System and method for asset health monitoring using multi-dimensional risk assessment

A power distribution network includes a plurality of power source nodes and component nodes, which direct power from the plurality of power source nodes to a plurality of load nodes; a plurality of sensors positioned to sense power flow information for the plurality of power source and component nodes; and a data warehouse housing the power flow information and diagnostic data for the plurality of power source and component nodes. The power distribution network also includes a control system configured to retrieve the power flow information and the diagnostic data; generate stress and health indices and compute a criticality for each of the power source and component nodes; and generate a risk index for each of the power source and component nodes based on their respective stress index, health index, and criticality. The risk index of each node represents the risk the node poses to the power distribution network.

BACKGROUND OF THE INVENTION

The present invention relates generally to asset health monitoring and, more particularly, to a system and method for asset health monitoring using a multi-dimensional risk assessment.

A power distribution system/network or electrical grid/network ordinarily requires many components or assets to supply and transmit electrical power to loads that are connected to the power system. A power system may include, for example, generators, power stations, transmission systems, and distribution systems. Generators and power stations supply electrical power to transmission systems, which then transmit the electrical power to distribution systems. Distribution systems deliver the electrical power to loads such as, for example, residential, commercial, and industrial buildings. The necessary components or equipment to operate the transmission and distribution systems may include, for example, transformers, circuit breakers, relays, reclosers, capacitor banks, buses, and transmission lines. Those components can be quite expensive to replace, especially in a large power system with thousands of those components. To keep track of the condition of those components, many power systems implement asset health monitoring.

Asset health monitoring includes analyzing data about power system components in order to assess the risk of failure. Once the risk of failure has been determined, decisions can be made about when to perform maintenance on or replace the power system components and how to reconfigure the power flow in the system in order to perform the maintenance on or replace the components. In other words, if the asset health monitoring reveals that a power system component needs to be repaired or replaced, a course of action can be planned ahead of a system fault. However, maintenance personnel are usually limited in number and need to service a large number of assets over a fixed amount of time. Thus, it is crucial to manage the time spent by the maintenance personnel as efficiently as possible. If maintenance of a power system is not managed properly or disregarded entirely, the power system will eventually fail.

Various asset health monitoring techniques are used to determine when to perform maintenance on a power system component. A depth first approach may be used for network model maintenance. Predictive modeling techniques such as, for example, clustering, classification, association analysis, pattern discovery, regression, and anomaly detection may also be used. Mean absolute percentage error for pattern recognition may be implemented to forecast the load on the power system.

Depending on the technique used, the technique may leverage data from several different sources. The data used to manage power system maintenance may include information from an advanced metering infrastructure that may have a variety of meters in the system; a phasor measurement unit used to measure the electrical waves of the electrical grid; intelligent electronic devices that monitor, control, automate, and/or protect monitored equipment within the power system; or individual component sensors, for example. Offline data such as, for example, historical sensor data, field test and service data, or network model data may also be used.

However, the above-referenced asset health monitoring techniques suffer from deficiencies. In general, the asset health monitoring techniques do not take advantage of all the information relevant to assessing how much risk a deteriorating component poses to a power system. For example, while an asset health monitoring technique may consider data concerning equipment being monitored, it may not take into account data concerning other power system equipment that may be relevant to the future operation and of the monitored equipment. As an additional example, some asset health monitoring techniques use only historical fault and maintenance data to predict when a component will fail without incorporating any current information relevant to the condition of the component.

Furthermore, asset health monitoring techniques typically do not consider all of the factors influenced by the information collected. For example, asset health monitoring techniques often ignore the impact of a component failure or taking the component offline for maintenance. Moreover, asset health monitoring techniques fail to take into account the availability and accuracy of diagnostic information for power system equipment.

It would therefore be desirable to provide a system and method for asset health monitoring that assesses the risk of failure of power system component using all relevant data in order to optimize the efficiency of maintenance personnel.

BRIEF DESCRIPTION OF THE INVENTION

Embodiments of the present invention provide a system and method for assessing the risk posed to a power distribution network by components within the power distribution network by analyzing the stress, health, and criticality of the components within the network.

In accordance with one aspect of the invention, a power distribution network includes a plurality of power source nodes and component nodes. The component nodes direct power from the plurality of power source nodes to a plurality of load nodes. The power distribution network further includes a plurality of sensors positioned to sense power flow information for the plurality of power source and component nodes and a data warehouse housing the power flow information and diagnostic data for the plurality of power source and component nodes. The power distribution network additionally includes a control system configured to retrieve the power flow information and the diagnostic data for the plurality of power source and component nodes from the data warehouse. The control system is also configured to generate a stress index for each of the power source and component nodes from the power flow information, generate a health index for each of the power source and component nodes from the diagnostic information, and compute a criticality for each of the power source and component nodes using a model of the power distribution network. Furthermore, the control system is configured to generate a risk index for each of the power source and component nodes based on their respective stress index, health index, and criticality. The risk index of each node represents the risk the node poses to the power distribution network.

In accordance with another aspect of the invention, a control system for assessing the risk of node failure to a power system having at least one power source node and a plurality of component nodes positioned to provide power from the at least one power source node to at least one load node is configured to extract power flow information and diagnostic information for the plurality of component nodes and the at least one power source node. The control system is also configured to convert the power flow information into a stress index for each component node, convert the diagnostic information and the stress index into a health index for each component node, and determine the criticality of each component node. The control system is further configured to convert the stress index, the health index, and the criticality of each component node into a respective risk index that symbolizes the risk each component node poses to the power system.

In accordance with yet another aspect of the invention, a method for assessing the risk that power system assets pose to a power system includes defining at least one power source node and a plurality of component nodes within the power system. The plurality of component nodes are positioned to provide power from the at least one power source node to at least one load node. The method further includes obtaining power flow information and diagnostic information for the power source and component nodes from a data warehouse, transforming the power flow information into a stress index for each of the power source and component nodes, transforming the diagnostic information into a health index for each of the power source and component nodes, and evaluating a criticality for each of the power source and component nodes using a model of the power distribution network. The method also includes transforming the stress index, the health index, and the criticality of each power source and component node into a respective risk index, each risk index indicating the risk a node poses to the power distribution network.

DETAILED DESCRIPTION

Embodiments of the invention relate to a system and method for assessing the risk that power system components pose to a power system by converting information on the power flow within the power system, diagnostic information for power system components, and information from a network model into a health index, a stress index, and a criticality for each power system component. The health index, stress index, and criticality of each component are transformed into a risk index that indicates how much risk the component poses to the power system. The risk indices for the various components may be used to develop a maintenance strategy and/or re-route power flow within the power system.

Referring toFIG. 1, a diagram of a power distribution system or network10is illustrated, according to an embodiment of the invention. The diagram of power system10is a network model and includes a number of nodes18-40that represent power sources, components, and loads and edges42-72that represent an electrical link connecting any two nodes such as, for example, overhead lines and underground cables. Nodes12,14,16represent power sources that supply power within power system10. The power sources may include any type of power source such as, for example, power stations, generators, and alternative energy sources (solar, hydroelectric, wind, etc.). Nodes18,20,22,24,26,28,30,32represent components within power system10. The components may include any type of component necessary for transmitting or distributing power within a power system such as, for example, load and generator buses, circuit breakers, capacitor banks, reclosers, and relays. Nodes34,36,38,40represent loads. The loads may include any type of load that may be powered by a power system such as, for example, residential, commercial, and industrial buildings.

Each edge42-72is a vector indicating a direction of power flow using an arrow. For example, edge42shows that power flows from power source node12to component node18, edge44shows that power flows from component nodes18to component node30, and edge46shows that power flows from component node30to component node34. As such, the network model of power system10may be considered a directed graph. The directed graph may be created using topology and power flow information available for power system10.

The power flow information and diagnostic information for components in power system10may be available from a sensor system (not shown) having a variety of sensing devices such as, for example, a plurality of individual sensors placed at various nodes and edges in power system10, an advanced metering infrastructure having a variety of meters in power system10, and/or a phasor measurement unit measuring the electrical waves of power system10. In addition, any of component nodes18-40may be intelligent electronic devices that monitor, control, automate, and/or protect monitored equipment within the power system such as, for example, differential, distance, directional, feeder, overcurrent, voltage, breaker failure, generator, and motor relays, voltage regulator controls, automation controllers, remote terminal units, bay controllers meters, recloser controls, communications processors, computing platforms, programmable logic controllers, programmable automation controllers, and input and output modules. Also, since having a multitude of sensors within a power system can be quite expensive, some power flow information may be derived instead of using a sensor. Offline data such as, for example, historical sensor data, field test data, or network model data, may also be used in creating the directed graph.

Power system10further includes data management and control system73including a control system74, a cloud-based data warehouse76, and a centralized database78. As will be described in more detail below with respect toFIG. 2, control system74is able to access cloud-based data warehouse76to analyze power flow information and diagnostic information for power system10in order to determine the best course of controlling power source and component nodes12-32. While control system74is shown as one control system in one location, control system74may include a variety of controllers at multiple locations so that power system10may be controlled as efficiently as possible.

The information from the sensor system of power system10is typically stored in database78at one centralized location chosen based on economic considerations. As a non-limiting example, the information may be stored at the headquarters for power system10. The information from the sensor system of power system10is then uploaded to data warehouse76. A cloud-based data warehouse is generally used in order to allow the information about power system10to be accessed from any location. However, the information may only be stored on physical databases. Data warehouse76is also able to access various other types of data from the Internet and from other databases (not shown) of power system10such as, for example, forecasting data for loads, power generation, and weather and logistic data including costs for repairs of power system components and availability of critical components.

Referring now toFIG. 2, a block diagram of data management and control system73of power system10ofFIG. 1is shown, according to an embodiment of the invention. As described above, centralized database78receives information from the sensor system of power system10. The sensor information includes power flow information and diagnostic information for power source and components nodes12-32as well as edges42-72. The sensor information and forecasting and logistic data is uploaded to the cloud-based data warehouse76. From the data stored in data warehouse76, control system74is able to calculate a risk index for each power source and component node12-32in power system10that is indicative of the risk that each power source and component node12-32poses to power system10.

Control system74retrieves or extracts forecasting, power flow, diagnostic, and network model information or data80,82,84,86as needed from data warehouse76as inputs to a stress calculator88, and health calculator90, a criticality calculator92, and a risk calculator94to determine the risk posed to power system10by each power source and component node12-32. Power flow information82includes any type of information relevant to the stress on the power sources and components of power system10such as, for example, data on current, voltage, and power surges, sags, and failures. Control system74analyzes power flow information82and, using stress calculator88, generates a stress index for each power source and component node12-32indicative of how much stress each respective power source and component node12-32is experiencing. The environmental conditions of a power source or component node may also be taken into account if the node has been under extreme temperatures, wind, and other environmental stresses. The stress index for any node12-32is calculated based on how the current state of the power flow in the node compares to the nominal rating of the power flow within the node. As a non-limiting example, a circuit breaker with a higher than rated current flowing through it is under stress, and control system74will, therefore, give the circuit breaker a higher stress index.

Diagnostic information84retrieved from data warehouse76by control system74includes information relevant to the health of the power source and component nodes12-32such as, for example, historic fault and maintenance information and present diagnostic information. The historic fault and maintenance information for a node may include information such as, for example, how many times the power source or component has failed or been repaired. The present diagnostic information may be online sensor information from the sensor system of power system10or may be derived from other known diagnostic information. By using derived diagnostic information, power system10does not need as many sensors to save on cost. Control system74inputs the stress index generated by stress calculator88and diagnostic information84into health calculator90.

FIG. 2illustrates that health calculator90analyzes the stress index and diagnostic data84and generates a health index for each power source and component node12-32. However, in various embodiments, health calculator90generates the health index for each power source and component node12-32based on an analysis of diagnostic data84alone. In addition, sometimes diagnostic information is unavailable or inaccurate for some power source and component nodes12-32for a variety of reasons such as, for example, no sensors are positioned to sense data for that node or only some of data associated with a node is sensed. In that case, health calculator90generates a health index based on the stress index for that node and the fact that no diagnostic information is available. The availability of diagnostic information for a node is an important factor for evaluating the risk the node poses to power system10. A critical node with no available diagnostic information will have a high degree of risk associated with it. Without diagnostic information on a node, there is no way for the control system to determine exactly how healthy it is.

Network model information86obtained from data warehouse76by control system74includes updates to the network model that represents power system10. As a non-limiting example, an additional component or power source node may be added to the network model, which may also change the direction of power flow on edges42-72of power system10. As will be described in more detail with respect toFIG. 3, control system74analyzes the network model and determines a criticality for each power source and component node12-32in power system10using criticality calculator92. The criticality of the power source and component nodes12-32are based on how many consumers will lose power if the node fails. The criticality of a node is independent of the stress on the node because it only pertains to the availability of power.

Once a stress index, a health index, and a criticality of each power source and component node12-32of power system10is calculated by respective stress, health, and criticality calculators88,90,92, these risk factors are normalized according to how much risk they pose to power system10. The normalization process may be nonlinear or linear and can be manipulated to suit the needs of power system10. In one embodiment, each of the risk factors is normalized on a scale of 1 to 10.

Control system74inputs the normalized stress index, health index, and criticality for each power source and component node12-32into risk calculator92. Control system74also inputs forecasting information80into risk calculator92. Forecasting information80includes information relevant to the future power flow in and stresses on power system10such as, for example, load forecasting, power generation forecasting, and weather forecasting information. In various embodiments, forecasting information80is also normalized before being input into risk calculator92. Risk calculator92analyzes the stress index, health index, criticality, and forecasting information for each power source and component node12-32and generates a respective risk index. Control system74then ranks or prioritizes each power source and component node12-32according to how much risk it poses to power system10in a prioritized list. Control system74then continuously updates the prioritized list of power source and component nodes12-32so that the risk posed to power system10is continually monitored.

There are various ways a risk index may be calculated and various ways power source and component nodes12-32may be ranked by the amount of risk they pose to power system10. The following is a non-limiting example of how to calculate a risk index for and rank the power source and component nodes12-32of power system10. First, the stress index, health index, and criticality of each node are normalized on a scale of 1 to 10. Next, any node having a health index greater than or equal to 9 is ranked at the highest priority regardless of the stress and criticality of the node. This is because a health index of 9 or higher indicates that the node is close to failing and there is not much time left to fix it. Then, the nodes with a health index less than 9 are examined according to stress index. If the health index of a node multiplied by the stress index of a node is greater than or equal to 72, that node is identified as the second highest priority irrespective of the criticality of the node because these nodes are fast deteriorating due to the stress on them and face immediate threat of bringing down part of power system10.

After the highest and second highest priority nodes have been identified the remaining nodes are prioritized by multiplying each of their respective stress indices, health indices, and criticalities together. Once power source and component nodes12-32have been ranked according to health index, stress index, and criticality, control system74analyzes forecasting information80to determine if any nodes are expected to experience bad weather, high load, or low power generation. Control system74will then reprioritize the nodes accordingly. After forecasting information80has been taken into account each power source and component node12-32has been ranked or prioritized according to the risk it poses to power system10.

Regardless of the method used to create the prioritized list of power source and component nodes12-32, control system74may use that prioritized list to perform control actions96on power system10and develop a maintenance strategy98. Control actions96include any action taken by control system74to alter the power flow within power system10. As a non-limiting example, control system74may re-route power to a load node34-40through a different component node18-32if a component node18-32is close to failing or deteriorating quickly. As another non-limiting example, control system74may shed a non-critical load, such as a residence, for a period of time to reduce stress on power source or component nodes12-32or to reserve power for critical loads such as a data center or a hospital. Control system74may keep a prioritized list of loads for the purpose of load shedding. As yet another non-limiting example, control system74may take a power source node12-16offline in anticipation of a failure and re-route power from the other power source nodes to component and load nodes18-40to compensate for the offline power source node. For instance, control system74may disconnect power source node12from component node18if power source node12is about to fail or is deteriorating quickly and re-route power from power source nodes14,16to component nodes18,44and load node46to compensate for the loss of power source node12.

In developing maintenance strategy98, control system74uses not only the prioritized list of power source and component nodes12-32, but also logistic information or data100extracted from data warehouse76. Logistic information100may include information including, but not limited to, the cost involved in repair of a component and the availability and cost of replacement components. Control system74may perform a cost analysis on repairing and replacing components using logistic information100. Thus, maintenance strategy98may include a recommendation to immediately service or replace power source or component nodes12-32that pose a high risk to power system10. The prioritized list and maintenance strategy98may then be used by maintenance personnel to schedule maintenance on power source and component nodes12-32that have the most need for maintenance so they can better manage their time. Because the risk each power source and component node12-32poses to power system10is known, maintenance personnel can determine how best to manage their time to address the needs of power system10without allowing any node of power system10to fail.

Referring now toFIGS. 3-4, a flow chart setting forth exemplary steps of a technique102for calculating the criticality of nodes of a power distribution system is shown, according to an embodiment of the invention. Process102begins at STEP104when a control system, such as control system74of data management and control system73of power system10, begins calculating the risk a node poses to a power distribution system. At STEP106, the control system retrieves or extracts a network model from a data warehouse or database such as cloud-based data warehouse76of data management and control system73. At STEP108, the control system assigns a unit criticality to all the nodes of the power system. At STEP110, the control system assigns a criticality to each load node. At STEP112, after each load node has been assigned a criticality, the control system moves to analyze the next level of the power system having the nodes directly upstream from the load nodes.

At STEP114, the control system scans a node on the newly entered level of the power system. At STEP116, the control system scans a node directly downstream from the node scanned at STEP114. At STEP118, the control system determines whether the node scanned at STEP116has more than one incoming edge. In other words, the control system determines if the node scanned at STEP116has more than one node directly upstream from it. If the node scanned at STEP116does not have any additional incoming edges, process102moves to STEP120, and the control system adds the criticality of the node scanned at STEP116to the node scanned at STEP114. If the node scanned at STEP116does have additional incoming edges, process102moves to STEP122, and the control system does not add the criticality of the node scanned at STEP116to the criticality of the node scanned at STEP114.

After performing either STEP120or STEP122, process102moves to STEP124, and the control system determines whether all nodes downstream from the node scanned at STEP114have been scanned. If not all the nodes downstream from the node scanned at STEP114have been scanned, process102moves to STEP116, and the control system scans an additional downstream node. If all the nodes downstream from the node scanned at STEP114have been scanned, process102moves to STEP126, and control system assigns a criticality to the node scanned at STEP114criticality summation made at STEPS120and122.

At STEP128, the control system determines whether all the nodes on the same level as the node scanned at STEP114have been scanned. If not all the nodes on the same level as the node scanned at STEP114have been scanned, then process102moves to STEP112, and the control system scans an addition node. If all the nodes on the same level as the node scanned at STEP114have been scanned, then process102moves to STEP130, and the control system determines whether the network has been completely scanned. If the network has not been completely scanned, process102moves to STEP112, and the control system moves to analyze the next level of the power system having the nodes directly upstream from the nodes on the same level as the node scanned at STEP114. If the network has been completely scanned, process102ends at STEP132.

Referring now toFIGS. 4A, 4B, and 4C, a flow chart illustrates an example criticality calculation134of power source and component nodes12-32of power distribution system10ofFIG. 1according to technique102ofFIG. 3. Example criticality calculation134is shown by way of six panels136,138,140,142,144,146. Panel136shows the network model of power system10and the initial assignment of the nodes (Level0) at STEPS106and108of process102. Control system74of data management and control system73of power system10assigns each power source and component node12-32a unit criticality or a criticality of 1. In panel138, control system74assigns load nodes34,36,38, and40a criticality of 20, 15, 5, and 10, respectively, (Level1) at STEP110of process102. In this particular example, it is assumed that each load node34,36,38,40has sub-loads of 20, 15, 5, 10, respectively.

In panel140, control system74moves to the level of nodes directly upstream from load nodes34-40including component nodes28,30,32(Level2) at STEP112of process102. Control system74then follows STEPS114-128of process102to determine the criticality component nodes28,30,32before moving on to the next level of nodes of power system10in panel144, as described in more detail below. At STEP114of process102, control system74scans component node28. Control system74then scans load node38directly downstream from component node28at STEP116of process102, determines that load node38does not have any edges in addition to edge56linking component node28and load node38at STEP118of process102, and adds the criticality of load node38to the criticality of component node28for a total criticality of 6 at STEP120of process102. Control system74determines that component node28has no more downstream nodes at STEP124of process102and assigns a criticality of 6 to component node28at STEP126of process102. Control system74then determines at STEP128of process102that component nodes30and32still need to be scanned and scans component node30at STEP114of process102.

At STEP116of process102, control system74scans load node34and determines that load node34does not have any edges in addition to edge46linking component node30and load node34at STEP118of process102. At STEP120of process102, control system74adds the criticality of load node34to the criticality of component node30for a total criticality of 21 and determines that load node36needs to be scanned at STEP124of process102. Control system74scans load node36at STEP116of process102, determines that load node36has an incoming edge52in addition to incoming edge48linking component node30and load node36at STEP118of process102, and does not add the criticality of load node36to the criticality of component node30at STEP122of process102. Control system74determines that component node30has no more downstream nodes at STEP124of process102and assigns a criticality of 21 to component node30at STEP126of process102. Control system74then determines at STEP128of process102that component nodes32still needs to be scanned and scans component node32at STEP114of process102.

At STEP116of process102, control system74scans load node40and determines that load node40does not have any edges in addition to edge66linking component node32and load node40at STEP118of process102. At STEP120of process102, control system74adds the criticality of load node40to the criticality of component node32for a total criticality of 11. Control system74determines that component node32has no more downstream nodes at STEP124of process102and assigns a criticality of 11 to component node32at STEP126of process102. At STEP128of process102, control system74determines that all the nodes on the current level have been scanned and determines that the complete network has not been scanned at STEP130of process102.

In panel142, control system74moves to the level of nodes directly upstream from component nodes28,30,32including component nodes18,20,22,24,26(Level3) at STEP112of process102. At this level of the network, control system74follows STEPS114-128of process102for component nodes20,22,26and does not add any criticality to component nodes20,22,26because all nodes directly downstream therefrom have more than one incoming edge. However, for component node18, control system74follows STEPS114-128of process102, determines that the criticality of component node30should be added to component node18because component node30has no incoming edges other than incoming edge44linking component nodes18,30, and assigns a total criticality of 22 to component node18. For component node24, control system follows STEPS114-128of process102, determines that the criticality of component node32should be added to component node24because component node32has no incoming edges other than incoming edge64linking component nodes24,32, and assigns a total criticality of 12 to component node24. At STEP130of process102, control system74determines that the complete network has not been scanned.

In panel144, control system74moves to the level of nodes directly upstream from component nodes18,20,22,24,26including power source nodes12,14,16(Level4) at STEP112of process102. At this level of the network, control system74follows STEPS114-128of process102for power source nodes12,14,16and determines in each case that the criticality of a downstream node should be added to its criticality. For power source node12, control system74determines that the criticality of component node18should be added to power source node12because component node18has no incoming edges other than incoming edge42linking power source node12and component node18and assigns a total criticality of 23 to power source node12. For power source node14, control system74determines that the criticality of component node26should be added to power source node12because component node26has no incoming edges other than incoming edge58linking power source node14and component node26and assigns a total criticality of 2 to power source node14.

For power source node16, control system74determines that the criticality of component node22should be added to power source node16because component node22has no incoming edges other than incoming edge68linking power source node16and component node22and assigns a total criticality of 2 to power source node16. At STEP130of process102, control system74determines that the complete network has been scanned. Control system74stops executing process102at STEP132and outputs the resulting criticalities of each power source and component node12-32as shown in panel146to risk calculator92.

Beneficially, embodiments of the invention thus provide a control system for determining the risk each node of a power system poses to the power system, ranking the nodes according to risk, and developing a maintenance strategy for preventing power system failure. The control system retrieves power flow, diagnostic, and network model information for power system components and power sources and converts or transforms that information into a stress index, a health index, and a criticality for each power source and component in the power system. The stress index, health index, and criticality of each power source and component, along with forecasting information, are transformed or converted into a risk index for each power source and component of the power system. The health, stress, and risk indices, the criticality, and logistic information are used to create a maintenance strategy that allows maintenance personnel to optimize their time spent maintaining the power system in order to prevent power system failure.

According to one embodiment of the present invention, a power distribution network includes a plurality of power source nodes and component nodes. The component nodes direct power from the plurality of power source nodes to a plurality of load nodes. The power distribution network further includes a plurality of sensors positioned to sense power flow information for the plurality of power source and component nodes and a data warehouse housing the power flow information and diagnostic data for the plurality of power source and component nodes. The power distribution network additionally includes a control system configured to retrieve the power flow information and the diagnostic data for the plurality of power source and component nodes from the data warehouse. The control system is also configured to generate a stress index for each of the power source and component nodes from the power flow information, generate a health index for each of the power source and component nodes from the diagnostic information, and compute a criticality for each of the power source and component nodes using a model of the power distribution network. Furthermore, the control system is configured to generate a risk index for each of the power source and component nodes based on their respective stress index, health index, and criticality. The risk index of each node represents the risk the node poses to the power distribution network.

According to another embodiment of the present invention, a control system for assessing the risk of node failure to a power system having at least one power source node and a plurality of component nodes positioned to provide power from the at least one power source node to at least one load node is configured to extract power flow information and diagnostic information for the plurality of component nodes and the at least one power source node. The control system is also configured to convert the power flow information into a stress index for each component node, convert the diagnostic information and the stress index into a health index for each component node, and determine the criticality of each component node. The control system is further configured to convert the stress index, the health index, and the criticality of each component node into a respective risk index that symbolizes the risk each component node poses to the power system.

According to yet another embodiment of the present invention, a method for assessing the risk that power system assets pose to a power system includes defining at least one power source node and a plurality of component nodes within the power system. The plurality of component nodes are positioned to provide power from the at least one power source node to at least one load node. The method further includes obtaining power flow information and diagnostic information for the power source and component nodes from a data warehouse, transforming the power flow information into a stress index for each of the power source and component nodes, transforming the diagnostic information into a health index for each of the power source and component nodes, and evaluating a criticality for each of the power source and component nodes using a model of the power distribution network. The method also includes transforming the stress index, the health index, and the criticality of each power source and component node into a respective risk index, each risk index indicating the risk a node poses to the power distribution network.