Genetic algorithms solution to generator maintenance scheduling with modified genetic operators

The applicability of genetic algorithms (GA) to the generator maintenance scheduling (GMS) problem with modified genetic operators (MGO), such as string reversal and reciprocal exchange mutation (REM) is demonstrated. The main contribution is the use of ‘probabilistic production simulation’ (PPS) with an equivalent energy function method, which outperforms other methods in terms of computation time and accuracy. The performance of the algorithm has been tested on 5- and 21-unit test systems with integer encoding, binary for integer encoding, and real encoding. The GMS problem is solved to minimise the expected energy production cost (EEPC) and maximising the reserve objectives under a series of constraints. Results are compared with solution by conventional methods. This paper places in proper perspective the effect of MGO, with an explicit case study and simulation results. It is placed in evidence that only integer coding GA finds the global optimum solution, irrespective of the nature of the objective function and system size. Faster convergence is enhanced with the implementation of MGO for integer GA only.

[1]  D. Chattopadhyay A practical maintenance scheduling program mathematical model and case study , 1998 .

[2]  Jay Yellen,et al.  A decomposition approach to unit maintenance scheduling , 1992 .

[3]  L. L. Garver,et al.  Adjusting Maintenance Schedules to Levelize Risk , 1972 .

[4]  Rong-Ceng Leou A Flexible Unit Maintenance Scheduling Considering Uncertainties , 2001 .

[5]  Chin E. Lin,et al.  An expert system for generator maintenance scheduling using operation index , 1992 .

[6]  A. H. Palmer,et al.  A Technique for the Automated Scheduling of the Maintenance of Generating Facilities , 1972 .

[7]  太田 宏 Mitsuo Gen and Runwei Cheng著, Genetic Algorithms & Engineering Design, John Wiley & Sons Inc., 441頁, 1997年, 定価89.95ドル , 1999 .

[8]  Koichi Nara,et al.  Maintenance scheduling by using simulated annealing method (for power plants) , 1991 .

[9]  Tharam S. Dillon,et al.  An Experimental Method of Determination of Optimal Maintenance Schedules in Power Systems Using the Branch-and-Bound Technique , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  J.F. Dopazo,et al.  Optimal generator maintenance scheduling using integer programming , 1975, IEEE Transactions on Power Apparatus and Systems.

[11]  Edmund K. Burke,et al.  Hybrid evolutionary techniques for the maintenance scheduling problem , 2000 .

[12]  Yasuhiro Hayashi,et al.  An algorithm for thermal unit maintenance scheduling through combined use of GA, SA and TS , 1997 .

[13]  J. R. McDonald,et al.  Generator maintenance scheduling of electric power systems using genetic algorithms with integer representation , 1997 .

[14]  H. Khatib,et al.  Maintenance Scheduling of Generating Facilities , 1979, IEEE Transactions on Power Apparatus and Systems.

[15]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[16]  H. M. Merrill,et al.  Power plant maintenance scheduling: optimizing economics and reliability , 1991 .

[17]  Zia A. Yamayee,et al.  A Computationally Efficient Optimal Maintenance Scheduling Method , 1983 .