Method and system for detection and notification of power outage and power quality

Abstract

To provide methods and systems for detection and notification of electrical power outages and power quality.SOLUTION: A sensor coupled to a circuit transmits a keepalive packet to a server. The sensor detects an input signal generated by an electrical activity and generates an output signal on the basis of the input signal. The sensor monitors the output signal, during a clock cycle, determines whether a rising edge occurs and transmits a fault packet to the server when the rising edge occurs prior to a predetermined clock value or when no rising edge occurs. The server receives the fault packet from the sensor and listens for keepalive packets. The server transmits a power outage notification when no keepalive packets are received for at least a defined time period after the fault packet is received. The server transmits a power restoration notification when one or more keepalive packets are subsequently received.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE The subject matter of this application relates generally to methods and systems for power outage and power quality detection and notification in electrical systems. [Background technology]

[0002] Consumers continue to rely more and more on uninterrupted electricity for a wide range of activities, including powering communications devices, computing devices, medical devices, heating and cooling appliances, and refrigerators. However, according to the U.S. Energy Information Administration (EIA), U.S. electricity consumers were without power for an average of 250 minutes in 2016 and experienced 1.3 power outages. In 2017, the length of time consumers were without power nearly doubled to an average of 470 minutes (7.8 hours) and experienced an average of 1.4 power outages. In 2016, the longest power outage was around 20 hours, but in 2017, this increased to just over 40 hours. These outages are often unplanned and in some cases may go unnoticed, for example by homeowners who are away from their homes. These outages can have a significant impact on almost every aspect of daily life, including health and safety, making instant detection and notification of outages critical.

[0003] Currently available techniques for detecting power outages typically rely on the use of backup batteries and / or generators that can temporarily provide power to the power outage detection device and supporting communication equipment. However, batteries have a limited life span and add cost to the power outage detection device. Battery replacement also creates an ongoing maintenance burden for the user. Furthermore, in some cases, activating the backup battery can result in an undesirable delay between the start of a power outage and when the backup electrical supply is activated and able to power the detection device.

[0004] Furthermore, power generation, transmission, and distribution systems are becoming more and more complex. The transition to less carbon dioxide (CO2) emitting energy sources means combining many different generation methods, including wind, solar, nuclear, batteries, natural gas, and coal. Homes and businesses will increasingly have on-premise energy generation methods, all of which will share the grid with different levels of aging and exposure to the environment. Switching between different generation types can cause voltage surges and sags, as well as other power quality concerns. Aging and exposure to the environment cause deterioration and failure of transformers and electrical interconnections. Surge, sag, and equipment deterioration can cause failure of home electronics and appliances, and can also create very dangerous situations where electrocution and electrical fires can occur. In residential environments, fires often start in unseen cavities, such as within walls, and spread to significant heat before being detected by the home's occupants or smoke detectors, leading to significant damage. Electrical malfunctions are one of the leading causes of residential fires. Due to their hidden nature, electrical fires are also a disproportionate cause of death, with an estimated 420 deaths, 1,370 injuries, and $1.4 billion in damages to homes caused by electrical fires annually.

[0005] Current technology does not provide homeowners with much needed information about the quality of the power they receive from their utility companies. For example, homeowners may notice flickering lights or frequent outages of sensitive electronic devices, but may not be alerted to very serious problems with their electrical connection to their home or within their electrical grid. Additionally, damage and deterioration of the U.S. power grid increases risks and liabilities for grid owners and their customers. For example, a recent Pacific Gas & Energy (PG&E) fire incident data report revealed that PG&E experienced over 2,400 grid-related fires from 2014 to 2019. These fires resulted in over $13 billion in liabilities and quickly led PG&E to file for bankruptcy. In another example, a study by the Texas Wildfire Mitigation Project found that in less than four years prior to the study, 4,000 fires were caused by transmission or distribution system events, most of which were localized and of minor consequence, but some became major fires. In addition to wildfires, an increase in transformer fires and explosions, as well as other catastrophic grid events, has been linked to the deterioration of utility equipment. In one frightening example, in mid-July 2019, firefighters responded to downtown Madison, Wisconsin, where a high-voltage transformer had exploded and started a fire. Another recent event was the late July 2019 transformer explosion and fire at an American Electric Power Texas substation. Finally, in February 2021, a devastating winter storm caused a devastating power grid failure in Texas, resulting in deaths and leaving millions without electricity in subfreezing temperatures.

[0006] As mentioned above, fires caused by electric utility grids result in very large economic losses each year and often significant fatalities. While many power system components (e.g., switches, insulators, transformers) provide decades of fault-free service, transmission and distribution components eventually fail. Wildfires, and other damage to property and life, can be triggered through several mechanisms, including downed wires on the ground, vegetation contact, conductor contact, damaged or deteriorated equipment arcing, repetitive faults, and equipment failure. Rapid detection and mitigation of these problems is therefore crucial to prevent catastrophic fire incidents. Summary of the Invention

[0007] Therefore, there is a need for a method and system for detecting outages and power quality in an electrical system in real time or near real time and notifying relevant users (such as power grid operators and / or government officials) of outages and / or undesirable changes in power quality or hazardous conditions in the electrical system. Advantageously, the techniques described herein provide for detection of outages, assessment of power quality, and identification of electrical system hazards based on the detected frequency of alternating current (AC) electricity received by an outage detection device, and for immediate notification of the outage to remote devices, including, in some embodiments, using communication equipment powered by the affected electrical system just before the outage takes the communication equipment offline. Also, in some embodiments, the methods and systems described herein utilize one or several monitoring devices that connect to existing outlets instead of installing other complex, expensive, or dangerous outage detection devices and / or monitoring components (such as those that connect to circuit breakers or distribution boards). The techniques described herein are not only advantageous in providing homeowners and business owners with insight into power quality and potential hazards, but if multiple residences utilize the methods and systems described herein, they also provide power companies with insight into any problems and the ability to proactively resolve issues before they become full-blown hazards.

[0008] In one aspect, the invention features a system for power outage detection and notification. The system includes a sensor device coupled to a circuit and a server computing device. The sensor device periodically transmits keep-alive packets to the server computing device. The sensor device detects an input signal generated by electrical activity on the circuit. The sensor device generates an output signal based on the detected input signal. The sensor device monitors the generated output signal during each of a number of clock cycles having a predefined duration. During each clock cycle, the sensor device determines whether a rising edge has occurred in the generated output signal, and transmits a fault packet to the server computing device when the rising edge occurs before a predefined clock value in the clock cycle or when no rising edge occurs during the clock cycle. The sensor device starts a new clock cycle. The server computing device receives the fault packet from the sensor device. The server computing device listens for one or more keep-alive packets from the sensor device. The server computing device transmits a power outage notification to the one or more remote computing devices when it does not receive a keep-alive packet from the sensor device for at least a predetermined period of time after receiving the failure packet, and transmits a power restored notification to the one or more remote computing devices when it receives one or more keep-alive packets from the sensor device after transmitting the power outage notification.

[0009] The invention, in another aspect, features a computerized method for power outage detection and notification. A sensor device coupled to a circuit periodically transmits keep-alive packets to a server computing device. The sensor device detects an input signal generated by electrical activity on the circuit. The sensor device generates an output signal based on the detected input signal. The sensor device monitors the generated output signal during each of a number of clock cycles having a predetermined duration. During each clock cycle, the sensor device determines whether a rising edge occurs in the generated output signal, and when the rising edge occurs before a predetermined clock value during the clock cycle or when no rising edge occurs during the clock cycle, the sensor device transmits a failure packet to the server computing device. The sensor device initiates a new clock cycle. The server computing device receives the failure packet from the sensor device. The server computing device listens for one or more keep-alive packets from the sensor device. The server computing device transmits a power outage notification to the one or more remote computing devices when, after receiving the failure packet, it does not receive a keep-alive packet from the sensor device for at least a predetermined period of time, and transmits a power restoration notification to the one or more remote computing devices when, after transmitting the power outage notification, it receives one or more keep-alive packets from the sensor device.

[0010] Any of the above aspects may include one or more of the following features. In some embodiments, the input signal includes an alternating current (AC) voltage sine wave having multiple zero crossings. In some embodiments, the output signal is a voltage curve having multiple rising edges corresponding to the zero crossings of the input signal. In some embodiments, the keep-alive packet includes power quality data including one or more of any number of measures of root mean square (RMS) voltage, frequency of the voltage sine wave, relative phase angle of the voltage sine wave, amplitude of harmonics of the voltage sine wave, or high frequency noise amplitude. In some embodiments, each clock cycle has a predefined duration of 9 milliseconds. In some embodiments, the predefined clock value during the clock cycle is 8.33 milliseconds.

[0011] The invention, in another aspect, features a system for power quality detection and notification. The system includes one or more sensor devices coupled to a circuit and a server computing device. The one or more sensor devices detect an input signal generated by electrical activity on the circuit. The one or more sensor devices generate an output signal based on the detected input signal. The one or more sensor devices transmit power quality data based on the output signal to the server computing device. The server computing device receives the power quality data from the one or more sensor devices. The server computing device analyzes the power quality data in conjunction with historical power quality data received from the one or more sensor devices to detect one or more power quality events. The server computing device transmits a power quality notification to one or more remote computing devices based on the detected power quality events.

[0012] The invention, in another aspect, features a computerized method for power quality detection and notification. A sensor device coupled to a circuit detects an input signal generated by electrical activity on the circuit. The sensor device generates an output signal based on the detected input signal. The sensor device transmits power quality data based on the output signal to a server computing device. The server computing device receives the power quality data from the sensor device. The server computing device analyzes the power quality data in conjunction with historical power quality data received from the sensor device to detect one or more power quality events. The server computing device transmits power quality notifications to one or more remote computing devices based on the detected power quality events.

[0013] Any of the above aspects may include one or more of the following features. In some embodiments, the detected one or more power quality events include one or more of a surge event, a surge jump event, a sag event, a sag jump event, a brownout event, a swell jump event, a high frequency (HF) filter jump event, a frequency jump event, a recurrent power quality problem, a phase angle jump event, a loose neutral event, or a generator start-up event. In some embodiments, the server computing device further (i) correlates the detected one or more power quality events with zero or more external events, and / or (ii) correlates the detected power quality events from the first sensor device with the detected power quality events from one or more other sensor devices.

[0014] In some embodiments, the server computing device detects a loose neutral event by analyzing, for a sensor device, the number and amplitude of surge events, surge jump events, and sag events recorded by the sensor device within a predetermined time period that are uncorrelated with matching power quality events from any other sensor devices in the vicinity of the sensor device, and generating a loose neutral event when the average number of surge events is greater than a first predetermined number per day, or when the average number of surge jump events having a magnitude greater than a predetermined percentage of the nominal voltage is greater than a second predetermined number per day, or when the average number of sag events is greater than a third predetermined number per day.

[0015] In some embodiments, the output signal includes one or more of any number of measurements of root mean square (RMS) voltage, frequency of a voltage sine wave, relative phase angle of a voltage sine wave, amplitude of a harmonic of a voltage sine wave, or high frequency noise amplitude. In some embodiments, the server computing device detects the surge event by analyzing multiple consecutive data points of the RMS voltage from one or more sensor devices and generating a surge event when the RMS voltage is greater than a predefined threshold percentage of the nominal voltage over a number of consecutive data points. In some embodiments, the predefined threshold percentage varies based on the number of consecutive data points for which the RMS voltage is greater than a minimum threshold percentage.

[0016] In some embodiments, the server computing device detects the brownout event by analyzing multiple consecutive data points of the RMS voltage from one or more sensor devices and generating a brownout event when the RMS voltage is less than a predefined threshold percentage of the nominal voltage over a number of consecutive data points, in some embodiments the predefined threshold percentage varies based on the number of consecutive data points for which the RMS voltage is less than a minimum threshold percentage.

[0017] In some embodiments, the server computing device detects sag jump events by analyzing multiple consecutive data points of RMS voltage from one or more sensor devices and generating a sag jump event for each one or more drops in the RMS voltage that occur in the multiple consecutive data points and that are greater than a predetermined threshold percentage of the nominal voltage.

[0018] In some embodiments, the server computing device detects swell jump events by analyzing multiple consecutive data points of RMS voltage from one or more sensor devices and generating a swell jump event for each one or more rises in the RMS voltage that occur in the multiple consecutive data points that are greater than a predetermined threshold percentage of the nominal voltage.

[0019] In some embodiments, the server computing device detects the HF filter jump event by analyzing multiple consecutive data points of HF amplitude data from one or more sensor devices, calculating an average of the HF amplitude data, and generating an HF filter jump event when the HF amplitude data increases by more than a threshold multiple of the average when the average is greater than one, or generating an HF filter jump event when the HF amplitude data increases by more than a predefined threshold when the average is less than one.

[0020] In some embodiments, the server computing device detects a frequency jump event by analyzing multiple consecutive data points of frequency data from one or more sensor devices, calculating an average of the frequency data, calculating a standard deviation of the frequency data, and generating a frequency jump event when the frequency increases from the average by more than a predefined threshold or generating a frequency jump event when (i) the standard deviation changes from below a first frequency to above a second frequency, or (ii) the standard deviation changes from above the second frequency to below the first frequency.

[0021] In some embodiments, the server computing device detects a generator start-up event by analyzing, for a sensor device, whether any power outage events and frequency events have been detected by the one sensor device during a predetermined time period, and generating a generator start-up event when the one sensor device detects a power outage event followed by a change in standard deviation of frequency above a predetermined threshold within a predetermined time period from the power outage event, where the change in standard deviation of frequency is not associated with a correlated external event.

[0022] In some embodiments, the one or more external events include a lightning activity event, a power grid monitoring event, and an energy pricing event. In some embodiments, the detected power quality event from the first sensor device and the detected power quality event from the one or more other sensor devices are of the same event type. Power quality events having the same event type (including, but not limited to, the same or similar power quality characteristics, duration, start time, end time, geographic location, etc.) are referred to herein as "correlated events" or "coincident events."

[0023] Other aspects and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example only the principles of the invention. The above-mentioned and further advantages of the present invention may be better understood by reference to the following description taken in conjunction with the accompanying drawings, which are not necessarily to scale and in which the focus is generally upon illustrating the principles of the invention. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a block diagram illustrating a system for power outage detection and notification. [Figure 2A] 1 is a flow diagram illustrating a computerized method for power outage detection and notification. [Figure 2B] 1 is a flow diagram illustrating a computerized method for power outage detection and notification. [Diagram 3] FIG. 2 illustrates an exemplary 60 Hz voltage signal captured by a power outage detection device. [Figure 4] 2A-2C illustrate exemplary input signals received by a power failure detection device and exemplary output signals generated by the power failure detection device. [Figure 5A] 4 is a detailed timing diagram illustrating the output signals generated by the power failure detection device and the corresponding values ​​of the global power failure flag when a power failure is detected by the power failure detection device; [Figure 5B] 1 is a graph showing output signals of a number of different power outage detection devices when a power outage occurs. [Figure 6A-6B] FIG. 6A is an exemplary user interface of a remote computing device depicting a power outage notification received from a server computing device, and FIG. 6B is an exemplary user interface of a remote computing device depicting a power restoration notification received from the server computing device. [Figure 7] 13A and 13B are diagrams showing the results of testing the detection efficiency of a power outage detection device. [Figure 8] Graph of the ITI(CBEMA) curve. [Figure 9] 1 is a block diagram illustrating a networked system for power quality detection and notification. [Figure 10] 1 is a flow diagram illustrating a computerized method for analyzing power quality data. [Figure 11] 1 is a graph illustrating output signals generated by a number of different power outage detection devices during a power grid surge event. [Figure 12] 1 is a graph illustrating output signals generated by a number of different power failure detection devices during a brownout event. [Figure 13] Graph showing output signals generated by several different power outage detection devices during a sag and jump event. [Figure 14] Graph showing the output signals generated by several different power outage detection devices during a swell jump event. [Figure 15] Graph showing the output signals produced by several different power outage detection devices during an HF filter jump event. [Figure 16] 1 is a graph illustrating output signals generated by a number of different power failure detection devices during a frequency jump event. [Figure 17A] A graph showing the nominal root mean square (RMS) voltage reading taken by a power outage detection device. [Figure 17B] 4 is a graph showing voltage RMS readings taken by a power outage detection device, showing an exemplary loose neutral connection; [Figure 17C] Graph showing voltage RMS readings taken by a power outage detection device before and after the removal of a loose neutral connection. [Figure 18] FIG. 1 illustrates a user interface displayed on a remote computing device showing historical voltage RMS readings detected by a power outage detection device. [Figure 19] 1 illustrates a user interface displayed on a remote computing device showing a month's worth of power quality events detected by a power outage detection device. [Figure 20A-20B]FIG. 20A is a diagram showing a user interface showing a push notification alert sent by a server computing device to a remote computing device and displayed, and FIG. 20B is a diagram showing a user interface showing a list of power quality notifications related to the power outage detection device. [Fig. 20C-20D] FIG. 20C is a diagram showing a user interface showing a detailed power surge event alert notification sent by a server computing device to a remote computing device and displayed, and FIG. 20D is a diagram showing a user interface showing a detailed power brownout event alert notification sent by a server computing device to a remote computing device and displayed. [Figure 20E] 13 illustrates a user interface showing a recurrent power quality problem alarm notification sent by a server computing device to a remote computing device for display; [Figure 21A] 1 illustrates a geographic map generated by a server computing device depicting the locations of power outage events detected by a network of power outage detection devices. [Figure 21B] FIG. 1 illustrates a detailed view of a geographic map generated by a server computing device depicting the location of power outage events detected by a network of power outage detection devices. [Figure 22A] 1 illustrates a geographic map generated by a server computing device depicting the locations of power grid surge and brownout events detected by a network of power outage detection devices. [Figure 22B] FIG. 1 illustrates a detailed view of a geographic map generated by a server computing device depicting the locations of power grid surge and brownout events detected by a network of power outage detection devices. [Figure 23] FIG. 1 illustrates a detailed view of a geographic map generated by a server computing device depicting the locations of power grid sag and jump events detected by a network of power outage detection devices. [Figure 24] FIG. 1 illustrates a detailed view of a geographic map generated by a server computing device depicting the locations of power grid swell jump events detected by a network of power outage detection devices. [Figure 25A] 1 illustrates a geographic map generated by a server computing device depicting the locations of power grid frequency jump events detected by a network of power outage detection devices. [Figure 25B] FIG. 1 illustrates a detailed view of a geographic map generated by a server computing device depicting the location of power grid frequency jump events detected by a network of power outage detection devices. [Figure 26] 1 illustrates a geographic map generated by a server computing device depicting the locations of high frequency (HF) filter jump events detected by a network of power outage detection devices. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] FIG. 1 is a block diagram of a system 100 for power outage detection and notification. The system 100 includes a power outage detection device 102, a communication medium 104, and a remote computing device 106. The power outage detection device 102 includes an optical isolator 102a that connects to the voltage and neutral of a power system to monitor current (e.g., 120 VAC, 60 Hz) for purposes described herein. The optical isolator 102a is also connected to ground (GND). An exemplary 60 Hz voltage signal captured by the power outage detection device is shown in FIG. 3. The optical isolator 102a generates an output signal (Out) based on the received current provided to a processor 102b of the power outage detection device 102. The processor 102b analyzes the output signal from the optical isolator 102a and transmits data (e.g., packetized communication) to a server computing device 106, which can transmit a power outage notification to one or more remote computing devices (not shown) based on the data received from the power outage detection device 102. An exemplary power outage detection device 102 is a Ting™ sensor manufactured by Whisker Labs, Inc. (Germantown, Maryland, USA).

[0026] The power outage detection device 102 also includes an analog-to-digital (A / D) converter 102c that reads input signals from the voltage and neutral lines. In some embodiments, the input signals may be filtered or converted before being read by the A / D converter 102c. The A / D converter 102c generates specific power quality data for transmission to the server, such as, but not limited to, RMS voltage, peak voltage, frequency, phase, high frequency (HF) amplitude, and amplitude of harmonics. It can be understood that other power quality measurements may be calculated and this list is not exhaustive. The power quality data is transmitted to the server computing device periodically at regular intervals and is included as part of a "keep alive packet" as shown in FIG. 1 and described herein.

[0027] The communication medium 104 allows other components of the system 100 to communicate with each other to perform the power outage detection and notification processes described herein. The medium 104 may be a local network, such as a LAN, including one or more components (e.g., routers, modems, etc.) connected to the electrical wiring monitored by the system 100, or a wide area network, such as the Internet and / or a cellular network. In some embodiments, the network 104 is comprised of several discrete networks and / or sub-networks (e.g., cellular to Internet) that allow the components of the system 100 to communicate with each other. The communication medium 104 may comprise wired and / or wireless components.

[0028] The server computing device 106 is a combination of hardware, such as one or more dedicated processors and one or more physical memory modules, and specialized software modules executed by the processor of the server computing device 106 to perform the functions of receiving data from and transmitting data to other components of the system 100, and performing the power outage detection and notification functions described herein. In some embodiments, the server computing device 106 includes an alert module 106a, which is a specialized set of computer software instructions programmed into the dedicated processor of the server computing device 106 and may include specifically designated memory locations and / or registers for executing the specialized computer software instructions. Further description of the specific processing performed by the alert module 106a is provided below.

[0029] As can be appreciated, in some cases, it is advantageous to correlate other external events (e.g., events outside the power grid / power system) with power quality events. For example, if a lightning strike is closely correlated with a surge power quality event, the system 100 can issue more urgent notification alerts (e.g., visual and audio alert messages on multiple end user devices) to end users, especially if the amplitude of the surge event is such that it could cause significant damage to appliances in the home. A homeowner may be especially vigilant in monitoring direct hazards in their home if they are familiar with very strong surges caused by direct lightning strikes. It may also be advantageous to correlate power quality events with other power grid monitoring devices, such as automatic circuit recloser devices. In this manner, power companies may utilize the techniques described herein to correlate recloser device events with residential or business power quality events. External events may be correlated with power quality events to restrict distribution or increase the urgency of notifications to end users.

[0030] In some embodiments, the power outage detection device 102 is coupled via a 120 VAC plug to a branch circuit outlet of a building's electrical system connected to a power utility grid. While FIG. 1 shows one power outage detection device 102, it should be understood that the system 100 may include two or more power outage detection devices positioned to sense electrical activity in the electrical grid. Multiple sensors may transmit data to a server computing device to improve sensitivity and may cooperate to provide information about electrical system and / or grid outages and power quality. It should also be understood that multiple power outage detection devices may be installed at a location (e.g., a residence) and the system 100 may be configured to receive data from multiple power outage detection devices installed at different locations (as described below with reference to FIG. 9).

[0031] As described above, the power outage detection device 102 is communicatively coupled to the server computing device 106 via the communication medium 104. In one embodiment, the power outage detection device 102 includes communication components (e.g., antennas, network interface circuitry) that enable the power outage detection device 102 to communicate with the server computing device 106 via a wireless connection (i.e., using wireless components such as a router and / or modem of the communication medium 104).

[0032] 2A and 2B include a flow diagram of a computerized method 200 for power outage detection and notification using the system 100 of FIG. 1. A power outage detection device (e.g., power outage detection device 102) coupled to a branch circuit of an electrical distribution system detects (202) an input signal generated by electrical activity on the branch circuit. The power outage detection device 102 generates (204) an output signal based on the detected input signal. FIG. 4 is a diagram of an example input signal 402 received by the power outage detection device 102 and an example output signal 404 generated by the power outage detection device 102. As shown in FIG. 4, the input signal 402 includes a normal alternating current (AC) voltage signal (e.g., an AC sine wave) with zero crossings (e.g., 402a, 402b) occurring at intervals of approximately 8.33 milliseconds (ms).

[0033] The optical isolator 102a of the power failure detection device 102 receives an input signal 402 via a line and neutral connection to an electrical distribution system (including a power grid) and converts the input signal 402 into an output signal 404. As shown in Figure 4, the output signal 404 constitutes a voltage curve having a number of rising edges (e.g., 404a, 404b) that generally correspond to the zero crossings of the input signal 402 in that they occur at intervals of approximately 8.33 ms. Diagram 406 of Figure 4 shows the input signal 402 and the output signal 404 superimposed to show the correspondence between the two signals. It should be appreciated that depending on the electrical system to which the power outage detection device 102 is coupled (including the spectrum of electronic devices that may be coupled to the electrical system), one or more different points on the rising edges of the output signal 404 may correspond to zero crossings of the input signal 402, e.g., the beginning of the rising edge of the output signal 404 may correspond to a zero crossing of the input signal 402, the midpoint between the beginning and the peak of the rising edge may correspond to a zero crossing, or the peak of the rising edge may correspond to a zero crossing. It should further be appreciated that the same points (e.g., beginning, midpoint, peak) on each rising edge occur at intervals of approximately 8.33 ms. The output signal 404 is transmitted to a processor incorporated in the power outage detection device (see FIG. 1 ) that monitors the output signal 404 as described below. It should be appreciated that the exact timing of the zero crossings described herein is for illustrative purposes only and corresponds to standard timings of the U.S. power system. The algorithms and techniques described herein can be modified to automatically detect and adjust for various timings of international power systems.

[0034] Returning to FIG. 2A, when the power outage detection device 102 is powered on and connected to the server computing device 106, the power outage detection device 102 periodically transmits keep-alive packets to the alarm module 106a of the server computing device 106 (206). The keep-alive packets are used to inform the server computing device 106 that the power outage detection device 102 is receiving power (i.e., there is no power outage at the corresponding location) and that the power outage detection device 102 is online. For example, the processor 102b of the power outage detection device 102 executes a main thread that runs continuously and sends keep-alive packets to the server computing device 106 at regular intervals of ¼ second. In some embodiments, the keep-alive packets may further include other periodically monitored data generated by the A / D converter 102c as described above, such as any number of measurements of the measured root mean square (RMS) voltage of the power distribution system, the frequency of the voltage sine wave, the relative phase angles of the sine waves, the amplitude of the sine wave harmonics, and the amplitude of high frequency noise. Examples of certain non-limiting types of power quality data that may be captured and transmitted by the power outage detection device 102 are described in U.S. Patent No. 10,641,806, entitled "Detection of Electric Discharges that Precede Fires in Electrical Wiring," which is incorporated herein by reference.

[0035] Additionally, the main thread monitors a global power outage flag. When the power outage flag is set (e.g., to 1), the main thread sends a failure packet (e.g., indicating a power outage) to the server computing device 106 and resets the power outage flag to 0. Further details about the power outage flag and failure packets are provided below.

[0036] The power failure detection device 102 monitors the generated output signal in each of a number of clock cycles having a predetermined length (208). For example, the processor 102b of the power failure detection device 102 may create a timer (or timeout clock) that periodically counts down from 9 ms (at a frequency of 55 Hz) to 0 and then resets. When the timer reaches zero, the processor 102b calls an interrupt to the main thread (the "timeout interrupt"). Additionally, the processor 102b calls an interrupt to the main thread (the "opto-isolator interrupt") when the processor detects a rising edge in the output signal received from the opto-isolator 102a (i.e., signal 404 in FIG. 4).

[0037] When the processor 102b invokes the opto-isolator interrupt, the processor 102b of the power failure detection device 102 determines (210) whether a rising edge has occurred in the generated output signal 404. For example, the processor 102b counts how many clock ticks have occurred since the last opto-isolator interrupt. This indicates a loss of power when (i) a rising edge has occurred in the generated output signal 404 before a certain clock value in the clock cycle (e.g., less than 7.6 ms (at a frequency of 65 Hz) since the last opto-isolator interrupt occurred) or (ii) a rising edge has not occurred in the generated output signal 404 during the clock cycle (e.g., more than 9 ms since the last opto-isolator interrupt occurred, thereby triggering the timeout interrupt described above). As a result, the processor 102b sets a global power failure flag to 1. As described above, the main thread executed by processor 102b monitors the global power outage flag, and when the main thread sees that this flag is set to 1, the processor of the power outage detection device 102 transmits (212) a fault packet to the alarm module 106a of the server computing device 106 and begins a new clock cycle (214) of monitoring the generated output signal 404 (e.g., by reloading or resetting the timeout clock to 9 ms).

[0038] FIG. 5A is a detailed timing diagram illustrating the output signals generated by the power outage detection device 102 and the corresponding values ​​of the global power outage flag when a power outage is detected by the power outage detection device 102. As shown in FIG. 5A, trace 510 corresponds to the output signal 404 generated by the power outage detection device 102. The solid black line 502 indicates the value of the global power outage flag of the main thread of the processor 102b, and the dotted black line 504 indicates when the main thread finishes sending a failure packet to the server computing device 106. For example, the processor 102b detects a power outage at time t1 and sets the global power outage flag to 1. Then, at time t2, the main thread of the processor 102b reads the global power outage flag as 1 and starts transmitting a failure packet to the server computing device 106. At time t3, the main thread finishes sending the failure packet to the server computing device 106. Processor 102b detects a second power outage at time t4 (which may correspond to the same global power outage) and begins transmitting a second failed packet to the server computing device at time t5. Then, at time t6, processor 102b finishes sending the second failed packet.

[0039] Alternatively, the opto-isolator interrupt processing does not set the global power failure flag to 1 (and does not trigger a timeout interrupt) when a rising edge occurs in the generated output signal 404 at a given clock value during the clock cycle (i.e., at approximately 8.33 ms). As a result, the processor 102b of the output sensing device 102 simply begins (214) a new clock cycle monitoring the generated output signal 404 (e.g., by reloading or resetting the timeout clock to 9 ms).

[0040] FIG. 5B is a graph of the output signals of a number of different power outage detection devices as a power outage occurs. As shown in FIG. 5B, each line (e.g., line 512) corresponds to a voltage reading of the output signal of a different power outage detection device. Shortly before 05:48, a power outage occurs, which is reflected in the graph of line t1, where the readings from each device suddenly stop. Over the next few minutes, power to each power outage detection device is restored at different times, as shown by the arrows labeled "power restored."

[0041] 2B, when the power outage detection device 102 transmits a failure packet to the server computing device 106, the alert module 106a receives the failure packet (216) and listens for a keep-alive packet from the power outage detection device 102 (218). As described above, the main thread of the processor 102b of the power outage detection device 102 is configured to transmit a keep-alive packet to the server computing device 106 every ¼ second. When the alert module 106a receives the failure packet and does not detect a keep-alive packet for at least a predefined period (e.g., 5 seconds) after receiving the failure packet, the alert module 106a transmits a power outage notification to one or more remote computing devices (220).

[0042] In some embodiments, the alert module 106a can transmit the power outage notification to a remote computing device, such as a mobile phone, smart watch, smart device, tablet, laptop, etc., via one or more communication channels and / or protocols, such as email, text (e.g., SMS), automated call, etc. In some embodiments, the alert module 106a can transmit the power outage notification to a computing device associated with a different organization, for example, via a webhook API callback, or by utilizing a cloud computing provider's service, such as Amazon Web Services™ (AWS) Simple Notification Service. In one example, the remote computing device can include a mobile application (app) configured to receive a push notification from the alert module 106a that, when received, automatically activates functionality of the mobile app to alert a user of the remote computing device that a power outage is occurring at the corresponding location of the power outage detection device 102 (e.g., a pop-up message, an audible alert, and / or a tactile alert (vibration)). 6A is an exemplary user interface of a remote computing device (e.g., a smartphone) depicting a power outage notification received from the alert module 106a. As shown in FIG. 6A, the power outage notification includes a visual symbol 602 indicating the power outage event and a detailed description 604 including the time and location of the power outage.

[0043] After transmitting the power outage notification, the alert module 106a goes back to listening for keep-alive packets from the power outage detection device 102. If the alert module 106a detects one or more keep-alive packets (within a predefined period or after a predefined period has elapsed) after transmitting the power outage notification, the alert module 106a transmits (222) a power restored notification to one or more remote computing devices. As described above in connection with the power outage notification, the remote computing device can receive the power restored notification from the alert module 106a and activate a function of the remote computing device that alerts a user of the device that power has been restored. FIG. 6B is an exemplary user interface of a remote computing device depicting a power restored notification received from the alert module 106a. As shown in FIG. 6B, the power restored notification includes a visual symbol 606 indicating the power restored event and a detailed description 608 including the time and location of power restoration.

[0044] It should be appreciated that the reliability of the power outage detection methods described herein may depend on the connectivity between the power outage detection device 102 and the server computing device 106, as well as the availability of intermediate equipment supporting that connectivity. For example, the power outage detection device 102 may be connected to a local WiFi router (e.g., installed in a home or business), which may be connected to an Internet modem or router, which may be connected to other components in the corresponding Internet provider's network, before the connection finally reaches the server computing device 106 that detects the power outage event. As a result, the failure packet transmitted by the power outage detection device 102 must traverse each segment of the communication medium 104 (e.g., network), which may or may not be powered by the same power grid and power lines, before one or more of those devices lose power and are unable to transmit, retransmit, and / or relay the failure packet to the next link in the entire connection. It should be appreciated that if the failure packets do not reach the server computing device 106, the server computing device 106 cannot distinguish whether the keep-alive packets stopped due to a loss of communication connectivity (e.g., an Internet outage) or due to a power outage at the location of the power outage detection device 102. Thus, prompt detection of a power outage and transmission of the failure packets is critical to the advantages provided by the methods and systems described herein.

[0045] To determine the effectiveness of detecting power outages using this method, a test was performed with two power outage detection devices installed in two different locations. Each power outage detection device was placed on a programmable switch such that it could automatically turn off the power once a day for a period of seven months. Figure 7 illustrates the results of this test. As shown in Figure 7, the power outage detection device 102 was successful in detecting power outages on average 95% of the time, with a maximum detection efficiency of 100% and a minimum of 71%.

[0046] Another advantage provided by the technology described herein is that the efficiency of power outage detection and notification increases with the scale of installation of power outage detection devices in close proximity to each other. For example, the efficiency of power outage detection increases when multiple power outage detection devices are in close proximity to each other, for example on the same transformer that supplies power to multiple homes. A power outage detection device 102 installed in at least one of the homes on the same electrical network can communicate a fault packet to the server computing device 106, and if the keep-alive packets stop arriving simultaneously, a power outage notification can be sent to a remote device associated with each customer on the network. The same approach can also be used for power outages in a larger power grid. That is, if several dozen homes are powered by the same substation, when a power outage occurs at that substation, only one power outage detection device 102 installed in one of the several dozen homes served by that substation needs to successfully transmit a fault packet to the server computing device 106 to generate a power outage notification to the remote device associated with each customer of the several dozen homes. Conversely, if a dozen homes are powered by the same substation and the power outage detection devices 102 installed in each of those homes do not transmit a fault packet prior to a loss of communication of keep-alive packets, it can be safely assumed that the outage is the result of another non-power related event (e.g., an Internet Service Provider (ISP) outage) and not the result of a loss of power.

[0047] Another important feature of the methods and systems described herein is the ability of the power outage detection device 102 of the system 100 to monitor the power quality of the power it receives and provide information about the power monitoring to both the customer (e.g., the homeowner in which the power outage detection device 102 is installed) and the power company. As previously mentioned, the power outage detection device 102 can capture additional data about the power during routine monitoring for outages (e.g., the measured root mean square (RMS) voltage of the power distribution system, the frequency of the voltage sine waves, the relative phase angles of the sine waves, the amplitude of the sine wave harmonics, any number of measurements of the amplitude of high frequency noise, etc.), and the power outage detection device 102 can transmit this data to the server computing device 106 (e.g., as part of the transmission of a keep alive packet to the server computing device).

[0048] As described in more detail below, the power outage detection device 102 can send a 5Vrms sample of the voltage to the server computing device 106 every 1 / 4 second (or a 20Vrms reading every second). This allows the server computing device 106 to monitor for voltage surges and sags that may also damage appliances or sensitive electronic devices that are also tied to the power grid. FIG. 8 is a graph called an ITI (CBEMA) curve. This graph shows the allowable voltage levels within an envelope of voltage amplitude over a period of time. For example, a very short period (~16 milliseconds) of a large voltage amplitude (300 volts) will generally not affect devices powered on the circuit, but if the voltage amplitude remains at 300 volts for more than 5 seconds, it may damage any device on the electrical network. Additionally, customers are encouraged to contact their utility company when multiple power outages or surge and sag events occur at one location over a period of days. Recurring power quality problems may be an indication of more serious problems with the power lines serving the home that could damage appliances and sensitive electronic equipment and even cause electrical fires.

[0049] For example, if the amplitude and duration of the voltage data flowing to the alert module 106a crosses the boundaries of the CBEMA curve and rises above the nominal voltage level, the alert module 106a sends a "power surge" notification message to the end user's device. If the amplitude and duration of the voltage data flowing to the alert module 106a crosses the boundaries of the CBEMA curve and falls below the nominal voltage level, the alert module 106a sends a "power brownout" notification to the end user's device.

[0050] In some embodiments, other information gleaned from the voltage signal may also indicate a power quality issue. For example, a measurement of the frequency of the voltage sine wave with a very large variation may indicate that the home is no longer on utility power and has switched to generator backup. A large jump in the frequency of the sine wave may indicate that there has been some change on the grid, such as a power plant going offline. This typically results in a large drop in frequency. Alternatively, this event may indicate that a new power plant has come online, which may result in a large jump in frequency. The system 100 may use an algorithm to detect when a generator has been turned on in a home, such as measuring the variation in frequency over the last 5 seconds, and if the variation exceeds a threshold, after the outage is detected, the system 100 may send a notification to the end user's device and / or the utility company's monitoring device that the home is now on generator power. When the frequency variation returns to nominal variation, the system 100 may send a notification that the home has returned to utility power.

[0051] Large deviations in RMS voltage outside the limits of the CBEMA curve are the most damaging to equipment in the home, but smaller changes in RMS voltage can be indicators of activity in the home that can also alert the homeowner to a faulty device or a dangerous condition. One such dangerous condition is called a loose neutral. A loose neutral occurs when the neutral wire that normally holds the voltage level in the home to "ground" becomes disconnected. The result of a loose neutral is that the voltage on one leg becomes much less stable and positive jumps (voltage increases) occur much more frequently. This is due to the imbalance in impedance on each leg that occurs as independent devices on each leg turn on and off. Larger devices operating at 220V will not make a noticeable difference in voltage jumps.

[0052] Additionally, customers are encouraged to contact their utility company when multiple outages or surge and sag events occur at one location over a period of days. Recurring power quality problems may also indicate a more serious problem with the power lines serving the residence, which could damage appliances and sensitive electronics and cause an electrical fire. Recurring power quality problems may be limited to one residence, in which case it is likely a loose neutral, or may occur periodically at multiple residences on the same transformer or substation. In those cases, recurring power quality problems may also indicate a fault in the common transformer or substation.

[0053] FIG. 9 is a block diagram of a networked power quality detection and notification system 900. The system 900 of FIG. 9 uses many of the same devices described above in connection with FIG. 1, and so their description will not be repeated here. As shown in FIG. 9, the system 900 includes a plurality of power outage detection devices 902, 910, 912 installed at different locations (e.g., residences, businesses) within a geographic region. These power outage detection devices 902, 910, 912 provide power quality data to a server computing device 906 over a communications network 904 as described above. The server computing device 906 utilizes one or more power quality analysis algorithms (described below) to analyze the power quality data received from the plurality of power outage detection devices 902, 910, 912 to detect power quality events and generate event notification messages that are distributed to one or more remote computing devices 908a, 908b controlled by end users (e.g., homeowners, business owners, power companies, etc.). It should be understood that a power outage detection device (e.g., device 902) may be co-located with a remote computing device (e.g., device 908a), e.g., a homeowner may install a power outage detection device 902 in his or her own residence and view the notification message on any of a number of different computing devices associated with him or her, such as a computing device located in his or her residence or a mobile device carried by the user.

[0054] 10 is a flow diagram of a computerized method for analyzing power quality data using the system 900 of FIG. 9. Upon receiving the power quality data, the server computing device 906 detects 1002 one or more power quality events occurring based on the received power quality data. The server computing device 906 may store the captured power quality data as historical data for future reference, as described herein. In some examples, it may be understood that no power quality events have occurred in the power quality data received by the server computing device 906, and therefore no further action is taken.

[0055] When one or more power quality events are detected, the server computing device 906 correlates (1004) the detected one or more power quality events with one or more external events that may have contributed to or affected the power quality events. For example, weather activity, such as lightning strikes, may occur within the same geographic area as the homes or businesses monitored by the outage detection devices 902, 910, 912. In another example, equipment on the power grid may perform an automatic reclosure event where a circuit recloser on the power grid senses a fault condition and temporarily interrupts power to a portion of the power grid that serves the homes and businesses in which the outage detection devices 902, 910, 912 are installed. In yet another example, a change in electrical demand (e.g., an energy pricing event) may occur on a particular portion of the power grid that affects the quality of power to the homes and businesses utilizing the outage detection devices 902, 910, 912.

[0056] Once the correlation step is complete, the server computing device 906 analyzes 1006 the power quality events and the external events using one or more power quality algorithms described herein. It should be understood that these power quality algorithms are exemplary and that other types of algorithms can be utilized with the methods and systems described herein. It should also be understood that in some embodiments, the outage detection devices 902, 910, 912 installed at each location can also perform some or all of the power quality analyses described herein as being performed by the server computing device 906 (and provide, for example, real-time customized power quality analyses for a particular location).

[0057] After analyzing the power quality events and external events, the server computing device 906 performs 1008 a historical power event analysis, for example, for a particular location, geographic region, and / or any number of outage detection devices 902, 910, 912. As described below, the system can leverage the historical event data to detect recurring power quality issues (e.g., sags, surges, etc.) that may indicate structural defects (e.g., loose neutrals) with respect to the power grid and / or residential wiring system that can only be detected by analyzing power quality data over longer periods of time, such as days, weeks, or months. Finally, the server computing device 906 transmits 1010 one or more event notification messages (e.g., alert messages) based on the above power event analysis, which are then distributed to one or more remote computing devices 908a, 908b, as described in more detail below.

[0058] The general structure of the power quality algorithm executed by the server computing device is as follows: 1. The server computing device 906 captures and queues multiple seconds of incoming power quality data.

[0059] 2. The server computing device 906 detects power quality events in the incoming power quality data based on predefined data types and predefined rules (e.g., determined from a historical data queue), such as by running one or more particular power quality algorithms on the incoming data. The server computing device 906 labels the detected power quality events with particular data points, such as the time of the event, the location of the event, and various detected or calculated quantities related to the event (e.g., any number of measurements of the measured root mean square (RMS) voltage of the power distribution system, the frequency of the voltage sine wave, the relative phase angles of the sine waves, the amplitudes of the sine harmonics, and the amplitude of high frequency noise).

[0060] 3. The server computing device 906 adds the detected power quality event to an event correlation queue to detect a correlated event (i.e., from multiple outage detection devices or external events). For example, the external event data can be obtained from one or more remote computing devices, and in the case of a lightning strike, the server computing device 906 can, for example, communicate with a server in a lightning detection network to identify the lightning event. As will be appreciated, a correlated event typically occurs within a location delta of the location of the outage detection devices 902, 910, 912, within a time delta of the detected power quality event, and is generally of the same event type (such as, but not limited to, the same or similar power quality characteristics, duration, start time, stop time, and geographic location). In some embodiments, a minimum number of outage detection devices may be required to match before a correlated event is detected to minimize false positives.

[0061] 4. The server computing device 906 stores the detected power quality events and correlation events in long-term storage, such as, for example, a NoSQL database or other type of archival storage.

[0062] 5. The server computing device 906 transmits power quality notification messages to one or more remote computing devices 908a, 908b based on the detected power quality events, correlated events, and in some embodiments historical events (e.g., generated by tracking power quality data for a particular outage detection device 902, 910, 912 and / or geographic region over a period of time).

[0063] Below are examples of algorithms that the server computing device 906 may use to detect certain power quality conditions and events. Surge Event Generally, a surge event occurs when conditions on the power grid cause excessive voltage to be delivered to a home. To detect a surge event, the server computing device 906 can analyze the incoming power quality data as follows:

[0064] 1. The server computing device 906 captures and queues a predefined amount (eg, 6 seconds worth) of incoming RMS voltage data. 2. If the server computing device 906 determines that the RMS voltage exceeds a predefined threshold percentage (e.g., 120%) of the nominal voltage for several consecutive data points (or is otherwise outside the upper portion of the CBEMA curve), then the server computing device 906 adds a “surge” event to the event queue along with, for example, the UTC time of the event, a location (e.g., location data such as GPS, latitude / longitude, cellular-based data, etc.) associated with the installation point of the power outage detection device that captured the incoming data, and the maximum value of the RMS voltage. It should be appreciated that in some embodiments, the predefined threshold percentage may vary based on the number of consecutive data points for which the RMS voltage is greater than the minimum threshold percentage.

[0065] 3. The server computing device 906 adds the detected surge event to a correlated event queue for evaluation along with other surge events. The correlated event queue is evaluated to generate correlated events, such as "grid surge events" within a predefined time period (e.g., 400 milliseconds) and within a predefined proximity (e.g., 10 kilometers) from the detected surge event, and in some embodiments may require a minimum number of events to match.

[0066] 4. The server computing device 906 stores the detected surge events and correlated power grid surge events, for example in long term storage. 5. The server computing device 906 transmits alert notification messages relating to the detected surge event and correlated events to a remote computing device 908a (e.g., mobile phone, smart device, wearable device, etc.) associated with the individual homeowner in whose hands the surge event was detected, and / or transmits notification messages relating to the detected surge event and correlated events (including external events, if detected) to a remote computing device 908b of the associated utility or other power grid operator.

[0067] 11 is a graph illustrating output signals generated by different power outage detection devices during a power grid surge event. As shown in FIG. 11, each line on the graph (e.g., line 1102) represents an output signal from a different power outage detection device. At approximately 14:17:10 (time t1), a surge event occurs, causing a power outage. The RMS voltage signals received by the server computing device 906 from each power outage detection device have increased significantly from their previous levels, indicating that the power outage detection devices have experienced a voltage surge.

[0068] Brownout Event Generally, a brownout event occurs when conditions on the power grid cause a prolonged drop in voltage to be delivered to a residence. To detect a brownout event, the server computing device 906 can analyze the incoming power quality data as follows:

[0069] 1. The server computing device 906 captures and queues a predefined amount (eg, 6 seconds worth) of RMS voltage data. 2. If the server computing device 906 determines that the RMS voltage is below a predefined threshold percentage (e.g., 70%) of the nominal voltage (or is otherwise below the CBEMA curve) for several consecutive data points, then the server computing device 906 adds a "brownout" event to the event queue, for example with the UTC time of the event, the location, and the minimum RMS voltage. It should be appreciated that in some embodiments, the predefined threshold percentage may vary depending on the number of consecutive data points for which the RMS voltage is below the minimum threshold percentage.

[0070] 3. The server computing device 906 adds the detected brownout event to a correlation event queue for evaluation along with other brownout events. The correlation event queue is evaluated to generate correlated events, such as a "power grid brownout event" within a predefined time period (e.g., 400 milliseconds) and within a predefined proximity (e.g., 10 kilometers) from the detected brownout event, which in some embodiments may require a minimum number of events to match.

[0071] 4. The server computing device 906 stores the detected brownout events and correlated power grid brownout events, for example in long term storage. 5. The server computing device 906 transmits alert notification messages related to the detected brownout event and correlated events to a remote computing device 908a associated with the individual homeowner from whom the brownout event was detected, and / or transmits notification messages related to the detected brownout event and correlated events to a remote computing device 908b of an associated utility or other power grid operator.

[0072] 12 is a graph showing output signals generated by different power outage detection devices during a brownout event. As shown in FIG. 12, each line on the graph (e.g., line 1202) represents an output signal from a different power outage detection device. At approximately 21:02:34 (time t1), a brownout event occurs. The RMS voltage signals received by the server computing device 906 from each power outage detection device drop significantly for several cycles before returning to approximately the same voltage level, indicating that the brownout event was captured by the power outage detection device.

[0073] Sag-Jump Event Generally, a sag jump event occurs when conditions on the power grid cause a brief drop in voltage to be delivered to a residence. To detect a sag jump event, the server computing device 906 may analyze the incoming power quality data as follows:

[0074] 1. The server computing device 906 captures and queues a predefined amount (eg, 6 seconds worth) of RMS voltage data. 2. The server computing device 906 determines whether one or more RMS voltage drops have occurred that are greater than a predefined threshold percentage (e.g., 2.5%) of the nominal voltage, and the server computing device 906 generates a “sag jump” event for each detected voltage drop.

[0075] 3. The server computing device 906 adds the detected sag jump event (including, for example, the UTC time of the event, the location, and the minimum RMS voltage drop) to a correlation event queue.

[0076] 4. The server computing device 906 evaluates all sag jump events in the correlated event queue to identify sag jump events that occurred within a predefined time period (e.g., 400 milliseconds) and within a predefined proximity to each other (e.g., 10 kilometers). In some embodiments, a minimum number of events may be required to identify a correlated event.

[0077] 5. The server computing device 906 stores the identified sag-jump events and correlation events, for example in long-term storage. 5. The server computing device 906 transmits alert notification messages related to the detected sag jump events and correlated events to a remote computing device 908a associated with the individual homeowner from whom the sag jump event was detected, and / or transmits notification messages related to the sag jump events and correlated events to a remote computing device 908b of an associated electric utility or other power grid operator.

[0078] FIG. 13 is a graph showing output signals generated by different power failure detection devices during a sag jump event. As shown in FIG. 13, each line on the graph (e.g., line 1302) represents an output signal from a different power failure detection device. At approximately 21:02:35 (time t1), a sag jump event occurs. The RMS voltage signal received by the server computing device 906 from each power failure detection device drops significantly and almost immediately returns to approximately the same voltage level, indicating that a sag jump event was captured by the power failure detection device.

[0079] Swell Jump Event Generally, a swell jump event occurs when conditions on the power grid cause an increase in voltage to be delivered to a residence. In some cases, a swell jump event may occur when a large load is turned off in the electric system. To detect a swell jump event, the server computing device 906 may analyze the incoming power quality data as follows:

[0080] 1. The server computing device 906 captures and queues a predefined amount (eg, 6 seconds worth) of RMS voltage data. 2. The server computing device 906 determines whether one or more RMS voltage rises have occurred that are greater than a predefined threshold percentage (e.g., 2.5%) of the nominal voltage, and the server computing device 906 generates a “swell jump” event for each detected voltage rise.

[0081] 3. The server computing device 906 adds the detected swell jump event (including, for example, the UTC time of the event, the location, and the maximum RMS voltage rise) to a correlation event queue.

[0082] 4. The server computing device 906 evaluates all swell jump events in the correlated event queue to identify swell jump events that occurred within a predefined time period (e.g., 400 milliseconds) and within a predefined proximity to each other (e.g., 10 kilometers). In some embodiments, a minimum number of events may be required to identify a correlated event.

[0083] 5. The server computing device 906 stores the identified swell jump events and correlation events, for example in long term storage. 6. The server computing device 906 transmits alert notification messages related to the detected swell jump events and correlated events to a remote computing device 908a associated with the individual homeowner in which the swell jump event was detected, and / or transmits notification messages related to the swell jump events and correlated events to a remote computing device 908b of an associated electric utility or other power grid operator.

[0084] FIG. 14 is a graph showing output signals generated by different power failure detection devices during a swell jump event. As shown in FIG. 14, each line on the graph (e.g., line 1402) represents an output signal from a different power failure detection device. At approximately 01:33:50 (time t1), a swell jump event occurs. The RMS voltage signals received by the server computing device 906 from each power failure detection device almost immediately rise to a higher voltage level, indicating that a swell jump event has been captured by the power failure detection device.

[0085] High Frequency (HF) Filter Events As described above, the power outage detection devices 902, 910, 912 may monitor certain power quality data including high frequency amplitudes of power flowing to a residence that may cause damage to the wiring and appliances of the residence. To detect a high frequency filtering event, the server computing device 906 may analyze the incoming power quality data as follows:

[0086] 1. The server computing device 906 captures and queues a predefined amount (eg, 6 seconds worth) of high frequency (HF) amplitude data. 2. The server computing device 906 calculates a sliding mean of the high frequency (HF) amplitude data. If the mean is greater than one, the server computing device 906 generates an HF filter event when the HF amplitude data jumps by more than a predefined multiple (e.g., 5) of the mean. If the mean is less than one, the server computing device 906 generates an event when the HF amplitude data jumps by more than a predefined threshold (e.g., 5). It may be appreciated that in some embodiments, the selected jump threshold for identifying an HF filter event may vary based on various signal-to-noise ratio thresholds or other characteristics of the HF data.

[0087] 3. The server computing device 906 adds the HF filter event (eg, including the event's UTC time, location, and HF amplitude jump magnitude) to a correlation event queue.

[0088] 4. The server computing device 906 evaluates all FH filter jump events in the correlated event queue to identify HF filter events that occurred within a predefined time period (e.g., 400 milliseconds) and within a predefined proximity of each other (e.g., 10 kilometers). In some embodiments, a minimum number of events may be required to identify a correlated event.

[0089] 5. The server computing device 906 stores the identified HF filter events and correlation events, for example in long term storage. 6. The server computing device 906 transmits alert notification messages related to the detected HF filter events and correlated events to a remote computing device 908a associated with the individual homeowner from whom the HF filter event was detected, and / or transmits notification messages related to the HF filter events and correlated events to a remote computing device 908b of an associated electric utility or other power grid operator.

[0090] FIG. 15 is a graph showing output signals generated by different power outage detection devices during an HF filter jump event. As shown in FIG. 15, each line on the graph (e.g., line 1502) represents an output signal from a different power outage detection device. At approximately 14:15:20 (time t1), an HF filter jump event occurs. The frequency signals received by the server computing device 906 from each power outage detection device almost immediately rise to a higher level and each signal has a large variation in frequency and is much noisier, indicating that an HF filter jump event has been captured by the power outage detection device.

[0091] It should be appreciated that the system can be further configured to detect an incipient arcing event on the power grid. For example, when the system detects multiple HF filter events with no change in phase angle voltage or frequency over time, the system can determine that the HF filter events correspond to an incipient arcing event, thereby detecting a dangerous condition at a very early stage. The system can then alert the power grid operator and / or utility company that an arcing condition exists and the geographic area or location where the condition may occur, so the operator can quickly assess and respond to the problem.

[0092] Frequency Events As described above, the power outage detection devices 902, 910, 912 may monitor certain power quality data including the frequency of the power flowing to the residence that may cause damage to the home's wiring and appliances under certain conditions, such as a surge or dip. To detect a frequency event, the server computing device 906 may analyze the incoming power quality data as follows:

[0093] 1. The server computing device 906 captures and queues a predefined amount of frequency data (eg, 6 seconds worth). 2. The server computing device 906 calculates a sliding average of the frequency data. If the frequency jumps from this average by more than a predefined threshold (e.g., 0.05 Hz), the server computing device 906 generates a frequency event. The server computing device 906 also calculates the standard deviation of the frequency and generates a frequency event if (i) the standard deviation changes from below a lower threshold (e.g., 0.025 Hz) to above an upper threshold (e.g., 0.05 Hz), or even if the standard deviation changes from above an upper threshold (e.g., 0.05 Hz) to below a lower threshold (e.g., 0.025 Hz).

[0094] 3. The server computing device 906 adds the frequency event (including, for example, the event's UTC time, location, maximum frequency jump, and standard deviation of the frequency) to a correlation event queue.

[0095] 4. The server computing device 906 evaluates all frequency events in the correlated event queue to identify frequency events that occurred within a predefined time period (e.g., 400 milliseconds) and within a predefined proximity to each other (e.g., 10 kilometers). In some embodiments, a minimum number of events may be required to identify correlated events.

[0096] 5. The server computing device 906 stores the identified frequency events and correlation events, for example in long-term storage. 6. The server computing device 906 transmits alert notification messages related to the detected frequency events and correlated events to a remote computing device 908a associated with the individual homeowner from whom the frequency event was detected, and / or transmits notification messages related to the frequency events and correlated events to a remote computing device 908 of an associated electric utility or other power grid operator.

[0097] FIG. 16 is a graph showing output signals generated by different power outage detection devices during a frequency jump event. As shown in FIG. 16, each line on the graph represents an output signal from a different power outage detection device. Note that in this graph, the output signals of each power outage detection device are very close together, making the graph appear as a single line. At approximately 19:53:00 (time t1), a frequency event occurs. The frequency signals received by the server computing device 906 from each power outage detection device drop to a lower frequency and then begin to rise, indicating that a frequency event has been captured by the power outage detection device.

[0098] Loose Neutral Event As mentioned above, a loose neutral is a very dangerous condition that can exist in residential wiring. Typically, a loose neutral will break away from its contacts, which can cause abnormally high or low voltage conditions at the outlets in the home. In some cases, current will flow through other devices in the home to ground, such as through a television set to a cable TV connection. As high currents can flow through cable TV cables or other conductors that are not designed to handle high currents, this can result in arcing or a situation where the conductor gets very hot, burning off its insulation and damaging its surroundings, and possibly even leading to an electrical fire. In some instances, the neutral can be resistive. The resistance of the neutral is low enough to carry some electricity, but too high to carry the current it is meant to carry. In these cases, the neutral can get very hot where the resistance is higher than normal, which can also cause a fire. To detect a loose neutral event, the server computing device 906 can analyze the incoming power quality data as follows:

[0099] 1. The server computing device 906 retrieves, for example from long-term storage, historical power quality event data and historical correlation event data for a particular power outage detection device 902, 910, 912. For example, as described above, the system may capture and record power quality data from power outage detection devices in a particular residence over time and store the data in long-term storage to establish a historical record of power quality events associated with the residence.

[0100] 2. The server computing device 906 evaluates, for a power outage detection device 902, the number and amplitude of surge events, surge jump events, and sag events recorded for that power outage detection device 902 within a predetermined period of time (e.g., within the last 7 days) that are not correlated with other events.

[0101] 3. If the average number of surge events is greater than a predefined threshold (e.g., 1) per day, or if the average number of surge jump events having a magnitude greater than a predefined threshold percentage (e.g., 10%) of the nominal voltage is greater than a predefined threshold (e.g., 10) per day, or if the average number of sag events is greater than a predefined threshold (e.g., 10) per day (these are example conditions), the server computing device 906 may generate a loose neutral event and store the loose neutral event data, for example, in long-term storage.

[0102] 4. The server computing device 906 transmits a notification message relating to the loose neutral event to a remote computing device 908a associated with, for example, the user. In some embodiments, the notification message may also be sent to a remote computing device 908b associated with the utility company serving the residence to enable the utility company to determine possible repairs to remedy the loose neutral condition.

[0103] As an example, Figures 17A-17C are graphs generated from power quality data captured by the power outage detection device 902 relating to a loose neutral event. Figure 17A shows a graph of nominal voltage RMS readings captured by the power outage detection device, and as shown in Figure 17A, the voltage RMS value is relatively constant over time. However, when a loose neutral is present, as shown in the graph of Figure 17B, the power outage detection device 902 captures large positive jumps in the voltage RMS value (e.g., > +10% of nominal). Similarly, Figure 17C is a graph showing voltage RMS readings captured by the power outage detection device 902 before and after the clearance of the loose neutral. As shown in Figure 17C, the voltage RMS readings include a number of large jumps before the clearance point 1702, after which the voltage RMS readings return to the nominal range without the large jumps.

[0104] Below is a table detailing the number of loose neutral connections detected by a power outage detection device during a real-world deployment of the system described herein.

[0105] [Table 1] As shown above, in each case, the system detected one or more loose neutral events associated with a particular outage detection device and generated an alarm notification to the associated end user device to make the homeowner aware of the presence of a loose neutral. In each case, the loose neutral was subsequently verified and repaired by the utility company. An additional 49 cases are currently open and in the process of being resolved. The above data illustrates the significant advantages provided by the systems and methods described herein in terms of quickly and accurately detecting power quality issues within a residence before a potential loss of property or loss of life occurs.

[0106] Recurrent power quality problem events Other types of recurring power quality problems (e.g., frequent surges, sags, etc.) may occur when delivering electricity to a residence. To detect these problems, the server computing device 906 may analyze the incoming power quality data as follows:

[0107] 1. The server computing device 906 retrieves historical power event data and historical correlation event data for a particular power outage detection device 902, 910, 912, for example from long term storage.

[0108] 2. The server computing device 906 evaluates, for a power outage detection device 902, the number and amplitude of surge and sag events recorded for that power outage detection device 902 within a predefined period (e.g., within the last 30 days).

[0109] 3. If more than a predetermined number of surge events (e.g., 4) or more than a predetermined number of sag events (e.g., 10) occur (these are example conditions), the server computing device 906 may generate a recurring power quality problem event and store the recurring power quality problem event data, for example, in long-term storage.

[0110] 4. The server computing device 906 transmits a notification message relating to the recurring power quality problem event to a remote computing device 908a, e.g., associated with the user. In some embodiments, the notification message may also be sent to a remote computing device 908b, e.g., of an electric utility company serving the residence, to enable the electric utility company to determine possible repairs to remedy the recurring power quality problem.

[0111] Generator on / off events The methods and systems described herein may also be used to detect scenarios in which an alternate power generation system (such as a residential generator) is activated in response to a power outage. To detect these situations, the server computing device 906 may analyze the incoming power quality data as follows:

[0112] 1. The server computing device 906 retrieves historical power event data and historical correlation event data for a particular power outage detection device 902, 910, 912, for example from long term storage.

[0113] 2. The server computing device 906 evaluates, for one power outage detection device 902, whether a power outage event and a frequency event have occurred. 3. If one of the power outage detection devices 902 records a power outage event followed by a change in the standard deviation of the frequency above a predefined threshold (e.g., 0.05 Hz) within a predefined period (e.g., 60 seconds) of the power outage event and the change in the standard deviation of the frequency is not associated with a correlated external event, the server computing device 906 may generate a “generator on” event and transmit a corresponding notification message, for example to a remote computing device 908a associated with a user of the power outage detection device 902.

[0114] 4. If one power outage detection device 902 that previously met the conditions for a “generator on” event records a subsequent frequency event for which the standard deviation of the frequency change is less than 0.025 Hz, the server computing device 906 may generate a “generator off” event and transmit a corresponding notification message, for example, to a remote computing device 908a associated with a user of the power outage detection device 902.

[0115] As discussed above, the server computing device 106 and / or the outage detection device 102 can be configured to generate alert notifications, reports, maps, charts, and the like for display to a user on an associated remote computing device. FIG. 18 is an illustration of a user interface displayed on a remote computing device (e.g., a cell phone) showing historical RMS voltage (Vrms) readings captured by the outage detection device 102. As shown in FIG. 18, a graph 1802 shows the Vrms readings captured by the outage detection device 102 over the course of a day, and a meter 1804 shows low Vrms readings, high Vrms readings, and average Vrms readings. This user interface provides an easy to understand depiction of the power quality at a particular location. This user interface can also include a list 1806 of associated power quality events, such as brownouts or power surges. For example, this user interface shows a power brownout that occurred at 10:40 pm, marked with a yellow triangle on the graph 1802. The server computing device 106 can also generate a historical calendar view of power quality events that have occurred for the power outage detection device 102, as shown in FIG. 19, allowing a user to view a month's worth of power quality events (e.g., brownouts, surges, outages) to get a complete picture of the overall power quality of the electrical network serving their residence.

[0116] In connection with the power quality reports and graphs described above, the server computing device 106 may also generate and transmit power quality notifications to one or more remote computing devices. FIG. 20A is a user interface diagram illustrating a push notification alert sent by the server computing device 106 to a remote device for display. As shown in FIG. 20A, the push alert includes a description of the associated power quality event along with the time the event occurred. Similarly, FIG. 20B is a user interface diagram illustrating a list of power quality notifications related to a power outage detection device. FIGS. 20C, 20D, and 20E are detailed power quality event alert notifications displayed to a user of a remote computing device. FIG. 20C is a power surge notification that provides a user with detailed information regarding the timing, location (e.g., the location where the power outage detection device captured data related to the event), and voltage readings of the surge event. FIG. 20D is a power brownout notification that provides a user with detailed information regarding the timing, location, and voltage readings of the brownout event. FIG. 20E is a recurrent power quality problem notification. and power surge notifications that provide users with detailed information regarding the rhythm and amount of power quality problems occurring in a residential electrical system. As a result, system 100 is configured to automatically generate and rapidly transmit these alerts to relevant users so that they can stay up to date on power quality events affecting their homes or businesses.

[0117] In addition to event notification messages, the systems and methods described herein can generate maps that display the locations of specific power quality events in relation to one another, allowing a user (e.g., a consumer, a grid operator, a utility company) to quickly and easily determine whether a particular geographic area is experiencing a type of power quality event that needs to be resolved.

[0118] FIG. 21A is an illustration of a geographic map generated by the server computing device 106 that plots the locations of power outage events detected by a network of power outage detection devices. As shown in FIG. 21A, each circle on the map represents one or more power outage events, with larger circles indicating more widespread power outages. A user can interact with the map of FIG. 21A (e.g., by clicking on one of the circles) to see a more detailed view of a particular geographic area, as shown in FIG. 21B. In FIG. 21B, a user has selected a power outage event 2102 in the greater Houston, Texas area, and the server computing device 106 has generated a zoomed-in view 2104 of the map that includes a plot 2106 of the location of that particular power outage detection device, and an information area 2108 that includes details about the power outage event, such as the number of customers affected, a summary of readings taken by the power outage detection devices, correlated events 2110, and other power quality data information.

[0119] FIG. 22A is an illustration of a geographic map generated by the server computing device 106 depicting the locations of power grid surge and brownout events detected by a network of power outage detection devices. As shown in FIG. 22A, each square on the map represents one or more power grid surge and / or brownout events, with larger squares indicating more widespread events. A user can interact with the map of FIG. 22A (e.g., by clicking on one of the squares) to see a more detailed view of a particular geographic area, as shown in FIG. 22B. In FIG. 22B, a user has selected a brownout event 2202 in the greater Los Angeles area, and the server computing device 106 has generated a zoomed-in view 2204 of the map including a plot 2206 of the location of that particular power outage detection device, and an information area 2208 containing details about the brownout event, such as the number of customers affected, a summary of readings taken by the power outage detection devices, correlated events 2210, and other power quality data information.

[0120] Similarly, Figure 23 is an illustration of a detailed view of a map depicting power grid sag jump events detected in a particular geographic region (i.e., Los Angeles). As shown in Figure 23, a user has selected a sag jump event 2302 near Los Angeles, and the server computing device 106 has generated a zoomed-in view 2304 of the map including a plot 2306 of the location of that particular outage detection device, and an information area 2308 containing details about the power grid sag jump event, such as the number of customers affected, a summary of readings taken by the outage detection device, correlated events 2310, and other power quality data information.

[0121] 24 is an illustration of a detailed view of a map depicting a power grid swell jump event detected in a particular geographic region (i.e., near San Antonio, Texas). As shown in FIG. 24, a user has selected the swell jump event 2402 near San Antonio, and the server computing device 106 has generated a zoomed-in view 2404 of the map that includes a plot 2406 of the location of that particular outage detection device, and an information area 2408 that includes details about the swell jump event, such as the number of customers affected, a summary of readings taken by the outage detection device, correlated events 2410, and other power quality data information.

[0122] FIG. 25A is an illustration of a geographic map generated by the server computing device 106 depicting the location of a power grid frequency event detected by a network of power outage detection devices. As shown in FIG. 25A, each octagon on the map represents one or more power grid frequency events, with larger octagons indicating more widespread events. As with the maps above, a user can interact with the map of FIG. 25A to see a more detailed view of a particular geographic area, as shown in FIG. 25B. In FIG. 25B, a user has selected a frequency event 2502 in the greater Los Angeles area, and the server computing device 106 has generated a zoomed-in view 2504 of the map including a plot 2506 of the location of that particular power outage detection device, and an information area 2508 containing details about that power outage event, such as the number of customers affected, a summary of readings taken by the power outage detection devices, correlated events 2510, and other power quality data information.

[0123] Figure 26 is an illustration of a geographic map generated by the server computing device 106 that plots the locations of HF filter jump events detected by a network of outage detection devices. As shown in Figure 26, each pentagon on the map represents one or more power grid frequency events, with larger pentagons indicating more widespread events. As with the maps above, a user can interact with the map of Figure 26 to see a more detailed view of a particular geographic area.

[0124] It should be appreciated that the server computing device 106 may generate a map that includes multiple different outage and / or power quality events over the same geographic area. For example, in some embodiments, a user may select multiple different events (e.g., with a checkbox menu) and the server computing device 106 may display each type of event with a different indicator, such as a different shape or color.

[0125] The techniques described above can be implemented in digital and / or analog electronic circuitry, or in computer hardware, firmware, software, or combinations thereof. The implementation may also be as a computer program product, i.e., a computer program tangibly embodied in a machine-readable storage device, for execution by or controlling the operation of a data processing apparatus, such as a programmable processor, a computer, and / or multiple computers. The computer program may be written in any form of computer or programming language, such as source code, compiled code, interpreted code, and / or machine code, and the computer program may be implemented in any form, such as an independent program or as a subroutine, element, or other unit suitable for use in a computing environment. The computer program may be installed and executed on one computer, or on multiple computers in one or multiple locations.

[0126] The method steps may be performed by one or more processors executing a computer program to perform the functions of the technique by manipulating input data and / or generating output data. The method steps may also be performed by, or an apparatus may be implemented by, special purpose logic circuitry, such as, for example, an FPGA (field programmable gate array), an FPAA (field programmable analog array), a CPLD (complex programmable logic device), a PSoC (programmable system on chip), an ASIP (application specific instruction set processor), or an ASIC (application specific integrated circuit). A subroutine may also refer to a portion of a stored computer program and / or a processor and / or special purpose circuitry that performs one or more functions.

[0127] Processors suitable for executing computer programs include, by way of example, both general purpose and special purpose microprocessors, and any one or more processors of any kind of digital or analog computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory, or both. The essential elements of a computer are a processor for executing instructions, and one or more memory devices for storing instructions and / or data. Memory devices, such as a cache, can be used to store data temporarily. Memory devices can also be used for long-term data storage. Generally, a computer includes one or more mass storage devices for storing data, such as, for example, magnetic disks, magneto-optical disks, or optical disks, or is operatively coupled to such mass storage devices to receive data from them, transfer data to them, or both. A computer can also be operatively coupled to a communication network to receive instructions and / or data from the network and / or transfer instructions and / or data to the network. Computer-readable storage media suitable for carrying computer program instructions and data include, by way of example, all forms of volatile and non-volatile memory, including semiconductor memory devices, such as DRAM, SRAM, EPROM, EEPROM, and flash memory devices, magnetic disks, such as internal hard disks or removable disks, optical disks, such as CDs, DVDs, HD-DVDs, Blu-Ray® disks, etc. The processor and the memory may be supplemented by and / or incorporated in special purpose logic circuitry.

[0128] To achieve interaction with a user, the techniques described above can be implemented on a computer in communication with a display device, such as a CRT (cathode ray tube), plasma, or LCD (liquid crystal display) monitor, for displaying information to the user, as well as a keyboard and a pointing device, such as a mouse, trackball, touchpad, or motion sensor, for allowing the user to provide input to the computer (e.g., interact with user interface elements). Other types of devices can also be used to achieve interaction with a user, e.g., feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, such as acoustic input, speech input, and / or tactile input.

[0129] The techniques described above can be implemented in a distributed computing system that includes a back end component. The back end component can be, for example, a data server, a middleware component, and / or an application server. The techniques described above can also be implemented in a distributed computing system that includes a front end component. The front end component can be, for example, a client computer having a graphical user interface, a web browser that allows a user to interact with the exemplary implementation, and / or other graphical user interface for a transmission device. The techniques described above can be implemented in a distributed computing system that includes any combination of such back end, middleware, or front end components.

[0130] The components of a computing system may be interconnected by a transmission medium, which may include any form of medium (e.g., a communications network) for digital or analog data communication. The transmission medium may include one or more packet-based networks and / or one or more circuit-based networks in any configuration. Packet-based networks may include, for example, the Internet, carrier Internet Protocol (IP) networks (e.g., local area networks (LANs), wide area networks (WANs), campus area networks (CANs), metropolitan area networks (MANs), home area networks (HANs)), private IP networks, IP private branch exchanges (IPBXs), wireless networks (e.g., radio access networks (RANs), Bluetooth, Wi-Fi, WiMAX, general packet radio service (GPRS) networks, HiperLANs), and / or other packet-based networks. Circuit-based networks may include, for example, the public switched telephone network (PSTN), a traditional private branch exchange (PBX), a wireless network (e.g., a RAN, a code division multiple access (CDMA) network, a time division multiple access (TDMA) network, a Global System for Mobile Communications (GSM) network), and / or other circuit-based networks.

[0131] The transfer of information over the transmission medium may be based on one or more communication protocols, which may include, for example, Ethernet protocol, Internet Protocol (IP), Voice over IP (VOIP), Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol (HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway Control Protocol (MGCP), Signaling #7 (SS7), Global System for Mobile Communications (GSM) protocol, Push-to-Talk (PTT) protocol, PTT over Cellular (POC) protocol, and / or other communication protocols.

[0132] The devices of the computing system may include, for example, computers, computers with browser devices, telephones, IP telephones, mobile devices (e.g., mobile phones, smart phones, personal digital assistant (PDA) devices, laptop computers, email devices), and / or other communication devices. Browser devices include, for example, computers (e.g., desktop computers, laptop computers) with World Wide Web browsers (e.g., Microsoft® Internet Explorer® from Microsoft Corporation, Mozilla® Firefox from Mozilla Corporation). Mobile computing devices include, for example, iOS®-enabled devices such as the iPhone® and iPad® from Apple, Inc., and Android®-enabled devices such as the Galaxy™ from Samsung Corp., the Pixel® from Google®, Inc., and the Kindle Fire® from Amazon®, inc.

[0133] "Comprising," "including," and / or each plural form are open ended and include the listed parts and potentially additional unlisted parts. "And / or" is open ended and includes one or more of the listed parts, as well as combinations of the listed parts.

[0134] Those skilled in the art will appreciate that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof, and therefore, the foregoing embodiments are to be considered in all respects as illustrative rather than limiting of the invention described herein.

Claims

1. 1. A power outage detection and notification system, comprising: a sensor device coupled to the circuit, transmitting keep-alive packets periodically to a server computing device; Detecting an input signal generated by electrical activity on the circuit; generating an output signal based on the detected input signal; monitoring the generated output signal during each of a plurality of clock cycles having a predetermined duration; During each clock cycle, determining whether a rising edge has occurred in the generated output signal; transmitting a fault packet to said server computing device when a rising edge occurs before a predetermined clock value during said clock cycle or when no rising edge occurs during said clock cycle; Start a new clock cycle The sensor device configured as described above; a server computing device communicatively coupled to the sensor device, receiving the failure packet from the sensor device; listening for one or more keep-alive packets from the sensor device; transmitting a power outage notification to one or more remote computing devices upon not receiving a keep-alive packet from the sensor device for at least a predefined period of time after receiving the failure packet; transmitting a power restored notification to the one or more remote computing devices upon receiving one or more keep-alive packets from the sensor device after transmitting the power loss notification. The server computing device configured as described above. A system comprising:

2. The system of claim 1 , wherein the input signal comprises an alternating current (AC) voltage sine wave having multiple zero crossings.

3. The system of claim 2 , wherein the output signal is a voltage curve having a number of rising edges corresponding to the zero crossings of the input signal.

4. 3. The system of claim 2, wherein the keep-alive packets include power quality data including one or more of any number of measurements of root mean square (RMS) voltage, frequency of a voltage sine wave, relative phase angle of the voltage sine wave, amplitude of harmonics of the voltage sine wave, or high frequency noise amplitude.

5. 2. The system of claim 1, wherein each clock cycle has a default duration of 9 milliseconds.

6. 6. The system of claim 5, wherein said predetermined clock value during said clock cycle is 8.33 milliseconds.

7. 1. A computerized method for power outage detection and notification, comprising: a sensor device coupled to the circuit periodically transmitting a keep-alive packet to a server computing device; detecting, by the sensor device, an input signal generated by electrical activity on the circuit; generating an output signal based on a detected input signal by the sensor device; monitoring an output signal generated by the sensor device during each of a plurality of clock cycles having a predetermined duration; During each clock cycle, the sensor device determining whether a rising edge has occurred in the generated output signal; transmitting a fault packet from the sensor device to the server computing device when a rising edge occurs before a predetermined clock value during the clock cycle or when no rising edge occurs during the clock cycle; the sensor device starting a new clock cycle; receiving, by the server computing device, the failure packet from the sensor device; the server computing device listening for one or more keep-alive packets from the sensor device; transmitting, by the server computing device, a power outage notification to one or more remote computing devices upon not receiving a keep-alive packet from the sensor device for at least a predefined period of time after receiving the failure packet; transmitting, by the server computing device, a power restored notification to the one or more remote computing devices upon receiving one or more keep-alive packets from the sensor device after transmitting the power outage notification; The method includes:

8. The method of claim 7 , wherein the input signal comprises an alternating current (AC) voltage sine wave having multiple zero crossings.

9. The method of claim 8 , wherein the output signal is a voltage curve having a number of rising edges corresponding to the zero crossings of the input signal.

10. 9. The method of claim 8, wherein the keep-alive packets include power quality data including one or more of any number of measurements of root mean square (RMS) voltage, frequency of a voltage sine wave, relative phase angle of the voltage sine wave, amplitude of harmonics of the voltage sine wave, or high frequency noise amplitude.

11. 8. The method of claim 7, wherein each clock cycle has a default duration of 9 milliseconds.

12. 12. The method of claim 11, wherein the predetermined clock value during the clock cycle is 8.33 milliseconds.

13. 1. A power quality detection and notification system, comprising: one or more sensor devices each coupled to the circuit, Detecting an input signal generated by electrical activity on the circuit; generating an output signal based on the detected input signal; transmitting power quality data based on the output signal to a server computing device. the one or more sensor devices each configured as follows: The server computing device, receiving the power quality data from the one or more sensor devices; analyzing the power quality data in conjunction with historical power quality data received from the one or more sensor devices to detect one or more power quality events; Transmitting a power quality notification to one or more remote computing devices based on the detected power quality event. The server computing device configured as described above. A system comprising:

14. 14. The system of claim 13, wherein the one or more detected power quality events include one or more of a surge event, a surge jump event, a sag event, a sag jump event, a brownout event, a swell jump event, a high frequency (HF) filter jump event, a frequency jump event, a recurrent power quality problem, a phase angle jump event, a loose neutral event, or a generator start-up event.

15. 15. The system of claim 14, wherein the server computing device is further configured to: (i) correlate the detected one or more power quality events with zero or more external events; and / or (ii) correlate the detected power quality events from a first sensor device with detected power quality events from one or more other sensor devices.

16. the server computing device: analyzing, for a sensor device, the number and amplitude of surge events, surge jump events, and sag events recorded by the sensor device within a predetermined time period that are uncorrelated with matching power quality events from any other sensor device in the vicinity of the sensor device; generating a loose neutral event when the average number of the surge events is greater than a first predetermined number per day, or when the average number of the surge jump events having a magnitude greater than a predetermined percentage of the nominal voltage is greater than a second predetermined number per day, or when the average number of the sag events is greater than a third predetermined number per day.

16. The system of claim 15, wherein the system detects a loose neutral event by:

17. 16. The system of claim 15, wherein the output signal comprises one or more of any number of measurements of root mean square (RMS) voltage, frequency of a voltage sine wave, relative phase angle of a voltage sine wave, amplitude of a harmonic of a voltage sine wave, or high frequency noise amplitude.

18. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a surge event when the RMS voltage is greater than a predefined threshold percentage of the nominal voltage for a number of consecutive data points; 20. The system of claim 17, wherein the system detects a surge event by:

19. 20. The system of claim 18, wherein the predetermined threshold percentage varies based on the number of consecutive data points for which the RMS voltage is greater than a minimum threshold percentage.

20. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a brownout event when the RMS voltage is less than a predefined threshold percentage of the nominal voltage for a number of consecutive data points; 16. The system of claim 15, wherein the brownout event is detected by:

21. 21. The system of claim 20, wherein the predetermined threshold percentage varies based on the number of consecutive data points for which the RMS voltage is less than a minimum threshold percentage.

22. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a sag jump event for each of one or more drops in RMS voltage greater than a predefined threshold percentage of the nominal voltage occurring in the plurality of consecutive data points; 16. The system of claim 15, wherein the system detects sag jump events by:

23. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a swell jump event for each of one or more rises in RMS voltage greater than a predefined threshold percentage of the nominal voltage occurring in said plurality of consecutive data points; 16. The system of claim 15, wherein the system detects a swell jump event by:

24. the server computing device: Analyzing a plurality of consecutive data points of HF amplitude data from one or more sensor devices; Calculating the average of the HF amplitude data; generating an HF filter jump event when said HF amplitude data increases by more than a threshold number times said average when said average is greater than one; or When the average is less than one, generating an HF filter jump event when the HF amplitude data increases beyond a predefined threshold.

16. The system of claim 15, wherein the system detects an HF filter jump event by:

25. the server computing device: Analyzing a plurality of consecutive data points of frequency data from one or more sensor devices; calculating an average of the frequency data; Calculating the standard deviation of the frequency data; generating a frequency jump event when the frequency increases from said average by more than a predefined threshold; or (i) generating a frequency jump event when the standard deviation changes from below a first frequency to above a second frequency, or (ii) when the standard deviation changes from above the second frequency to below the first frequency.

16. The system of claim 15, wherein the system detects a frequency jump event by:

26. the server computing device: Analyzing, for a sensor device, whether any power outage events and frequency events are detected by the sensor device during a predetermined time period; generating a generator start event when the one sensor device detects a power outage event followed by a change in standard deviation of frequency above a predefined threshold within a predefined time period from the power outage event, the change in standard deviation of frequency not being associated with a correlated external event; 16. The system of claim 15, wherein the system detects a generator start-up event by:

27. The system of claim 15 , wherein the one or more external events include a lightning activity event, a power grid monitoring event, and an energy pricing event.

28. 16. The system of claim 15, wherein the detected power quality event from the first sensor device and the detected power quality event from one or more other sensor devices are of the same event type.

29. 1. A computerized method for power quality detection and notification, comprising: a sensor device coupled to the circuit detecting an input signal generated by electrical activity on the circuit; generating an output signal based on a detected input signal by the sensor device; transmitting, by the sensor device, power quality data based on the output signal to a server computing device; receiving, by the server computing device, the power quality data from the sensor device; analyzing, by the server computing device, the power quality data in conjunction with historical power quality data received from the sensor devices to detect one or more power quality events; transmitting, by the server computing device, a power quality notification to one or more remote computing devices based on the detected power quality event; A method comprising:

30. 30. The method of claim 29, wherein the one or more detected power quality events include one or more of a surge event, a surge jump event, a sag event, a sag jump event, a brownout event, a swell jump event, a high frequency (HF) filter jump event, a frequency jump event, a recurrent power quality problem, a phase angle jump event, a loose neutral event, or a generator start-up event.

31. 31. The method of claim 30, wherein the server computing device further: (i) correlates the detected one or more power quality events with zero or more external events; and / or (ii) correlates the detected power quality events from a first sensor device with detected power quality events from one or more other sensor devices.

32. the server computing device: analyzing, for a sensor device, the number and amplitude of surge events, surge jump events, and sag events recorded by the sensor device within a predetermined time period that are uncorrelated with matching power quality events from any other sensor device in the vicinity of the sensor device; generating a loose neutral event when the average number of the surge events is greater than a first predetermined number per day, or when the average number of the surge jump events having a magnitude greater than a predetermined percentage of the nominal voltage is greater than a second predetermined number per day, or when the average number of the sag events is greater than a third predetermined number per day.

32. The method of claim 31 , further comprising detecting a loose neutral event by:

33. 32. The method of claim 31 , wherein the output signal comprises one or more of any number of measurements of root mean square (RMS) voltage, frequency of a voltage sine wave, relative phase angle of a voltage sine wave, amplitude of a harmonic of a voltage sine wave, or high frequency noise amplitude.

34. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a surge event when the RMS voltage is greater than a predefined threshold percentage of the nominal voltage for a number of consecutive data points; 34. The method of claim 33, wherein a surge event is detected by:

35. 35. The method of claim 34, wherein the predetermined threshold percentage varies based on the number of consecutive data points for which the RMS voltage is greater than a minimum threshold percentage.

36. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a brownout event when the RMS voltage is less than a predefined threshold percentage of the nominal voltage for a number of consecutive data points; 34. The method of claim 33, wherein the brownout event is detected by:

37. 37. The method of claim 36, wherein the predetermined threshold percentage varies based on the number of consecutive data points for which the RMS voltage is less than a minimum threshold percentage.

38. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a sag jump event for each of one or more drops in RMS voltage greater than a predefined threshold percentage of the nominal voltage occurring in the plurality of consecutive data points; 34. The method of claim 33, wherein a sag jump event is detected by:

39. the server computing device: Analyzing a plurality of consecutive data points of the RMS voltage from one or more sensor devices; generating a swell jump event for each of one or more rises in RMS voltage greater than a predefined threshold percentage of the nominal voltage occurring in said plurality of consecutive data points; 34. The method of claim 33, further comprising detecting a swell jump event by:

40. the server computing device: Analyzing a plurality of consecutive data points of HF amplitude data from one or more sensor devices; Calculating the average of the HF amplitude data; generating an HF filter jump event when said HF amplitude data increases by more than a threshold number times said average when said average is greater than one; or When the average is less than one, generating an HF filter jump event when the HF amplitude data increases beyond a predefined threshold.

34. The method of claim 33, further comprising detecting an HF filter jump event by:

41. the server computing device: Analyzing a plurality of consecutive data points of frequency data from one or more sensor devices; calculating an average of the frequency data; Calculating the standard deviation of the frequency data; generating a frequency jump event when the frequency increases from said average by more than a predefined threshold; or (i) generating a frequency jump event when the standard deviation changes from below a first frequency to above a second frequency, or (ii) when the standard deviation changes from above the second frequency to below the first frequency.

34. The method of claim 33, further comprising detecting a frequency jump event by:

42. the server computing device: Analyzing, for a sensor device, whether any power outage events and frequency events are detected by the sensor device during a predetermined time period; generating a generator start event when the one sensor device detects a power outage event followed by a change in standard deviation of frequency above a predefined threshold within a predefined time period from the power outage event, the change in standard deviation of frequency not being associated with a correlated external event; 34. The method of claim 33, further comprising detecting a generator start-up event by:

43. 34. The method of claim 33, wherein the one or more external events include a lightning activity event, a power grid monitoring event, and an energy pricing event.

44. 34. The method of claim 33, wherein the detected power quality event from the first sensor device and the detected power quality event from one or more other sensor devices are of the same event type.

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