Abstract:
A method for power distribution network analysis includes receiving power distribution network event data from a plurality of data sources, filtering the power distribution network event data, normalizing the power distribution network event data, storing the power distribution network event data in an event database and retrieving, in a querying engine, the power distribution network event data.

Description:
BACKGROUND OF THE INVENTION 
     The subject matter disclosed herein relates to power distribution networks, and more particularly to systems and methods for capturing, analyzing and replaying power distribution network events. 
     Power distribution networks can experience several events that include corresponding data from a variety of sources. For example, if there is a blackout in a power distribution network, there may be several corresponding pieces of data such as weather data showing that there was increased electrical storm activity, data from an electrical grid showing that several power transformers broke down, and media data showing that there were several conventions in the area thus increasing power demand on the power distribution network. Often, these data sources are in a variety of formats that have to be individually collected. Manual correlations are then made to make a determination of what data sources contributed to the power distribution network event. 
     BRIEF DESCRIPTION OF THE INVENTION 
     According to one aspect of the invention, a method for power distribution network analysis is described. The method includes receiving power distribution network event data from a plurality of data sources, filtering the power distribution network event data, normalizing the power distribution network event data, storing the power distribution network event data in an event database and retrieving, in a querying engine, the power distribution network event data. 
     According to another aspect of the invention, a computer program product including a non-transitory computer readable medium storing instructions for causing a computer to implement a method for power distribution network analysis is described. The method includes receiving power distribution network event data from a plurality of data sources, filtering the power distribution network event data, normalizing the power distribution network event data, storing the power distribution network event data in an event database and retrieving, in a querying engine, the power distribution network event data. 
     According to yet another aspect of the invention, a system for power distribution network analysis is described. The system includes a querying engine, an event database communicatively coupled to the querying engine, a normalization layer coupled to the event database and configured to transform a plurality of data structure types to a single data structure type and a plurality of event filters coupled to the normalization layer and configured to retrieve relevant power distribution network event data from a plurality of data sources. 
     These and other advantages and features will become more apparent from the following description taken in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
       The subject matter, which is regarded as the invention, is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  diagrammatically illustrates an exemplary system for power distribution network event analysis; 
         FIG. 2  illustrates a method for a power distribution network analysis in accordance with exemplary embodiments; and 
         FIG. 3  illustrates an exemplary embodiment of a system that can be implemented for analyzing power distribution network events as described herein. 
     
    
    
     The detailed description explains embodiments of the invention, together with advantages and features, by way of example with reference to the drawings. 
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  diagrammatically illustrates an exemplary system  100  for power distribution network event analysis. The system  100  includes a variety of data sources  105 . The data sources  105  are potentially unlimited. However, typical sources include power distribution network data, weather data (e.g., temperature, wind direction and speed, barometric pressure), space weather data (e.g., solar flares), catastrophe data (e.g., earthquake information), media data and the like. It will be appreciated that the systems and methods described herein contemplate receiving any data source with any format, which can be related to power distribution network events. Power distribution events include but are not limited to: blackouts, switch states (i.e., open/close), and load values that exceed a predetermined threshold value. In exemplary embodiments, the power distribution network can be any electrical distribution system for the delivery of electricity to consumers. As such, the power distribution network can include, but is not limited to, power generation plants, transmission lines, transformers, substations, demand centers and the like. The input data from the data sources  105  may also come from different power distribution systems, so the analytical capabilities of the system  100  go beyond the analysis of events within a single power distribution network, being able to correlate events across power distribution networks. Additionally, event correlation is performed based on any number of different parameters beyond time. 
     The system  100  further includes event filters  110  for each of the data sources  105 . The event filters  110  are configured to select events in the power distribution network that may be beyond certain predetermined thresholds. In exemplary embodiments, the event filters  110  are aware of the normal (predetermined) range of input values at a particular point in time (e.g., temperature values for a particular place at a particular time of the year). Depending on the input data source  105 , the corresponding event filter  110  may be more elaborate. For example, the event filter  110  can apply rules-based processing, patterns-based analysis, Bayesian analysis and the like. The event filter  110  may also be more generic such as a neural network-based filter. 
     The system  100  can further include a normalization layer  115  coupled to the event filters  110  and configured to receive all of the data from the data sources  105  and correspondingly filtered by the event filters  110 . The normalization layer  115  is configured to transform the various data types into a single data format that can then be stored in an event database (event store)  120 . As such, the normalization layer  115  can be a dynamically updated layer that is adaptable to any data (structure) type. It will be appreciated that the normalization layer  115  can be initially programmed to convert various known data types from the data sources  105 . As new data sources are identified, the normalization layer  115  can be appropriately updated to receive and convert the new data type to the normalized data type that is stored in the event database  120 . As such, the system  100  consumes vast amounts of data and data types from a multitude of sources that identify power distribution network events represented by data that falls out of the “normal” range, and stores those events in the event database  120 . 
     The system  100  further includes a querying engine  125  coupled to the event database  120  and configured to extract the normalized data from the event database  120 . In exemplary embodiments, the querying engine  125  is configured to access information such as but not limited to: data values and ranges; events similar to a particular current event; and time data. With regard to data values and ranges, the querying engine  125  can be implemented to retrieve all data values and ranges for all of the data sources  105  related to a particular power distribution network event or a particular point in time. In addition, as the event database  120  grows and evolves, it can keep a history of power distribution network events and the data related to those historic power distribution network events. As such, the querying engine  125  can be implemented to retrieve events that are similar to a particular event. For example, events that are surrounded by a set of input data from the data sources  105  that matches a particular event are given matching criteria. In this way, a user can input criteria into the querying engine  125  in order to retrieve historic events. The user can then use this retrieved data to analyze a current event that is similar to the historic event and thereby save time and effort in figuring out how to manage the current event. For example, if there is going to be a large city-wide convention in a given area, a user can query the event database  120  to determine if there was a similar city-wide convention, and any resulting power distribution network events that occurred during the convention. In this way, the user can take action or preemptive measures to avoid a similar power distribution network event. The querying engine  125  is also configured to retrieve all input (time) data (or ranges) for all time points (given a certain granularity) for a particular time period. In this way, a user can analyze a given time period and corresponding data from the data sources  105  during the given time period. 
     In exemplary embodiments, the querying engine  125  can be coupled to various interfaces in order to access the event database  120  for analysis. For example, a user interface  130  can be coupled to the querying engine  125  to provide basic querying functionality in order to query the event database  120 . The querying engine  125  can include various types of analysis tools  135 , which can be various software interfaces, for example, to obtain system state information surrounding any particular event or any particular point in time. The analysis tools  135  can therefore enable the querying engine  125  to use advanced analysis applications to perform root-cause analyses of events, to perform correlation analyses, to generate prediction models, to drive simulations, to rerun events, to be the source of semantic-based queries and the like. A semantic interface  140  can also be coupled to the querying engine  125 . The semantic interface  140  can be any layered, open architecture providing device-independent access to the event database  120 . As such, any suitable external system  145  can be used, such as but not limited to computing devices, mobile devices, smart devices and the like. The various interfaces described herein, which are only illustrative and can include various other interfaces, provide visualization and analysis of all circumstances surrounding a particular power distribution network event. The system  100  therefore enables sophisticated forensic analysis of power distribution network events, which allows root-cause analyses, prediction models and power distribution network planning. 
       FIG. 2  illustrates a method  200  for power distribution network analysis in accordance with exemplary embodiments. At block  205 , the system receives event-related data from the data sources  105 . At block  210 , the event filters  110  filter the event-related data to retrieve the relevant data for power distribution network events. At block  215 , the relevant power distribution network event data is normalized at the normalization layer  115  as described herein. At block  220 , the normalized relevant data for power distribution network events is stored in the event database  120  as described herein. At block  225 , the data stored in the event database  120  is retrieved by the querying engine  125  for dissemination to a variety of interfaces. As described herein, the data from the event database can be retrieved via a variety of interfaces. In addition, at block  230 , the data from the event database  120  can be analyzed and processed in a variety of ways as described herein. 
     The querying engine  125  and the external system  145  described herein can implement any suitable computing device as now described.  FIG. 3  illustrates an exemplary embodiment of a system  300  that can be implemented for analyzing power distribution network events as described herein. The methods described herein can be implemented in software (e.g., firmware), hardware, or a combination thereof. In exemplary embodiments, the methods described herein are implemented in software, as an executable program, and are executed by a special or general-purpose digital computer, such as a personal computer, workstation, minicomputer, or mainframe computer. The system  300  therefore includes general-purpose computer  301 . 
     In exemplary embodiments, in terms of hardware architecture, as shown in  FIG. 3 , the computer  301  includes a processor  305 , a memory  310  coupled to a memory controller  315 , and one or more input and/or output (I/O) devices  340 ,  345  (or peripherals) that are communicatively coupled via a local input/output controller  335 . The input/output controller  335  can be, but is not limited to, one or more buses or other wired or wireless connections, as is known in the art. The input/output controller  335  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  305  is a hardware device for executing software, particularly that stored in the memory  310 . The processor  305  can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computer  301 , a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, or generally any device for executing software instructions. 
     The memory  310  can include any one or a combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), tape, compact disc read only memory (CD-ROM), disk, diskette, cartridge, cassette or the like, etc.). Moreover, the memory  310  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  310  can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processor  305 . 
     The software in the memory  310  may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. In the example of  FIG. 3 , the software in the memory  310  includes the power distribution network event analysis methods described herein in accordance with exemplary embodiments and a suitable operating system (OS)  311 . The OS  311  essentially controls the execution of other computer programs, such as the power distribution network event analysis systems and methods as described herein, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. 
     The power distribution network event analysis methods described herein may be in the form of a source program, executable program (object code), script, or any other entity comprising a set of instructions to be performed. When a source program, then the program needs to be translated via a compiler, assembler, interpreter, or the like, which may or may not be included within the memory  310 , so as to operate properly in connection with the OS  311 . Furthermore, the power distribution network event analysis methods can be written as an object oriented programming language, which has classes of data and methods, or a procedure programming language, which has routines, subroutines, and/or functions. 
     In exemplary embodiments, a conventional keyboard  350  and mouse  355  can be coupled to the input/output controller  335 . Other output devices such as the I/O devices  340 ,  345  may include input devices, such as but not limited to a printer, a scanner, a microphone, and the like. Finally, the I/O devices  340 ,  345  may further include devices that communicate both inputs and outputs, such as but not limited to a network interface card (NIC) or modulator/demodulator (for accessing other files, devices, systems, or a network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, and the like. The system  300  can further include a display controller  325  coupled to a display  330 . In exemplary embodiments, the system  300  can further include a network interface  360  for coupling to a network  365 . The network  365  can be an IP-based network for communication between the computer  301  and any external server, client and the like via a broadband connection. The network  365  transmits and receives data between the computer  301  and the external systems. In exemplary embodiments, the network  365  can be a managed IP network administered by a service provider. The network  365  may be implemented in a wireless fashion, e.g., using wireless protocols and technologies, such as WiFi, WiMax, etc. The network  365  can also be a packet-switched network such as a local area network, wide area network, metropolitan area network, Internet network, or other similar type of network environment. The network  365  may be a fixed wireless network, a wireless local area network (LAN), a wireless wide area network (WAN), a personal area network (PAN), a virtual private network (VPN), an intranet or other suitable network system and includes equipment for receiving and transmitting signals. 
     If the computer  301  is a PC, workstation, intelligent device or the like, the software in the memory  310  may further include a basic input output system (BIOS) (omitted for simplicity). The BIOS is a set of essential software routines that initialize and test hardware at startup, start the OS  311 , and support the transfer of data among the hardware devices. The BIOS is stored in ROM so that the BIOS can be executed when the computer  301  is activated. 
     When the computer  301  is in operation, the processor  305  is configured to execute software stored within the memory  310 , to communicate data to and from the memory  310 , and to generally control operations of the computer  301  pursuant to the software. The power distribution network event analysis methods described herein and the OS  311 , in whole or in part, but typically the latter, are read by the processor  305 , perhaps buffered within the processor  305 , and then executed. 
     When the systems and methods described herein are implemented in software, as is shown in  FIG. 3 , the methods can be stored on any computer readable medium, such as storage  320 , for use by or in connection with any computer related system or method. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     In exemplary embodiments, where the power distribution network event analysis methods are implemented in hardware, the power distribution network event analysis methods described herein can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc. 
     Technical effects include providing a single normalized source of information for visualization and analysis tools. 
     While the invention has been described in detail in connection with only a limited number of embodiments, it should be readily understood that the invention is not limited to such disclosed embodiments. Rather, the invention can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described, but which are commensurate with the spirit and scope of the invention. Additionally, while various embodiments of the invention have been described, it is to be understood that aspects of the invention may include only some of the described embodiments. Accordingly, the invention is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.