A METHOD FOR ASSESSING COMPONENT IMPORTANCE BASED ON RESILIENCE IN POWER DISTRIBUTION NETWORKS

Abstract

The present invention relates to a method for assessing component importance based on resilience in power distribution networks. The method involves collecting real-time data from a plurality of components within the network using a network data collection system and the disruptions are then detected through a disruption detection system. Following this, network resilience is defined, and it is modeled using binary state variables to represent each component's operational status over time, assessment of each component's vulnerability and recoverability probabilities, which are then used to calculate resilience metrics such as the Resilience Impact Index (RII) and the Resilience Worth Index (RWI), components are ranked based on these metrics using the Copeland Score method, which determine their relative importance, then the recovery task prioritization system is used to prioritize the recovery tasks. The restoration process is then monitored and adjusted by a monitoring and adjustment system to ensure efficient and effective recovery.

Specification

Description:FIELD OF INVENTION:
The present invention generally relates to electrical power systems. More specifically, the present invention relates to a method for assessing component importance based on resilience in power distribution networks during recovery processes after disruptive events which enhances the efficiency of resource allocation and optimizes overall network resilience.

BACKGROUND AND PRIOR ART:
In the realm of electrical power systems, the resilience and recovery of power distribution networks following disruptive events, such as natural disasters or system failures, are critical to maintaining reliable service.

Traditional methods for managing and restoring power distribution networks often focus heavily on protective measures and immediate fault isolation, sometimes at the expense of recovery efficiency and systematic repair prioritization. In many existing systems, there is a significant gap in evaluating and improving the resilience of power networks during the recovery phase and a lack of a comprehensive approach to systematically rank and prioritize network components based on their importance to overall system functionality and recovery. This can result in inefficiencies, prolonged downtime, and suboptimal resource allocation.

EP4199305A1 discloses a method for evaluating power distribution networks by frequently sampling electric parameter values and analyzing these values against predefined thresholds to assess reliability and potential maintenance needs. This method involves calculating event scores based on the ratio of event time periods to circuit action time periods, accumulating these scores, and indicating the reliability of the network based on the accumulated scores. The approach focuses on monitoring and assessing the reliability of power distribution phases by tracking deviations from normal operational thresholds and using these deviations to generate reliability indicators.

The above-given prior art primarily focuses on evaluating power distribution network reliability based on electric parameter values, such as current and voltage, and their deviations from threshold values, it does not include detailed metrics for evaluating recovery processes or optimizing resource allocation. The method in prior art does not address the optimization of resource allocation during the recovery phase.

The present invention addresses the need for an effective approach that prioritizes the repair and restoration of critical components based on their impact on overall network resilience.
OBJECTS OF THE INVENTION:
The main object of the present invention is to provide a method for assessing component importance that enhances the resilience of power distribution networks by assessing and ranking the importance of various components during the recovery process after disruptive events.

Another object of the present invention is to provide a method for assessing component importance that implements methodologies, such as the Resilience Worth Index (RWI) and Resilience Impact Index (RII) to optimize the restoration process and ensure a quicker return to full operational capacity.

Another object of the present invention is to provide a method for assessing component importance that reduces the overall recovery time of the power distribution network.

Another object of the present invention is to provide a method for assessing component importance that enables proactive planning and decision-making by integrating real-time data collection and analysis.



SUMMARY OF THE INVENTION:
The present invention pertains to a novel method for assessing the importance of components within power distribution network, focusing on their resilience and recovery during and after disruptive events.

In an aspect of the present invention, the method for assessing component importance based on resilience in a power distribution network, comprising collecting real-time data from a plurality of components within a power distribution network through a network data collection system, detecting disruptions in the power distribution network using a disruption detection system, calculating the Resilience Worth Index (RWI) and Resilience Impact Index (RII) for each component through a processor, ranking the components using the Copeland Score (CS) method via the processing unit, ranking the plurality of components based on a combination of RWI and RII values, wherein the ranking of the plurality of components performed by a recovery task prioritization system, monitoring and adjusting the restoration process using a monitoring and adjustment system.

In an aspect of the present invention, the network data collection system comprises a resource database for storing collected data on network components

In an aspect of the present invention, the recovery task prioritization system ranks components based on their Resilience Worth Index (RWI) and Resilience Impact Index (RII) values.

In an aspect of the present invention, the disruption detection system comprises a plurality of sensors deployed across the power distribution network.

In an aspect of the present invention, the Copeland Score (CS) method includes performing pairwise comparisons to evaluate the relative significance of each component in enhancing overall network resilience.

In an aspect of the present invention, the system for prioritizing the repair of components in a power distribution network, comprising a processor and a computing system configured to define, model, assess, calculate, rank, and monitor network resilience and recovery processes, comprises an input module including a network data collection system and a disruption detection system, a system operation module including a resilience assessment system and a recovery task prioritization system, an output module including a recovery implementation system and a monitoring and adjustment system.

In an aspect of the present invention, the resilience assessment system is configured to calculate the Resilience Worth Index (RWI) and Resilience Impact Index (RII) for each component.

In an aspect of the present invention, the recovery implementation system consists of a repair schedule creation module for optimizing the distribution of restoration resources based on the prioritized ranking of components, creating a repair schedule.

In an aspect of the present invention, the monitoring and adjustment system having a progress tracking module for tracking the repairs process.

BRIEF DESCRIPTION OF DRAWINGS:
The invention has other advantages and features which will be more readily apparent from the following detailed description of the invention and the appended claims, when taken in conjunction with the accompanying drawings, in which:
Fig. 1 illustrates the flowchart of a system for component importance measures based on resilience.
Fig.2 illustrates the flowchart of a method for component importance measures based on resilience.
Fig.3 illustrates the IEEE 33-bus test system used for validation.
Fig.4 illustrates the graphic representation of the example distribution of the Resilience Worth Index (RWI) for a critical component.
Fig.5 illustrates the graphic representation of the example distribution of the Resilience Impact Index (RII) for a critical component.

DETAILED DESCRIPTION OF THE INVENTION:
The invention is described herein in detail with the help of figures appended at the end of the specification. The figures illustrate the preferred embodiment as well as other embodiments that define the scope of the present invention. However, it may be understood that the figures presented herein are intended to exemplify the scope of the invention only. The person skilled in art may note that by no means the figures limit the scope of the invention. Any variation in the drawings by any other person will be falling in the scope of the present invention.

Throughout the specification and claims, the following terms take the meanings explicitly associated herein unless the context clearly dictates otherwise. The meaning of "a", "an", and "the" include plural references. The meaning of "in" includes "in" and "on." Referring to the drawings, like numbers indicate like parts throughout the views. Additionally, a reference to the singular includes a reference to the plural unless otherwise stated or inconsistent with the disclosure herein.

The present invention discloses a method for assessing and ranking the importance of components within power distribution networks to enhance their resilience during recovery from disruptive events. The method addresses existing gaps in recovery processes by providing systematic metrics that prioritize repairs, optimize resource allocation, and accelerate the restoration of full operational capacity.

Fig. 1 illustrates the flowchart of the system for assessing and ranking the importance of network components based on their impact on system resilience. The system comprises a processor and computing system to define, model, assess, calculate, rank, and monitor network resilience and recovery processes, which includes the input module, the system operation module, and the output module.

The input module consists of two essential systems: the network data collection system and the disruption detection system. The Network Data Collection System is responsible for gathering real-time data from various components within the power distribution network. This data is stored in a Resource Database, which includes operational status. The disruption detection system is equipped with a plurality of sensors and continuously monitors the network to identify and report disruptions as they occur. The plurality of sensors include current transformers, voltage or potential transformers, phasor measurement units (PMUs), vibration sensors, and smart meters, which are employed to gather real-time data from the network.

The system operation module processes the data collected by the input module to assess network resilience. The collected data includes voltage and current levels to monitor power flow and load conditions, while temperature data is used to track critical equipment like transformers and switchgear for potential overheating, fault detection data identifies fault locations and evaluates the severity of disruptions. Additionally, phasor data, consisting of synchronized voltage and current measurements, is utilized to analyze system stability. The system operation module includes the Resilience Assessment System and the Recovery Task Prioritization System. The resilience assessment system calculates key metrics such as the Resilience Worth Index (RWI) and the Resilience Impact Index (RII) for each network component. The RWI measures the time required to restore a component to full functionality after a disruption, while the RII evaluates the impact of each component on the overall resilience of the network. By analyzing these metrics, the system determines the relative importance of each component in maintaining network stability. The recovery task prioritization system uses the assessed RWI and RII values to rank components based on their criticality and the urgency of their repair.

The Resilience Worth Index (RWI) and Resilience Impact Index (RII) are two critical metrics used to prioritize the repair of components within a power distribution network, ensuring that the network's resilience is maximized during the recovery process. The RWI is calculated to determine the precise time at which a failed component should be repaired to restore the network's functionality as quickly and efficiently as possible. This is mathematically represented as,

Here, R_t represents the resilience of the network at time t.

The Resilience Impact Index (RII) quantifies the potential loss in network resilience due to delays in repairing a component. It provides a clear measure of the impact of not prioritizing the repair of a particular component, thus aiding in recovery decisions. The RII is expressed as,

Where, R_max is the maximum achievable resilience and R_t is the resilience at time t.

The output module includes the recovery implementation system and the monitoring and adjustment system. The recovery implementation system includes the repair schedule creation module, which generates a detailed repair schedule based on the prioritized list of components. this schedule outlines the specific actions required for each component and allocates resources accordingly. the monitoring and adjustment system includes a progress tracking module that monitors the progress of restoration efforts in real-time.

Fig.2 depicts the flowchart of the method for assessing component importance based on resilience in a power distribution network. The method involves a systematic approach to enhance network recovery after disruptions. Initially, real-time data is collected from a plurality of components within the network through a network data collection system. Concurrently, a disruption detection system actively monitors the network to identify any disruptions. Following disruption detection, defining of network resilience R(t_x│E) as a function of time t_x and disruptive events E and modeling network resilience using binary state variables s_ij (t) through a processor to represent the operational status of each component over time. The method assesses each component's vulnerability and recoverability probabilities via a processor, which are then used to calculate resilience metrics such as the Resilience Impact Index (RII) and the Resilience Worth Index (RWI). Components are ranked based on these metrics using the Copeland Score (CS) method through a processor, which helps determine their relative importance. The recovery task prioritization system further refines this ranking, ensuring that resources are allocated efficiently and that critical components are restored first. Throughout the restoration process, a monitoring and adjustment system continuously tracks progress, allowing for real-time adjustments to the recovery plan, thereby ensuring a timely and effective restoration of the power distribution network.

Function of resilience: The resilience of a network at a specific time is denoted as R(t_x│E), is quantified to reflect the network's ability to recover from a disruptive event E at a specific time tx. This resilience metric is calculated by assessing the ratio of recovered load to the total load lost due to the event. The formula,


Where,
F(t_x ) represents the load restored at time tx,
F(t_pe ) is the load at the start of the recovery phase, tpe,
F(t_o ) is the load before the disruptive event occurred.

In the above given formula, the resilience metric R(t_x│E)is defined within the range [0,1], where R(t_x│E)= 0 represents minimal recovery when F(t_x )= F(t_pe) and R(t_x│E)= 1 signifies complete restoration when F(t_x )= F(t_0). This function quantifies the ratio of the restored load to the total lost load, providing an indication of how much of the network's functionality has been recovered. It assesses both the extent of recovery and the speed with which recovery is achieved.

Network Resilience Optimization: The optimization of network resilience post-disruption involves strategically managing the repair sequence of network components to ensure maximum system functionality throughout the recovery period. When a disruption affects multiple components, the sequence and prioritization of repairs are crucial for maximizing overall system resilience. The optimization process aims to achieve optimal cumulative system functionality over the recovery period. This includes determining the sequence for repairing damaged components and allocating repair resources efficiently.

In a distribution network represented by N(B, K), where B is the set of buses and K is the set of branches, the network's performance is evaluated based on the maximum flow received by load buses. In the network optimization framework, buses are categorized into four distinct groups to effectively manage and evaluate network performance. Buses with distributed generation (DG) are represented by B1, while those with tie line switches are denoted as B2. Buses that feature both DG and tie line switches are grouped as B12, where B12 is the intersection of B1 and B2. The remaining buses, which lack both DG and tie line switches, are classified as B3. This categorization ensures a comprehensive analysis of network components and their roles. Each branch ij in the network has an associated capacity C, which defines the maximum flow it can handle. Buses with DG have a supply capacity per time unit denoted by S, whereas load buses have a demand per time unit represented by D. To evaluate the network's performance, the maximum flow that can be received by load buses is assessed, providing key insights into the network's capacity to meet demand and maintain functionality. This assessment is mathematically represented by the system performance function:

where fi(t) denotes the flow received by load buses at time t.

The recovery optimization framework aims to efficiently restore critical infrastructure by managing the repair of network branches in a strategic manner to maximize overall system resilience. In this framework, the repair process is treated as a series of discrete tasks, where each task addresses the repair of a single branch at a time. This discrete nature implies that, at any given time, only one branch is repaired, necessitating a discrete temporal framework denoted by t=1,2,…,T. This contrasts with the continuous time model used in resilience quantification, such as in Equation (3), which measures resilience over a continuous range of time.

In this framework, binary state variables s_ij (t) are introduced, where i,j ∈K and t=1,2,…, T, to represent the operational status of each branch ij at each time unit. Specifically, s_ij (t)=1 indicates that the branch is operational, while s_ij (t)=0 indicates that the branch is non-operational during the time unit t. This optimization approach involves selecting a subset of branches for repair and determining the optimal order for these repairs to maximize system resilience throughout the restoration process. The performance of the network is evaluated by assessing the maximum flow that can be received by load buses, quantified by the system performance function F(t). This function sums the flow received by all load buses at time t, providing an indicator of the network's ability to meet demand during the recovery phase.

Network Component Vulnerability and Recoverability: Network component vulnerability and recoverability are critical aspects of maintaining power distribution networks, particularly in response to disruptive events. Vulnerability is the network's initial response to disruptive events, where a significant reduction in functionality is observed, such as a decrease in power flow through network branches. When an extreme event E occurs, it causes a reduction in the functionality of network components, such as a decrease in the power flow through branches. The impact of this event on a specific branch ij can be quantified by the parameter Q, which represents the extent of performance reduction. The state of branch ij immediately after the event, at time t0, can be described by the following equation:


Here:
s_ij (t)is the operational status of branch ij at the initial time t0 after the event.
s_ij (t_pe |Q)is the pre-event operational status of branch ij, adjusted for the impact Q.
Q is the reduction in system performance for branch ij, where Q=1 indicates a complete loss of functionality.
The stochastic nature of Q reflects the uncertainty associated with the characteristics of the disruptive event E and the subsequent behavior of the network component ij. The range of Q is defined within a specific interval [α,β], where, α and β are both within [0,1]. The probability that Q falls within this range is given by:


Here, f(Q) is the probability density function of Q, describing the likelihood of different levels of impact on system functionality.

Recoverability refers to the ability of the network to restore its functionality after a disruption. It is measured by the time taken for a branch ij to regain full operational status after an event. The recovery time is influenced by the vulnerability factor Q, making it a stochastic variable. The probability that a branch ij recovers before a given time tx is expressed as:
P(t_x≥V>t_pe )=∫_(t_pe)^(t_x)▒〖f(V)dV〗 (7)
Here:
V: Recovery time for branch ij, influenced by Q.
tpe: Time just after the disruptive event.
tx: Specific time by which the recovery is expected.

The assumption is that the state of the branch s_ij (t) remains equal to s_ij (t_pe) until recovery time tr is achieved. This assumption can be modified with more complex models, such as linear, convex, or concave relationships, to represent the behavior of s_ij (t) over the recovery period (tpe, tr).

The assumption P(t_x≥V>t_pe )=P(t_x≥V^'>t_pe )for all positive values of vulnerability V′ implies that the recovery time V is invariant with respect to different magnitudes of Q. In other words, the recovery time does not change regardless of the severity of the disruption, provided that Q>0. This simplifies the modeling but may not capture all real-world complexities where the severity of damage can influence recovery time.

The metric T_(s_ij (t_0 ) ) (E) quantifies the total duration required for the entire system to return to its original state in the context of the recoverability of each component, as stated in Equation (7). This metric is calculated from the initiation of recovery activities at tpe until the system reaches its original service function value F(t0) at time tr. It provides a comprehensive measure of recovery efficiency and effectiveness. This metric assesses the total duration of recovery activities, beginning at the disruption event's initiation time tpe and extending until the system's service is fully restored to its original performance level F(to) by the designated time tr. It encompasses the entire period from the start of recovery efforts to the complete restoration of the system's functionality. The random value of tr′ is defined such that it satisfies the condition R(t_r^'│E)=1,ensuring full recovery, and R[(t_r-∆t)|E]<1, for Δt>0, just before full restoration. Given the stochastic nature of the recovery time T_(s_ij (t_0 ) ) (E), the probability of achieving network service resilience by time tr is expressed as P_(s_ij (t_0 ) ) (t_r )=P[T_(s_ij (t_0 ) ) (E)≤t_r].

Network vulnerability is a critical factor within the operational framework, shaping how the network reacts to disruptive events. When such events occur, causing a significant drop in power flow through network branches, the system immediately evaluates the impact on its performance. For instance, if an extreme event diminishes the performance of a particular branch, the system's preparedness measures such as protective mechanisms and robust network design aim to mitigate initial impacts and bolster overall resilience. The stochastic nature of vulnerability highlights the uncertainty tied to extreme events and the behavior of network components, emphasizing the need for flexible strategies to manage disruptions across varying conditions effectively. Recoverability focuses on the speed at which the network can regain full functionality after a disruption. The system carefully manages the recovery time for each component, taking into account its initial condition and the recovery process's trajectory. This approach ensures rapid restoration of operational stability following any disruption. The operational framework assumes that a branch's status remains unchanged until complete recovery, while also acknowledging the potential for more detailed modeling of system behavior during recovery.

The method involves ranking network branches based on resilience-related Component Importance Measures (CIMs) , which assess the impact of each branch on system resilience at time tx. This ranking helps identify branches that pose the highest risk to system resilience during disruptive events, ranging from the branch ij with the greatest negative impact to the one with the least. Simultaneously, the analysis highlights branches that, if unaffected by disruption, significantly contribute to enhancing system resilience.

To facilitate this analysis, an algorithm is introduced that generates an ordered list of component importance, specifically focusing on the resilience measure R_1 (t_x│E). However, the algorithm is adaptable and can be applied to another resilience measure, R_2 (t_x│E), providing a unified approach for assessing component importance across both metrics.

The algorithm operates in several steps:
Step 1: The process begins by iterating through each branch 𝑖𝑗. For each branch, the algorithm uses probability distributions, as outlined in Equations (6) and (7), to generate realizations of Q(recovery) and V (vulnerability).

Step 2: For a specified time range tx∈(tpe, tr), the algorithm calculates the resilience measure R_1 (t_x│E). This process is repeated for a selected number of iterations η.

Step 3: In each iteration, the algorithm generates a distribution of R_1 (t_x│E) for each time period tx∈(tpe, tr).

Step 4: After obtaining the distributions of R_1 (t_x│E)for each branch 𝑖𝑗, the branches are ranked based on ascending values of R_1 (t_x│E). This ranking provides an ordered assessment of each branch's significance relative to its impact on resilience.

Additionally, in scenarios where event E affects multiple branches, developing recoverability strategies, including predefined recovery orders, becomes critical.

A method based on the Copeland Score (CS) has been developed to generate the stochastic order of network branches. The CS is a simple, non-parametric ranking technique that does not require information about decision-maker preferences. This approach uses a multi-indicator matrix consisting of z objects, each characterized by ϕ attributes. The CS method involves pairwise comparisons between objects based on these attributes to derive a ranking order. To compute the CS, each branch is compared against others in terms of specific attributes. The score for each branch is determined by evaluating the difference between the number of times it is considered superior to other branches regarding a given attribute and the number of times it is deemed inferior. This method ranks branches based on their performance across selected attributes in the network. After calculating the CS for each branch through these pairwise comparisons, the branch with the highest CS is selected, indicating the branch with the highest prominence relative to the chosen attribute. The CS method does not require normalization since comparisons are made individually for each attribute, making it a straightforward assessment tool. The CS method assumes equal importance for all attributes in the ranking process. If there are specific preferences for different attribute weights, parametric ranking techniques, like ordered weighted averaging, might be more suitable. These methods allow for customized ranking by assigning different weights to attributes according to decision-maker preferences.

To apply the CS method, the cumulative distribution function of R_1 (t_x│E) for each branch is analyzed first. The method then compares the cumulative distribution functions of branches 𝑖𝑗 and ¯ij against a set of predetermined percentiles. Attribute An corresponds to the nth percentile, and the comparison assesses the relative positions of branches 𝑖𝑗 and ¯ij at these percentiles.

The problem of stochastic ordering is simplified to ranking branches based on their positions in relation to these attributes. The branch with the highest rank in this process significantly contributes to overall system resilience. The value G_n (ij,¯ij)is determined by comparing branch 𝑖𝑗 with branch ¯ij for each percentile An as defined by Equation (8):


Before reaching the initial percentile A1, the value G_1 (ij,¯ij) is initialized to zero. The iterative process described in Equation (8) is then applied across all φ percentiles to calculate the evolving values of G_n (ij,¯ij) as the comparisons progress through these percentiles. To determine the Copeland Score (CS) for a branch ij, the method involves summing G_n (ij,¯ij) for all 𝒊𝒋 ≠¯ij, where each ¯ij represents the other branches in the set. This summation is outlined in Equation (9):


In the context of managing network resilience, this methodology is used to evaluate and oversee the resilience of network branches in the aftermath of disruptive events. The method ranks branches according to their potential impact on resilience at a specific time tx. This ranking process identifies which branches present the greatest risk during disruptions and which ones contribute most positively when they remain unaffected. By assessing resilience-based Component Importance Measures (CIMs), the analysis highlights key branches that, when resilient to disruptions, significantly enhance the overall resilience of the network.

A system operational module is developed to achieve effective component ranking for resilience measures like R_1 (t_x│E). This module uniformly orders components based on their importance, ensuring a consistent approach across various resilience measures. The process starts by iterating through each network branch, using probability distributions to simulate potential impacts and vulnerabilities. The resilience metric R_1 (t_x│E) is then calculated over a specified time range for each branch through multiple iterations. Once the distributions of R_1 (t_x│E) for all branches are computed, the system performs a stochastic ranking process. This process organizes branches in ascending order based on their R_1 (t_x│E) values, clearly assessing each branch's importance within the network. This methodology effectively identifies and prioritizes branches crucial to resilience. Additionally, the Copeland Score (CS) method has been integrated into the system for generating stochastic branch orders. The CS method conducts pairwise comparisons among branches across multiple attributes, determining which branches perform better relative to others. This non-parametric ranking technique simplifies the assessment without needing decision-maker preferences, offering a straightforward and relevant ranking based on resilience attributes. The same procedure for assessing and ranking component importance for R_1 (t_x│E) is also applied to R_2 (t_x│E).

DETAILS OF EXPERIMENT:

As illustrated in Fig.3, the methodology was validated using the IEEE 33-bus distribution test system, a widely recognized model for simulating power grid networks. The simulations showed that components linking generator nodes to other nodes and those uniquely connected to demand nodes were prioritized for repairs, highlighting their critical role in overall network resilience.

Simulation Procedure:
Step 1: Simulate disruptive events on the network to understand their impact on system performance and component resilience.
Step 2: Calculate metrics such as Resilience Worth Index (RWI) and Resilience Impact Index (RII) for each network component, providing insights into their recovery needs and significance.
Step 3: Apply Copeland's pairwise aggregation method to rank the components based on their resilience attributes, identifying those most critical for network recovery.

Fig. 4 illustrates the graphical representation of the distribution of Resilience Worth Index (RWI) values across various components. A higher Copeland score for a branch signifies a greater likelihood of rapid restoration compared to others. For instance, branch <12,22> achieves the highest Copeland score of 880 a.u., indicating it has the greatest probability of being restored first after a disruption, given the current network topology. Conversely, branch <26,27> has a Copeland score of 48 a.u., suggesting it is likely to be restored last.

Fig. 5 illustrates the graphical representation of the variation of the Resilience Impact Index (RII) metric for a critical component across different simulated scenarios. This figure demonstrates how the RII metric fluctuates under various conditions. A higher Copeland score for a branch indicates that its outage significantly impacts the network during a disruption. Branch <11,12> has the highest Copeland score of 910 a.u., suggesting that the average load loss is greatest when this branch is non-operational. In contrast, branch <9,10>, with a Copeland score of 160 a.u., exhibits the least impact when it is out of service.

In an embodiment, the method employs Monte Carlo simulations to generate a wide range of random scenarios, capturing the inherent uncertainty and variability associated with disruptive events. By simulating numerous possible impacts on network components, these simulations provide a comprehensive understanding of potential disruptions and their effects on network performance.

In an embodiment, the Copeland's pairwise aggregation method compares each component against every other component to assess their relative importance. This thorough comparison process produces a robust ranking of components based on their criticality to network resilience.

In an embodiment, the application of resilience metrics such as Resilience Worth Index (RWI) and Resilience Impact Index (RII) shifts the focus from merely identifying critical components to prioritizing their recovery. This prioritization is crucial for effective planning and resource allocation during post-disruption recovery efforts, enhancing the overall resilience of the network by ensuring that the most vital components are addressed promptly.

In an embodiment, the Copeland Score (CS) method includes performing pairwise comparisons to evaluate the relative significance of each component in enhancing overall network resilience.

In an embodiment, the data is gathered through a plurality of sensors placed strategically throughout the system. The voltage and current sensors monitor electrical parameters, while temperature sensors monitor thermal conditions. Power quality sensors detect variations in voltage and frequency, and fault detectors identify the locations of faults. The Phasor Measurement Units (PMUs) collect synchronized electrical data, and gas and moisture sensors are used to assess transformer health. The smart meters at customer points provide energy consumption data, facilitating thorough monitoring and management of the system.

In an embodiment, the plurality of components include buses, distribution lines, and other critical components. The distribution substations lower the voltage for distribution, while feeders and primary distribution lines transmit electricity to various locations. The distribution buses serve as connection points within the network, and distribution transformers further decrease the voltage for delivery through secondary distribution lines and service lines to consumers. Switchgear provides control and protection for the network, capacitors and reactors assist with voltage regulation, and meters record consumer usage. The protective devices such as fuses and relays safeguard the system against faults and overloads.

Although a particular exemplary embodiment of the invention has been disclosed in detail for illustrative purposes, it will be recognized by those skilled in the art that variations or modifications of the disclosed invention, including the rearrangement in the configurations of the parts, changes in steps and their sequences may be possible. Accordingly, the invention is intended to embrace all such alternatives, modifications, and variations as may fall within the spirit and scope of the present invention.

The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching.


ADVANTAGES OF THE INVENTION:

The present invention provides a method for assessing component importance based on resilience that enhances the overall resilience of the power distribution network by focusing on components that have the greatest impact on system performance.
The present invention provides a method for assessing component importance based on resilience that offers a structured approach to assessing and improving network resilience.
The present invention provides a method for assessing component importance based on resilience that facilitates the strategic allocation of restoration resources.
The present invention provides a method for assessing component importance based on resilience that reduces downtime and enhances service continuity compared to traditional centrality measures.
, Claims: A method for assessing component importance based on resilience in a power distribution network, comprising:
collecting real-time data from a plurality of components within a power distribution network through a network data collection system;
detecting disruptions in the power distribution network using a disruption detection system;
defining network resilience R(t_x│E)) as a function of time tx and disruptive events E through a system operational module;
modeling network resilience using binary state variables sij(t) through a processor to represent the operational status of each component over time;
assessing the vulnerability P(Q) and recoverability P(V) probabilities for each component by a processor;
calculating component importance-based resilience measures by determining the Resilience Impact Index (RII) and the Resilience Worth Index (RWI) metrics through a processor for each component;
ranking the components based on their importance using the Copeland Score (CS) method by a processor;
prioritizing recovery tasks using a recovery task prioritization system;
ranking the plurality of components based on a combination of RWI and RII values, wherein the ranking of the plurality of components performed by a recovery task prioritization system;
monitoring and adjusting the restoration process using a monitoring and adjustment system.
The method as claimed in claim 1, wherein the network data collection system comprises a resource database for storing collected data on network components.
The method as claimed in claim 1, wherein the recovery task prioritization system ranks components based on their Resilience Worth Index (RWI) and Resilience Impact Index (RII) values.
The method as claimed in claim 1, wherein the disruption detection system comprises a plurality of sensors are deployed across the power distribution network.
The method as claimed in claim 1, wherein the Copeland Score (CS) method includes performing pairwise comparisons to evaluate the relative significance of each component in enhancing overall network resilience.
A system for prioritizing the repair of components in a power distribution network, comprising:
a processor and a computing system configured to define, model, assess, calculate, rank, and monitor network resilience and recovery processes, comprises:
an input module including a network data collection system and a disruption detection system;
a system operation module including a resilience assessment system and a recovery task prioritization system;
an output module including a recovery implementation system and a monitoring and adjustment system.
The system as claimed in claim 6, wherein the resilience assessment system is configured to calculate the Resilience Worth Index (RWI) and Resilience Impact Index (RII) for each component.

The system as claimed in claim 6, wherein the recovery implementation system consists of a repair schedule creation module for optimizing the distribution of restoration resources based on the prioritized ranking of components, creating a repair schedule.
The system as claimed in claim 6, wherein the monitoring and adjustment system having a progress tracking module for tracking the repairs process.

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