A genetic-evolved fuzzy system for maintenance scheduling of generating units

doi:10.1016/S0142-0615(97)00080-X

International Journal of Electrical Power & Energy Systems

Volume 20, Issue 3, March 1998, Pages 191-195

https://doi.org/10.1016/S0142-0615(97)00080-X Get rights and content

Abstract

An application of a genetic-evolved fuzzy (GEF) system for maintenance scheduling of generating units is presented in this paper. In the proposed system, the fuzzy system was formulated with respect to multiple objectives and soft constraints, where genetic algorithms were applied to tune the membership functions in the solution process. In this way, parameters of membership functions can be optimally adjusted. The performance of the computation is also enhanced. The proposed approach has been tested on a practical Taiwan Power system (Taipower) through the utility data. The feasibility and effectiveness of the approach for generator maintenance scheduling applications were solidified through the test results.

References (20)

C.J. Huang et al.
Fuzzy approach for generator maintenance scheduling
Journal of Electric Power System Research
(1992)
S.J. Huang
Generator maintenance scheduling — A fuzzy system approach with genetic enhancement
Journal of Electric Power System Research
(1997)
J.F. Baldwin et al.
Dynamic programming for fuzzy systems with fuzzy environment
Journal of Mathematical Analysis and Applications
(1982)
Z. Yamayee et al.
A computationally efficient optimal maintenance scheduling
IEEE Transactions on Power Apparatus and Systems
(1983)
J.G. Waight et al.
Scheduling of generation and reserve margin using dynamic and linear programming
IEEE Transactions on Power Apparatus and Systems
(1981)
P. Hans et al.
Scheduling of generation and allocation of fuel using dynamic and linear programming
IEEE Transactions on Power Apparatus and Systems
(1984)
J.F. Dopazzo et al.
Optimal maintenance scheduling using integer programming
IEEE Transactions on Power Apparatus and Systems
(1975)
H. Kim et al.
An algorithm for thermal unit maintenance scheduling through combined use of GA SA and TS
T. Satoh et al.
Maintenance scheduling by using simulated annealing method
K. Yoshimoto et al.
Extended Hopfield neural network applied to maintenance scheduling of generating units based on decomposition
Expert System Applications to Power Systems
(1993)

There are more references available in the full text version of this article.

Cited by (31)

A Discrete Chaotic Jaya algorithm for optimal preventive maintenance scheduling of power systems generators
2022, Applied Soft Computing
Citation Excerpt :
The reliability constraints appoint the degree of risk for a given maintenance plan, the sequence constraints prevent to make any maintenance to some generators unless after a duration of making the maintenance to other units [25,29]. The transmission network constraints show the transmission ability in power systems [23,26]. Traditional methods, throughout history, have been used for solving GMS problems.
The main role of the optimal Generator Maintenance Scheduling (GMS) problem in power systems is to develop an optimal preventive maintenance scheduling of the generation part units. An optimal GMS provides power systems with higher operational reliability, extends the generators lifetime and reduces the cost of generators maintenance. The GMS problem is formulated as an optimisation problem. This problem should satisfy both load’s power demand and workforce constraints with ensuring the reliability of power systems at economical operation cost. The GMS problem has been studied for many years when exact mathematical methods have been used in the past to reach exact solutions for small-scale problems. However, these conventional mathematical approaches have many limitations and they suffer from unreasonable computational efforts as system dimension increases. Traditional approximate methods have been adopted to overcome the limitations of exact methods for medium-scale power systems. However, they provide approximate solutions and they require a large computational effort for wide-area systems of big dimensions. Recently, modern methods based on metaheuristics optimisation have taken a long part to solve the GMS problem and to overcome the limitations of approximate methods. In this paper, a proposed Discrete Chaotic Jaya Optimisation (DCJO) algorithm is employed to perform the preventive maintenance scheduling of electric power systems generators. The proposed algorithm is based on a cooperation between the discrete Jaya optimisation algorithm and a proposed move rule based on Chaotic Local Search (CLS) technique to improve both exploration and exploitation phases. The GMS problem is modelled based on the reliability criterion of an objective function of a sum of the squares of the reserves of generation. The optimisation process is performed through minimising an evaluation function of a weighted sum of the objective function and the penalty function for violations of the constraints. The proposed approach has been tested in a 21-unit test system over a planned horizon of 52 weeks in which the peak load is 4739 MW and the maximum generation is 5688 MW and there is a total number of 35 workforce available per week to perform the maintenance tasks. The proposed method has been compared through several statistical tests with recent algorithms of the related works. The obtained results show the effectiveness of the proposed algorithm for solving the GMS problem as compared to other recent algorithms. This approach can be relied at the present upon to solve maintenance scheduling problems of generation units in power systems.
Spinning reserve contribution using unit responsibility criterion incorporating preventive maintenance scheduling
2015, International Journal of Electrical Power and Energy Systems
Generators maintenance scheduling is addressed as a crucial issue that may affect both economy and reliability of power systems. System reserve procurement may facilitate preventive maintenance scheduling in such way to guarantee system reliability as well as security. In this paper, a new deterministic criterion for determining operating reserve capacity is introduced. In the proposed model, the unit reserve provision is handled based upon unit responsibility criterion (URC) that depends on unit capacities as well as number of committed units. Therefore, a new formulation for reserve assessment based upon URC incorporating preventive maintenance scheduling (PMSURC) is developed. The proposed model is structured as a mixed integer programming (MIP) and is solved using CPLEX solver. Several analyses are conducted to investigate the impact of unit responsibility criterion on the reserve assessment expenditure. An IEEE Reliability Test System (RTS) is employed to demonstrate the effectiveness of the proposed methodology and simulation results are promising.
Application of a novel cost reduction index to preventive maintenance scheduling
2014, International Journal of Electrical Power and Energy Systems
Citation Excerpt :
Reference [17], expressed a method based on the Monte Carlo simulation by integrating GA to tackle the preventive maintenance problem. In [18–20], a hybrid Fuzzy-genetic algorithm has been proposed while in [21] an MILP model based upon goal programming has been expressed for maintenance problem of thermal power plants. In [22], Tabu search (TS) algorithm has been used while an improved formulation by using ant colony (AC) technique to schedule the optimum power plant maintenance scheme has been used in [23,24].
Preventive maintenance scheduling of generating units is addressed as a crucial issue that affects on both economy and reliability of power system. In this paper, a new formulation of preventive maintenance scheduling associated with a novel cost reduction index (CRI) is developed. Mainly, the objective of the cost-based preventive maintenance scheduling consists of the operation as well as maintenance cost over a specified time horizon. A cost reduction index is introduced in such a way to minimize the operation cost while determining the most appropriate maintenance scheme. Here, the proposed framework is structured as a mixed integer linear programming (MILP) and solved using CPLEX solver. Several analyzes are conducted to investigate the impacts of CRI on maintenance scheme as well as system expenditures. The IEEE reliability test system (RTS) is utilized to demonstrate the effectiveness of the proposed structure and simulation results are promising.
Speed control of permanent magnet synchronous motors using fuzzy controller based on genetic algorithms
2012, International Journal of Electrical Power and Energy Systems
In this study, speed control of a permanent magnet synchronous motor (PMSM) with genetic based fuzzy controller has been simulated. Earlier studies have focused on different components of fuzzy controllers such as rule base or data base in the literature. However, in this study, the whole knowledge base is parameterized and optimized to obtain an optimal fuzzy controller without expert knowledge. In the developed control scheme, there are three closed loops. The two inner loops are the current and the velocity feedbacks, respectively. The other outer feedback is the genetic algorithm (GA) which optimizes the rule base and data base of the fuzzy controller simultaneously. This outer loop is an iterative process. To make comparisons, a conventional fuzzy controller also has been designed and the PMSM speed control has been simulated for both the proposed and conventional fuzzy controller. The comparative results have proved that the proposed controller has a better dynamic response than that of the conventional one.
Application of Artificial Intelligence in maintenance modelling and management
2012, IFAC Proceedings Volumes (IFAC-PapersOnline)
Over the past 3 decades many attempts have been made to apply Artificial Intelligence (AI) techniques in maintenance modeling and management. Essentially the use of AI is an attempt to replace human intelligence with machine intelligence. The ultimate objective is to achieve more effective maintenance management and in some cases to make achieving this goal a viable option. The AI techniques used are numerous ranging from the classic expert systems that utilizes rule based reasoning to the more cumbersome optimization techniques used in Genetic Algorithms. Over the past decade there has been a shift towards developing hybrid intelligent management systems in operations that use more than one AI technique. The application areas of AI in maintenance extends widely from the intelligent maintenance optimization models to the more practical applications such as cost budgeting of maintenance projects and selecting optimal repair methods. This paper presents an overview of the applications of AI techniques in maintenance over the past decades identifying specific applications and extent of use of techniques. The paper discusses the applications of AI techniques and recent trends.
A Simulated Annealing based approach to solve the generator maintenance scheduling problem
2011, Electric Power Systems Research
Citation Excerpt :
The authors implemented genetic operators to prevent the premature convergence of the simulation together with efficient encoding/decoding techniques concluding that GA's are very effective in dealing with the GMS problem. The approach described in [10] uses Genetic Algorithms together with fuzzy membership functions to model the two objectives included in this formulation – the reserve margin and the generation cost. Regarding the constraints, this formulation considers limitations on the number of available maintenance crews, limitations on the number of generators in maintenance in the same geographical area in order to limit power transfers between areas and the definition of a window of weeks during which each generator maintenance should be scheduled.
The scheduling of maintenance actions of generators is not a new problem but gained in recent years a new interest with the advent of electricity markets because inadequate schedules can have a significative impact on the revenues of generation companies. In this paper we report the research on this topic developed during the preparation of the MSc Thesis of the second author. The scheduling problem of generator maintenance actions is formulated as a mixed integer optimization problem in which we aim at minimizing the operation cost along the scheduling period plus a penalty on energy not supplied. This objective function is subjected to a number of constraints detailed in the paper and it includes binary variables to indicate that a generator is in maintenance in a given week. This optimisation problem was solved using Simulated Annealing. Simulated Annealing is a very appealing metaheuristic easily implemented and providing good results in numerous optimization problems. The paper includes results obtained for a Case Study based on a realistic generation system that includes 29 generation groups. This research work was proposed and developed with the collaboration of the third and fourth authors, from EDP Produção, Portugal.

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A genetic-evolved fuzzy system for maintenance scheduling of generating units

Abstract

Journal of Electric Power System Research

Journal of Electric Power System Research

Journal of Mathematical Analysis and Applications

A computationally efficient optimal maintenance scheduling

IEEE Transactions on Power Apparatus and Systems

Scheduling of generation and reserve margin using dynamic and linear programming

IEEE Transactions on Power Apparatus and Systems

Scheduling of generation and allocation of fuel using dynamic and linear programming

IEEE Transactions on Power Apparatus and Systems

Optimal maintenance scheduling using integer programming

IEEE Transactions on Power Apparatus and Systems

An algorithm for thermal unit maintenance scheduling through combined use of GA SA and TS

Maintenance scheduling by using simulated annealing method

Extended Hopfield neural network applied to maintenance scheduling of generating units based on decomposition

Expert System Applications to Power Systems