Mechanical failure mechanisms in power plant distribution systems provide serious difficulties that might impair system dependability and cause operations to be disrupted. One typical mechanical failure mode is bearing failure, which occurs when problems like wear, contamination, or a lack of lubrication affect the bearings that support spinning shafts and other components [11] . Increased vibration levels, device failure, and catastrophic damage can result from this. In addition, common problems include tooth failure and gear wear, which are frequently brought on by overloading, improper lubrication, or misalignment and can reduce performance and result in equipment failures [12] . Risks include structural decay and corrosion, which erode the integrity of infrastructure and raise the possibility of failures. Furthermore, as they can result in fluid loss, pollution, and decreased system performance, seal and gasket leaks are a cause for concern [13] . It takes initiative-taking measures to address these mechanical failure modes. Initiative-taking maintenance techniques, like routine inspections, condition monitoring, and timely replacements, are necessary to address these mechanical failure mechanisms and maintain the dependability and lifespan of power plant distribution systems.

Electrical failure modes in power plant distribution systems refer to a variety of potential problems that could impair system reliability and cause operations to be disrupted. Insulation breakdown is a common failure event that results in short circuits or arcing because dielectric materials are unable to tolerate electrical stress. Another frequent problem is overloading of electrical circuits and components, which is caused by high current flow and overheating from increased loads or insufficient cooling [14] . Arc flash events are extremely dangerous. They happen when an electric arc forms between wires or against the ground, generating a great deal of heat and light and damaging equipment and causing significant injury. Distribution networks are also hampered by power quality problems and voltage variations, which impair equipment functionality [15] . To guarantee the dependability and security of power plant distribution systems, addressing these electrical failure types necessitates a comprehensive maintenance strategy that includes insulation testing, load monitoring, arc flash mitigation techniques, and power quality monitoring.

In the realm of maintenance strategy implementation in a power distribution facility, the combination of Failure Mode and Effects Analysis (FMEA) Pareto Analysis as shown in Figure 1 and Figure 2 . with Pareto Analysis presents a robust methodology for enhancing maintenance operations [16] . FMEA is employed to systematically recognize potential failure modes in the components of the facility and evaluate their impact on operational performance. This systematic approach aids in prioritizing the most critical failure modes based on severity, occurrence, and detection ratings, resulting in a Risk Priority Number (RPN) assigned to each failure mode. Following the prioritization of failure modes, Pareto Analysis is utilized to pinpoint the vital few important failures/problems that are responsible for many issues, in alignment with the 80/20 principle [17] . This analytical method brings to light the most crucial concerns that, if addressed, can lead to substantial enhancements in both reliability and effectiveness. By concentrating on these pivotal areas, resources can be efficiently allocated to achieve maximum impact. This approach guarantees that decision-making is thorough and well-balanced, considering all pertinent factors. The integration of FMEA and AHP

with Pareto Analysis promotes a data-centric, prioritized strategy for maintenance activities [18] . This consolidated approach empowers power distribution facilities to diminish risks, elevate system dependability, minimize downtime, and optimize maintenance assets, ultimately leading to a more effective and resilient operation.

Using Pareto Analysis to develop a maintenance strategy for a power distribution plant entails several organized processes to ensure that maintenance activities are prioritized effectively. The first step in identifying all potential failure modes and their frequencies is data gathering; maintenance records, failure logs, and performance reports are acquired. The impact of each failure mode on plant operations is then ascertained by classifying and quantifying this data, considering aspects like downtime, repair costs, and safety concerns. Plotting the failure modes on a Pareto chart is how the Pareto Principle is implemented after the data has been organized. The cumulative impact of each failure mode is usually shown in this chart in descending order. The horizontal axis lists the failure modes, while the vertical axis shows the frequency or impact (such as cost, downtime) of failures. Next, the research pinpoints the key few, which are typically about 20% of the failure modes and account for 80% of the issues. The maintenance approach concentrates on these high-impact failure types in accordance with this prioritization. Preventive maintenance, more frequent inspections, or component upgrades for the most important portions are some possible courses of action. By focusing on the most important problems, this targeted strategy makes sure that maintenance resources are used effectively, improving reliability, lowering unplanned failures, and maximizing maintenance costs [19] .

A methodical technique called Failure Mode and Effect Analysis (FMEA) is employed to determine probable failure modes in a system, product, or process and evaluate the consequences of those failures. By disassembling complex systems into their component parts and examining the potential failure modes of each part, Failure Mode and Effect Analysis (FMEA) assists companies in anticipating problems and improving dependability. Failure modes can be determined for each equipment and the system behavior can be then examined from the viewpoint of reliability via failure modes and effects analysis (FMEA) [20] . Failure modes are failure of one or a combination of network equipment that cause the system to malfunction or collapse. FMEA, on the other hand, is a procedural method to analyze failure modes and their effects on reliability. These steps include defining the system, identifying potential failure modes in the system, determining the effects and consequences of the occurrence of each failure mode, ranking the negative effects, determining all potential sources of failure modes, determining tracking methods, identifying potential sources of failure modes, and eventually using methods to eliminate the root of the failure or reduce the sensitivity of the system performance to the failures.

Figure 3 illustrates the mechanism for identifying the failure mode and estimating its rate. As shown in Figure 3 , in the first step (failure tree), it is necessary, for example for a power transformer, to include the components such as bushing, coil, tank, pulse changer, oil, cooler, and core. A failure tree must be drawn for each of these components separately. Finally, by selecting an appropriate estimation model, the failure rate is calculated and incorporated in the reliability assessments, maintenance planning, and related applications.

In the deployment of maintenance strategies for power distribution plants, understanding and prioritizing Protection and Automation data through tools like Failure Mode and Effects Analysis (FMEA) and Pareto analysis are crucial steps. The Failure Mode and Effect Analysis (FMEA) methodology facilitates a methodical process for recognizing possible failure modes in the Automation and Protection systems, evaluating their impact, and estimating the probability of their occurrence. With the help of this technique, maintenance staff can proactively fix vulnerabilities before they become serious malfunctions. Pareto analysis highlights the most important factors that could lead to possible failures, which is a supplement to FMEA. The bulk of risks or failures may be attributed to a critical few variable, thus maintenance resources can be spent effectively and efficiently. Pareto analysis is useful in identifying the most frequent failure modes or problems that could jeopardize the power distribution system’s dependability and safety when applied to data related to protection and automation. By offering an organized method for allocating resources and prioritizing tasks according to the protection and automation systems’ level of criticality, the combination of FMEA and Pareto analysis improves the process of implementing maintenance strategies. This strategy improves overall system longevity and performance while reducing the chance of unplanned downtime. Power distribution plants can ultimately guarantee the highest levels of operational efficiency, safety, and dependability by utilizing these approaches. Table 1 tabulates the number of fault for protection and automation; meanwhile Table 2 tabulates cumulative number of faults for protection and automation. Figure 4 depicts Pareto analysis of protection and automation.

High-Voltage Direct Current (HVDC) systems are essential for transporting electricity over long distances with low losses in the field of power distribution. However, a strong maintenance plan is necessary to guarantee the dependability and effectiveness of HVDC systems. The application of Failure Modes and Effects Analysis (FMEA) in conjunction with Pareto analysis is a useful method for creating such a plan. Pareto analysis is used to rank failure modes according to how they affect system dependability and performance after they have been found. Maintenance teams can direct their resources and efforts where they are most needed by using Pareto analysis, which identifies the critical few failure modes that account for the bulk of system failures. Targeted maintenance plans can be developed by utilizing FMEA Pareto analysis for HVDC systems in the context of maintenance strategy implementation on power distribution plants. Maintenance efforts can be improved to improve system dependability, reduce downtime, and guarantee a continuous supply of energy by focusing on the most significant failure scenarios first. In addition to increasing overall system safety and operational efficiency, this proactive strategy also lowers costs. As per Table 3 is the tabulated for faults category with their corresponding number of faults for HVDC. Meanwhile Table 4 tabulates Cumulative Number of Fault of HVDC and Figure 5 depicts the Pareto Analysis of HVDC respectively.

The FMEA in Figure 6 presented six potential failure modes for a system, accompanied by their respective Risk Priority Numbers (RPNs), relative proportions, and cumulative effects. The foremost risk factor recognized is inadequate maintenance, characterized by an RPN of 200, representing 29.5% of the overall risk, and cumulatively amounting to 30%. Subsequently, errors in sensor calibration (RPN 144, 21.3%) constitute a considerable portion of the risk landscape, encompassing more than half of the total risk at 51% when combined with inadequate maintenance.

According to Table 5 for the tabulated RPN for Remote Terminal Units, noteworthy is the substantial risk posed by incorrect installation procedures, evidenced by an RPN of 120, contributing 17.7% and elevating the cumulative risk to 69%. Failures in the Power Supply Unit (PSU), along with issues related to humidity, and component corrosion, follow suit with diminishing RPN values of 105, 63, and 45 respectively. These factors represent 15.5%, 9.3%, and 6.6% of the total risk, progressively heightening the cumulative risk to 100%. The examination indicates that prioritizing enhancements in maintenance protocols, sensor calibration, and installation practices would effectively alleviate a majority of the identified risks.