Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization

Jagtap, Hanumant P.; Bewoor, Anand K.; Kumar, Ravinder; Ahmadi, Mohammad Hossein; Chen, Lingen

doi:10.1016/j.ress.2020.107130

Cited by 39 publications

(17 citation statements)

References 25 publications

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“…There is a set of measurements that help in the process of assessing the importance of different aspects of a system. The performance measurement of maintenance factors assists managers in making strategic decisions for the good performance of the solar plant 67,68 . Maintenance indicators assess the quality of maintenance in terms of time consumed for different interventions, as well as related costs; which is why they are classified into technical and economic indicators.…”

Section: Resultsmentioning

confidence: 99%

Management of operation and maintenance practices in photovoltaic plants: Key performance indicators

Rediske

Michels

Siluk

et al. 2022

Intl J of Energy Research

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Summary The number of large photovoltaic (PV) power plants is increasing around the world. Energy sale usually follows demand contracts with clearly defined obligations, subject to nonsupply penalties. Not supplying the amount of contracted energy is a critical issue to PV plant performance, which can be mitigated with operation and maintenance (O&M) good practices. Furthermore, as the PV plant ages, O&M practices become increasingly important to improve or maintain a good performance. Performance assessment is a complex process that involves several parameters to collect, indicators to measure, and action plans to solve the identified problems. The objective of this study is to identify relevant factors in performance evaluation of O&M practices of medium to large PV plants. Through a systematic literature review and Delphi method with specialists, 33 key performance indicators (KPIs) were determined and classified in operation or maintenance categories, and further in technical or economic subcategories. This paper also organized a set of parameters that PV plant managers should collect to determine the KPIs and evaluate the O&M practices performance. Efficient management of O&M practices results in an indirect increase in generation capacity, as well as ensuring compliance with electricity supply contracts.

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Section: Resultsmentioning

confidence: 99%

Management of operation and maintenance practices in photovoltaic plants: Key performance indicators

Rediske

Michels

Siluk

et al. 2022

Intl J of Energy Research

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“…These algorithms have achieved more efficient results for various problems such as availability optimization of process industries. Also, GA and PSO are not influenced by the size and non-linearity of problems [49]. There are many other algorithms such as Chimp optimization, dynamic Levy flight chimp, and weighted Chimp optimization [54][55][56][57][58][59][60][62][63][64][65][66] that can be applied in the future for estimating their performances in availability computations of STPP.…”

Section: Optimization Strategiesmentioning

confidence: 99%

Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower Using Metaheuristic Algorithms

et al. 2022

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Metaheuristic algorithms are extensively utilized to find solutions and optimize complex industrial systems' performance. In this paper, metaheuristic algorithms are utilized to predict the optimum value of the operational availability of a cooling tower in a steam turbine power plant. These techniques have some flaws like poor convergence speed, being stuck in local optima, and premature convergence. For this purpose, a novel efficient stochastic model is proposed for a cooling tower that is configured with six subsystems. The Markovian birth-death process is utilized to develop the Chapman-Kolmogorov differentialdifference equations. All the random variables are statically independent, and repairs are perfect. Failure rates are exponentially distributed, while repair rates follow the arbitrary distribution. Steady-state availability (SSA) of the system is derived concerning various failure and repair rates. The sensitivity analysis of SSA is also performed to identify the most critical component. Further, system availability is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) because they are found to be more suitable for such types of problems. It is revealed that the PSO outperforms GA in predicting the availability of cooling towers used in steam turbine power plants.

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“…GA and PSO are most popular algorithms under evolutionary and swarm intelligence categories respectively 47 . Also, these techniques are more efficient for such type of problems (availability optimization of process industries) and not influenced by size and non‐linearity of problems 48 . The main objective of applying these techniques to derive values of decision variables (i.e., failure and repair rates) and the optimum value of the availability function.…”

Section: Availability Optimizationmentioning

confidence: 99%

Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

Saini

Gupta

Shankar

et al. 2022

Quality & Reliability Eng

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The prominent objective of the present study is to optimize the availability of condenser units in steam turbine power plants. For this purpose, a novel stochastic model is proposed by considering a condenser as a system consisting of seven subsystems. All the time‐dependent random variables associated with failure rates followed exponential distribution while repair rates are arbitrarily distributed. Furthermore, to attain the optimum value of system availability nature‐inspired algorithm has been applied. Predicting the future phenomena is very difficult and most of the existing algorithms have some limitations like stuck at local minima, and slow convergence rate, and so forth. So, in this study proposed model has been optimized using a genetic algorithm (GA) and particle swarm optimization (PSO) The expressions for steady‐state availability of the system have been assessed using Chapman‐Kolmogorov differential‐difference equations developed by applying the Markov birth‐death process. The supplementary variable technique has been adopted to solve differential‐difference equations For a comparative analysis, the steady‐state values, GA, and PSO results are compared. It is revealed that the GA gives the maximum value of availability as 0.9886 corresponding to crossover probability 0.65, population size 50, evaluation 300, and mutation probability 0.5. While using PSO optimum availability value is 0.998152 at 27 iterations, population 55, inertia weight 1, damping ratio 0.85, personal best 1.5, and global best 2. So, PSO results outperform the steady‐state and GA results. It is revealed that mixed bed filter and ejector are the most sensitive components of the condenser those influences most the system effectiveness.

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Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization

Cited by 39 publications

References 25 publications

Management of operation and maintenance practices in photovoltaic plants: Key performance indicators

Management of operation and maintenance practices in photovoltaic plants: Key performance indicators

Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower Using Metaheuristic Algorithms

Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

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