AU2020104355A4 - IoT and MACHINE LEARNING BASED POWER QUALITY IMPROVEMENT SYSTEM FOR MICRO-GRID - Google Patents

Abstract

"IoT and MACHINE LEARNING BASED POWER QUALITY IMPROVEMENT SYSTEM FOR MICRO-GRID" Exemplary aspects of the present disclosure are directed towards the IoT and Machine Learning Based Power Quality Improvement System for Micro-Grid consists of the plurality of Line Monitoring & Control System (LMCS) 101 connected to Central Monitoring & Control System (CMCS) 103 through Communication-Network 102. LMCS 101 is an integration of microcontroller 101a with advanced power-management chip 101b, two pair of three potential-transformers 101d and current-sensors 101c, Circuitry for Circuit-Breaker 10le control, and Capacitor-Banks 101f. LMCS 101 uses the Machine-Learning (ML) algorithm to predict power quality issues and operates respective Capacitor-Banks 101f for mitigating voltage and frequency profiles. All the power data received by CMCS 103 is normalized and fed to relevant ML algorithm to identify and predict, load, line stress and power quality issues and mitigate the problems. Based on mitigation CMCS 103 isolates the section if necessary by operating the concerned Circuit-Breaker 101e or switch on the concerned capacitor-banks. Page 1 500 501 *Acquire Current and Voltage magnitudes and respective wave forms through Power IC 101b. Assign time stamping to the acquired values along with LMS ID 502 Compare waveforms of two sets of currents and voltages of that line and compute the change in currents and voltages Al AV and Power Factor. 503 Using relevant machine learning algorithm, ascertain whether Al and AV values are in limits in comparison them with set values 504 If anomaly is found in Al and AV, then feed the values directly to machine learning algorithm and predict the type of power quality issue alert the CMCS-MAIN and await for the mitigation strategy. 505 Based on mitigation strategy, connect capacitors of the respective capacitor bank. 506 Acquire Al and AV after energies the capacitor banks and fed the values to respective MLA for determining and predicting their safe values. 507 4 If the PQ issues persisting, then increase the capacitor bank 10 lg values and again Acquire Al and AV 508 1 If Al is high and persistent after the step 507, then ascertain the type of PQ issues and intimate the CMCS with time stamping & LMCS ID and upload power data and type of PQ issue. FIG 5 500 Process for Executed in LMS

Description

501

*Acquire Current and Voltage magnitudes and respective wave forms through Power IC 101b. Assign time stamping to the acquired values along with LMS ID 502 Compare waveforms of two sets of currents and voltages of that line and compute the change in currents and voltages Al AV and Power Factor.

503 Using relevant machine learning algorithm, ascertain whether Al and AV values are in limits in comparison them with set values

504 If anomaly is found in Al and AV, then feed the values directly to machine learning algorithm and predict the type of power quality issue alert the CMCS-MAIN and await for the mitigation strategy. 505

Based on mitigation strategy, connect capacitors of the respective capacitor bank. 506

Acquire Al and AV after energies the capacitor banks and fed the values to respective MLA for determining and predicting their safe values.

507 4 If the PQ issues persisting, then increase the capacitor bank 10 lg values and again Acquire Al and AV

508 1 If Al is high and persistent after the step 507, then ascertain the type of PQ issues and intimate the CMCS with time stamping & LMCS ID and upload power data and type of PQ issue.

FIG 5 500 Process for Executed in LMS

TITLE OF THE INVENTION

IoT and Machine Learning Based Power Quality Improvement System for Micro-Grid

PREAMBLE TO THE DESCRIPTION

The following specification particularly describes the invention and the manner in which it is to be performed

DESCRIPTION TECHNICAL FIELD

[0001] The present disclosure generally relates to the field of Electrical Engineering, more precisely monitoring, protection, Power management and method thereof for resolving power quality issues in a micro grid system by implementing suitable Internet of Things (IoT) protocol and relevant Machine Learning Algorithms (MLA).

BACKGROUND

[0002] The vast Electrical grid for effective technical management in the present day has been divided into micro-grids. The modem day micro-grid with the advent of IoT and Artificial Intelligence has become smart, in that the dynamic state of the micro-grid is determined using appropriate sensors. The new era in power system has opened up the chapter of smart micro grid systems wherein small units of generating systems (distributed generation) are connected to loads. The gravity of fault conditions in this type of system are very dangerous due to presence of various generation systems such as PV based generation systems. This is generally termed as islanding of micro grid systems and it can be avoided if a predictive system is in place along with supervisory and control system.

[0003] Though Microgrid provide low cost, clean energy to limited geographic localities improving their energy stability and operations, they suffer from some drawbacks such as load islanding, load balancing and PQ issues. Wherein, the major problems relate to PQ issues which results in unreliable and unbalanced power which may lead to grid failure.

[0004] In the field of electrical engineering, transmission line plays a vital role, and in the process of Electrical Automation, line monitoring and protection is a major constrain.

Line protection is made mainly through a differential protection system, under and over voltage protection system, temperature base protection system, and vibrational base protection system. All the types of protection systems realized with relays, sensors, and microcontroller interface, but they have reasonable failures of the system. Power Quality (PQ) issues are usually reported in the categories of Voltage dips and swells, Short interruptions, Long interruptions, Harmonics, Surges and transients, and lastly in the category of Flicker, unbalance, earthing and electromagnetic compatibility (EMC) problems. Most of the Microgrid are loosely monitored and they lack sophisticated monitoring or control system to avert the PQ issues.

[0005] Though several inventors and researchers have overcome the disadvantages by implementing machine learning and other artificial intelligence-based algorithms in existing microcontroller architecture but weren't able to integrate all the protection systems efficiently and unable to integrate with the total power distribution system.

[0006] Numerous prior arts have made attempts to automate the grid with numerous prototyping but haven't achieved a more desirable feature in a single unit for the power distribution network.

[0007] Similarly, several prior art disclosures have ascertained that power quality issues particularly Voltage Sag and Swell tend to malfunction or failure of equipment which can cause large financial losses to various manufacturers. When dealing with such a PQ issue their deeper understanding assists us to ascertain their impact and mitigate the issues effectively. Voltage dips and swells are defined in different manners based on their characteristics and they are Instantaneous, Interruption and Momentary sag and swells.

[0008] In the prior art KR101778772B1, with title The System and Method of Detecting Events by Checking Frequency in Microgrid explained A method for detecting a microgrid anomaly by an anomaly detection system, the method comprising: collecting frequency information per unit time relating to a voltage of each power facility constituting a microgrid; Measuring a frequency change per unit time through the collected information; Calculating overtime when the frequency change per unit time exceeds a threshold of any of a plurality of thresholds, And determining that an abnormal phenomenon corresponding to any one of the thresholds has occurred in the microgrid when the calculated excess time is equal to or longer than a predetermined time, And a predetermined time of the microgrid abnormality detecting method.

[0009] Another prior art US20200313430A1, titled Configuring, optimizing, and managing micro-grids ascertained methods and systems for controlling electrical distribution grids. The method includes determining premises in an electrical distribution grid that include an energy resource. The method further includes determining a configuration of the electrical distribution grid including a micro-grid, the micro-grid including the one or more premises. The method further includes electrically isolating, monitoring and controlling the micro-grid from the electrical distribution grid through the use of a micro-grid manager.

[0010] Another Prior art document US9455577B2 by Gopal K. BHAGERIAJean Gael F. REBOUL, titled Managing devices within micro-grids - explained An approach to provide power from power supply devices to power-consuming devices based on characteristics of the power consuming devices and/or the power supply devices. The approach includes a method that includes receiving information of a power-consuming device from an energy management system that determines criticality of the power consuming device. The method further includes receiving power supply information of one or more power supply devices associated with an electric grid. The method further includes receiving a power request from the power-consuming device. The method further includes determining that the power-consuming device receives power from the power supply device, based on the information and the power supply information.

[0011] Similar prior art US9960637B2 .with title Renewable energy integrated storage and generation systems, apparatus, and methods with cloud distributed energy management services- discloses A software platform in communication with networked distributed energy resource energy storage apparatus, configured to deliver various specific applications related to offset demand monitoring, methods of virtual power plant and orchestration, load shaping services, methods of reducing demand at aggregated level, prioritizing computer programs related to virtual energy pool, energy cloud controllers methods, charge discharge orchestration plans of electric vehicles, distributed energy resources, machine learning predictive algorithms, value optimizing algorithms, autonomous sensing event awareness, mode selection methods, capacity reservation monitoring, virtual power plant methods, advanced DER-ES apparatus features, energy management system for governing resources and methods, aggregated energy cloud methods, load shaping methods, marginal cost cycle-life degradation, load shaping API, forward event schedule, on-demand request, and load service state request methods.

[0012] In Prior art document US20060158037A1 by Douglas Danley et al, with title Fully integrated power storage and supply appliance with power uploading capability disclosed a Systems, methods and devices for integrating alternative energy sources and energy storage components into a single device with systems for control and safety monitoring to provide for use of the generated power on the premises, resale of power to the utility, and for power supply from storage and/or the alternative energy sources in the event of an interruption of supply from a utility's power grid.

[0013] Another prior art document US20170228479A1 describes a Systems and methods for real-time modelling of a microgrid are disclosed. An analytics server communicatively connected to a microgrid. The analytics server comprises a virtual system modelling module, an analytics module; a simulation module; and a communications module. The virtual system modelling module is operable to generate predicted data for the microgrid utilizing a virtual system model of the microgrid. The analytics module is operable to initiate a calibration and synchronization operation to update the virtual system model based on a difference between the predicted data and real-time data from the microgrid. The simulation module is operable to forecast at least one aspect of the microgrid. The communications module is operable to provide at least one forecasted aspect to a microgrid operator and a microgrid operator.

[0014] Another prior art document US8751421B2 by Roger N. Anderson Et al disclosed an invention Machine learning for power grid wherein he proposed A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection offiltered propensity to failure metrics of like components within the electrical grid.

[0015] Referring to another document US10599175B1, discloses a Time synchronized frequency and voltage regulation of electric power balancing areas. Wherein Systems, methods, and computer program products are described for controlling power of a balancing area having a plurality of distributed energy resources of a power system. A high frequency controller having a memory and at least one data processor of one distributed energy resource of the balancing area continuously receives (i) data including a phasor data stream having time-synchronized phasor measurements derived from the plurality of distributed energy resources of the balancing area and (ii) real-time energy levels of the plurality of distributed energy resources.

[0016] US20150051856A1 document titled Method for estimating voltage stability discussed a method for estimating voltage stability includes establishing a multi-port equivalent model and the measurement-based equivalent impedance; calculating the reactive power response factor through two consecutive samples from wide-area phasor measurement unit measurement; finding the mitigation factor; constructing the modified coupled single port model with the modified impedance and voltage; and using the modified maximal loading parameter for voltage stability assessment.

[0017] CN104242462A presented an invention relates WAMS (wide-area measurement system) and SCADA (supervisory control and data acquisition) integrated data based grid forced oscillation source positioning method. The method includes: firstly, positioning general directions of the oscillation sources by adopting an energy flow function method based on 500kV grid WAMS data, then adopting different accurate oscillation source positioning methods according to properties of three common oscillation sources, computing tie lines of a local weak tie network by adopting a SCADA data-based oscillation extreme point judgment method, and judging whether oscillation exists in the weak tie network or not; adopting WAMS data of generator excitation voltage phase to judge whether oscillation is caused by a generator exciting system or not; adopting an extreme point computation method of SCADA data of valve or guide vane opening of a generator set to judge whether grid oscillation is caused by a unit prime mover or not.

[0018] In an early document CN102323494B, The invention relates to a novel method for distinguishing multiple harmonic sources, which can be used for providing a theoretical foundation for positioning a disturbance source in a multiple harmonic source system, managing harmonic waves, rewarding or punishing the harmonic waves and the like and has a broad application prospect and favourable social and economic benefits. The method comprises the following steps of (1) defining a harmonic influence index; (2) solving the harmonic influence index; (3) acquiring and pre-processing data; and (4) selecting and analyzing the data.

[0019] In a prior document CN102221655A, The invention discloses a random-forest model-based power transformer fault diagnosis method, which comprises the following steps of: acquiring transformer state overhauling data, training a random forest model by utilizing the transformer state overhauling state, checking the sensitivity of the random forest model, and diagnosing a fault of a transformer by using the trained and checked random forest model. The method provided by the invention is high in adaptability and interpretability, and criticality between normality and the fault is separated by utilizing a k-means clustering method, and a system is endowed with fault early warning capability.

[0020] In an invention stated in a document CN102377180B, disclosed an invention relates to a power system load modelling method based on an electric energy quality monitoring system, belonging to the field of power system measurement and load model identification.

[0021] The present invention provides an effective maintenance system for a microgrid system with the integration of the plurality of the Smart Line Monitoring & Control System (SMCS). Whereas SMCS can work alone with Machine Learning Algorithm and as well as in coordination control with other plurality of SMCS and CMCS. Wherein CMCS on a whole can execute advanced Machine Learning Algorithms to ascertain PQ issues and stress on the lines and isolate the faulty or overloaded systems.

[0022] The present invention addresses the shortcomings mentioned above of the prior art.

[0023] All publications herein are incorporated by reference to the same extent as if each publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.

SUMMARY

[0024] The following presents a simplified summary of the disclosure in order to provide a basic understanding of the reader. This summary is not an extensive overview of the disclosure, and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[0025] Exemplary embodiments of the present disclosure are directed towards the IoT and Machine Learning-Based Power Quality Improvement System For Micro-Grid.

[0026] An exemplary object of the present disclosure is directed towards a system that monitors and controls the PQ issues and improve the power factor, Voltage sag or swell and any line fault conditions in a Microgrid system.

[0027] Another exemplary object of the present disclosure is directed towards the integration of microcontroller 101a with Power IC 10lb and capacitor banks to make Line Monitoring & Control System (LMCS) 101. Whose primary function is to monitor vital Line parameters especially voltages, currents, frequency waveforms.

[0028] Another exemplary object of the present disclosure is directed towards the integration of microcontroller 101a with a capacitor bank which can switch on required capacitance on-demand to mitigate the voltage and power factor issues.

[0029] An exemplary aspect of the present subject matter is directed towards the implementation of the Machine Learning Algorithm in microcontroller 101a for ascertaining the type of PQ issue that occurred in the particular line where the LMCS is connected.

[0030] An exemplary aspect of the present subject matter is directed towards the use of the Machine Learning Algorithm in microcontroller 101a for predicting the PQ issues that may be caused by excess loading and pre-fault conditions.

[0031] An exemplary aspect of the present subject matter is directed towards the implementation of the Machine Learning Algorithm in microcontroller 101a for predicting the PQ event and finding suitable mitigation method.

[0032] Another exemplary aspect of the present disclosure is directed towards the integration of the plurality of LMCS with CMCS through IoT protocol that is WiFi Mesh Network. Where in each LMCS acts as a node and Trans-receive the WiFi signals.

[0033] Another exemplary aspect of the present disclosure is directed towards the implementation of the Machine Learning Algorithm in LMCS for supporting decisions of the plurality of LMCS during PQ anomaly and as well as fault conditions and prediction of failures.

[0034] Another exemplary aspect of the present disclosure directed towards the implementation of the Machine Learning Algorithm in LMCS for determining the stress on line and loading capacity.

[0035] Another exemplary aspect of the present disclosure directed towards the removal of the faulty section or transformer from the system to avert total system failure.

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In the following, numerous specific details are set forth to provide a thorough description of various embodiments. Certain embodiments may be practiced without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others.

[0037] FIG.1 is a diagram depicting the100 Machine Learning-Based Power Quality Improvement System for Micro-Grid, according to an exemplary embodiment of the present disclosure.

[0038] FIG. 2 is a Block diagram 101 Line Monitoring & Control System (LMCS) 101 according to an exemplary embodiment of the present disclosure.

[0039] FIG. 3 is an actual representation of the microcontroller 101a, according to an exemplary embodiment of the present disclosure.

[0040] FIG. 4 is an actual representation of Custom Made PCB for POWER IC (ADE7861), according to an exemplary embodiment of the present disclosure.

[0041] FIG. 5 is a flow chart 500 depicting the Process executed on SMCS.

[0042] FIG. 6 is a flow chart 600 depicting the process carried in CMCS.

[0043] Fig 7 depicts the actual representation of Two Step Capacitor Bank Array

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0044] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components outlined in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0045] The use of "including", "comprising" or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms "first", "second", and "third", and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0046] The nomenclature used hereafter for ease of understanding grouped and termed here as follows. Microgrid system means a plurality of loads and a source connected in a ring main to maintain continuous supply to all the loads by deriving the power from one or more power sources. Microcontroller means a microcomputer capable of executing Artificial intelligence and machine language algorithms. Machine Learning Algorithm (MLA) implies a generalized term for the complete process of data collections, noise removal, data formatting, cleansing, usage of random forest method, training the model with -80 ration, model evaluation, parameter tuning, prediction, and weight adjustment. Power IC 101b is an advanced chip custom made to collect power data in a bus bar/connected terminals having a six-channel current sensor in which three connected in input side and another three connected to secondary of the particular bus. Power quality issues which are mainly in discussions are Voltage Sag and Swell only. Another set of nomenclature used hereafter are defined when and where required.

[0047] Referring to FIG. 1 is a diagram depicting a Machine Learning Based Power Quality Improvement System for Micro-Grid. Which comprises of a Central Monitoring & Control System (CMCS) 103, a plurality of Line Monitoring & Control System (LMCS) 101 connected through WiFi Mesh network 102 to CMCS 103 in a ring main microgrid system. CMCS is a central management unit capable of executing parallel and complex Machine Learning Algorithms (MLA) and calculations. Plurality of the LMCS

101 are enabled with WiFi mesh network which eliminates the necessity of any special routers or modems and this feature is an advanced IoT function that removes the redundancy and reliable data transfer.

[0048] Further to it, CMCS & SMCS uses a trained MLA based on a random forest model for predicting faults, line loading, load flow analysis, and PQ issue determination. Data transmitted by the plurality of SMCS 101 is collected, labelled as per SMCS ID and time interval, and stored in the database. The power data thus received is cleansed and fed to the MLA for predicting stress on the feeder and line, the capacity of other components in the line as well as the components which might create PQ issues. And also, MLA predicts the faults and analyzes the fault events which take place in or outside the busbar and justifies the actions taken by SMCS 101. CMCS 103 is a centralized unit which typically consists of a computing device which is capable of interacting, acquiring the data, processing the data and sending necessary commands to the plurality of SMCS 101.

[0049] Additional to it, CMCS can isolate a faulty section or an overstressed section based on the prediction model. This feature enables the microgrid to maintain its stability and reliability through its operation time. For the said model, a five bus system is considered wherein four loads are connected to four busses and one bus is considered as power sources bus. Hence, it is to understand that, the generating bus is capable of supplying the required power demand from all the four loads. This system is considered by taking into account of a microgrid formed in cement industry.

[0050] During the islanding process and as well as uncertain loading conditions, PQ events may occur and which may create serious problems if not mitigated in time. For mitigating the PQ event we must act swiftly when an anomaly is found. For finding the PQ anomaly the placement of LMCS devices is much more important. Hence, one LMCS are connected across each line as shown in fig 1. It is worthwhile to note that for easy of understanding, each LMCS is connected near bus bars only wherein three current transformers and three potential transformers form group A and others form group B. Grop A and Group B sensors are connected to one side of bus bar. Here it is understand that, to form a line monitoring and control system (LMCS), Group A sensors of LMCS-2 and Group b sensors of LMCS-1 form a pair to monitoring and control the line.

[0051] Though LMCS l0lare connected across the bus bar, a set of the three current transformers and three potential transformers from Group B of LMCS1 continuously compares the voltage and currents waveforms concerning Group A of LMCS-2. LMCS-1 retrieves the data from Group B sensors (a set of the three current transformers and three potential transformers) and transmit the data to LMCS-2. Wherein LMCS-2 compares the retrieved data with its Group A sensor data by executing relevant MLA to find any anomaly. This configuration and assembly help in ascertaining exact PQ anomaly and pinpoint the place of origin.

[0052] In accordance with a non-limiting exemplary embodiment of the present subject matter, FIG. 2 is a representation of the Line Monitoring & Control System (LMCS) 101. As depicted in the drawings of FIG 2, LMCS 101 consists of microcontroller 101a, Power IC (ADE7816) 101b, Current sensors 101c, Potential sensors 101d, embedded connection to Circuit breakers 101e controlled through relays, Temperature sensor 101f and Capacitor Bank 101g. Current sensors 101c divided into a set of three and connected to the two-lines adjoining concerned bus bar. Similarly, Potential transformers 101d divided into a set of three and connected in the same fashion as that of current transformers 101c. The arrangement of Current sensors 101c forms a bus bar protection system and also for identifying PQ events. The combinational performance analysis of temperature sensor 101f and current sensor 101c enables the relevant MLA to predict the future faults arising of line temperature variations concerning the predicted and present load on a the line. Microcontroller 101a controls the circuit breakers concerned with that bus bar, which are arranged in incoming and outgoing lines, as indicated in FIG 1. Based on the output of concerned MLA, Microcontroller 101a decided to operate concerned circuit breaker 10le for averting PQ anomaly and faults and disconnecting the faulty transformer or line. All the components of the LMCS concealed in a custom container with IP & IK protection norms and for ease of installation. Powered by solar, and as well as +12/+5 V DC supply, makes the LMCS more convenient for deployment.

[0053] Referring to FIG 3 is a diagram depicting the actual image of Microcontroller 101a, which acts as a backbone for the present invention. Tweaked with ZRAM and additional external storage system 101a-i makes the microcontroller 101a suitable for the present invention. In microcontroller 101a, Machine Learning Algorithms can be executed in parallel to validate several parameters of the Microgrid. The GPRS Module and Wi-Fi Modules make the microcontroller a versatile Internet of things (IoT) device.

[0054] In accordance with a non-limiting exemplary embodiment of the present subject matter, FIG. 4 is an actual image depicting the Custom Made PCB for POWER IC(ADE7816) 101b. The Power IC 101b- is a state of the art chip designed particularly for accurate power measurement and integrated with easy. By utilizing this feature, in the present invention, the same Power IC 10lb-i is developed on a custom size PCB to hold the required components such as power supply and sensor connectors and controller connectors. Custom PCB integrated with microcontroller 10la for Main Line anomaly detection and mitigation. The Current Transformers 101c, which are split-core type connected to any sort of cables/wire up to 200A current rating. Three current transformers 101c form a group that is hooked up in either of the primary or secondary inputs of the bus bar. Power IC 101b-I collects the sensor data and accurately measures and presents power data, which are Active Power, Reactive Power, Apparent Power, Individual phase and line currents and voltages, along with RMS and True values and concerned waveform.

[0055] According to a non-limiting exemplary embodiment of the present disclosure, FIG.5 is a flow chart 500 depicting Process for executed in LMCS 101. LMCS considered as principal protection and monitoring device for the Line and busbar in the way it acts by monitoring its performance with sensors and power IC and controlling its operations when fault or PQ abnormality arises. The step 501 starts by acquiring Current and Voltage magnitudes and respective waveforms through Power IC 101b. Assign time stamping to the acquired values along with LMCS ID. Timestamping is done to the acquired value so that the occurrence can be known precisely for the present and future process. Whereas LMCS ID is attached to the data acquired as this data is further transmitted to CMCS for justification and prediction of the faults and other PQ events, and this LMCS ID makes the CMCS pinpoint the occurrence point in the system accurately. In successive step 502, microcontroller 101a compare waveforms of two currents and voltages at that bus and as well at the line (Group A sensor of this LMCS with that of other LMCS Group B sensors) to compute the change in waveforms of currents and voltages Al and AV. In a balanced system, when so anomaly is present then the Al and AV must be nearly zero. It is to be noted that, Al and AV may be the difference is busbar voltages and currents or maybe the difference between operating norms. Therefore, in step 503 microcontroller executes relevant Machine Learning Algorithm (MLA) to ascertain whether Al and AV values are in limits by comparing them with set values. For speedy of response, the microcontroller first compares the Al and AV values with user-defined set values to determine an ongoing anomaly or a fault. In step 504, then feed the values directly to the machine learning algorithm and predict the type of power quality issue alert the CMCS-MAIN and devise the mitigation strategy. The mitigation strategies which are not limited to but maybe energizing capacitor banks in a stepwise manner or eliminating the source/load which causes the PQ anomaly. And also communicating the anomaly to CMCS 103 by LMCS 101 may also lead to mitigation strategy which may be localized at that fault or maybe on overall prospective of the grid. The mitigation strategies at grid level which are not limited to but maybe increasing the generation, increasing the capacitor involvement or maybe removing a section.

[0053] Further to step 505, based on mitigation strategy, connect capacitors of the respective capacitor bank. This step is crucial for eliminating any false PQ issue indication arisen from several constraints. Any PQ event arose of any condition is mitigated away in this step. This is to be noted that, Power IC 10lb s capable of identifying Voltage sag and Swell in 1 / 1 0 th of a cycle and the processor 101a is capable of executing the relevant MLA and mitigate the same in less than a half-power cycle. Next step 506 starts with microcontroller 101a acquire Al and AV after energies the capacitor banks and fed the values to respective MLA for determining their safe values. And in the following step 507, microcontroller 101a If the PQ issues persisting, then microcontroller 10la increase the capacitor bank values and again Acquire Al and AV through power IC 101b. It is worthwhile to note that, capacitor bank 101g is a two-step value device, which is capable of delivering the required capacitance to mitigate the PQ event. The capacitance value may be designed exactly based on the grid parameters and predicted PQ events. Wherein step 508, a conclusion about the PQ event or anomaly which has aggrieved from step 507, and the microcontroller 101a then ascertain the type of PQ issues and intimate the CMCS with time stamping & LMCS ID and upload power data and type of PQ issue.

[0054] According to a non-limiting exemplary embodiment of the present disclosure, FIG.6 is a flow chart 600 depicting the process carried in CMCS, which starts at step 601 wherein CMCS acquire all the parameters of the Grid through respective LMCS and allocate time-stamping along with LMCS ID. Data is not restricted to sensors, but it also includes the MLA predicted outcome as well as other derivatives. As data collected from the plurality of LMCS, then the data cleansing should take place. Therefore in step 602. Data cleansed to meet the requirements of the MLA present in CMCS. In consecutive step 603, power data, that is Al, AV, AT, and APL of all the plurality of LMCS fed to the concerned machine learning algorithm MLA.

[0055] Further, in step 604, with the help of predicted outcome from one of the MLAs concerning current and voltage variations in all the lines, CMCS ascertains the exact load causing the concerned PQ issues and intimates the LMCS for deploying appropriate mitigation method. In consecutive step 605, if microcontroller in LMCS once received the mitigation method, it executes the necessary actions and intimates the same to CMCS through communication network 102. In step 606, CMCS monitor the actions of relevant SMCS and acquires new data of Al, AV, Af and APL of all the plurality of LMCS and feed to concerned MLA. Subsequently in step 607, If the concerned PQ issue is not resolved to improvise the mitigation strategy and send new mitigation command to concern LMCS. Step 608, CMCS execute the steps 605 to 607 till the PQ issues are resolved, else send a command to relevant LMCS to disconnect the concerned load.

[0056] In an embodiment, LMCS collects the data of Line temperature through temperature sensor 101f. This data is normalized and fed to relevant MLA to predict the nearby fault conditions and stress on the Line due to excessive currents or losses in that line. It is to note that, excessive heat lead to PQ issues due to corona or skin effect and may result in fatal Line failure. If predicted results show adverse loading condition and stress on the line, CMCS removes the faulty session or transformer.

[0057] In another embodiment, the capacitor banks 101g which are designed to just suit the requirements of the microgrid thus considered. The well-known fact that excessive power electronic components usage in microgrids introduces the excessive reactive power in the system and this results in inrush current during commencement and as well as load decommissioning. Hence, the capacitor bank 101g initially if required to mitigate this issue may be energized. This step is initiated by the CMCS 103 based on relevant MLA output. The relevant output predicts the initial condition based on its training model and the data provided.

Editorial Note 2020104355 There is 1 page of Claims only.

Claims (4)

1. STATEMENT OF CLAIMS

   We Claim:

   The IoT and Machine Learning Based Power Quality Improvement System for Micro-Grid consisting of :

   A plurality of Line Monitoring & Control System (LMCS) 101 connected to Central Monitoring & Control System (CMCS) 103 through Communication-Network 102; and Where in Line Monitoring & Control System (LMCS) 101 is an integration of Microcontroller 101a, Power Management IC 101b, Current Sensors 101c, Potential Transformers 101d, circuitry for controlling Circuit Breaker 101e and capacitor banks 101g; and LMCS 101 capable of running suitable Machine Learning Algorithms for predicting and ascertaining the type of PQ issue, fault and its timeline; and LMCS 101 capable of running suitable Machine Learning Algorithms for predicting fault and PQ events due to current and voltage variations in the line; and CMCS is comprising a computer capable of acting as a server and parallel execution of different Machine Learning Algorithms to ascertain decisions taken by LMCS; and CMCS capable of executing a type of Machine Learning algorithm to predict the loading effect and can isolate the session based on PQ event in that section; and LMCS is capable of mitigating the PQ event by one or more methods which may include the introduction of capacitor banks or isolation of the PQ event causing agent.
2. 2. The device as claimed in claim 1, wherein LMCS 101 contains the integration of Microcontroller 101a and Power IC (ADE7816) 101b capable of making a protection scheme for the Line and determine/ predict PQ events by using suitable Machine Learning Algorithm.
3. 3. The device as claimed in claim 1, wherein a combination of a plurality of LMCS 101 and CMCS 103 employed for monitoring and Voltage sag and swell in a microgrid and compensate it by employing capacitor banks 101g.
4. 4. The device as claimed in claim 1, wherein two LMCS dedicates three CTs and 3 PTs from them to form a group in total to protect the particular line.