Sohrab Asgarpoor

Probabilistic security assessment for power system operations

Control room operators are faced with frequent security-economy decision-making situations necessitated by stressed system operating conditions, and there is increased need for security-economy decision-support tools. Although probabilistic methods are promising in this regard, they have been mainly used in planning environments. This task force paper explores their use for operational decision-making, comparing them to the more traditional deterministic approach. Two examples are used to facilitate this comparison via overload and low voltage security assessment to identify secure regions of operation for a small 5-bus system and for the IEEE Reliability Test System. The results of this comparison show that the probabilistic approach offers several inherent advantages.

Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households

In the near future, a smart grid will accommodate customers who are prepared to invest in generation-battery systems and employ energy management systems in order to cut down on their electricity bills. The main objective of this paper is to determine the optimum capacity of a customers distributed-generation system (such as a wind turbine) and battery within the framework of a smart grid. The proposed approach involves developing an electricity management system based on stochastic variables, such as wind speed, electricity rates, and load. Then, a hybrid stochastic method based on Monte Carlo simulation and particle swarm optimization is proposed to determine the optimum size of the wind generation-battery system. Several sensitivity analyses demonstrate the proper performance of the proposed method in different conditions.

Reliability Evaluation of Equipment and Substations With Fuzzy Markov Processes

An algorithm for integrating uncertain parameters in Markov analysis is proposed. Fuzzy Markov models for aging equipment and substations are developed in which the transition rates/probabilities with uncertainty are represented by fuzzy membership functions. An extension principle and nonlinear optimization-based approaches are utilized for calculation of fuzzy reliability indices. Sensitivity studies for analyzing the impact of various fuzzy membership functions on reliability indices are provided, and the characteristics of the results are discussed. The fuzzy Markov model presented in this paper has valuable application in extending current Markov analysis with the ability to incorporate uncertainties associated with data collected on maintenance activities, etc.

Maintenance optimization of equipment by linear programming

Optimal levels of preventive maintenance performed on any system ensures cost-effective and reliable operation of the system. In this paper, a component with deterioration and random failure is modeled using Markov processes while incorporating the concept of minor and major preventive maintenance. The optimal mean times to preventive maintenance (both minor and major) of the component is determined by maximizing its availability with respect to mean time to preventive maintenance. Mathematical optimization programs Maple 7 and Lingo 7 are used to find the optimal solution, which is illustrated using a numerical example. Further, an optimal maintenance policy is obtained using Markov Decision Processes (MDP). Linear Programming (LP) is utilized to implement the MDP problem

Optimum Maintenance Policy with Inspection by Semi-Markov Decision Processes

A method which incorporates modeling of equipment deterioration, inspection, and minor & major maintenance for optimal maintenance decision is proposed in this paper. Optimal inspection and maintenance policy to maximize equipment availability and minimize the cost is evaluated by semi-Markov processes and semi-Markov decision processes. An example illustrates the implementation of proposed method for maintenance evaluation of circuit breakers. Also the results of how inspection and maintenance rate impact equipment availability and benefit are discussed. This study is valuable for utilities to determine optimal maintenance policy of equipment.

Reliability and Maintainability Improvement of Substations With Aging Infrastructure

The increasing size, aging equipment, and complexity of power systems, coupled with present day financial constraints, have made the use of probabilistic methods and reliability indices a necessity for maintaining continuity and quality of service to customers. Proper maintenance-related decisions should address all of these issues to improve system reliability while meeting limited budget constraints. This paper proposes algorithms that enable system-level reliability assessment with detailed modeling of maintenance for aging equipment. Stochastic-based reliability modeling of substations with aging equipment is presented, which enables the study of equipment aging, failures, and maintenance and their effect on substation-level availability and frequency of failure. Several case studies are provided which describe optimization of maintenance activities and the impact on maximum substation reliability. The algorithms provide a valuable tool for processing detailed models of aging equipment and maintenance of individual pieces in system reliability assessment applications. These algorithms are consistent with existing reliability models and are capable of being integrated into utility asset-management programs.

Preventive Maintenance Using Continuous-Time Semi-Markov Processes

A method is presented to determine the amount of preventive maintenance to be performed on equipment in order to maximize availability. The method uses a continuous-time semi-Markov process that assumes equipment can fail due to both deterioration and random occurrences. Preventive maintenance can be performed from working state to prevent deterioration failure. An example is used to demonstrate the method using MATLAB and Maple software. Additional flexibility in equipment modeling using semi-Markov processes is described.

Risk-based Failure Mode and Effect Analysis for wind turbines (RB-FMEA)

Failure Mode and Effect Analysis (FMEA) has already been used as a qualitative measure for identifying failure modes and causes, in order to mitigate the effects of failure in different sectors of power systems. This paper presents a quantitative approach called Risk-Based-FMEA, based on the failure probabilities and incurred failure costs instead of rating scales. As a case study, this approach has been applied to a direct drive wind turbine. The results show that the definition of failure modes priorities based on their contribution to the total failure cost of the wind turbine is more realistic and practical than the common FMEA approach. Using MS Excel spreadsheet platform, the proposed method can be generalized for different types of wind turbines. In addition, the effective failure cost factors are investigated through sensitivity analysis, by which the wind turbine owner can determine the suitable approach to reduce the total failure cost.

Voltage analysis of distribution systems with DFIG wind turbines

Wind energy is becoming the most viable renewable energy source mainly because of the growing concerns over carbon emissions and uncertainties in fossil fuel supplies and the government policy impetus. The increasing penetration of wind power in distribution systems may significantly affect voltage stability of the systems, particularly during wind turbine cut-in and cut-off disturbances. Currently, doubly fed induction generator wind turbine (DFIG-WT) is the most popular wind turbine. This paper investigates the issues of voltage stability improvement and grid-loss reduction of distribution systems which include DFIG-WTs under unbalanced heavy loading conditions. Simulation studies are carried out in IEEE 34-bus test system using DIgSILENT software to examine these issues during steady-state and transient operations of the system. Optimal locations of the WTs are determined based on this analysis. A new index (system unbalanced voltage variance) is proposed to evaluate system unbalanced voltage. The dynamic impacts between WTs and motor loads are also examined.

Maintenance Optimization Of Equipment By Linear Programming

Optimal levels of preventive maintenance performed on any system ensures cost-effective and reliable operation of the system. In this paper a component with deterioration and random failure is modeled using Markov processes while incorporating the concept of minor and major preventive maintenance. The optimal mean times to preventive maintenance (both minor and major) of the component is determined by maximizing its availability with respect to mean time to preventive maintenance. Mathematical optimization programs Maple 7 and Lingo 7 are used to find the optimal solution, which is illustrated using a numerical example. Further, an optimal maintenance policy is obtained using Markov Decision Processes (MDPs). Linear Programming (LP) is utilized to implement the MDP problem.