. The strings and arrays are connected to the DC combiner box to aggregate the power. AC LT phase converts the DC power generated by solar panels into AC power by means of plurality of inverters. The said AC power is stepped-up to high voltage signals in AC HT phase, by means of plurality of transformers. This high voltage signals are utilised in HT panels and further exported into power grids via DP panel and switch yard. As stated before, the electric digital twin (100) calculates losses at each of these phases.

Fig. 3 illustrates schematics of DC side of the solar PV power plant where the ohmic losses are calculated by the electric digital twin (100). The anatomy of a typical solar PV power plant is as follows: Plurality of PV cells form one panel, several panels in series connection form a string, plurality of several such strings when connected in parallel form an array, and several such arrays are input to the DC combiner box. As demonstrated in Fig. 3, the ohmic losses are measured at the end of each of the strings, arrays and combiner boxes. The solar panels, which generates dc power by converting sunlight via photoelectric effect undergoes through the several gains and losses over the period of time. Some of the solar panels gains and losses includes, but not restricted to, are temperature, quality, mismatch, light induced diffusion (LID), potential inducted diffusion (PID), soiling, shading, reflection and defects. All the industry standard gains and losses phenomenon are incorporated in the characterisation of solar panels. The typical DC cable, consisting of +vs and -ve terminal, undergoes through ohmic losses due to increase in ambient temperature and the resistivity of cable material which typically are made up of copper and aluminium metal.

Fig. 4 illustrates schematics of low transmission of AC side (ACLT) of solar PV power plant, where the ohmic losses are calculated by the electric digital twin (100). At the low transmission phase, the inverters are connected to plurality of transformers to step up the voltage at a desired value. The electrical digital twin (100) calculates the ohmic losses between the inverters and the transformers. There are inverter internal losses which occurs during the operating age. Some of the inverter losses includes, but not restricted to, are DC-AC power convention, maximum power point tracking (MPPT), clipping, deration and faults. These losses are being captured in the inverter characterisation of the digital twin engine.

At the AC-side High Transmission (ACHT), as illustrated in Fig. 5, the stepped-up voltage from the transformers is routed to the High Tension (HT) panels which in turn are connected to double pole (DP) panel. The ohmic losses occurring at the output sides of each of these points are measured & calculated by the electric digital twin (100). There are transformer internal losses which occurs during the operating age. Some of the transformer losses includes, but not restricted to, are power transfer loss, core loss, winding loss, voltage tap loss and faults. These losses are being captured in the transformer characterisation of the digital twin engine. There are losses in HT and DP panel due to contact resistance and power aggregation. The same losses are incorporated in the digital twin engine. The AC cables, consisting of 3 phase R Y B, undergoes through the same ohmic losses as DC cables, where instead of material resistance, impedance is considered by capturing the ac power factor of the cable. There are safety devices such as vacuum circuit breaker, protection relay and power evacuation system i.e. switch yard which connects to the power grid system.

With reference to Fig. 6, the invention further discloses an automated method and orchestration of engineering functions to generate performance and loss predictions for efficient O&M and asset management of a solar PV power plant. The orchestrations can be controlled via either user defined or autogenerated settings. The said method employs the electrical digital twin engine, wherein the electrical digital twin engine is configured to perform following steps in an automated manner:

Initialise the system by reading and validating solar PV power plant’s configuration and operational requirements (step 601);

Characterise and predict irradiation losses based on operational data received from plurality of weather station sensors and plant configuration (step 602);

Characterise and predict PV cells performances and losses based on steps 601 and 602 (step 603);

Characterise and predict performance and losses in inverter based on steps 601 and 603 (step 604);

Characterise and predict performance and losses in transformer based on steps 601 and 604 (steps 605);

Characterise and predict HT panel performance and loss based on steps 601 and 605 (step 606);

Characterise and predict DP panel performance and loss based on steps 601 and 606 (step 607);

Characterise and predict cable performance and losses based on actual cable length and step 601, steps 603 to steps 606 (steps 608);

- Recalibrate and predict PV cells performance under inverter due to losses in steps 604 & 605 (steps 609);

Calculating voltage, current, power and energy losses from steps 601 to 608 (step 610); and Storing all performance and losses predictions from steps 601 to 609 (step 611).

The aforesaid steps are initiated once a user is authorised upon requesting the predictive analysis. The said performance and losses predictions typically included, and not restricted to, voltage, current, power and component related internal loss parameters.

In an embodiment, multiple weather stations are present to provide innumerable data to the system of the present invention.

Figure 7 (a) & (b) graphically illustrate the comparative power output data in a span of operating hours of a solar pv power plant. The comparison is between the predicted output power with the help of the present invention and actual power output at the site. Fig. 7(a) shows the graph of comparative power output data at ground mounted fixed title system at the capacity of 2MVA AC under cloudy weather conditions. Fig. 7(b) provides graphical representation comparative data at ground mounted single axis tracker system having the capacity 10 MV A AC. It may be observed from the graphs that the predicted output power with the assistance of the system of the present invention has an accuracy of greater than 95% with that of the output power. More comprehensive analysis and testing of the solar pv power plant electrical digital twin engine has been carried out for more than 500 MVA AC operating capacity across the different geographical locations and under wide weather conditions. The electrical digital twin engine consisted of multiple technology OEM devices and topology resulting in very heterogenous solar pv power plant deigns. It may be noted that, to generate heterogeneous performance and loss prediction of as-built or as-operated solar power plant, a virtual electrical solar pv power plant is instantiated based on configuration requirements. Thereafter, the time-stamped operational data is parsed through the electrical digital twin engine (100). Fig. 8 illustrates that the electrical digital twin (100) and the system of present invention may be tied to plurality of solar PV power plants. In other words, the system of the present invention is capable to in simultaneously predicting output of multiple solar PV power plant and export the performance and losses predictions to multiple consumer of system. Advantages of the present invention:

The system of the present invention is able to run on any heterogenous plant configuration, type of solar power plant (ground mounted (Utility), rooftop mounted (Distributed) and solar part of the hybrid plant) and OEM devices. The said system is flexible for deployment and can be deployed on premises for a customer or a remote server.

The system can be operated for any age solar power plant.

The data is collected in real-time or on-demand mode

The system can be integrated to any 3^rd party system, which is capable to receive data.

The system is capable to predict performance & losses at a disaggregated level and not just one levels of power plant.

The system can be auto configured or manually configured if in case required.

The system can take multiple inputs from multiple consumers of the digital twin engine, hence making it highly scalable allowing multiple predictions happening at the same time.

The system can be operated on a subscription mode for any known consumer system of digital twin.

The system do not store consumer data under the guidelines of GDPR and only keeps the operation log files for system optimisation.

The system prediction efficiency is comparable when compared to machine learning or artificial intelligence-based system and more importantly its realistic in nature.

The system is capable to detect abnormal ageing & degradation in the OEM devices i.e. solar panels, inverters, transformers, sun trackers and cables. This will generate valuable performance insights for the O&M and asset management of a underperforming solar pv power plant. The system is intended to improve the power forecast accuracy of the solar pv power plant using geolocation weather conditions and its operational status.

Conventional systems depend upon machine learning and artificial intelligence. Due to the provision in the present invention, the degradation and losses at various points in the plant can be quantified. This is justified by the intermediate output giving mechanism of the system. In AI dependent systems, the stated variables cannot be quantified since AI based devices are viewed as blackbox.

Claims

WE CLAIM,

1. An automated system implementing an electrical digital twin (100) for efficient operation & maintenance and asset management of a solar PV power plant comprising:

plurality of weather sensors (400) for collecting time-stamped operational data of geo-location specific solar power plant in real-time or on demand;

a“plant build” module (300) to configure an as-built or as-operated solar PV power plant;

a“component” module (200) to configure device specific data related to the as-built or as-operated solar power plant;

an electrical digital twin engine (100) for generating performance and losses predictions by utilizing weather data of the weather sensors (400) and configuration data generated by the plant build module (300) and the component module (200);

a remote server to store, execute and deliver the predictions data to consumers.

2. The system as claimed in Claim 1, wherein the plant build module (300) acquires design engineering data of as-built or as-operated solar PV power plant as one of the configuration requirement.

3. The system as claimed in Claim 2, wherein the said configuration requirement is acquired from engineering documents such as AC SLD, DC SLD, plant array layout, cable schedule and such.

4. The system as claimed in Claim 1 to 3, wherein the plant build module (300) is upgraded in response to changes in the engineering data during O&M.

5. The system as claimed in Claim 1, wherein the component module (300) accepts datasheet specification of specific device type as the other configuration requirement and integrates with the plant configuration process.

6. The system as claimed in Claim 1, wherein a virtual electrical solar pv power plant is instantiated based on configuration requirements and the time-stamped operational data thereafter is parsed through the electrical digital twin engine to generate heterogeneous performance and loss prediction of as-built or as-operated solar power plant.

7. The system as claimed in Claims 1 to 6, wherein the electrical digital twin engine to generate performance and loss predictions is pre-configured to Initialise the system by reading and validating solar power plant’s configuration and operational requirements (step 601);

Characterise and predict cable performance and losses based on actual cable length and step 601, steps 603 to steps 606 (steps 608); - Recalibrate and predict PV cells performance under inverter due to losses in steps 604 & 605 (steps 609);

Calculating voltage, current, power and energy losses from steps 601 to 608 (step 610); and

- Storing and exporting all performance and losses predictions from steps 601 to 609 (step 611).

8. The system as claimed in Claim 1, wherein the electrical digital twin engine (100) is able to parallelly generate performance and losses predictions of multiple solar pv power plant.

9. The system as claimed in Claim 1, wherein time-stamped operational data of geo-location specific solar power plant is alternately acquired from weather forecast agencies.

10. An electrical digital twin (100) for generating performance and losses predictions in efficient operation & maintenance and asset management of a solar PV power plant by utilizing weather data of plurality of weather sensors (400) and configuration data generated by a plant build module (300) and a component module (200), wherein the electrical digital twin engine (100) is pre-configured to

Initialise the system by reading and validating solar power plant’s configuration and operational requirements (step 601);

Characterise and predict performance and losses in inverter based on steps 601 and 603 (step 604); Characterise and predict performance and losses in transformer based on steps 601 and 604 (steps 605);

- Characterise and predict DP panel performance and loss based on steps 601 and 606 (step 607);

Storing and exporting all performance and losses predictions from steps 601 to 609 (step 611).