Reliability Assessment Of Generation Systems Research Articles

Reliability assessment for electrical power generation system based on advanced Markov process combined with blocks diagram

This paper presents the power generation system reliability assessment using an advanced Markov process combined with blocks diagram technique. The effectiveness of the suggested methodology is based on HL-I of IEEE_EPS_24_bus. The proposed method achieved the generation reliability and availability of an electrical power system using the Markov chain which based on the operational transition from state to state which represented in matrix. The proposed methodology has been presented for reliability performance evaluation of IEEE_EPS_24_bus. MATLAB code is developed using Markov chain construction. The transition between probability states is represented using changing the failure and repair rates. The reduced number of generation system are used with Markov process to assess the availability, unavailability, and reliability for the generation system. Additionally, the proposed technique calculates the frequency, time duration of states, the probability of generation capacity state which get out of service or remained in service for each state of failure, and reliability indices. A considerable improvement in reliability indices is found with using blocks diagram technique which is used to reduce the infinity number of transition states and assess the system reliability. The proposed technique succeeded at achieving accurate and faster reliability for the power system.

Reliability evaluation of generating systems considering aging processes

Equipment aging has been a major concern among electric utilities’ planners, since quality of service can be put at risk. This paper proposes a new method that allows the effects of the aging of generators to be properly included into the reliability analysis of power systems. First, a new model for the failure rate capable of representing the stages of normal operation and wear-out, replacing the adoption of constant failure rates, is described. The effects of repairable and non-repairable (i.e., catastrophic) failures on the reliability assessment of generation systems are also discussed. The proposed method is built from the previous models using a sequential Monte Carlo simulation and variance reduction techniques. To validate the proposed method, the IEEE Reliability Test System is used with modifications in which different ages are defined for existing generating units that are progressively aging over the years. Several tests are described together with strategies for adopting preventive refurbishments and the corresponding results are duly discussed.

Predictive Reliability Assessment of Generation System

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

Generating System Wellbeing Index Evaluation Using Genetic Algorithm

Reliability assessment of generation system is a crucial task used to be done using deterministic approaches. However, due to the practical limitations of these approaches, they have been gradually replaced by probabilistic techniques. Nevertheless, there is a considerable reluctance in many electric power utilities to completely abandon deterministic considerations. To fulfill the industry need, wellbeing analysis has been developed to combine the deterministic and the probabilistic approaches in a single framework. Analytical techniques or Monte Carlo Simulation have been used to evaluate wellbeing indices. However, analytical approaches are complicated and mathematically demanding and simulation technique requires a huge amount of computing time, and large memory size. This still prevents the utilities to benefit from the wellbeing framework. This paper presents a novel Genetic Algorithm (GA) based technique to calculate the wellbeing indices. Hopefully, this will encourage the industry to benefit from the wellbeing analysis. The features of the GA are utilized to collect and identify the health, marginal and at risk wellbeing states and to calculate the associated wellbeing indices. The proposed technique is applied to the IEEE-RBTS and the resulting wellbeing indices are compared to those obtained using a conventional analytical technique. The results show that the outcome of both techniques is virtually identical. The effect of the GA parameters on the wellbeing indices is examined. The proposed GA based technique in the manner applied in this study is simple, practical and valid to calculate the wellbeing indices.

Probabilistic Wind Energy Modeling in Electric Generation System Reliability Assessment

The power grid reliability impacts could be significant when a large amount of variable wind generation is integrated with the electric power system. The widely used deterministic reliability assessment method is invalid when modeling intermittency of wind energy sources. The energy based probabilistic reliability assessment models are required in system reliability impact assessment in order to consider the stochastic characteristic of wind resources. This paper investigates different stochastic characteristics in wind energy integration, including resource availability, generation facility outage and transmission availability. A probabilistic framework of reliability modeling for renewable resource integration such as wind energy conversion system is proposed in this paper. Using the proposed reliability models and framework, the cost of wind energy integration with the power gird for maintaining system adequacy and reliability can be evaluated realistically. The IEEE Reliability Test System (IEEE-RTS) system is utilized to demonstrate the developed models and methods. The methodology can be used for modeling the reliability of industrial and commercial facilities being serviced by wind farms and transmission lines.

Incorporation and effects of different supporting policies in multi-area generation system reliability assessment

A new method for multi-area generation system reliability has been developed at the University of Saskatchewan. It is based on direct sampling of generating unit states, clustering and sampling of load states and a linear programming model for multi-area reliability evaluation. Area load uncertainty and correlation can be considered using a correlative distributed sampling technique. Reliability sensitivity indices of both overall system and area to area generation and tie line capacities can be calculated. A wide variety of supporting policies can be easily incorporated in this basic methodology. The authors examine the incorporation of different supporting policies and their effects on multi-area system reliability indices. Case studies of two test systems indicate that different supporting policies create different area reliability indices. It is therefore important to incorporate the supporting policy which is used in actual operation to obtain area indices which are consistent with reality. >

Monte Carlo Method for Multi-Area Generation System Reliability Assessment

Monte Carlo Method for Multi-Area Generation System Reliability Assessment

A Monte Carlo method for multi-area generation system reliability assessment

The authors present a method for multiarea generation system reliability assessment. It is based on direct sampling of generating unit states, the clustering technique, and the correlative normal distribution sampling technique of the load states. A linear programming model was used to minimize the total load curtailment. Area load uncertainty, correlation between area loads, and generating unit derated state were considered. Incorporation of these factors does not create a significant increase in computing time. Sensitivity indices of both system and each area to area generation and tie line capacities can be calculated. These indices provide information regarding which tie line or which area generating capacity should be reinforced from the overall system and each area point of view. Calculation results of a four-area system are given to demonstrate the effectiveness of the method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Reliability assessment of generation systems containing multiple hydro plant using simulation techniques

Models and evaluation techniques based on Monte Carlo simulation are described for the reliability assessment of mixed hydro-thermal generation systems. The effects that system operating and water management policies have on the reliability indices are considered. These effects are described and discussed using the basic IEEE Reliability Test System (RTS) extended by additional hydro plant data. The outcome of these studies is an improved understanding of the effects that operational policies have on the behavior of mixed hydro-thermal systems and, in particular, an improved knowledge of the response of the RTS.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>