Motor diagnostic system and method

Abstract

An electric machine diagnostic system and method. A causal diagnostic system and method for monitoring and predicting problems associated with an electrical system. A load diagnostic system coupled to a load within the electrical system obtains first data relating to the load, and an IED is connected within the electrical system closer to the power source upstream of the monitored load. The IED obtains second data related to the electrical system, the second data being at least one of energy related data and non-energy related data. A processor that receives and responds to the acquired first data and second data executes instructions to evaluate the first data for the second data to identify a correlation between the first data and the second data; evaluating the identified correlation to determine a condition of an electrical system associated with the IED; and taking at least one action to address a condition of an electrical system associated with the IED.

Description

Technical Field

[0001] The present disclosure relates generally to electrical / power systems, and more particularly to systems and methods for monitoring and evaluating energy-related and non-energy-related data in electrical systems. Background Art

[0002] Traditional condition-based monitoring (CBM) systems and techniques, particularly those related to motor diagnostics, focus on motor current signature analysis (MCSA) to analyze and diagnose motor problems. These systems utilize Fourier analysis to evaluate current signatures in the frequency domain, identifying harmful trends at relevant frequencies. Because MCSA, by definition, focuses on analyzing current signatures at discrete motor locations, it makes it difficult to assess the systemic causes of problems within the electrical system.

[0003] Commonly assigned U.S. Patent Application Publication No. 2023 / 0153389, the entire disclosure of which is incorporated herein by reference, discloses automatically analyzing electrical waveform data (i.e., waveform capture or WFC) to identify and reduce extraneous WFC generated / acquired from an electrical power monitoring system (EPMS) or any related components / elements (e.g., intelligent electronic devices (IEDs)).

[0004] Commonly assigned US Patent No. 11,695,427, the entire disclosure of which is incorporated herein by reference, discloses using at least one IED in an electrical system to capture at least one energy-related waveform.

[0005] Monitoring energy-related data in an electrical system includes processing energy-related data extracted or derived from an energy-related signal captured by at least one IED in the electrical system to identify at least one change / alteration in the energy-related signal as disclosed in commonly assigned U.S. Patent No. 11,740,266, the entire disclosure of which is incorporated herein by reference. Summary of the Invention

[0006] Aspects of the present disclosure correlate information from at least one CBM system and analyze the output against data from an electrical power management system (EPMS) to identify potential causes of load problems that originate at a system level. For example, the cause of a motor stator problem may originate from transients, overvoltage / undervoltage, voltage imbalance, etc., which in turn originate elsewhere inside or outside the end user's facility. Embodiments according to the present disclosure help identify systemic causes, which leads to more effective solutions and mitigation. Advantageously, aspects of the present disclosure allow load diagnostic data (e.g., MCSA) to be analyzed against electrical system data to identify common causes of problems that originate within the electrical system, which can simplify and reduce the overall cost of mitigating systemic problems, as well as provide useful context-based data and information back and forth between the CBM system and the EPMS.

[0007] In one aspect, a method for monitoring and predicting problems associated with an electrical system includes acquiring, by at least one load diagnostic system, first data related to a load within the electrical system monitored by the at least one load diagnostic system. The method also includes acquiring, by at least one IED, second data related to the electrical system, the second data including at least one of energy-related data and non-energy-related data. The at least one IED is electrically connected within the electrical system, upstream of the load monitored by the at least one load diagnostic system and closer to a power source for the system. The method also includes evaluating the first data acquired by the at least one load diagnostic system against the second data acquired by the at least one IED to identify a correlation between the first data and the second data, evaluating the identified correlation to determine a condition of the electrical system associated with the at least one IED, and taking at least one action to address the condition of the electrical system associated with the at least one IED.

[0008] In another aspect, a causal diagnostic system for monitoring and predicting problems associated with an electrical system includes: at least one load diagnostic system coupled to a load within the electrical system, the at least one load diagnostic system configured to acquire first data related to the load monitored by the at least one load diagnostic system; and at least one IED connected within the electrical system, upstream from the load monitored by the at least one load diagnostic system and closer to an electrical main of the electrical system. The at least one IED acquires second data related to the electrical system, the second data being at least one of energy-related data and non-energy-related data. The system also includes: at least one processor configured to receive and respond to the acquired first and second data; and at least one memory device coupled to the at least one processor. The at least one memory device stores processor-executable instructions that, when executed, configure the at least one processor to: evaluate first data acquired by the at least one load diagnostic system against second data acquired by the at least one IED to identify a correlation between the first and second data; evaluate the identified correlation to determine a condition of the electrical system associated with the at least one IED; and take at least one action to address the condition of the electrical system associated with the at least one IED.

[0009] Other objects and features of the invention will be in part apparent and in part pointed out herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 An example electrical system is shown according to an embodiment of the present disclosure.

[0011] Figure 2 An example electrical one-line diagram with an IED and a CBM is shown according to an embodiment of the present disclosure.

[0012] Figure 3 A system view of an electrical system with IEDs, CBMs, and software management elements (eg, gateways, edge software, and cloud-based applications / software) according to an embodiment of the present disclosure is shown.

[0013] Figure 4A and Figure 4B is a flow chart illustrating an example process for evaluating motor problems according to an embodiment of the present disclosure.

[0014] Figures 5A to 5C is a schematic diagram illustrating an example of a three-phase system according to an embodiment of the present disclosure.

[0015] Corresponding reference characters indicate corresponding parts throughout the drawings. DETAILED DESCRIPTION

[0016] The features and other details of the concepts, systems, and techniques for which protection is sought herein will now be described in greater detail. It will be understood that any specific embodiments described herein are presented by way of illustration and not as limitations of the disclosure and concepts described herein. Features of the subject matter described herein may be employed in various embodiments without departing from the scope of the concepts for which protection is sought.

[0017] According to embodiments of the present disclosure, problems primarily related to the susceptibility and impact of three-phase motors in electrical systems are identified, quantified, troubleshooted, and resolved. Two important aspects are identifying the source of three-phase electrical system problems (e.g., upstream or downstream of an intelligent electronic device (IED), or within the system of an IED) and quantifying the impact of such problems on equipment within the electrical system (e.g., heat generation, wasted energy, etc.). A general industry rule of thumb is that the energy consumed by a motor costs more than ten times its original purchase cost per year. This means that any improvement in motor energy efficiency can significantly save operating costs and emissions over time. In addition, embodiments according to the present disclosure provide the added benefit of reducing capital costs by increasing the life expectancy of equipment.

[0018] refer to Figure 1 An example electrical system 100 according to an embodiment of the present disclosure includes one or more IEDs 102 capable of sampling, sensing, or monitoring one or more parameters (e.g., power monitoring parameters) associated with one or more loads 106 (also sometimes referred to herein as "devices" or "equipment"). Although denoted by the same reference numerals, it should be understood that the IEDs 102 may be different from one another (e.g., IEDs with different features and capabilities, etc.), and the loads 106 may be different from one another (e.g., motors, lighting devices, computer servers, etc.), depending on the specific design and characteristics of the electrical system 100. In an embodiment, the loads 106 and IEDs 102 may be installed in one or more buildings or other physical locations, or they may be installed on one or more processes and / or loads within a building or other physical location. The building may correspond to, for example, a commercial building, an industrial building, a government building, or an institutional building. Other physical locations may include, for example, oil and gas platforms, parking areas, outdoor areas, or any other relevant area or zone associated with at least one of energy consumption or energy production.

[0019] like Figure 1As shown in FIG, each of the IEDs 102 is coupled to one or more loads 106, which in some embodiments may be located "upstream" or "downstream" of the IED. The loads 106 include, for example, machines or devices associated with a particular application (e.g., an industrial application), application, and / or process, and some, all, or nearly all of the machines, either inside or outside the facility. The machines may include, for example, electrical or electronic equipment. The machines may also include control devices and / or auxiliary equipment associated with the equipment. According to aspects of the present disclosure, the loads 106 include a mix of single-phase, phase-to-phase, and three-phase loads (e.g., motors), or any related electrical configuration required to operate equipment associated with an end-user's system or facility.

[0020] In one or more embodiments, the IEDs 102 can monitor and, in some embodiments, analyze parameters (e.g., energy-related parameters) associated with the loads 106 to which they are coupled. For example, the IEDs 102 (e.g., metering devices) capture energy-related waveforms in the electrical system 100. As used herein, an IED is a computing electronic device optimized to perform a specific function or group of functions. Examples of IEDs 102 include smart utility meters, power quality meters, microprocessor relays, digital fault recorders, and other metering devices. IEDs 102 can also be embedded in variable speed drives (VSDs), protection devices, programmable logic controllers (PLCs), uninterruptible power supplies (UPSs), circuit breakers, relays, transformers, or any other electrical device. Furthermore, IEDs 102 can be used to perform measurement / monitoring and control functions in a variety of installations. Facilities can include utility systems, industrial facilities, warehouses, office buildings or other commercial complexes, campus facilities, computing colocation centers, data centers, power distribution networks, or any other structure, process, or load that uses electrical energy. For example, if IED 102 is a power monitoring device, it may be coupled to (or installed in) a power transmission or distribution system and configured to sense / measure and store data (e.g., waveform data, logged data, I / O data, etc.) as electrical parameters representing operational characteristics of the power distribution system (e.g., voltage, current, waveform distortion, power, etc.). These parameters and characteristics can be analyzed by a user to assess, for example, potential performance, reliability, and / or power quality-related issues. One or more of IEDs 102 may include at least a controller (which, in some IEDs, may be configured to run one or more applications simultaneously, serially, or both), firmware, memory, a communication interface, and connectors that connect the IED to external systems, devices, and / or components of any voltage level, configuration, and / or type (e.g., AC, DC). At least some aspects of the monitoring and control functionality of IED 102 may be embodied in a computer program (e.g., PLC data, etc.) accessible by the IED.

[0021] As used herein, the term "IED" may refer to multiple IEDs operating in parallel and / or tandem (series) and hierarchical structures of IEDs. For example, an IED may correspond to an aspect of a hierarchy of energy meters, power meters, and / or other types of resource meters. The hierarchy may include a tree-based hierarchy, such as a binary tree, a tree with one or more child nodes descending from each parent node or multiple parent nodes, or a combination thereof, wherein each node represents a specific IED. In some instances, the hierarchy of IEDs may share data or hardware resources and may execute shared software. It should be understood that the hierarchy may be non-spatial, such as a billing hierarchy, where the IEDs grouped together may be physically unrelated.

[0022] According to another aspect, the IED 102 can detect overvoltage and undervoltage conditions (e.g., transient overvoltages), as well as other parameters such as temperature, including ambient temperature. According to another aspect, the IED 102 can provide an indication of the monitored parameters and detected conditions, which can be used to control the loads 106 and other devices in the electrical system 100 in which the loads 106 and the IED 102 are installed. The IED 102 can perform a variety of other monitoring and / or control functions, and the aspects and embodiments disclosed herein are not limited to the IED 102 operating as described in the examples mentioned above.

[0023] It should be understood that the IEDs 102 can take various forms and can each have an associated complexity (or set of functional capabilities and / or features). For example, one IED 102 can be a "basic" IED, while another IED 102 can be an "intermediate" IED, and yet another IED 102 can be an "advanced" IED. In such embodiments, the intermediate IED can have more functionality (e.g., energy measurement features and / or capabilities) than the basic IED, and the advanced IED can have more functionality and / or features than both the intermediate and basic IEDs. For example, in embodiments, the IED 102 (e.g., an IED with basic capabilities and / or features) can be capable of monitoring instantaneous voltage, current energy, demand, power factor, average value, maximum value, instantaneous power, and / or long-duration rms variation, and / or the IED 102 (e.g., an IED with advanced capabilities) can be capable of monitoring additional parameters, such as voltage transients, voltage fluctuations, frequency slew rates, harmonic power flows, and discrete harmonic components, all at higher sampling rates, etc. It should be understood that this example is for illustrative purposes only, and that also in some embodiments, an IED with basic capabilities may be able to monitor one or more of the above energy measurement parameters indicated as being associated with an IED with advanced capabilities. It should also be understood that in some embodiments, the IEDs 102 each have independent functionality.

[0024] Further references Figure 1 One or more loads 106 may be monitored by a load diagnostic system, such as a condition-based monitoring (CBM) system 108. Condition-based monitoring allows users to extract data related to one or more of vibration, acoustics, temperature, current, voltage, etc., to help answer critical questions about the condition of the monitored loads 106. Such condition-based monitoring of loads 106 can help users transition from a reactive to a proactive maintenance approach by highlighting potential problems and notifying the system of abnormal behavior before failure. Motor Current Signature Analysis (MCSA) for condition-based monitoring offers several advantages over traditional vibration, acoustic, and thermal monitoring techniques. MCSA measures minute fluctuations in both the current draw and the supply voltage of the power lines feeding a motor or other rotating equipment. These electrical "signatures" can provide early indications of impending failures with increased sensitivity and accuracy compared to other methods. The technology can diagnose specific failure modes or their primary causes, whether mechanical or electrical. Although referenced using the same reference numerals, it should be understood that CBM systems 108 may differ from one another depending on the specific design, characteristics, loads, and / or requirements of the electrical system 100. It should be understood that MCSA is used as an example of a CBM that performs electrical signal analysis and is not limited to current signature analysis alone. Analysis can be performed on at least one directly measured parameter (e.g., voltage, current, vibration, temperature, etc.) or an inferred / derived / calculated parameter (e.g., power, power factor, waveform distortion, etc.).

[0025] For example, MCSA for condition-based monitoring allows prediction of both known and unknown failure modes and global "wear and tear" estimates, while providing longer lead times in failure prediction. This gives maintenance teams more time to order spare parts and schedule repairs while minimizing the impact on operations. In addition, MCSA fault analysis can also provide clues to electrical conditions (e.g., power quality anomalies) occurring upstream of the load 106 that may cause specific problems.

[0026] exist Figure 1 In an example embodiment of the present invention, the IED 102 is communicatively coupled to a central processing unit (CPU) 140 (and associated memory) via a data communications network, illustrated as a “cloud” 150. In some embodiments, the IED 102 may be directly communicatively coupled to the cloud 150. In other embodiments, the IED 102 may be indirectly communicatively coupled to the cloud 150, for example, via an intermediary device, such as a cloud-connected hub 130 (or gateway), in place of or in conjunction with the gateway / hub 130, providing the IED 102 with access to the cloud 150 and the CPU 140 or edge software.

[0027] Commonly assigned U.S. Patent Application Publication No. 2023 / 0152833, the entire disclosure of which is incorporated herein by reference, discloses a cloud-connected electrical system in which aspects of the present disclosure may be used.

[0028] Although shown locally in conjunction with the associated load 106, it will be understood that the CBM system 108 can be implemented in a distributed manner. One advantage of the MCSA is the ability to upload data to the cloud 150 using, for example, cellular communications. The MCSA can be uploaded to a central, cloud-based asset management system, such as the CPU 140, which may include support from expert services. A set of machine learning algorithms first utilizes the associated normal motor current signatures to build a model of the "initial normal" motor behavior. If the motor operating behavior begins to deviate from the normal operating range, the anomaly is detected and classified by the changes in the identified motor current signatures. These sensing techniques and analysis methods can be used to detect and diagnose a wide range of potential failure modes, including stator short circuits, bearing degradation, loose rotor bars, coupling misalignment, mechanical or electrical imbalance, general aging, etc.

[0029] Figure 2 An example electrical system 200 is shown on which aspects of the present disclosure may be used to identify potential causes of failure at a system level. In particular, Figure 2 An exemplary electrical single line diagram is shown that includes components (i.e., IEDs / metering devices) of an electric power monitoring system (EPMS) for capturing, analyzing, and compressing data (e.g., energy-related waveforms) according to an embodiment of the present disclosure. Figure 2 As shown in FIG, an EPMS typically includes an array of various IEDs, shown as a plurality of meters 202, installed throughout an electrical system 200 for monitoring a mix of single-phase and three-phase downstream loads 206. The meters 202 (or IEDs) may have varying levels of capabilities and feature sets; some more and some less. For example, energy consumers typically monitor the location where the electricity enters their premises (i.e., Figure 2 High-end (best / most capable) IEDs are installed at M1 (in the example). This is done to gain the broadest possible understanding of the quality and characteristics of the electrical signals received from the source (usually the utility). Because metering budgets are typically fixed, and energy consumers typically require the most extensive metering possible across the electrical system, conventional wisdom dictates the use of IEDs with progressively lower costs as the metering point is installed closer to the load (with some exceptions). With this in mind, many facilities include more low- / mid-range IEDs than high-end IEDs. Figure 2 Also shown is a system 200 having two step-down transformers 204 and a plurality of CBM systems 208, each of which is associated with one of the loads 206. It should be understood that Figure 2 Electrical system 200 is but one embodiment of countless potential embodiments for teaching the concepts described herein.

[0030] Now refer to Figure 3 Aspects of the present disclosure aggregate information from two or more data sources associated with a load 306 to identify potential problems originating at a system level. In the illustrated embodiment, the data sources include at least one CBM system 308 (e.g., CBM system 108 or 208) and at least one IED 302 (e.g., IED 102 or meter 202), which are components of an EPMS. In alternative embodiments, the data sources include two or more CBM systems 308 in addition to an EPMS. For example, the cause of a motor stator problem may originate from transients, overvoltage / undervoltage, voltage imbalance, etc., which in turn originate elsewhere within or outside the end-user's facility. Embodiments according to the present disclosure help identify systemic causes, which leads to more effective solutions and mitigation. Advantageously, aspects of the present disclosure allow load diagnostic data (e.g., MCSA) to be analyzed against electrical system data to identify common causes of problems originating within the electrical system, simplifying and reducing the overall cost of mitigating systemic problems and providing useful context-based data and information back and forth between the CBM system and the EPMS.

[0031] In some embodiments, MCSA data provided by at least one CBM system 308 and energy-related data derived from or derived from energy-related signals captured by at least one IED 302 are processed on at least one of the following: at least one IED 302, a cloud-based system 310, edge or field software 312, or a gateway 314 (e.g., a hub 130) (which may alternatively or collectively be referred to herein as a "head-end" system 316). One or more of these elements (310, 312, 314) constitute the software management aspect of the head-end system 316 and may be capable of performing at least one of the following: managing applications, monitoring, optimizing, observing, processing, analyzing, hosting, storing, facilitating, aggregating, logging, searching, and / or virtualizing. Accordingly, a database may also be managed by at least one of these software management elements. Additionally, analytics (e.g., artificial intelligence, machine learning, statistical processing, etc.) may be performed in whole or in part on any one or more of these elements, and one or more outputs from these analytics may be provided in whole or in part to any other element within the system (302, 306, 308, 310, 312, 314, or others) as needed.

[0032] Figure 4A and Figure 4BAn example process 400 embodying aspects of the present disclosure is shown that allows for analysis of data acquired from multiple CBM systems 108 , 208 to identify system-level potential causes of problems within an electrical system, such as electrical system 100 or electrical system 200 .

[0033] Process 400 begins at 402 and, at 404, assesses whether the EPMS and CBM systems have been correctly configured for the application. Various assessments may be performed on various aspects of the EPMS and CBM, which may include: device type, signal sampling rate, load type, customer type, process type, alarm settings, logging intervals, load metadata (e.g., motor nameplate data, etc.), EPMS metadata, process, location, time context (e.g., timestamps, synchronization, etc.), and the like. In the event that one or more aspects of the EPMS and / or at least one CBM are not adequately and / or properly configured, instructions may be provided to the end user to resolve the issue, or alternatively, embodiments of the present disclosure may automatically configure or resolve the issue itself at 406 before returning to 402. Optionally, aspects of the present disclosure include reassessing the configuration to ensure that the application no longer has any configuration issues. Furthermore, it may be noted that the EPMS may evolve over time to reflect changes to the electrical installation (e.g., electrical modifications, additions to the building or changes to processes and equipment used in the field, adding more meters to the EPMS, etc.). When changes are made, the configuration of both systems can be updated to reflect the known changes. This will aid in diagnosing related systemic events and will also enable these related and systemic issues to be distinguished from non-related issues (e.g., installing another load elsewhere in the EPMS, installing another IED, changing the location of a load in the field / electrical facility, etc.).

[0034] Correlation can be considered narrowly (i.e., statistically). For example, a recurring or continuous event / problem may be systematically detected / identified at the EPMS and at least one associated CBM system. In this case, calculating correlation will identify a high statistical correlation value between the EPMS and at least one associated CBM system. If at least a second associated CBM system does not systematically detect / identify the recurring or continuous event / problem, the statistical correlation value will be lower than the statistical correlation value of the first at least one associated CBM system.

[0035] In a broader sense, correlation can refer to the identification and / or characterization of relationships between issues, events, measurements, locations, systems, IED types, segments, etc. The relationships can be based on any number of factors or methods, including: calculations, knowledge, recommendations, insights, new data, metadata, observations, new analytics, new systems (e.g., additional CBM systems, etc.), and so on.

[0036] Once the configuration of the application, EPMS, and CBM has been resolved, a CBM learning cycle can be initiated at 408. In an alternative embodiment, the CBM learning cycle at 408 is initiated before the configuration at 406 is completed. The purpose of the CBM learning cycle is to establish a baseline for the operation of the target load. The CBM acquires relevant data for analysis at 410. In this example, the CBM can evaluate various parameters (e.g., current, voltage, temperature, displacement, velocity, acceleration, frequency / spectrum, pressure, speed, associated metadata, etc.) to develop reference levels. Additionally, once typical parameter ranges have been determined / identified, thresholds can be established.

[0037] In embodiments, parameter data can be continuously evaluated to identify relevant changes, deviations, and / or trends that may indicate abnormal or degraded conditions. Statistical methods and / or trends can be used to identify, categorize, and / or distinguish the relevance of any one or more changes, deviations, change points, and / or trends. Parameter data can be analyzed, evaluated, charted, stored, alarmed, and / or the like as needed, configured, or requested. Having relevant parameter data (and / or, optionally, a valid equipment model) to perform the evaluation is an important part of the application. Depending on the parameter being analyzed or the evaluation being performed, a minimum number of parameter data points may be required. If a statistical analysis or evaluation is to be performed, a certain number of parameter data points may be required to generate statistically relevant results. The logging / sampling intervals from the CBM and EPMS systems may or may not be relevant, depending on the underlying issue being analyzed. For example, parameters such as harmonic distortion or imbalance are considered "steady-state" phenomena; therefore, a general approximation of the appearance of corresponding data may be acceptable. In such cases, it may be important that the load / motor is actually operating so that the data is available for comparison and / or analysis.

[0038] In some cases, two or more parameters may be required for a particular type of analysis or assessment. For example, the current and voltage from each of the three phases can be used to identify, assess, and quantify the severity of the unbalanced condition and the corresponding source (i.e., system or motor). Assuming sufficient data is available for analysis, as determined at 412, the learning cycle ends. If sufficient data is not available, as determined at 412, the learning cycle continues and / or more relevant data is obtained. If two or more systems (e.g., an EPMS system and several CBM systems) are implemented or connected at different times / dates, the learning cycle can be restarted or updated to utilize the mutual analysis / information extracted from each system. This can help the two systems distinguish between systemic problems that originate at the motor and systemic problems that originate elsewhere in the facility (as identified from the EPMS system / analysis).

[0039] Further references Figure 4A , process 400 continues at 414. Once data (e.g., measured data, metadata, etc.) and / or information is available for analysis, it may be useful to determine the operating state / condition of the motor loads associated with the CBM. For example, in most cases, a de-energized motor will not provide relevant / useful information; however, determining the operating state of one or more motors automatically (e.g., using an algorithm, etc.), manually (e.g., human intervention, etc.), or through some external input (e.g., using I / O, etc.) will allow the present invention's algorithm to more accurately identify and assess problems associated with systemic causes rather than isolated (discrete) motor problems.

[0040] Figure 4A and Figure 4B The example process 400 performs one or more sub-processes at 416 to 432. At 416, various electrical problems exist in the motor being evaluated by the CBM. For example, transient overvoltages may cause motor winding insulation failure, voltage sags may stress the motor (depending on the characteristics of the voltage sag, the voltage sag may (in some cases) produce motor contactor bounce causing a transient event (leading to the aforementioned problem of transient overvoltages)), steady-state unbalance and harmonic distortion may cause motor overheating and degradation of its winding insulation, and steady-state overvoltage and undervoltage conditions may also cause motor overheating and degrade winding insulation, to name a few. Excessive amounts of any one or more of these problems may lead to motor stress and premature motor failure.

[0041] This feature at 416 correlates the problem identified by one or more CBMs with a more systemic problem identified by the EPMS. For example, a transient overvoltage may cause a short circuit between windings (e.g., turn-to-turn short, phase-to-ground short). Although the origin of the transient voltage may be external / upstream of the motor load, the effect (e.g., turn-to-turn short, phase-to-ground short) occurs within the motor. The CBM will identify the short circuit and provide notification to the end user accordingly, regardless of whether the origin of the source of the short circuit is external to the motor. However, a properly configured, capable EPMS can determine the presence of one or more transient overvoltage events that caused a short circuit in the motor.

[0042] The CBM system's database may contain information associated with identified or potential motor load issues. It may also contain information that has not yet been analyzed or has not yet been determined as a problem. In the event that at least one or more identified or potential problems are indicated by the CBM system, the timestamps associated with the relevant data may be evaluated to determine correlation with systemic problems from the EPMS or other CBM-based systems on the same electrical system. At 418, it is possible that systemic conditions such as overvoltage, undervoltage, harmonic distortion, imbalance, sag, swell, etc. may be causing the motor problem (see step 416 above). Again, correlating data from the EPMS with data from the CBM can help identify potential systemic problems. An important part of this feature evaluation is when two or more CBMs are used. Aggregating and jointly evaluating EPMS and CBM data from multiple points can help determine the presence of a systemic problem, especially if discrete problems from the CBM-based system are related or correlated. An example might be overheating in multiple motors leading to frequent motor failures. Evaluation of the data may indicate that a common (systemic) voltage imbalance condition is a significant factor in these failures.

[0043] Now refer to Figure 4A At step 420, CBM systems often employ a "learning cycle" to establish a baseline of "what is normal," after which they begin actually monitoring and evaluating the motor's operating parameters to determine "what is abnormal and potential impacts." This approach presents several potential complications, including: 1) determining how long the learning cycle should be, 2) the occurrence of some external electrical disturbance or change during the learning cycle (unknown to the CBM system), and 3) pre-existing issues with the load being baselined. Any of these issues could affect the quality of the data during the learning cycle, skew the determination of "what is normal," and the results could be ignored or obscure relevant information provided by the CBM system about the health of the load / motor under consideration.

[0044] To mitigate this risk, data from the EPMS can be incorporated to identify external electrical issues that could skew the baseline data accumulated during the learning cycle. For example, when a condition is detected by the EPMS that could skew the baseline data for the CBM system, this feature restarts the learning cycle, expelling the affected data used to create the baseline for the CBM system, or providing an indication to the end user that such an event has occurred and / or should be considered / re-evaluated. In future versions of the CBM system, inputs can be stored and characterized (e.g., defining signatures from a database or library) to distinguish between discrete load-related issues and systemic EPMS system-related issues.

[0045] At 422, this aspect of the present disclosure utilizes multiple (two or more) applications (e.g., EPMS, CBM) to evaluate data from the same electrical system. When one application identifies a problem that should or could have been identified by at least one other application, an action is taken. For example, one action may be to notify the end user (e.g., an alarm, email, etc.) that at least one (and optionally, which) of the applications appears to be not performing correctly and that remedial action should be taken. Another action that may be taken may be to evaluate the application that appears to be not performing correctly and identify potential misconfigurations (e.g., misconfigured alarm thresholds, etc.) or any other issues with the application. If two or more applications (e.g., discrete CBMs, etc.) identify similar / related issues that should and could have been identified by a third or more applications (e.g., EPMS, CBMs, etc.), the probability that the third or more applications have a problem is higher, and resolving the differences may be given a higher priority. Any attempts (successful or unsuccessful) to resolve the issues with the third or more applications may be stored for future evaluation or for historical purposes.

[0046] At 424, a monitoring system is used to ensure that the others are configured correctly. As part of step 422 above, the CBM and EPMS settings are evaluated to ensure that each is set up correctly (with or without any application issues). Because at least two applications (e.g., EPMS, CBM) work together in the disclosed embodiments, it is important that they are configured so. For example, a cloud-based application may be able to upload and evaluate configuration settings, capabilities / features, historical information and / or other metadata associated with each connected application (e.g., EPMS, CBM, etc.). Any problems, differences, opportunities and / or other improvements can be identified from a system perspective (e.g., in the cloud, edge software, etc.), and action can be taken to remedy or enhance system (e.g., EPMS, CBM, etc.) performance accordingly. In some cases, the end user can be notified that part or all of the application should be reconfigured to better optimize its performance and results. This multi-system analysis and / or configuration optimization can also be performed by a professional service engineer relying on the principles and methods described in this application and using data and knowledge from both systems. Additionally, at 424 , process 400 may also provide the measurement capabilities or computational / inference features of one system to the other system if the other system has more limited capabilities.

[0047] When a problem (or potential / imminent problem) is detected, where at least one CBM operates independently of the EPMS or other CBMs, process 400 indicates (via some communication means) at 426 that a sampling rate, logging interval, and / or any other technique should be increased to capture more / better relevant information to assess, troubleshoot, and / or resolve or mitigate the problem. Additionally, at least one alarm threshold may be lowered to ensure that relevant or useful data related to the problem (or potential / imminent problem) is captured.

[0048] For example, the CBM may indicate to a second or more related systems via some communication means that a problem (or potential / imminent problem) has occurred, is occurring, may occur, or will occur. Upon receiving the indication of the problem, the second or more related systems may take some action to improve the quality of data acquired from the devices associated therewith. The second or more related systems may be an EPMS having, for example, several IEDs communicatively coupled thereto. The original indication from the CBM (and transmitted to at least one aspect of the EPMS) may cause the EPMS to change the sampling rate or logging interval of one or more related IEDs to ensure that extensive and relevant data (including waveform capture) related to the problem is measured and / or captured. This extensive and relevant data may be used to verify that the problem actually exists, improve troubleshooting, assess the impact on other areas of the electrical system, provide a broader mitigation assessment and opportunities, etc.

[0049] At 428, CBM applications are typically discrete, meaning one CBM (e.g., a vibration sensor) monitors one load or load acquisition point. In the case of motor diagnostics, a single CBM device would monitor a single motor. Due to its high cost, end users may choose to install CBM on a subset of their systems, processes, or loads. If / when this is the case, problems, potential problems, susceptibility, and / or significant trends can be inferred for areas and loads that do not have a direct CBM connection.

[0050] For example, a CBM monitoring a motor load may detect interharmonic current frequencies that indicate potential rotor bar fractures in the motor. An IED connected to the EPMS and located upstream of the motor's CBM may also measure these interharmonic current frequencies, as well as supplementary interharmonic current frequencies that are unrelated / undetected by the CBM. In this case, the measured current frequency values ​​(including interharmonic current frequencies) detected by the motor's CBM can be "subtracted" from the measured current frequency values ​​(also including interharmonic frequencies) detected by the IED connected to the EPMS to quantify the remaining current frequency values ​​(including the remaining interharmonic frequency values). The remaining interharmonic current frequencies can then be evaluated / analyzed to identify potential issues associated with other loads downstream of the IED, excluding the measured current frequencies from the motor's CBM. While this approach may not provide specific indications of issues with a specific downstream load, it can provide clues to which downstream load is at fault. Alternatively, it can provide a general indication of a downstream problem that needs to be addressed. If this is the case, the present invention can provide recommendations for additional monitoring (i.e., IED or CBM) as needed. It should be noted that the voltage and current amplitudes and phase angles at discrete harmonic and interharmonic frequencies can be used to determine the harmonic and interharmonic power flow direction. Using this determination of the remaining interharmonic frequencies will indicate whether the source of the remaining interharmonic frequencies is upstream or downstream of the IED connection point on the electrical system. It should also be noted that from the upstream IED point, it is possible to determine two or more loads that are turned on / off at different times and identify which of these loads are generating harmonic or interharmonic currents.

[0051] Now refer to 430, Figure 4A and Figure 4BThe example process 400 (such as indicated at steps 422, 424, and 428 above) can be used to identify potential system improvements. These recommendations can be initiated by a problem as indicated from measurement data, metadata, analysis output, or by some other means. For example, metadata may indicate that several large (e.g., 500 H.P.) motors are installed within the end user's electrical system; however, only one motor CBM is employed. Because large motors are expensive to purchase, operate, and have a greater impact when experiencing inefficiencies, motor CBMs may be purchased and installed prudently accordingly. In this case, perhaps only two of the multiple motors are operating properly. In this case, a process embodying aspects of the present disclosure can help prioritize recommendations for additional CBMs (based on certain criteria such as operating hours). Similarly, supplemental IEDs may be installed prudently to the EPMS to assist in the evaluation or provide redundancy for distributed CBMs. It should be noted that motor starting characteristics (e.g., inrush current magnitude and shape, steady-state current magnitude, voltage characteristics, affected phases, synchronization of events across phases, reactive power and energy characteristics, etc.) can be used to approximately determine and / or size motor applications through upstream connected IEDs, which may be useful in this aspect of the invention.

[0052] At 432, another interesting use of the algorithm is to utilize data from the CBM to correlate data from the EPMS, thereby resulting in beneficial improvements to the CBM and EPMS (and associated IED) algorithms. For example, a CBM application can be used to assess the condition of a motor over time for the purpose of identifying motor bearing issues that ultimately experience failure. The CBM data is then partially or fully imported into a database that also contains one or more data sets from one or more IEDs, captured during the same time period as the CBM data. Even though the EPMS (and / or its associated IEDs) may not employ the same targeted and / or complex algorithms as those employed by the CBM, the characteristics of the CBM data (including motor trends and eventual failures) can be utilized to train and / or identify seemingly innocuous trends in the EPMS (and associated IED) data that are indicative of eventual failures. This type of enriched data can help end users of the EPMS system distinguish between the causes of alarms between two or more systems (e.g., EPMS, CBM1, CBM2). This information can also be used to train algorithms (e.g., machine learning, etc.) to identify potential problems in unmonitored systems, processes, and loads. Additionally, as shown at 440, aspects of the present disclosure further evaluate the data for a confidence level in the evaluation results.

[0053] Because each electrical system and its loads are unique, there are subtle limitations or specific constraints associated with how a system or algorithm responds to different events. For example, it is possible that one algorithm may perform better than another under certain conditions. Assessing an event or problem from a variety of locations at different points within the electrical system is useful for many reasons, including: 1) it helps locate the source of the problem, 2) it helps quantify the severity of the problem, and 3) it provides redundant assessments of the event or problem from different "viewpoints."

[0054] This third reason is important because it increases the confidence level of the results and / or determinations made by the algorithm. For example, a CBM system may be evaluating a motor current signature that indicates a broken rotor bar. Similarly, an IED in an EPMS upstream of the motor load may also indicate the presence of a broken rotor bar downstream of its location. The CBM system and the IED (two independent systems) both provide comparable results, providing a more convincing conclusion that the motor's rotor bar is indeed cracked or broken.

[0055] Determining a "confidence level" can be performed in many ways. For example, a simple statistical approach could be to evaluate the results from each independent system (e.g., CBM, IED, etc.) and combine them accordingly. In the above example, the two independent systems agree that there is a rotor bar fracture on the motor downstream of the IED and evaluated by the CBM. Both results are consistent with each other, so the confidence level is higher than relying solely on the results from a single system (e.g., CBM). For example, the first system (e.g., CBM) can also be weighted more heavily than the second system (e.g., EPMS). In one implementation, the first system may be considered by an expert in the field to be more effective or more applicable. Therefore, during configuration / setup or during the expert's analysis / assessment, the expert may weight the first system more heavily. In another implementation, the first system (e.g., CBM) is considered twice as likely to be correct as the second system (e.g., EPMS). In the event that the results from the two systems are not similar, the conclusion of the first system (e.g., CBM) will be used because it has a higher probability of being correct than the second system. There are many statistical and mathematical methods and techniques for combining results from different systems that are understood or considered by those of ordinary skill in the art.

[0056] proceed to Figure 4BIn step 434, after performing any one or more assessments / analyses according to the above-mentioned applications / concepts, at least one recommendation may be provided to identify, prioritize, resolve and / or mitigate the problem. The recommendations and / or results of these applications / concepts may include at least one of the following: problem location, problem prevalence, severity, potential impact, required mitigation equipment, verification or validation of the problem and / or solution, confidence indicators, etc. According to some embodiments of the present disclosure, at least one recommended action is selected based on at least one of various factors, including, for example, cost, availability, time to implementation, safety improvement, whether the solution is hardware-based or software-based, etc. In response to the at least one recommended action provided by at least one of the EPMS system and / or the CBM system, at least one action may be taken or performed. For example, in an embodiment where at least one IED responsible for capturing energy-related signals is part of the EPMS, the EPMS may control one or more aspects of the electrical system (i.e., examples of at least one action) in response to the at least one recommended action provided.

[0057] At 436, the final step in the disclosed process 400 is the prioritization and management of its results / outputs. Data, issues, recommendations, assessments, improvements, results, impacts / affects, etc. can optionally be stored, alerted, displayed, analyzed / assessed, removed, deleted, edited, highlighted, and / or acted upon in any other manner. Regardless of the action, the results / output will be formatted and / or provided in a manner that is useful and / or relevant to the end user. At 436, which is optional in some embodiments, one or more types of related information can be stored, for example, for future use and / or analysis. For example, information collected in any step of process 400 (e.g., characteristics related to the event, impact, recovery metrics, actions taken, and / or status related to analysis of impact characteristics, recommendations for resolving, improving, and / or optimizing recovery, etc.) can be stored. This can be used to train existing or auxiliary (e.g., other) systems on new problems, for example, supervised learning or reinforcement learning by providing validated data. It can also be used to retrain these systems by adding this data to the training data, such as as measurement data, metadata, and / or as validated diagnostics. For example, analyzing load diagnostic data (e.g., MCSA) against electrical system data at 436 to identify common causes of problems originating within the electrical system can simplify and reduce the overall cost of mitigating systemic problems, as well as provide useful context-based data and information back and forth between the CBM system and the EPMS.

[0058] In embodiments where relevant information is stored, it should be understood that, for example, based on user-configured preferences, the relevant information may be stored locally (e.g., on at least one local storage device) and / or remotely (e.g., on cloud-based storage). For example, a user may indicate their preference in a user interface (e.g., of a user device) to store relevant information locally and / or remotely, and the relevant information may be stored based on the user-configured preferences. It should be understood that the location where relevant information is stored may be based on various other factors, including customer type / segment, process, memory requirements, cost, system (e.g., EPMS, CBM), etc.

[0059] It should also be understood that Figure 4A and Figure 4B The relevant information is stored during any step of the process 400 shown in FIG. In other words, step 436 does not necessarily need to be stored during any step of the process 400 shown in FIG. Figure 4A and Figure 4B Instead, it may occur at one or more points during process 400.

[0060] After taking these steps, Figure 4A and Figure 4B The example process ends at 438. Of course, the present invention may operate partially or fully, once, intermittently, randomly, regularly, or continuously.

[0061] Figures 5A to 5C The information obtained from various parameters to be analyzed (eg, three-phase current, three-phase apparent power, three-phase real power, etc.) is provided. Figure 5A An example of a simple three-phase application is shown. Figure 5A As shown in FIG, IED 502 (e.g., Figure 1 IED 102, Figure 2 Instrument 202, Figure 3 The IED 302) acquires electrical signals, data and information from three conductors 504 serving a three-phase delta winding motor load 506. It should be understood that acquiring data as described herein may include acquiring, measuring, capturing, logging, etc. Similarly, Figure 5B An IED 502 is shown acquiring electrical data and information from three conductors 504 feeding a three-phase wye-wound motor load 506 .

[0062] Figure 5C Other aspects of an example of a simple three-phase application according to one or more embodiments of the present invention are shown. Figure 5C In order to use, the IED 502 obtains electrical data and information from three conductors 504 feeding a three-phase delta winding motor load 506. In addition, the CBM system 508 (e.g., Figure 1 CBM 108 Figure 2 CBM 208 Figure 3 The CBM 308 of FIG. 5 monitors the load 506 based on one or more of vibration 510, acoustic 512, thermal (not shown), and MCSA 514 monitoring techniques. A head-end system 516 (such as Figure 3 The head-end system 316, including the cloud-based system 310 and / or the field or edge software 312 and / or the gateway 314, processes the data provided by the at least one CBM system 508 and the data derived from or derived from the energy-related signals captured by the at least one IED 502. The head-end system 516 performs any one or more evaluations / analyses according to the above-mentioned applications / concepts (see Figure 4A ) After that, at least one recommendation may be provided to identify, prioritize, resolve, and / or mitigate the problem. These application / concept recommendations and / or results may include at least one of the following: problem location, problem prevalence, severity, potential impact, trend, required mitigation equipment, verification or validation of the problem and / or solution, confidence level, etc.

[0063] In an embodiment, a method for monitoring and predicting problems associated with loads in an electrical system includes collecting at least one of energy-related data and non-energy-related data (or metadata) from at least one load diagnostic system (e.g., a CBM system or application) within the electrical system, and collecting at least one of energy-related data and non-energy-related data (or metadata) from at least one IED at a higher electrical connection point within the electrical system (e.g., electrically connected to the at least one load diagnostic system and / or electrically closer to an energy source of the electrical system). The method includes evaluating data collected from the at least one load diagnostic system against data collected from the at least one IED to identify at least one of a related characteristic, commonality, trend, and problem between the data collected from the at least one load diagnostic system and the data collected from the at least one IED. The method also includes evaluating at least one of the identified similar characteristics, commonality, trend, and problem to determine at least one of the electrical system characteristics, commonality, trend, and problem associated with the at least one IED, and taking at least one action to address the at least one of the electrical system characteristics, commonality, trend, and problem associated with the at least one IED. The loads include, for example, at least one motor, relay, transformer, and / or capacitor bank. Energy-related data includes, for example, at least one energy-related measurement (e.g., PQ, WFC, data logs, alarms, etc.), I / O status / state, trend, or other statistical derivation, and non-energy-related data includes, for example, at least one device characteristic, metadata information (e.g., segmentation, nameplate data, load type, etc.), operating characteristics, or external conditions. It should be noted that commonalities encompass all types of patterns, co-occurrences, and their respective analyses. For example, commonalities analysis can attempt to determine whether the IED and CBM system consistently identify a specific systemic issue. Alternatively, the CBM system may only detect partial co-occurrences, which may indicate multiple loads under the same IED, with the CBM monitoring only one load.

[0064] Aspects of the present disclosure may optionally utilize one or more digital or analog I / O signals to more optimally operate. For example, one or more embodiments may optionally utilize digital status input signals from at least one single-phase or three-phase load (e.g., a polyphase induction motor) to simplify processing and / or enhance its analysis, evaluation, results, and / or recommendations. Alternatively, one or more embodiments may utilize analog I / O signals from the load (e.g., a polyphase induction motor) to incorporate measured temperature (i.e., from thermocouples) into its analysis, evaluation, results, and / or recommendations. The I / O signals may be generated or utilized as needed by at least one of an IED, a gateway, a software system, a cloud-based system, or other application.

[0065] It should be understood that input is data received by the processor and / or IED, while output is data sent by the processor and / or IED. Inputs and outputs can be digital or analog. Both digital and analog signals can be discrete variables (e.g., two states such as high / low, one / zero, on / off, etc.). If digital, this can be a value. If analog, the system / IED can treat the presence of voltage / current as an equivalent signal or continuous variable (e.g., a continuous variable such as spatial position, temperature, pressure voltage, etc.). They can be digital signals (e.g., measurements in the IED from sensors that produce digital information / values) and / or analog signals (e.g., measurements in the IED from sensors that produce analog information / values). These digital and / or analog signals can include any processing step in the IED (e.g., deriving active power (kW), power factor, amplitude, relative phase angle in all derived calculations).

[0066] Processors and / or IEDs can convert / reconvert digital and analog input signals into digital representations for internal processing. Processors and / or IEDs can also be used to convert / reconvert internally processed digital signals into digital and / or analog output signals to provide an indication, action, or other response (such as an input to another processor / IED). Typical uses for digital outputs include signal relays for opening or closing circuit breakers or switches, signal relays for starting or stopping motors and / or other equipment, and operating other devices and equipment that can interface directly with digital signals. Digital inputs are often used to determine the operating state / position of equipment (e.g., whether a circuit breaker is open or closed) or to read input synchronization signals from a common pulse output. Analog outputs can be used to provide variable control of valves, motors, heaters, or other loads / processes in energy management systems. Finally, analog inputs can be used to collect variable operating data and / or be used in proportional control schemes.

[0067] Some more examples of utilizing digital and analog I / O data can include (but are not limited to): turbine control, electroplating equipment, fermentation equipment, chemical processing equipment, telecommunications, equipment, precision scaling equipment, elevators and moving walkways, compression equipment, wastewater treatment equipment, sorting and processing equipment, electroplating equipment temperature / pressure data logging, power generation / transmission / distribution, robotics, alarm monitoring and control equipment, as a few examples.

[0068] In some embodiments, the above methods (and / or other systems and / or methods discussed herein) may include one or more of the following features, alone or in combination with other features. For example, in some embodiments, energy-related signals captured by at least one IED may include at least one of the following: a voltage signal, a current signal, input / output (I / O) data, and a derived energy-related value. In some embodiments, the I / O data includes at least one of the following: on / off state, open / closed state, high / low state, temperature, pressure, and volume. Additionally, in some embodiments, the derived energy-related value includes at least one of the following: an additional energy-related value calculated, computed, estimated, derived, developed, interpolated, extrapolated, evaluated, or otherwise determined based on at least one of the voltage signal and / or the current signal. In some embodiments, the derived energy-related value includes at least one of: active power, apparent power, reactive power, energy, harmonic distortion, power factor, magnitude / direction of harmonic power, harmonic voltage, harmonic current, simple harmonic current, interharmonic voltage, magnitude / direction of interharmonic power, magnitude / direction of subharmonic power, individual phase current, phase angle, impedance, sequence component, total voltage harmonic distortion, total current harmonic distortion, three-phase current, phase voltage, line voltage, and / or other similar / related parameters. In some embodiments, the derived energy-related value includes at least one energy-related characteristic, including magnitude, direction, phase angle, percentage, ratio, level, duration, associated frequency component, impedance, energy-related parameter shape, and / or decay rate. It should be understood that the energy-related signal can include (or utilize) substantially any electrical parameter derived from at least one of the voltage and current signals (including the voltage and current themselves), including, for example, load level and mode, as will be understood from the further discussion below.

[0069] In some embodiments, the above-described methods (and / or other systems and / or methods described herein) can be implemented on at least one IED required by the above-described methods (and / or other systems and / or methods described herein). Additionally, in some embodiments, the above-described methods (and / or other systems and / or methods described herein) can be implemented partially or completely remotely from at least one IED, such as in a gateway, field software, edge software, a remote server, or the like (which may be collectively or alternatively referred to herein as a "head-end" system). It should be understood that "cloud-based software," "edge software," "edge systems," "management systems," "software management systems," and the like may be collectively or alternatively referred to herein as "head-end software" for purposes of this application. In some embodiments, at least one IED may be coupled to measure an energy-related signal, receive electrical measurement data from or derived from the energy-related signal at an input, and be configured to generate at least one or more outputs. The outputs may be used to identify at least one potential load type associated with at least one identified change / change in the electrical system that is characterized and / or quantified. Examples of the at least one IED may include a smart utility meter, a power quality meter, and / or another metering device (or devices). For example, the at least one IED may include a circuit breaker, a relay, a power quality correction device, an uninterruptible power supply (UPS), a filter, and / or a variable speed drive (VSD). Additionally, in some embodiments, the at least one IED may include at least one virtual (e.g., residual energy related signal measurement) meter.

[0070] In some embodiments, energy-related signals may be continuously or semi-continuously captured and / or recorded by at least one IED, and changes / alterations identified in the energy-related signals may be updated (e.g., evaluated / re-evaluated, prioritized / re-prioritized, tracked, etc.) in response thereto. For example, a change / alteration may be initially identified from energy-related signals captured a first time, and may be updated or revised in response to (e.g., including or in conjunction with) changes / alterations identified from energy-related signals captured a second time. For example, when a change / alteration is identified, the change / alteration may be characterized and / or quantified, information related to the characterized and / or quantified identified change / alteration may be appended to time series information associated with the energy-related data, and characteristics and / or quantities associated with the time series information may be evaluated to identify at least one potential load type associated with the characterized and / or quantified identified change / alteration. The appended information may include, for example, tag indications regarding the time series information, metadata, characteristics, and / or other information related to the characterized and / or quantified identified change / alteration. This can be performed by leveraging time series based statistics (e.g. local minima, maxima, mean or median, combined with variance such as standard deviation and interquartile range) or more advanced algorithms and solutions (such as “change point detection”, “model change detection”, etc.) to specify a few approaches among many possible candidate algorithms, tools and techniques.

[0071] As used herein, the terms "upstream" and "downstream" (also sometimes referred to as "upstream" and "downstream," respectively) are used to refer to electrical locations within an electrical system. More specifically, the electrical locations "upstream" and "downstream" are relative to the electrical location of the IED that collects data and provides that information. For example, in an electrical system that includes multiple IEDs, one or more IEDs may be located (or installed) at an electrical location upstream relative to one or more other IEDs in the electrical system, and one or more IEDs may be located (or installed) at an electrical location downstream relative to one or more other IEDs in the electrical system. A first IED or load that is located on an electrical circuit upstream of a second IED or load may, for example, be electrically closer to an input or source of the electrical system (e.g., a generator or utility feeder) than the second IED or load. Conversely, a first IED or load that is located on an electrical circuit downstream of a second IED or load may be electrically closer to an end or termination of the electrical system than the other IEDs (and thus, in this case, would be closer to a load or group of loads).

[0072] In an embodiment, a first IED or load electrically connected in parallel (e.g., in an electrical circuit) with a second IED or load can be considered "electrically" upstream of the second IED or load, and vice versa. In an embodiment, an algorithm for determining the direction (i.e., upstream or downstream) of a power quality event is located (or stored) in an IED, cloud, field software, gateway, etc. As an example, an IED can record voltage and current phase information of an electrical event (e.g., by sampling corresponding signals) and communicate this information to a cloud-based system. The cloud-based system can then analyze the voltage and current phase information (e.g., instantaneous, root mean square (RMS), waveform, and / or other electrical characteristics) to determine whether the source / origin of the energy-related transient (or other energy-related event) is electrically upstream or downstream of the location where the IED is electrically coupled to the electrical system (or network).

[0073] In some embodiments, energy-related data derived from or derived from energy-related signals captured by at least one IED is processed on at least one of the IED, a cloud-based system, field or edge software, a gateway, and any other head-end system associated with the electrical system. In these embodiments, for example, at least one IED may be communicatively coupled to at least one of the cloud-based system, field or edge software, a gateway, and any other head-end system on which electrical measurement data is processed, analyzed, and / or displayed.

[0074] In some embodiments, data associated with energy-related data is stored (e.g., in a memory device of at least one device or system associated with the electrical system) and / or tracked over a predetermined time period. For example, the predetermined time period can be a user-configured time period. In some embodiments, the stored and / or tracked data includes information associated with identifying at least one potential load type. The information associated with identifying at least one potential load type can include, for example, at least one of: at least one identified change / alteration, at least one identified change / alteration characterized and / or quantified, time series information, and assessed characteristics and / or quantities associated with the time series information. In some embodiments, the information associated with identifying at least one potential load type can be saved and / or tracked for future analysis / use. For example, the stored and / or tracked information can be used to generate a library of load types and associated start / run / change / stop characteristics and / or be added to a pre-existing library of load types and associated start / run / change / stop characteristics. In embodiments where there is a pre-existing library of load types and associated start / run / change / stop characteristics, at least one potential load type identified using the systems and methods described herein can be selected from a plurality of potential load types in the pre-existing library of load types and associated start / run / change / stop characteristics.

[0075] In some embodiments, the above-described system may correspond to a control system (e.g., the control system discussed previously) for monitoring or controlling one or more parameters associated with an electrical system. As previously mentioned, in some embodiments, the control system may be an instrument, an IED (e.g., at least one IED responsible for capturing energy-related signals), a programmable logic controller (PLC), head-end software (i.e., a software system), a cloud-based control system, a gateway, a system for routing data via Ethernet or some other communication system, and the like.

[0076] It should be understood that the systems and methods described herein can be responsive to changes in the electrical system in which the systems and methods are provided and / or implemented. For example, at least one identified change / alteration can be compared to one or more specified thresholds to determine whether the at least one identified change / alteration satisfies the specified threshold(s). The specified threshold(s) can be one or more dynamic thresholds that change in response to the change in the electrical system. For example, changes in the electrical system can be detected based on energy-related signals captured by at least one IED in the electrical system. In one example implementation, changes are detected after the system has been manually trained / taught to recognize the changes. For example, specific equipment (or processes) operating at a given time can be described to allow the system to learn (i.e., a form of machine learning). In another example implementation, changes are detected by automatically identifying operating patterns using state-of-the-art machine learning algorithms (e.g., using time series clustering, or using spectra or any other algorithm that facilitates analysis to identify patterns).

[0077] As will be further understood from the following discussion, among other features, the disclosed invention provides the ability to characterize voltage, current, and other derived signals to better understand upstream and downstream loads, their operation, and impact on the electrical system. The ability to automatically evaluate energy-related data for correlation, characterization, quantification, identification, and analysis helps end users (e.g., facility managers, maintenance personnel, energy managers, etc.) better understand the operation of their electrical systems. It can also provide many more service and solution opportunities to energy-related companies (such as Schneider Electric, the assignee of the present disclosure).

[0078] It should be understood that the at least one energy-related waveform capture described in conjunction with the above methods (as well as other methods and systems discussed below) can be associated with an energy-related signal captured or measured by at least one IED. For example, according to some embodiments of the present disclosure, at least one energy-related waveform capture can be generated based on at least one energy-related signal captured or measured by at least one IED. According to IEEE Standard 1057-2017, for example, a waveform is "a representation or indication (e.g., a graph, plot, oscilloscope representation, discrete time series, equation, table of coordinates, or statistics) or visualization of a signal." With this definition in mind, at least one energy-related waveform can correspond to a representation or indication or visualization of at least one energy-related signal. It should be understood that the above relationship is based on the definition of a waveform by a standards body (in this case, the IEEE), and that other relationships between waveforms and signals are certainly possible, as will be understood by one of ordinary skill in the art.

[0079] It should be understood that the energy-related signal or waveform captured or measured by the at least one IED can include (or utilize) substantially any electrical parameter derived from, for example, at least one of a voltage and current signal (including the voltage and current themselves). It should also be understood that the energy-related signal or waveform can be captured / recorded and / or transmitted and / or recorded by the at least one IED continuously or semi-continuously / periodically. As described above, the at least one captured energy-related waveform can be analyzed (e.g., in real-time, pseudo-real-time, or historically) to determine whether the at least one captured energy-related waveform can be compressed while maintaining relevant properties for characterization, analysis, and / or other purposes.

[0080] In some embodiments, the at least one IED that captures the energy-related waveform includes at least one metering device.The at least one metering device may, for example, correspond to at least one metering device in the electrical system that is capturing / monitoring the energy-related waveform.

[0081] It should be understood that the terms "processor" and "controller" are sometimes used interchangeably herein. For example, "processor" may be used to describe a controller. Additionally, "controller" may be used to describe a processor.

[0082] Embodiments of the present disclosure may include a special-purpose computer including various computer hardware, as described in more detail herein.

[0083] For illustrative purposes, programs and other executable program components may be shown as discrete blocks. However, it should be appreciated that such programs and components reside at different times in different storage components of a computing device and are executed by a data processor of the device.

[0084] Although described in conjunction with example computing system environment, the embodiments of various aspects of the present invention can operate together with other special computing system environments or configurations.Computing system environment is not intended to propose any restriction to the scope of use of any aspect of the present invention or function.In addition, computing system environment should not be interpreted as having any dependency or requirement relevant with any one component or component combination shown in the example operating environment.The example of computing system, environment and / or configuration that can be suitable for use together with various aspects of the present invention includes but is not limited to personal computer, server computer, handheld or laptop device, multiprocessor system, system based on microprocessor, set-top box, programmable consumer electronic product, mobile phone, network PC, minicomputer, mainframe computer, comprise any one distributed computing environment etc. in above system or device.

[0085] Embodiments of various aspects of the present disclosure may be described in the general context of data and / or processor-executable instructions, such as program modules stored in one or more tangible, non-transient storage media and executed by one or more processors or other devices. Typically, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform specific tasks or implement specific abstract data types. Various aspects of the present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules may be located in local and remote storage media including memory storage devices.

[0086] In operation, the processor, computer, and / or server may execute processor-executable instructions (eg, software, firmware, and / or hardware), such as those shown herein, to implement aspects of the present invention.

[0087] The embodiments may be implemented using processor-executable instructions. The processor-executable instructions may be organized into one or more processor-executable components or modules on a tangible processor-readable storage medium. Moreover, the embodiments may be implemented using any number and organization of such components or modules. For example, aspects of the present disclosure are not limited to the specific processor-executable instructions or specific components or modules shown in the figures and described herein. Other embodiments may include different processor-executable instructions or components having more or less functionality than shown and described herein.

[0088] Unless otherwise indicated, the order in which the operations of the various aspects of the present disclosure shown and described herein are performed or carried out is not required. That is, unless otherwise indicated, the operations may be performed in any order, and embodiments may include more or fewer operations than those disclosed herein. For example, it is contemplated that it is within the scope of the present invention to perform or carry out a particular operation before, simultaneously with, or after another operation.

[0089] When introducing elements of the present invention or the embodiments thereof, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements.

[0090] Not all depicted components shown or described may be required. In addition, some implementations and embodiments may include additional components. The arrangement and types of components may be varied without departing from the spirit or scope of the claims set forth herein. Additional, different, or fewer components may be provided, and components may be combined. Alternatively or additionally, a component may be implemented by multiple components.

[0091] The above description shows embodiments by way of example and not limitation. This specification enables those skilled in the art to make and use aspects of the present invention, and describes several embodiments, adaptations, variations, alternatives and uses of aspects of the present invention, including what are currently considered to be the best modes for carrying out aspects of the present invention. In addition, it should be understood that the aspects of the present invention are not limited in their application to the details of the construction and arrangement of the components set forth in the following description or shown in the accompanying drawings. Aspects of the present invention are capable of other embodiments and can be practiced or performed in various ways. Furthermore, it should be understood that the wording and terminology used herein are for descriptive purposes and should not be considered as limiting.

[0092] It will be apparent that modifications and variations may be made without departing from the scope of the invention as defined in the appended claims. As various changes may be made to the above structures and methods without departing from the scope of the invention, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not restrictive.

[0093] In view of the foregoing, it will be seen that the several advantages of aspects of the invention are achieved and other advantageous results attained.

[0094] The Abstract and Summary are provided to help the reader quickly ascertain the nature of the technical disclosure. They are submitted with the understanding that they will not be used to interpret or limit the scope or meaning of the claims. The Summary is provided to introduce a range of concepts further described in the Detailed Description in a simplified form. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the claimed subject matter.

Claims

1. A method for monitoring and predicting problems associated with an electrical system, the method comprising: acquiring, by at least one load diagnostic system, first data related to a load within the electrical system monitored by the at least one load diagnostic system; acquiring, by at least one intelligent electronic device (IED), second data related to the electrical system, the second data comprising at least one of energy-related data and non-energy-related data, the at least one IED being electrically connected within the electrical system and closer to a power source of the electrical system, upstream of a load monitored by the at least one load diagnostic system; evaluating the first data acquired by the at least one load diagnostic system against the second data acquired by the at least one IED to identify a correlation between the first data and the second data; evaluating the identified correlation to determine a condition of the electrical system associated with the at least one IED; as well as At least one action is taken to address a condition of the electrical system associated with the at least one IED.

2. The method according to claim 1, wherein The first data related to the load includes at least one of energy-related data and non-energy-related data, and optionally, the non-energy-related data related to the load includes one or more of device characteristics, metadata information, operating characteristics and external conditions.

3. The method according to claim 1 or claim 2, wherein: The correlation includes one or more of related characteristics, commonalities, trends, and issues between the first data acquired by the at least one load diagnostic system and the second data acquired by the at least one IED.

4. The method according to any one of claims 1 to 3, wherein The load includes at least one of a motor, a relay, a transformer, and a capacitor bank.

5. The method according to any one of claims 1 to 4, wherein The at least one load diagnostic system includes a condition-based monitoring (CBM) system coupled to the load, and the method further includes: evaluating, by the CBM system, one or more parameters associated with operation of the load; and A baseline operation of the load is learned for identifying deviations from the baseline operation, wherein the deviations from the baseline operation of the load are indicative of a condition of the load.

6. The method according to claim 5, wherein: Learning the baseline operation of the load includes adjusting the baseline operation of the load based on the second data acquired by the at least one IED.

7. The method according to any one of claims 1 to 6, wherein Obtaining the first data includes aggregating the first data obtained by a plurality of load diagnostic systems, the aggregated first data relating to a plurality of loads within the electrical system, each of the plurality of loads being monitored by one of the plurality of load diagnostic systems.

8. A causal diagnostic system for monitoring and predicting problems associated with an electrical system, the causal diagnostic system comprising: at least one load diagnostic system coupled to a load within the electrical system, the at least one load diagnostic system acquiring first data related to the load monitored thereby; at least one intelligent electronic device (IED), the at least one IED being connected within the electrical system and closer to a power source of the electrical system, upstream of a load monitored by the at least one load diagnostic system, the at least one IED acquiring second data related to the electrical system, the second data comprising at least one of energy-related data and non-energy-related data; at least one processor that receives and responds to the acquired first and second data; as well as at least one memory device coupled to the at least one processor, the at least one memory device storing processor-executable instructions that, when executed, configure the at least one processor to: evaluating the first data acquired by the at least one load diagnostic system against the second data acquired by the at least one IED to identify a correlation between the first data and the second data; evaluating the identified correlation to determine a condition of the electrical system associated with the at least one IED; as well as At least one action is taken to address a condition of the electrical system associated with the at least one IED.

9. The causal diagnosis system according to claim 8, wherein: The first data related to the load includes at least one of energy-related data and non-energy-related data; and optionally, wherein the non-energy-related data related to the load includes one or more of device characteristics, metadata information, operating characteristics and external conditions.

10. The causal diagnosis system according to claim 8 or claim 9, wherein: The correlation includes one or more of related characteristics, commonalities, trends, and issues between the first data acquired by the at least one load diagnostic system and the second data acquired by the at least one IED.

11. The causal diagnosis system according to any one of claims 8 to 10, wherein: The load includes at least one of a motor, a relay, a transformer, and a capacitor bank.

12. The causal diagnosis system according to any one of claims 8 to 11, wherein: The at least one load diagnostic system comprises a condition-based monitoring (CBM) system coupled to the load, and wherein the at least one memory device stores processor-executable instructions that, when executed, further configure the at least one processor to: evaluating one or more parameters associated with operation of the load; and A baseline operation of the load is learned for use by the CBM system to identify deviations from the baseline operation, wherein the deviations from the baseline operation of the load are indicative of a condition of the load.

13. The causal diagnosis system according to claim 12, wherein: Learning the baseline operation of the load includes adjusting the baseline operation of the load based on the second data acquired by the at least one IED.

14. The causal diagnosis system according to claim 12 or claim 13, wherein: The at least one memory device stores processor-executable instructions that, when executed, further configure the at least one processor to take at least one action to address the condition of the load.

15. The causal diagnosis system according to any one of claims 8 to 14, wherein: The at least one load diagnostic system includes a plurality of load diagnostic systems coupled to a plurality of loads within the electrical system, and wherein the first data is aggregated from each of the plurality of load diagnostic systems and the aggregated first data relates to the plurality of loads within the electrical system, each of the plurality of loads within the electrical system being monitored by one of the plurality of load diagnostic systems.

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