Abstract: An auto-discovery algorithm attempts a scattered-read of register addresses of a device in a utility monitoring system whose identity is unknown to a computer system. If the scattered read is successful, and data from the device includes a device ID code that is matched against a lookup table of devices. If unsuccessful, the algorithm attempts a block-read, and if unsuccessful, iteratively checks each register against the lookup table to determine whether a match exists until either one is found or the device reports an illegal data address exception, whereupon the algorithm stops attempting to read from subsequent addresses. The algorithm analyzes the response from the communications driver of the computer system to determine whether the response is valid, and if not, what type of exception is reported. If a timeout occurs, the algorithm flags the device for a later retry scan, and moves on to attempt to discover the next device.

Abstract: A method for automatically determining how monitoring devices in an electrical system having a main source of energy and at least one alternative source of energy (e.g., another utility source, a generator, or UPS system) are connected together to form a hierarchy. The end-user inputs identification information about the monitoring device(s) monitoring the alternative source of energy. The method receives time-series data from the monitoring devices and determines a model type of the electrical system by analyzing the monitoring device's time-series data. Once the model type is known, the method builds the complete monitoring system hierarchy in which the monitoring devices that are monitoring the main and alternative sources are placed properly. The method can also validate polarity nomenclature of the time-series data to account for end-user's varying polarity configurations.

Abstract: A voltage analysis algorithm for automatically determining anomalous voltage conditions in an electrical system monitored by a plurality of intelligent electronic devices (IEDs) and automatically making recommendations for ameliorating or eliminating the anomalous voltage conditions. The electrical system hierarchy is determined automatically or manually, and the algorithm receives voltage data from all capable IEDs. The voltage data is temporally aligned or pseudo-aligned to place the voltage data in both spatial and temporal context. The algorithm determines anomalous voltage conditions systemically by comparing measured voltage values against nominal or expected ones across the system. Based on the spatial and temporal context of the IEDs, the algorithm automatically identifies a source of the voltage deviation in the hierarchy, and recommends a modification associated with the source for mitigating the anomalous voltage condition.

Abstract: A process monitoring method that aggregates monitoring devices and optionally sensors into one or more groups that are each related to a process of a utility system. The monitoring devices are organized into a monitoring system hierarchy manually or automatically. A process algorithm determines from the hierarchy which monitoring devices are connected to a load. Monitored data from load-connected monitoring device pairs are correlated to produce a correlation coefficient that is compared against a correlation threshold selected between 0 and 1. When the correlation coefficient exceeds the threshold, the device pair is grouped into a process group. Other device pairs exceeding the threshold are likewise grouped into the process group. Multiple processes may be determined with the process algorithm. Sensors may also be grouped manually with the process group containing monitoring devices, which may include virtual monitoring devices.

Abstract: A system and method to evaluate characteristics of transient events in an electrical power system to determine the location of a transient source type, the source type of a transient event, and suggested mitigation measures that might reduce or eliminate the effects of the transient. The electrical power system has a plurality of monitoring devices that each may detect the occurrence of a transient event incident upon the power system. A controller is coupled to the monitoring devices. The monitoring devices measure signal parameter values associated with the transient event, and the location of the transient event is automatically determined by evaluating transient characteristics from each of the recording monitoring devices. Other measurements of signal parameters may be made to determine transient characteristics.

Abstract: A method and system to detect and evaluate harmonic distortion in an electrical power system is disclosed. A plurality of monitoring devices is installed in the electrical power system. The hierarchy of the electrical power system is received. Data indicative of harmonic distortion on the electrical power system is received from at least two of the monitoring devices. The data indicative of harmonic distortion is automatically aligned in a temporal or pseudo-temporal context. An electrical characteristic of the harmonic distortion from the data indicative of harmonic distortion is determined.

Abstract: A power monitoring system for monitoring characteristics of power transmitted through one or more power lines comprises a meter base and multiple option modules. The meter base includes a processor and associated circuitry for processing signals derived from sensors coupled to said power lines and producing output signals representing selected characteristics of the power transmitted through the power lines, and a housing containing the processor and the associated circuitry and having a first surface adapted to be mounted on a DIN rail, and a second surface containing multiple connectors for receiving multiple modules and electrically connecting the modules to the processor and the associated circuitry.

Abstract: A time synchronization device (TSD) that produces a synchronization signal and couples it onto energized power conductors in a power monitoring system. Monitoring devices coupled to the TSD include frequency detection algorithms, such as a Goertzel filter, for detecting the synchronization signal and interpreting the information encoded in the signal. The frequency of the synchronization signal may correspond to the fourth or tenth harmonic component of the fundamental frequency of the voltage on the power conductors. The magnitude of the signal is selected to be above the expected or established noise floor of the power monitoring system plus a predetermined threshold. The duration of the signal can be varied, such as lasting a full cycle of the fundamental frequency. Multiple TSD signals received in a predetermined sequence may be converted into digital words that convey time, configuration, reset, control, or other information to the monitoring device.

Abstract: A method and system to detect and evaluate improper grounding/grounded bonds in an electrical power system is disclosed. An example method is detecting improper grounding in an electrical power system having a plurality of monitoring devices coupled to a grounded conductor and a grounding conductor. Data of the voltage between the grounded conductor and the grounding conductor is received from the plurality of monitoring devices. The spatial orientation of the data from the plurality of monitoring devices is determined within a hierarchy of the electrical power system. The voltage data received from the plurality of monitoring devices is compared to determine a bond (or lack of a bond) between the grounded conductor and the grounding conductor.

Abstract: A system and method to evaluate characteristics of notches in sine wave type electrical signals. An example method includes measuring a voltage waveform. A reference waveform is derived from the measured voltage waveform. A series of threshold voltage values is determined based on corresponding voltages of the reference waveform. The voltages of the voltage waveform are compared with the corresponding threshold voltages. The presence of a notch is indicated when a voltage of the voltage waveform is lesser in magnitude than the corresponding threshold voltage.

Abstract: A system and method to evaluate characteristics of transient events in an electrical power system to determine the location of a transient source type, the source type of a transient event, and suggested mitigation measures that might reduce or eliminate the effects of the transient. The electrical power system has a plurality of monitoring devices that each may detect the occurrence of a transient event incident upon the power system. A controller is coupled to the monitoring devices. The monitoring devices measure signal parameter values associated with the transient event, and the location of the transient event is automatically determined by evaluating transient characteristics from each of the recording monitoring devices. Other measurements of signal parameters may be made to determine transient characteristics.

Abstract: An automated integrated monitoring (IM) algorithm that automatically puts data from a utility monitoring system into context by temporally aligning the data to a common reference point and by identifying the location of each monitoring device in a hierarchy relative to other devices. Frequency variation data is received from all meters. The data is automatically aligned to a common reference point, such as a precise zero crossing, using a cross-correlation algorithm to determine the time delay at which the data is most correlated. Once the data is aligned, power data is received from all meters in a hierarchy, and the monitoring system layout is auto-learned using a correlation algorithm to determine which two meters are most likely correlated with one another based upon their historical power readings. Once the layout is complete, additional decisions regarding hardware and software configuration can automatically be made by the IM algorithm.

Abstract: A noisy data alignment algorithm for determining cycle count offsets for noisy pairs of n monitoring devices. A direct cycle count offset matrix is determined based upon the highest correlation coefficients produced by correlating frequency variation data from each device pair Dij. For each direct cycle count offset Mij, indirect cycle count offsets are calculated as a function of at least Mk, where k?i?j, to produce indirect cycle count offsets. The statistical mode of these indirect offsets is compared with the corresponding Mij in the matrix. When they differ, Mij in the direct matrix is adjusted to be equal to the statistical mode. All indirect cycle count offsets for all other unique device pairs, Mij, are calculated to iterate to a single solution in which all indirect cycle count offsets are equal to the corresponding direct cycle count offset. An optional verification algorithm is also provided.

Abstract: An auto-learned hierarchy algorithm that learns the hierarchical layout of a power monitoring system. Historical power data from each meter is received and placed into a data table. The main is assumed to be at the top of the hierarchy and is designated as the reference. A check matrix is developed indicating whether a possible connection exists between each meter pair combination. A correlation coefficient matrix (CCM) is calculated based on the data table, and entries in which no connection is possible are zeroed. The column for the reference meter from the CCM is copied to a correlation reference array (CRA), and the meter having the highest correlation with the reference meter in the CRA is marked as connected to the reference meter in a connection table. That meter's power is subtracted from the data table and the procedure is repeated until all meters have been analyzed.

Abstract: A method and system to detect and evaluate harmonic distortion in an electrical power system is disclosed. A plurality of monitoring devices is installed in the electrical power system. The hierarchy of the electrical power system is received. Data indicative of harmonic distortion on the electrical power system is received from at least two of said plurality of monitoring devices. The data indicative of harmonic distortion is automatically aligned in a temporal or pseudo-temporal context. An electrical characteristic of the harmonic distortion from the data indicative of harmonic distortion is determined.

Abstract: A data alignment algorithm that automatically aligns data from multiple monitoring devices to the same zero-crossings at the same point in time. Cycle-by-cycle frequency data is received from each monitoring device and a cross-correlation algorithm is performed to determine a correlation coefficient between a reference monitoring device and another monitoring device. The data of the other monitoring device is shifted by one cycle and another correlation coefficient is calculated by the cross-correlation algorithm. The data of the two monitoring devices is aligned at the point at which the maximum correlation coefficient is calculated or the point at which the correlation coefficient exceeds a threshold value. The clocks of the monitoring devices can also be synchronized at the same point of alignment.

Abstract: An automated integrated monitoring (IM) algorithm that automatically puts data from a utility monitoring system into context by temporally aligning the data to a common reference point and by identifying the location of each monitoring device in a hierarchy relative to other devices. Frequency variation data is received from all meters. The data is automatically aligned to a common reference point, such as a precise zero crossing, using a cross-correlation algorithm to determine the time delay at which the data is most correlated. Once the data is aligned, power data is received from all meters in a hierarchy, and the monitoring system layout is auto-learned using a correlation algorithm to determine which two meters are most likely correlated with one another based upon their historical power readings. Once the layout is complete, additional decisions regarding hardware and software configuration can automatically be made by the IM algorithm.

Abstract: An auto-learned hierarchy algorithm that learns the hierarchical layout of a power monitoring system. Historical power data from each meter is received and placed into a data table. The main is assumed to be at the top of the hierarchy and is designated as the reference. A check matrix is developed indicating whether a possible connection exists between each meter pair combination. A correlation coefficient matrix (CCM) is calculated based on the data table, and entries in which no connection is possible are zeroed. The column for the reference meter from the CCM is copied to a correlation reference array (CRA), and the meter having the highest correlation with the reference meter in the CRA is marked as connected to the reference meter in a connection table. That meter's power is subtracted from the data table and the procedure is repeated until all meters have been analyzed.

Abstract: A system for accumulating and evaluating electromagnetic phenomena of at least one power quality category of a power distribution system, using a circuit monitor or processing system that summarizes and trends electromagnetic phenomena. The system provides for expressing the power quality index as a single number for each power quality category present and for combining multiple power quality indices into a single overall power quality index.

Abstract: A system for accumulating and evaluating electromagnetic phenomena of at least one power quality category of a power distribution system, using a circuit monitor or processing system that summarizes and trends electromagnetic phenomena. The system provides for expressing the power quality index as a single number for each power quality category present and for combining multiple power quality indices into a single overall power quality index.