Tuning of Microgrid Controllers using Cuckoo Search Algorithm

ABSTRACT

Microgrid at islanding mode is operated with renewable energy sources like Solar, Wind and non-renewable energy sources like Diesel generator and Battery which supply load to the system efficiently. With the change in load there is frequency deviation and controllers are required. There is a requirement to tune controllers to have optimal utilization of electrical energy and to maintain frequency at desired level. Cuckoo Search Algorithm (CSA) has been implemented to tune the controllers of microgrid. CSA gives optimal solutions in MATLAB using Integral Time Square Error principle (ITSE). The proposed results using CSA in comparison to the trial and error method is improving the steady state response of the considered microgrid, maintaining the system frequency constant. We have proposed a method for tuning the controller to have the frequency of the system at desired level.

KEYWORDS: Microgrid, Tuning, Diesel generator, Battery, Cuckoo Search Algorithm.

BLOCK DIAGRAM:

Fig. 1. The block diagram of microgrid with renewable energy sources and controllers.

SIMULATION RESULTS:

Fig. 2. Comparison of frequency deviation using PID, PI, and P controller for conventional method.

Fig. 3. The change in frequency (∆f) response of microgrid using PID controller by conventional and CSA method

Fig. 4. Response of the microgrid with battery using CSA method.

Fig. 5. The change in frequency (∆f) response of Microgrid with and without battery using PID controller.

CONCLUSION

In this paper the optimal values of the controller gains in the microgrid were calculated using the conventional methods and CSA. In this work renewable and non renewable energy sources are used to meet the load. The principle behind the new stratagem adopted is to somehow or other steady the system at the output end and independent of variations in the system due to load fluctuations on all counts. Thus the Cuckoo Search Algorithm has been implemented in the tuning of controller parameters MATLAB atmosphere. By trial and error method, the results achieved were comprehensively compared with Cuckoo Search Algorithm. The resultant conclusion proved that the latter is more efficient and better choice among other optimization techniques.

REFERENCES

[1] S. C. S. Chowdhury and P. Crossley, “Microgrids and active distribution networks,” Institution of Engineering and Technology, 2009.

[2] T. Senjyu, T. Nakaji, K. Uezato, and T. Funabashi, “A hybrid power system using alternative energy facilities in isolated island,” IEEE Transactions on Energy Conversion, vol. 20, no. 2, pp. 406–414, June 2005.

[3] N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Microgrids,” IEEE Power and Energy Magazine, vol. 5, no. 4, pp. 78–94, July 2007.

[4] P. Basak, S. Chowdhury, S. P. Chowdhury, and S. H. nee Dey, “Simulation of microgrid in the perspective of integration of distributed energy resources,” in 2011 International Conference on Energy, Automation and Signal, Dec 2011, pp. 1–6.

[5] M. Bhoye, S. N. Purohit, I. N. Trivedi, M. H. Pandya, P. Jangir, and N. Jangir, “Energy management of renewable energy sources in a microgrid using cuckoo search algorithm,” in 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Mar 2016, pp. 1–6.