The present invention relates generally to electrical power monitoring systems and, more particularly, to management systems for power monitoring systems containing a multiplicity of intelligent electronic devices (IEDs).
In a any software system (such as SCADA, HMI or DCS) that has to communicate with IEDs (devices), a tag (a description of how to read/write and format the data from a device) must be defined for every measurement that the software expects to read and write to an IED. This process is time-consuming and increases the time a systems integrator takes to install and commission a large software system. The more powerful a device's features/capabilities, the more tags/measurements that need to be defined and entered for use by the software.
The more sophisticated the capabilities of an IED (device), the more difficult it is to properly utilize the IED within a particular customer's system. Often how a device is used is very specific to the problems or issues that a customer is trying to solve, or around the market segment in which they are deployed. It is becoming increasingly difficult to find personnel that are experienced in a device's capabilities, and in the various market segments in which a particular device might be deployed. This lack of experienced personnel can result in poorly designed and implemented software solutions that do not truly meet the needs of the customer.
It is often difficult to do analysis on alarms and measurement data because the available information is insufficient. Often the creators of reports and/or analytical tools need to collect the required information after the fact, which requires extensive labor-intensive work.
According to one embodiment, a method of managing a power monitoring and control informational system that encompasses multiple intelligent electronic devices (IEDs) of varying types comprises
This invention is capable of significantly reducing the setup time for a SCADA project, especially when the project includes a variety of different devices. Specifically, setup time is reduced by creating a library of profiles and then, after automatically detecting all the IEDs in a system and creating a communication hierarchy, automatically applying the appropriate profile based on the type of device found and/or the market segment encountered. Using spatial alignment algorithms, a power distribution hierarchy can be created without the user having to enter all the devices' communication information.
Thus, the system capitalizes on the existing knowledge of an experienced engineer or market expert. These experts can create profiles based on their knowledge of devices and/or market segments, and these profiles can then be reused by less experienced personnel. Once created, the profiles can be used to both communicate with devices and enhance the user's ability to do analysis and reporting without any additional expert knowledge.
The system also reduces user errors. The profiles can be tested before they are stored in the library, and then they can be reused without repeating the testing. Automatic detection of the correct communication routing eliminates user errors that can occur during manual entry of such data. Automatic application of profiles also eliminates such user errors.
The invention may best be understood by reference to the following description taken in conjunction with the accompanying drawings, in which:
Although the invention will be described in connection with certain preferred embodiments, it will be understood that the invention is not limited to those particular embodiments. On the contrary, the invention is intended to cover all alternatives, modifications, and equivalent arrangements as may be included within the spirit and scope of the invention as defined by the appended claims.
Turning now to
As depicted in
The data from all the IEDs 102 is aligned automatically in temporal or pseudo-temporal context in the automated data alignment system 112, which produces data that is temporally or synchronously aligned such that it represents the data when it was actually seen simultaneously by the IEDs in the power monitoring system 100. The hierarchy classification system 113 automatically learns the hierarchy of monitoring devices present in the power monitoring system 100 and their positional relationships relative to one another. Examples of the hierarchy classification system 113 and the auto-learned hierarchy algorithms associated therewith are described in commonly assigned U.S. Pat. No. 7,272,518, titled “Automated Hierarchy Classification in Utility Monitoring Systems,” issued Sep. 18, 2007, and in commonly assigned PCT Patent Application No. PCT/US2006/034394, titled “Improvements in Hierarchy Determination for Power Monitoring Systems,” filed Nov. 5, 2007. A hierarchy as used herein includes a series of ordered groupings of things within a system. These relationships may be physical (based on a power system one-line diagram for example) or functional (based on cost centers or other organizational divisions). In an electrical power system context, a hierarchy describes the organization of the electrical power system (whether utility-side or demand-side of the point-of-common coupling (PCC)). As used herein, an “auto-learned hierarchy algorithm” refers to any of the auto-learned hierarchy algorithms disclosed in U.S. Pat. No. 7,272,518.
Each IED 102 measures characteristics of the power monitoring system 100, and quantifies these characteristics into data that can be analyzed by a computer. For example, the monitoring device may measure power, energy, voltage, current, or other characteristics of electricity. In the electrical context, the IED may be based on a PowerLogic® Series 3000/4000 Circuit Monitor or a PowerLogic® ION7550/7650 Power and Energy Meter available from Schneider Electric or any other suitable IED device such as a microprocessor-based circuit breaker, relay, metering device, or power meter.
Generally, the hierarchy classification system 113 utilizes an auto-learned hierarchy algorithm in the monitoring system software that is based on rules and statistical methods. Periodically, the monitoring system software polls each monitoring device in the power monitoring system 100 to determine certain characteristics or parameters of the power monitoring system 100 at that node (represented by an IED). Multiple samples of specified parameters are taken from each IED in the system at the same given point in time (synchronous) or at substantially the same point in time (pseudo-synchronous). Once the parameter data is collected from each IED in the power monitoring system 100, the auto-learned hierarchy algorithm analyzes the data and traces the relationships or links among the monitoring devices with respect to the time the data sample was taken and the associated value of the data sample. This analysis may be performed periodically to increase the probability that the hierarchy is accurate, or to ascertain any changes in the hierarchy. Once this iterative process reaches some predetermined level of statistical confidence that the determined layout of the power monitoring system 100 is correct, the auto-learned hierarchy algorithm ends. The final layout of the power monitoring system 100 is presented to the user for concurrence. As each IED's data is evaluated over time (the learning period) with respect to all other IEDs using the auto-learned hierarchy algorithm, a basic layout of the hierarchical structure of the power monitoring system 100 is determined based on the monitoring points available. In this respect, the auto-learned hierarchy algorithm uses historical trends of the data from each IED, and those trends are compared to determine whether any interrelationship (link) exists between the IEDs. A more detailed hierarchical structure can be determined with more monitoring points available for analysis.
Samples of specific electrical characteristics (such as power, voltage, current, or the like) are simultaneously taken from each IED 102 in the power monitoring system 100. This data is stored and analyzed with respect to the time the sample is taken, the associated value of the data point, and the IED 102 providing the data. Data taken from each IED 102 is compared with each other to determine whether any correlation exists between the IEDs. The data is analyzed for statistical trends and correlations as well as similarities and differences over a predetermined period of time.
The data alignment system 112 aligns data, such as power, voltage, current, time, events, and the like, from the multiple IEDs 102 in the power monitoring system 100. When data from all the IEDs 102 is aligned to the same point (or approximately the same point based on pseudo-temporal alignment) in time that the data occurred, the data can be put into a temporal (synchronous) or pseudo-temporal (pseudo-synchronous) context from which additional decisions regarding hardware and software configuration can be automatically made or recommended. The measured data from various IEDs may be synchronized or pseudo-synchronized with each other within a temporal or pseudo-temporal context. Temporal alignment is more precise than pseudo-temporal alignment. As used herein, temporal is synonymous with synchronous. Pseudo-temporal alignment takes data within an acceptable range based on load changes in the system. Pseudo-temporal alignment systems typically utilize a global positioning system (GPS) or network time protocol (NTP) for clock synchronization. Automatic temporal alignment implementations are described in commonly assigned U.S. patent application Ser. No. 11/174,099, filed Jul. 1, 2005, titled “Automated Precision Alignment of Data in a Utility Monitoring System.” In an automatic temporal alignment implementation, the data alignment system 112 synchronously aligns all IEDs 102 in an electrical system hierarchy to the zero-crossing of all three phase voltages without the use of additional hardware, notwithstanding potential phase shifts between various IEDs, such as for example, those caused by certain transformer configurations. When the data of the monitoring devices is aligned synchronously with each other, the system data is essentially aligned with respect to the time it occurred, making more complex data analyses feasible.
An exemplary IED 102 is shown as a functional block diagram in
Instructions from a computer are received by the IED 102 via the communications interface 123. Those instructions may include instructions that direct the controller 120 to mark the cycle count, to begin storing electrical parameter data, or to transmit to the computer 127 electrical parameter data stored in the memory 122. The electrical parameter data can include any data acquired by IEDs, including any combination of power, energy, current, voltage, frequency variations, amplitude variations, and phase variations.
The IED detection system 111 automatically detects the IEDs 102 in the power monitoring system 100. For example, the IED detection system 111 automatically discovers configuration or identification information published by a local computer (publisher) connected to the IEDs 102 in the power monitoring system 100 and to a server that determines whether to approve data packets sent by the publisher. The publisher attempts to send a data packet including identification information to the server, which throws an exception with error code(s) indicating which information is missing in order for the server to approve the pending data transfer. The publisher interprets the error code(s) and sends back to the server the missing information, which may be identification information, configuration information, or both. This process of the server throwing an exception and the publisher attempting to send data packets or configuration information again may iterate more than once until the server approves the publisher, the device, and all topic information associated with the device and accepts the data for storage in the server database. Systems and methods that automatically detects IEDs are described in commonly assigned U.S. Published Application No. 2007-0263643 A1, titled “TRANSFER OF ELECTRICAL DATA WITH AUTO-DISCOVERY OF SYSTEM CONFIGURATION,” dated Nov. 15, 2007.
Another example of how the IED detection system 111 automatically detects the IEDs 102 in the power monitoring system 100 is described in commonly assigned U.S. Published Application No. 2009-0287803 A1, titled “AUTOMATED DISCOVERY OF DEVICES IN LARGE UTILITY MONITORING SYSTEMS,” filed May 13, 2008. The IED detection system 111 includes an auto-discovery algorithm that attempts a scattered-read of register addresses of an IED 102 in the power monitoring system 100 whose identity is unknown. If the scattered-read is successful, data from the IED 102 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 IED 102 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 IED 102 for a later retry scan, and moves on to attempt to discover the next IED 102.
The data integrated monitoring system 100 also includes an IED configuration system 114 that automatically configures the power monitoring system 100 based upon the locations of the IEDs 102 in a hierarchy representing the spatial interrelationships of the IEDs. The software applications 110 on a host computer may execute any or all of an alarm aggregation algorithm for aggregating multiple alarms based on device location; a feature distribution algorithm for enabling/disabling selected device features; an evaluation algorithm for evaluating device applications; a device check algorithm for detecting flawed data; a custom configuration algorithm for customized configuration of thresholds on device-by-device basis; a host computer configuration algorithm for configuring the host computer; a redundancy algorithm for verifying an electrical event; an alarm configuration algorithm for configuring device thresholds; and a configuration error checking algorithm for detecting nomenclature issues. These algorithms are described in more detail in commonly assigned U.S. application Ser. No. 11/900,262, titled “AUTOMATED CONFIGURATION OF A POWER MONITORING SYSTEM USING HIERARCHICAL CONTEXT,” filed Sep. 11, 2007.
Creating or Editing Device Types
When a new IED is to be commissioned, the user accesses the main page of the user interface 116, as shown in
The default tab selected and displayed in
If the system has already been populated with IEDs and the user selects a Device Type Name that already exists in the system, the tags associated with the selected Device Type Name are listed under the tabs “Real Time Tags,” “Onboard Alarm Tags,” “Control Tags,” and “Reset Tags.” If the selected device type has been “Locked,” as indicated by the “Locked” icon in
Both options (1) and (2) from the main screen ultimately result in the user clicking on the “Add/Edit” button, which begins the execution of step 400 in a routine illustrated by the flow chart in
If the user chooses the “Create From” option, the routine proceeds to step 402 which generates a display containing a pull-down menu labeled “Device Type to Create From” that allows the user to select an existing device type from which to create the new device type, as seen in
Returning to step 401 and
Step 407 requires the user to check if any tags necessary for the current device type are missing from the “IEC Tags” list. If any necessary tags are not already available in the “IEC Tags” list, the user clicks on the “Add/Edit Custom Tags” button at the bottom of the “Add/Edit Device Type” screen (
Step 409 in the routine of
Step 412 of the routine in
Returning to the beginning of the routine shown in
Creating Device Profiles
When a new device profile is to be created, the user clicks on the “Create Device Profiles” tab on the main page of the user interface, as shown in
Both options (1) and (2) ultimately result in the user clicking on the “Add/Edit” button, which begins the execution of step 800 in a routine illustrated by the flow chart in
If the user chooses option (2) by selecting the “Create From” option, the routine of
The user then proceeds to step 803 of the routine of
Once the user has completed step 803 of the routine in
After checking all necessary tags, the user completes the routine of
Returning to
Returning to the beginning of the routine shown in
While particular embodiments and applications of the present invention have been illustrated and described, it is to be understood that the invention is not limited to the precise construction and compositions disclosed herein and that various modifications, changes, and variations may be apparent from the foregoing descriptions without departing from the spirit and scope of the invention as defined in the appended claims.
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