METHOD AND SYSTEM FOR DETECTING LOAD DROP RESULTING FROM A POWER QUALITY EVENT

Abstract

A power quality monitoring method includes: capturing a power quality event; determining that the captured power quality event is a voltage dip, rise, or interruption; in response to the determination that the captured power quality event is a voltage dip, rise, or interruption, selecting a pre-event interval and a post-event interval; comparing a pre-event load level and a post-event load level; and detecting a load loss based on the result of the comparison.

Description

METHOD AND SYSTEM FOR DETECTING LOAD DROP RESULTING FROM A POWER QUALITY EVENT FIELD OF INVENTION The concept described is generally related to a method and system for monitoring power quality and, in particular, to a system and method for detecting dropped loads resulting from an electrical power quality event. BACKGROUND OF THE INVENTION Power quality can be defined as the reliability, compatibility, or concept of energizing and grounding electronic equipment in a manner that is suitable for the operation of that equipment and compatible with the building's wiring system and other connected equipment. Power quality events can be classified into seven categories: (i) transients, such as impulsive and oscillatory transients, (ii) short-duration variations, such as voltage sags and voltage swells, (iii) long-duration variations, such as undervoltage and overvoltage, (iv) voltage imbalance, (v) waveform distortion, such as harmonics and interharmonics, (vi) voltage fluctuations, and (vii) power frequency variations. Loads can drop when power quality events occur, such as a voltage dip. The time i1 ti nn / eznz / B / YiAi Ref. 347131: Downtime of any critical process equipment results in operational and financial losses, including lost production and productivity. Currently, it is difficult to verify that certain loads have gone offline as a result of power quality events and therefore need to be restarted. For example, detecting voltage dips presents a challenge, as it is difficult to predict when such dips will occur. Furthermore, a root cause analysis of the process interruption is often required before restarting a process, extending the duration of the interruption. Identifying electrically driven loads, usually machinery, that have gone offline due to voltage dips or other disturbances requires time and effort.Conversely, automated disturbance notification often results in annoying alarms and / or email notifications, leaving the lost or dropped device unidentified and offline. A simple method or system is needed to detect the occurrence of power quality events and identify any resulting load loss that may lead to a loss of production or productivity. There is room for improvement in power quality monitoring methods and systems. i1 ti nn / eznz / B / YiAi BRIEF DESCRIPTION OF THE INVENTION These needs, and others, are met by at least one modality of the described concept in which a power quality monitoring method is provided for a power quality monitoring (PQM) system that includes a smart power device coupled to a plurality of loads.The method includes: capturing a power quality event that occurs over a portion of a plurality of time intervals, each of equal duration, the portion encompassing one or more time intervals; determining that the captured power quality event is a voltage dip, rise, or interruption; in response to the determination that the captured power quality event is a voltage dip, rise, or interruption, selecting a pre-event interval that includes a time interval preceding one or more time intervals and a post-event interval that includes a time interval following one or more time intervals; comparing a pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval; and detecting a load loss based on the result of the comparison. According to an illustrative modality of the described concept, a power quality monitoring system i1 ti nn / eznz / B / YiAi includes: a plurality of loads; a user device; and a smart power device coupled to the plurality of loads and structured to monitor power quality and energy within the power quality monitoring system, the smart power device including a power supply, a display, a measuring device, and a controller structured to control power quality and energy monitoring by the smart power device, the controller including a communication device communicatively coupled to a user device and the plurality of loads and a power quality monitoring device, wherein the power quality monitoring device includes: (i) a load loss detector structured to: capture a power quality event occurring over a portion of a plurality of time intervals each having an equal duration, the portion encompassing one or more time intervals;determine that the captured power quality event is a voltage dip, rise, or interruption; in response to the determination that the captured power quality event is a voltage dip, rise, or interruption, select a pre-event interval that includes a time interval preceding one or more time intervals and a post-event interval that includes a time interval following one or more time intervals; compare a pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval; and detect a load loss based on a result of the comparison;(ii) a power quality event report generator structured to generate a power quality event report that includes at least one of the captured power quality events, a detected load loss, and load loss parameters associated with the captured power quality event, and to transmit the power quality event report, including the captured power quality event, the detected load loss, and the load loss parameters associated with the captured power quality event, to the user device via the communication device; and (iii) a lookup table structured to store a plurality of data for use in power and power quality monitoring and that can be accessed by a user to at least one of the plurality of data or to categorize the captured power quality event via at least the user device. BRIEF DESCRIPTION OF THE FIGURES The invention can be fully understood from reading the following description of the preferred embodiments in conjunction with the accompanying figures, in which: Figure 1 is a diagram of a power quality monitoring system according to an illustrative modality of the described concept; Figure 2 is a diagram of a power quality monitoring system according to an illustrative modality of the concept described; Figure 3 is a block diagram of a power quality monitoring system controller according to an illustrative modality of the described concept; Figure 4 illustrates waveforms that capture a power quality event according to an illustrative modality of the described concept; Figure 5 illustrates waveforms that capture a power quality event according to an illustrative modality of the described concept; Figure 6 is a power quality event captured according to an illustrative modality of the described concept; Figures 7A-7B are a flowchart for a power quality monitoring method according to an illustrative modality of the described concept; and Figure 8 is a flowchart for a power quality monitoring method according to an illustrative modality of the described concept. DETAILED DESCRIPTION OF THE INVENTION The directional expressions used in this i1 ti nn / eznz / B / YiAi description, such as, for example, clockwise, counterclockwise, left, right, top, bottom, ascending, descending and derivatives thereof, relate to the orientation of the elements shown in the figures and do not limit the claims unless expressly stated therein. As used in the present description, the singular forms un, una, el and la include plural references unless the context clearly indicates otherwise. As used in the present description, the term number means one or an integer greater than one (i.e., a plurality). The most frequent power quality events include voltage dips and swells, harmonics, transients, and voltage and current imbalances. A voltage dip is a reduction in voltage magnitude, typically between 10 and 90% of the voltage for more than 8 milliseconds and less than one minute, and can occur as a result of system failures, the energizing of heavy loads, the starting of large inductive loads, or natural phenomena such as lightning. Harmonics are multiples of a fundamental frequency, and devices that conduct current for less than the full sine wave of the voltage are nonlinear loads and therefore generate harmonic voltage distortion, resulting in increased current and heat, decreased load efficiency (e.g., a motor), and reduced load life.Transients are momentary voltage excursions above the normal sine wave, typically caused by capacitor switching, current interruptions, power electronics operation, arc welding, etc. When they exceed electrical insulation ratings, transient voltages can result in abrupt equipment failure, semiconductor junction perforation, etc. Voltage imbalance is the voltage difference between the phases of a three-phase system, and it degrades performance and shortens motor life. Voltage imbalance can also cause high current imbalance, which can result in torque pulsations, increased vibration and mechanical stress, higher losses, motor overheating, etc. In some examples, loads can go offline when power quality events such as voltage dips occur. Downtime of any critical process equipment results in operational and financial losses, including lost production and productivity. Currently, it is difficult to verify that certain loads have gone offline as a result of power quality events and therefore need to be restarted. For example, it is difficult to predict when a power quality event, such as a voltage dip, will occur. Furthermore, a root cause analysis of the process interruption is often required before restarting a process, which extends the duration of the interruption. Identifying electrically driven loads, usually machinery, that have gone offline due to voltage dips or other disturbances requires time and effort.Conversely, automated disturbance notification often results in annoying alarms and / or email notifications. The illustrative modalities of the described concept address these problems. For example, power quality monitoring (PQM) methods and systems according to this description analyze power quality event waveforms to detect any load loss (e.g., identify a decrease in load levels) by comparing the load levels of loads measured over a predefined period (e.g., 200 milliseconds) before and after a power quality event. The difference between the minimum and maximum load in, for example, but not limited to, the 0.5 seconds before the power quality event can be used to establish the load variation tolerance. After a settling time following the power quality event, the pre-event average is compared to the post-event average. If the difference exceeds the tolerance, a load loss is detected.The differences apply to RMS amperes (root mean square value), real power (watts), apparent power (volt-amperes (VA)), and reactive power (volt-amperes reactive (VAR)). Changes in harmonic currents are determined from the harmonic spectrum, and the differences in RMS rates of change (i.e., slope) before and after the event are obtained by subtracting the pre-event spectrum from the post-event spectrum to characterize the load drop(s) (e.g., but not limited to, the largest changes, which are the fifth and seventh harmonics). After the load drop characterization, the PQM system reports the findings (e.g., but not limited to, the load drop(s) as a result of a voltage drop) in the event description, for example, in an event log or a PQM system lookup table.The PQM system then transmits the power quality event report to the user (e.g., customer) via email or SMS. Upon receiving the power quality event report, the user tags the event to categorize its consequences (e.g., compressor ABC #3 down). A tag includes the identity of the load that went down as a result of the power quality event. The user can remotely or manually store the categorization and / or the identity of the downed load(s) in the event log or lookup table. If a power quality event exhibiting similar pressure drop parameters has already occurred, the lookup table and / or event log already contains a label for the previously occurring power quality event. Therefore, after receiving the power quality event report, the user can inspect the reported pressure drop parameters and determine if they are similar to those of a previous power quality event. If such previous pressure drop parameters exist for a previously labeled power quality event, the user labels the current power quality event to match the label of the previous power quality event, categorizing the consequence of the current power quality event (i.e., ACB compressor #3 down).Therefore, the PQM system allows the user to receive a real-time power quality event report that detects load loss and remedy the situation (e.g., restart the dropped load) immediately without having to engage in the extensive root cause diagnostics required by the conventional power quality monitoring system.Such detection and real-time identification of one or more dropped loads as a result of a power quality event not only eliminates the need for the extensive root cause diagnostics that the user needs to perform in the conventional power quality monitoring system, but in fact replaces such diagnostics with simple steps of reading the power quality event report that has already identified the root cause (e.g., a voltage drop and a dropped load) and looking for the label of the previous power quality event to match the current power quality event, the label including the respective consequence (e.g., identity of the dropped load).Furthermore, for the detection and identification of one or more hot loads, the PQM system uses load loss parameters (pre-event and post-event load levels) that are already provided by conventional power quality monitoring systems but are not used for such detection and identification. Since detection and identification only require a simple comparison of the already available load loss parameters, no additional hardware needs to be added to the PQM system or any of its components. If a power quality event exhibiting similar load drop parameters has not yet occurred, there is no previous power quality event tag to which the current event can be tagged to match i1 ti nn / eznz / B / YiAi. Even in this situation, the user does not need to perform extensive diagnostics because the real-time power quality event report has already diagnosed the root cause (e.g., a voltage drop). Furthermore, to identify the dropped load(s), the user simply needs to compare the load drop parameters of the current power quality event with the power required for each load found in the lookup table. If only one load is dropped, the user must find a load that requires the load drop parameter for operation.If two or more loads are down, the user may find multiple loads whose sum of required power is equal to or substantially equal to the reported load loss parameters. Therefore, the PQM system facilitates and simplifies the user's identification of the downed load(s). After identification, the user creates a new event tag for the current power quality event based on the downed load(s) (e.g., Siemens® No. 2 ultrasound machine down) in the event log and / or lookup table. This new tag categorizes the consequence of the current power quality event and can be used to identify and categorize future power quality events with similar load loss parameters. Therefore, the PQM system, as described herein (i1 ti nn / eznz / B / YiAi), provides a simple mechanism for detecting a load drop and identifying a power loss in real time using readily available parameters without requiring additional hardware. This increases efficiency and convenience, reduces operational losses, and eliminates the costs associated with extensive conventional root cause diagnostics. Furthermore, the PQM system continuously expands its effectiveness by enabling constant updates to its lookup table and / or event log through user input, as well as automatic system updates. In some examples, the power quality monitoring system may include a machine learning algorithm trained to automatically identify a load drop, providing greater convenience and increased efficiency. Figure 1 is a schematic diagram of a power quality monitoring system 10 according to an illustrative embodiment of the described concept. The PQM system 10 includes a smart power device 1, loads 5 coupled to the smart power device 1, and a user device 20 communicatively coupled to the smart power device 1. The smart power device 1 can be, for example, but not limited to, a smart electric meter, a smart circuit breaker, or any smart power device for monitoring power quality and system energy 10.While Figure 1 illustrates the smart energy device 1 as a smart electric meter, in the examples where the smart energy device 1 is a circuit breaker, the circuit breaker can receive power from an AC power source, convert the alternating current (AC) to a direct current (DC) power signal, and supply power to the loads 5 in one or more rooms or offices via a load conductor (not shown), as well as detect a power quality event and identify a load loss. The smart electric meter 1 is structured to receive real-time parameters of the energy consumed by each load 5 and measure the energy consumed by the loads 5 over a time interval based on the real-time energy parameters.Real-time parameters can include voltage, current, frequency, and / or energy consumption readings and can be displayed as waveforms of, for example, voltage and current readings captured over a time interval. For example, real-time parameters can be real power (watts), apparent power (volt-amperes (VA)), reactive power (volt-amperes reactive (VAR)), current, harmonic currents, etc. The smart electric meter 1 is further structured to capture a power quality event, detect a load loss based on the waveforms, report the captured power quality event, detected load loss, and load loss parameters associated with the captured power quality event, and / or perform energy monitoring and management within the PQM 10 system.The smart electric meter 1 includes a power supply 100, a measuring device 200, a display 300 and a controller 400. Power supply 100 is designed to supply DC power to components of the smart electric meter 1. The measuring device 200 can be a structured measurement engine designed to receive real-time energy consumption parameters for each load 5, automatically read the energy consumed, and calculate measurement parameters based on these real-time parameters. For example, the measuring device 200 can measure line-to-line and line-to-neutral voltages and calculate RMS values ​​at intervals of, for example, but not limited to, 200 milliseconds. Additionally, the measuring device 200 can measure and average the system current per phase and calculate RMS values ​​at intervals of, for example, 200 milliseconds. Furthermore, the measuring device 200 can perform frequency measurements, for example, every 200 milliseconds using phase voltage A.Furthermore, the measuring device 200 can measure current energy consumption (e.g., but not limited to, user demand, i1 ti nn / eznz / B / YiAi energy, load profile, power factor, etc.) and calculate current values ​​including, but not limited to, apparent and displacement power factors for the system, apparent power for the system, real power for the system, reactive power for the system, etc. The measuring device 200 can also include a billing generator (not shown) to generate an invoice for a predefined period (e.g., 28 days, 3 months, etc.) and transmit the invoice to the user via the communication device 405 of the controller 200. The 300 display can be a liquid crystal display and structured to show real-time parameters and calculated measurement parameters, including RMS values ​​of voltage and / or current, frequency measurements, current energy consumption measurements, trends, minimum and maximum values ​​associated with each real-time parameter, calculated parameters of voltage, current, frequency and / or energy, which can also be displayed on a user device display screen 20 after its connection to the smart electric meter 1. The 300 display can have an option (e.g., a drop-down menu) to list all events that have occurred and display an event log or lookup table.All events (for example, but not limited to, power quality events) that have previously occurred and are currently occurring i1 ti nn / eznz / B / YiAi can be displayed on screen 300, as well as on the user device display screen 20. Screen 300 may include a power quality display showing, for example, but not limited to, minimum and maximum phase threshold currents with date and time stamps. The controller 400 includes a communication device 405, a power quality monitoring (PQM) device 410, a processor, and memory (not shown). The communication device 405 is configured to communicate with loads 5, the user device 20, and / or the utility cloud via wired (e.g., but not limited to, Ethernet 22A, USB cable 22B, etc.) or wireless (e.g., Bluetooth®, WiFi, LTE, LTE-A, New Radio, etc.) connections. The communication device 405 can transmit real-time and / or calculated measurement parameters, including RMS values ​​of voltage and / or current, frequency measurements, current power consumption measurements, trends, minimum and maximum values ​​associated with each real-time parameter, calculated voltage, current, frequency, and / or power.The 405 communication device can also transmit to the user a power quality event report, invoices, an alarm indicating the power quality event, etc. by means of an authorized communication channel (e.g., but not limited to, email, SMS, etc.). The Power Quality Monitoring (PQM) 410 device can be software, firmware, code, or instructions configured to detect and identify any load loss (e.g., identify a decrease in load levels) by comparing the load levels of loads measured over a predefined period (e.g., 200 milliseconds) before and after a power quality event. The difference between the minimum and maximum load in, for example, but not limited to, the 0.5 seconds before the power quality event can be used to establish the load variation tolerance. After a settling time following the power quality event, the pre-event average is compared to the post-event average. If the difference exceeds the tolerance, a load loss is detected.The differences apply to RMS amperes, real power (watts), apparent power (volt-ampere (VA)), and reactive power (volt-ampere reactive (VAR)). Changes in harmonic currents are determined from the harmonic spectrum, and the differences in RMS rates of change (i.e., slope) before and after the event are obtained by subtracting the pre-event spectrum from the post-event spectrum to characterize the lost load(s) (e.g., but not limited to, the largest changes being the fifth and seventh harmonics). After characterizing the lost load(s), the PQM 410 device reports the findings (e.g., but not limited to, the lost load(s) as a result of a voltage drop) in the event description, for example, in a PQM 410 event log 425.The PQM 410 device then transmits the power quality event report to the user (e.g., customer) via email or SMS. Upon receiving the power quality event report, the user labels the reported pressure drop parameters to categorize the consequences of the power quality event (e.g., compressor ABC No. 3 down) and thus identify the pressure drop resulting from the power quality event. The user can remotely store the categorization and / or the identity of the dropped load(s) in the PQM 410 device's event log (425) or lookup table (430). If a power quality event exhibiting similar load loss parameters has already occurred and been tagged, and lookup table 430 and / or event log 425 already contain a tag for the previously occurring power quality event, then the load loss parameters have already been tagged. Therefore, after receiving the power quality event report, the user can inspect the reported load loss parameters and determine if they are similar to those of a previously reported power quality event.If such previous load loss parameters exist from a previously labeled power quality event, then the user labels the current power quality event to match the label of the previous power quality event, categorizing the consequence of the current power quality event (i.e., ACB compressor no. 3 down). Therefore, the PQM 410 device allows the user to receive a real-time power quality event report that detects the load loss and enables the user to remedy the situation (e.g., restart the dropped load) immediately based on the report without having to engage in the extensive root cause diagnostics required by a conventional power quality monitoring system.Such real-time detection and identification of one or more dropped loads as a result of a power quality event not only eliminates the need for the extensive root cause diagnostics that the user needs to perform in the conventional power quality monitoring system, but in fact replaces such diagnostics with simple steps of reading the power quality event report that has already identified the root cause (e.g., a voltage drop and a dropped load) and looking for the i1 ti nn / eznz / B / YiAi tag of the previous power quality event to match the current power quality event, the tag that includes the consequence (e.g., identity of the dropped load).Furthermore, for the detection and identification of a load drop, the PQM 410 device uses load drop parameters (pre-event and post-event load levels) that are already provided by conventional power quality monitoring systems but are not used for such detection and identification. Since detection and identification only require a simple comparison of the already available load drop parameters, no additional hardware needs to be added to the PQM 410 device or any of its components. If a power quality event exhibiting similar load drop parameters has not yet occurred, there is no previous power quality event tag to which the current event can be matched. Even in this situation, the user does not need to perform extensive diagnostics because the real-time power quality event report has already diagnosed the root cause (e.g., a voltage drop). Furthermore, to identify the dropped load(s), the user simply needs to compare the load drop parameters of the current power quality event with the power required for each load found in the lookup table. i1 ti nn / eznz / B / YiAi If only one load is down, the user must find a load that requires the lost amount of power. If two or more loads are down, the user can find multiple loads whose sum of required power is equal to or substantially equal to the reported load loss. As such, the PQM system facilitates and simplifies the user's identification of the downed load(s). After identification, the user creates a new event tag for the current power quality event based on the lost load(s) (e.g., Siemens® Ultrasound Unit No. 2 down) in the event log and / or lookup table. This new tag categorizes the consequence of the current power quality event and can be used to identify and categorize future power quality events with similar load loss parameters. Therefore, by providing a simple mechanism for detecting a load loss and identifying a drop in real time using readily available parameters without requiring additional hardware, the PQM 410 device increases efficiency, reduces operational and / or financial losses, and eliminates prolonged downtime and the costs associated with extensive conventional root cause diagnostics. Furthermore, the PQM 410 device is continuously expanding its application and utility, for example, by allowing users to view and enter information, including labels for newly detected events with details and consequences, in the lookup table and / or event log through user input, as well as through automatic updates from the 410 device itself.In some examples, the PQM 410 device may include a machine learning algorithm trained to, for example, but not limited to, automatically identify a power outage, thereby providing greater convenience and efficiency. The machine learning algorithm can be trained by the user and / or the power quality monitoring system operator and updated wirelessly via the utility cloud (e.g., Bluetooth®, Wi-Fi, LTE, LTE-A, New Radio, etc.) or by inserting a hardware storage device (e.g., a USB drive). The processor may be, for example and without limitation, a microprocessor, a microcontroller, or some other suitable processing device or circuit system. Memory may be any one or more of a variety of internal and / or external storage media types such as, but not limited to, RAM, ROM, EPROM, EEPROM, FLASH, and the like, which provide a storage register, i.e., a machine-readable medium for storing data, such as in the form of an internal storage area of ​​a computer, and may be volatile or non-volatile memory.The memory may include a software device through which the smart electric meter 1 can be configured with parameters such as time duration, a storage medium, a communication channel for monitoring power quality events, billing functionality, generation and transmission of a power quality event report to the user, and / or updating of the lookup table based on user input that identifies the power loss. The software may be installed or uploaded to the smart electric meter 1 from the utility cloud or a storage device (e.g., a USB drive). The software device is capable of monitoring energy data for the loads 5 and operating continuously on the smart electric meter 1. Figure 2 is a diagram of a PQM system 10 according to an illustrative embodiment of the described concept. The PQM system 10 includes a smart energy device 1 as described with reference to Figure 1, user devices 20, 20', an industrial complex 30, and the utility cloud 40. In the illustrative embodiment shown in Figure 2, the smart energy device 1 is operating in an industrial complex 30. However, this is for illustrative purposes only, and the smart energy device 1 can be used to monitor power quality and energy consumption for any other entities (e.g., an office building, a plant, a laboratory, etc.). The smart energy device 1 operates and includes the same components described with reference to Figure 1; therefore, any overlapping descriptions of the smart energy device 1 are omitted for brevity.The smart energy device 1, for example, the smart electric meter 1, is communicatively coupled to a user device 20 on a LAN via wired (e.g., USB cables 22B) or wireless (Bluetooth®, WiFi, LTE, LTE-A, New Radio, etc.) connections. The user device 20 can also be coupled to another user device 20 on an IAN (Internet Area Network) if the end user is not an operator located on or near the site of the smart energy device 1 and receives communications via the wireless connection 22C (LTE, LTE-A, New Radio, etc.) from the user device 20 for power quality and energy monitoring. The user can manually or remotely access the smart energy device 1 and control and monitor power quality and energy within the PQM system 10. Figure 3 is a block diagram of a 410 power quality monitoring (PQM) device, according to an illustrative embodiment of the described concept. The device PQM 410 can be firmware, software, or configured instruction code designed to, for example, but not limited to, capture a power quality event, detect a load loss, and transmit a power quality event report to the user. The PQM 410 device includes a load loss detector 415, a PQE report generator 420, an event log 425, and a lookup table 430.The 415 load loss detector is structured to capture a power quality event occurring over a portion of a plurality of time intervals, each having an equal duration, the portion encompassing one or more time intervals; determine that the captured power quality event is a voltage dip, rise, or interruption; in response to the determination that the captured power quality event is a voltage dip, rise, or interruption, select a pre-event interval that includes a time interval preceding one or more time intervals and a post-event interval that includes a time interval following one or more time intervals; compare a pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval; and detect a load loss based on the result of the comparison.By comparing the pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval, the load loss detector i1 ti nn / eznz / B / YiAi is further structured to: establish a load variation tolerance using a load level measured during at least one time interval preceding one or more time intervals; obtain a difference between the pre-event load level and the post-event load level; and determine that the difference exceeds the established load variation tolerance. By obtaining the difference between the pre-event load level and the post-event load level, the load loss detector 415 is further structured to subtract the post-event load level from the pre-event load level, and where the detection of the load loss based on the result of the comparison is based on a determination that the difference exceeds the established load variation tolerance.The load loss parameters included in the power quality event report include volt-ampere (VA) load loss, current load loss, harmonic load loss, watt load loss, and reactive VA load loss (VAR). The established load variation tolerance includes volt-ampere (VA) load variation tolerance, current load variation tolerance, harmonic load variation tolerance, watt load variation tolerance, and reactive VA load variation tolerance. By detecting load loss based on the comparison result, the 415 load loss detector is structured to determine that VA load loss has occurred if a difference between the pre-event VA measured during the pre-event interval and the post-event VA measured during the post-event interval exceeds the VA load variation tolerance; determine that a current load loss occurred based on a determination that VA load loss has occurred and a determination that a difference between the pre-event current measured during the pre-event interval and the post-event current measured during the post-event interval exceeds the current load variation tolerance;determine a plurality of the largest pre-event harmonic currents and the corresponding post-event harmonic currents and determine if any of the differences between the pre-event harmonic currents and the corresponding post-event harmonic currents are greater than the harmonic load variation tolerance which includes a percentage of the determined current load loss; after determining if any of the differences between the pre-event harmonic currents and the corresponding post-event harmonic currents is greater than the harmonic current load variation tolerance, determine that a watt load loss has occurred if a difference between the pre-event watts measured during the pre-event interval and the post-event watts measured during the post-event interval exceeds the i1 ti nn / eznz / B / YiAi watt load variation tolerance;When determining that a watt load loss has occurred, a VAR load loss is determined to have occurred if the difference between the pre-event VARs measured during the pre-event interval and the post-event VARs measured during the post-event interval exceeds the VAR load variation tolerance. The VA load variation is up to 1% of the VA load level measured during a time interval in which the VA load variation tolerance is established. The difference is representative of the VA loss during the captured power quality event. The captured power quality event and VA load loss are reported. The current load loss is also reported. The harmonic current load variation tolerance is up to 10% of the current load loss. When determining that any of the harmonic losses is greater than the harmonic load variation tolerance, the harmonic load loss is reported. The PQE 420 report generator is structured to generate the power quality event report, which includes at least the captured power quality event, the detected load loss, and the load loss parameters associated with the captured power quality event, and transmit a power quality event report to the user via the 405 communication device. The load loss parameters include the load loss i1 ti nn / eznz / B / YiAi VA, current load loss, harmonic current load loss, watt load loss, and VAR load loss are determined by the load loss detector 415. A user input that includes a tag for the captured power quality event is received by at least one of the event log 425 or lookup table 430 via the communication device 405. The tag may match a tag from a previous power quality event that exhibits load loss parameters similar to the load loss parameters of the captured power quality event. Upon receiving the PQE report, the user inspects at least the load loss parameters associated with the captured power quality event;and categorizes the captured power quality event into at least one of a lookup table 430 or event log 425 within the smart power device 1. By categorizing the captured power quality event, the user determines whether the load loss parameters associated with the captured power quality event are similar to the load loss parameters of a previous power quality event;and if the load loss parameters associated with the captured power quality event are similar to the load loss parameters of a previous power quality event, label the captured power quality event to match a label for i1 ti nn / eznz / B / YiAi the previous power quality event, and enter the label into at least one of the lookup table or event log, where the label for the previous power quality event includes a consequence of the previous power quality event and the consequence includes the identity of a lost load as a result of the previous power quality event;or if the load loss parameters associated with the captured power quality event are not similar to the load loss parameters of the previous power quality event, generate a new label for the captured power quality event according to the detected load loss and enter the new label in at least one of lookup table 430 or event log 425.; The 425 event log is structured to capture and describe the occurrence of all events, including power quality events associated with the PQM 10 system. The 425 event log creates an entry for each event, including details and consequences, along with date and time stamps, and stores this information in memory. The entry can be a label that categorizes each event according to its details and / or consequences. The 425 event log can be displayed on the 300 screen or the user device's display screen (20,20') with an option for the user to select which event to display. The 425 event log can be accessed by the user manually or remotely via the user device (20,20') and is constantly updated automatically with new events and / or user input. Lookup table 430 can store multiple data points for use in energy and power quality monitoring and can be accessed by a user to view any of the data points or to categorize the power quality event captured by at least one user device. The lookup table is accessed by the user to categorize the captured power quality event based on a determination of whether the load loss parameters associated with the captured power quality event are similar to the load loss parameters of a previous power quality event; and if the load loss parameters associated with the captured power quality event are similar to the load loss parameters of the previous power quality event.The captured power quality event is tagged by the user to match a tag for the previous power quality event, and the tag is entered into lookup table 430 or event log 425, where the tag for the previous power quality event includes a consequence of the previous power quality event and the consequence includes the identity of a lost load such as i1 ti nn / eznz / B / YiAi resulting from the previous power quality event; or if the load loss parameters associated with the captured power quality event are not similar to the load loss parameters of the previous power quality event,A new tag is generated for the captured power quality event based on the detected load loss, and this new tag is entered into lookup table 425. The data includes all user-generated and / or PQM 410 device-generated tags for corresponding power quality events. The tags include the date and time, details, and consequences of each power quality event. Details include load loss parameters and the identity of the loads lost as a result of each power quality event. A newly detected power quality event may be tagged to match an existing tag from a previous power quality event exhibiting the same or similar load loss parameters. In such a case,The identity of the dropped load resulting from the new power quality event is the same as the dropped load identity included in the label of the previous power quality event. Lookup Table 430 can also include data regarding the power requirement of each load. Therefore, if a new power quality event has load drop parameters that are not the same as or similar to the existing load drop parameters i1 ti nn / eznz / B / YiAi from a previous power quality event, the user can look up the power requirement for each load and compare it to the load drop parameters to deduce the identity of the dropped load. Because power quality events occur at any time, Lookup Table 430 is constantly updated automatically by new events and / or by the user. Lookup Table 430 can be displayed on screen 300 or the user device's display screen 20.20'. In such cases, the user can select each stored label or event by typing a keyword that describes the load drop parameters (e.g., but not limited to, 35 amp current load drop and voltage drop, heated ultrasound machine and voltage drop, etc.). In some examples, the PQM 410 device may include a machine learning algorithm trained for PQM. Machine learning (ML), also called artificial intelligence (AI) or multivariate analysis (MVA), can be used to distinguish between two or more types of events, such as to discern the waveform images of a voltage drop from background waveforms or to differentiate the energy deposit patterns of electrons from photons in physics experiments. i1 ti nn / eznz / B / YiAi Machine learning (ML) can also be used in regression problems to estimate, for example, electron energy from the pattern of energy deposits. The use of such a machine learning method for uncategorized events is done in two stages. First, the training stage determines the structure and parameters that optimally separate the event classification based on the characteristic variables. Once the training stage is complete, the machine learning method, along with the structure and parameters, can evaluate the uncategorized events. Second, the evaluation stage places the uncategorized events into one of the classifications or assigns a probability to each event.In those examples, lookup table 430 can include data that has been previously evaluated and preprocessed for specific event testing needs and is therefore able to classify any power quality event based on the data. Figure 4 illustrates waveforms capturing a power quality event according to an illustrative modality of the described concept. The waveform is captured during a voltage dip S that occurred, for example, on April 2, 2021, at 12:45:36 over a time period T, during which the RMS voltage dropped approximately 400 V. The current A experiences a significant drop (for example, approximately 550 amperes during the time period T, which lasts approximately 90 ms) and, after the dip, remains reduced by approximately 35 A, as shown in D. Based on the reduced current as shown in D, it can be deduced that a load was lost or dropped during the voltage dip S.The smart electric meter 1 (specifically, the PQE 420 report generator) generates a power quality event report for the user. This report describes the voltage drop S, the date and time it occurred, the amount of the voltage drop S, the reduced current D after the voltage drop S, and the captured waveform. The user can inspect the reported load drop parameters and determine if they are similar to the load drop parameters of a previously tagged event(s). If so, the user views the power quality event report displayed on the user device 20, 20', determines the identity of the load drop by looking at lookup table 430, and immediately resets the identified load drop.Next, the user tags the reported power quality event to match the previously tagged power quality event(s) and categorizes the event consequence in the event description of event log 425 or lookup table 430 (e.g., ABC compressor #3 down). If the reported loss parameters are not similar to the pressure loss parameters of the previously tagged event(s), the user identifies the lost pressure based on the reported loss parameters and the power requirement for each load. After identifying the lost pressure, the user creates a new tag according to the reported pressure loss parameters. This new tag will be added to event log 425 or lookup table 430 by the user or automatically updated by the PQM 410 device. Figure 5 illustrates waveforms capturing a power quality event according to an illustrative modality of the described concept. Figure 5 shows that the waveforms were captured during a subsequent voltage drop S' that occurred on April 22, 2021, at 08:49:25. The voltage drop S' exhibits much smaller parameters than those of the voltage drop S as described with reference to Figure 4. As such, the drop loss is a load that requires much less power to operate (e.g., a TV). Figure 6 illustrates a power quality event captured according to an illustrative modality of the described concept. Figure 6 shows a voltage dip event S'' occurring during two marked intervals: three pre-event intervals and three post-event intervals, each lasting 200 ms. Since the PQM 410 device captures a power quality event, the RMS and FFT (Fast Fourier Transform) intervals are marked. The PQM 410 then selects a pre-event interval (e.g., -1, -2, etc.) before the marked event and a post-event interval (e.g., 1, 2, etc.) after the marked event. Finally, the PQM 410 selects a communication mechanism (e.g., user email or SMS) to report the marked event and its parameters. RMS and FFT are collected by the IEC 61000-4 standard.30 (i.e., 10 cycles at 50 Hz, 12 cycles at 60 Hz). RMS and FFT values ​​collected during a dip, rise, or interruption are marked as invalid for aggregation. The PQM 410 device compares the RMS values ​​before and after the marked intervals to detect a load drop. Figures 7A-B illustrate a flowchart for Method 700 for creating PQM load loss parameters according to an illustrative embodiment of the described concept. Method 700 can be implemented by a smart energy device 1 or its components as described with reference to Figures 1-3. In Figures 7A-B, the smart energy device 1 is illustrated as a smart electric meter 1; however, this is for illustrative purposes only, and the smart energy device 1 can be any other type of smart device capable of monitoring energy and power quality. In 705, the smart electric meter 1 determines whether the captured power quality (PQ) event i1 ti nn / eznz / B / YiAi is marked as a dip, a swell, or an outage. If not, method 700 stops. If so, method 700 continues in 710. In 710, the smart electric meter 1 selects the 200 millisecond interval (iris) (before marked intervals). In 715, the smart electric meter 1 selects a 200 ms post-event interval (after marked intervals). In 720, the smart electric meter 1 determines volt-ampere (VA) losses by subtracting the post-event VA from the pre-event VA. At 725, the smart electric meter 1 determines if the VA losses are greater than 1%. If not, method 700 stops. If so, method 700 advances to 730. In 730, the smart electric meter 1 reports the PQ event and VA load losses to a user. In 735, the smart electric meter 1 determines current load losses by subtracting the post-event RMS current from the pre-event RMS current. At 740, the smart electric meter 1 reports the current load loss. In 745, the smart electric meter 1 determines the five largest pre-event harmonic currents. At 750, the smart electric meter 1 determines the corresponding post-event harmonic currents. i1 ti nn / eznz / B / YiAi At 755, smart electric meter 1 determines if any harmonic losses exceed 10% of the current load loss. If so, at 760A smart electric meter 1 reports the harmonic load loss to the user. If not, at 760B smart electric meter 1 determines the watt load loss by subtracting the post-event watts from the pre-event watts. At 765, the smart electric meter 1 reports the watt load loss to the user. In 770, the smart electric meter 1 determines the VAR pressure drop by subtracting the post-event VAR from the pre-event VAR. At 775, the smart electric meter 1 reports the VAR load loss. In 780, the smart electric meter 1 sends the power quality event report to the user via email or SMS. Figure 8 is a flowchart for a user response method 800 to a pressure loss report, illustrating one aspect of the described concept. Method 800 can be implemented by a smart electric meter 1 or its components, as described with reference to Figures 1-3. While Figure 8 shows specific user-performed power quality monitoring steps, this is only part of the complete power quality monitoring method i1 ti nn / eznz / B / YiAi as described herein. In Figure 8, the smart energy device 1 is depicted as a smart electric meter 1; however, this is for illustrative purposes only, and the smart energy device 1 can be any other type of smart device capable of monitoring energy and power quality. In 810, the user receives an email or SMS that includes a power quality event report. In 820, the user inspects the reported pressure loss parameters. In 830, the user determines whether the pressure drop parameters are similar to the parameters of previous events. In 840, user labels reported one or more power quality events to match a previous label. In 850, the user labels the reported power quality event(s) with a new label according to a lost load(s). Although specific embodiments of the invention have been described in detail, those skilled in the art will appreciate that various modifications and alternatives to these details could be developed in light of the general principles of the description. Accordingly, the particular arrangements described are intended to be illustrative only and not to limit the scope of the invention, which should be given the full extent of the appended claims and any equivalents thereof. It is hereby stated that, as of this date, the best method known to the applicant for putting the aforementioned invention into practice is the one that is clear from the present description of the invention.

Claims

1. A power quality monitoring method for a power system that includes a smart power device coupled to a plurality of loads, characterized in that it comprises: capturing a power quality event occurring over a portion of a plurality of time intervals, each having an equal duration, the portion encompassing one or more time intervals; determining that the captured power quality event is a voltage dip, rise, or interruption; in response to the determination that the captured power quality event is a voltage dip, rise, or interruption, selecting a pre-event interval comprising a time interval preceding one or more time intervals and a post-event interval comprising a time interval following one or more time intervals;compare a pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval; and i1 ti nn / eznz / B / YiAi detect a load loss based on a result of the comparison.; 2. The method according to claim 1, characterized in that it further comprises: transmitting to a user a power quality event report comprising at least the captured power quality event, the detected load loss, and the load loss parameters associated with the captured power quality event; and in response to the detection of the load loss, categorizing the captured power quality event into at least one of a lookup table or event log within the smart power device.

3. The method according to claim 2, characterized in that the categorization of the captured power quality event comprises: determining whether the pressure drop parameters associated with the captured power quality event are similar to the pressure drop parameters of a previous power quality event;and in response to the determination that the load loss parameters associated with the captured power quality event are similar to the load loss parameters of a previous power quality event, label the captured power quality event to match i1 ti nn / eznz / B / YiAi with a label for the previous power quality event, and enter the label into at least one of the lookup table or event log, wherein the label for the previous power quality event comprises a consequence of the previous power quality event and the consequence includes the identity of a lost load as a result of the previous power quality event;or in response to the determination that the load loss parameters associated with the captured power quality event are not similar to the load loss parameters of the previous power quality event, generate a new label for the captured power quality event according to the detected load loss and enter the new label in at least one of the lookup table or event log.

4. The method according to claim 3, characterized in that the generation of the new label for the captured power quality event comprises determining the identity of a lost load based at least in part on the load loss parameters and the energy required for each load of the plurality of loads, and the new label includes the identity of the lost load.

5. The method according to claim 1, characterized in that comparing a pre-event load level measured during the pre-event interval and a post-event load level i1 ti nn / eznz / B / YiAi measured during the post-event interval comprising: establishing a load variation tolerance using a load level measured during at least one time interval preceding one or more time intervals; obtaining a difference between the pre-event load level and the post-event load level; and determining that the difference exceeds the established load variation tolerance.

6. The method according to claim 5, characterized in that obtaining the difference between the pre-event load level and the post-event load level comprises: subtracting the post-event load level from the pre-event load level.

7. The method according to claim 5, characterized in that detecting the load loss based on the comparison result is based on a determination that the difference exceeds the established load variation tolerance.

8. The method according to claim 5, characterized in that the established load variation tolerance comprises volt-amperes (VA) load variation tolerance, current load variation tolerance, harmonic load variation tolerance, watt load variation tolerance, and reactive VA (VAR) load variation tolerance.

9. The method according to claim 8, characterized in that detecting the load loss based on the result of the comparison comprises: determining that the VA load loss has occurred based on a determination that a difference between the pre-event VA measured during the pre-event interval and the post-event VA measured during the post-event interval exceeds the VA load variation tolerance; determining that a current load loss has occurred based on a determination that the VA load loss has occurred and a determination that a difference between the pre-event current measured during the pre-event interval and the post-event current measured during the post-event interval exceeds the current load variation tolerance;determine a plurality of the largest pre-event harmonic currents and the corresponding post-event harmonic currents and determine that any of the differences between the pre-event harmonic currents and the corresponding post-event harmonic currents is greater than the harmonic load variation tolerance comprising a percentage of the determined current load loss; in response to the determination that any i1 ti nn / eznz / B / YiAi of the differences between the pre-event harmonic currents and the corresponding post-event harmonic currents is greater than the harmonic current load variation tolerance, determine that a load loss of watts has occurred based on a determination that a difference between the pre-event watts measured during the pre-event interval and the post-event watts measured during the post-event interval exceeds the watt load variation tolerance;and in response to the determination that a watt load loss has occurred, determine that a VAR load loss has occurred based on a determination that a difference between the pre-event VARs measured during the pre-event interval and the post-event VARs measured during the post-event interval exceeds the VAR load variation tolerance.; 10. The method according to claim 9, characterized in that the load loss parameters associated with the captured power quality event comprise VA load loss, current load loss, watt load loss, and VAR load loss.

11. The method according to claim 9, characterized in that, based on the determination that any of the differences between the pre-event harmonic currents i1 ti nn / eznz / B / YiAi and the corresponding post-event harmonic currents is greater than the harmonic current load variation tolerance, the load loss parameters associated with the captured power quality event comprise harmonic load loss, VA load loss, current load loss, watt load loss, and VAR load loss.

12. The method according to claim 8, characterized in that the tolerance of the load variation VA is up to one percent of the pre-event VA measured during the pre-event interval.

13. The method according to claim 8, characterized in that the harmonic current load variation tolerance is up to ten percent of the current load loss.

14. The method according to claim 1, characterized in that the equal duration comprises 200 milliseconds.

15. A power quality monitoring system characterized in that it comprises: a plurality of loads; a user device; and a smart power device coupled to the plurality of loads and structured to monitor power quality and energy within the power quality monitoring system, the smart power device comprising a power supply, a display, a measuring device, and a controller structured to control the monitoring of power quality and energy by means of the smart power device, the controller comprising a power quality monitoring device and a communication device communicatively coupled to a user device and the plurality of loads,wherein the power quality monitoring device comprises: (i) a load loss detector structured to: capture a power quality event occurring over a portion of a plurality of time intervals, each having an equal duration, the portion encompassing one or more time intervals; determine that the captured power quality event is a voltage dip, rise, or interruption; in response to the determination that the captured power quality event is a voltage dip, rise, or interruption,select a pre-event interval comprising a time interval preceding one or more time intervals and a post-event interval comprising a time interval following one or more time intervals; compare a pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval; and detect a load loss based on the result of the comparison; (i) a power quality event report generator structured to generate a power quality event report that includes at least one of the captured power quality events,a detected load loss and the load loss parameters associated with the captured power quality event and transmit the power quality event report to the user device via the communication device; and (iri) a structured lookup table for storing a plurality of data for use in power and power quality monitoring and that can be accessed by a user to at least one of the plurality of data or categorize the captured power quality event via at least the user device.

16. The power quality monitoring system according to claim 15, characterized in that when comparing the pre-event load level measured during the pre-event interval and a post-event load level measured during the post-event interval, the load loss detector i1 ti nn / eznz / B / YiAi is structured to: establish a load variation tolerance using a load level measured during at least one time interval preceding one or more time intervals; obtain a difference between the pre-event load level and the post-event load level; and determine that the difference exceeds the established load variation tolerance.

17. The power quality monitoring system according to claim 16, characterized in that, when obtaining the difference between the pre-event load level and the post-event load level, the load loss detector is further structured to subtract the post-event load level from the pre-event load level, and wherein the detection of the load loss based on the result of the comparison is based on a determination that the difference exceeds the established load variation tolerance.

18. The power quality monitoring system according to claim 16, characterized in that the load loss parameters included in the power quality event report comprise volt-ampere (VA) load loss, current load loss, harmonic load loss, watt load loss, and reactive VA load loss (VAR).

19. The power quality monitoring system of i1 ti nn / eznz / B / YiAi according to claim 15, characterized in that the lookup table is accessed by the user to categorize the captured power quality event based on a determination that the load loss parameters associated with the captured power quality event are similar to the load loss parameters of a previous power quality event;and in response to the determination that the load loss parameters associated with the captured power quality event are similar to the load loss parameters of the previous power quality event, the captured power quality event is labeled by the user to match a label for the previous power quality event, and the label is entered into the lookup table, wherein the label for the previous power quality event comprises a consequence of the previous power quality event and the consequence includes the identity of a lost load as a result of the previous power quality event;or in response to the determination that the load loss parameters associated with the captured power quality event are not similar to the load loss parameters of the previous power quality event, a new label is generated for the captured power quality event according to the detected load loss and the new i1 ti nn / eznz / B / YiAi 55 label is entered into the lookup table.; 20. The method according to claim 19, characterized in that the new label includes the identity of a determined lost load based at least partly on the 5 lost load parameters and the energy required for each load of the plurality of loads.

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