**Author details**

178 Induction Motors – Modelling and Control

**6. Conclusions** 

Figure 10 shows the improved voltage profile at Bus 14071 under contingency 1 after implementing the final SVC solution. Figure 10 also shows the output of the two SVCs. The

The final investment solution for the coordinated planning problem includes SVC placements at buses 14071 and 14084 of maximum capacity 3.0 MVar and 2.85 MVar respectively1. The total cost is 32.25 M\$ under the cost assumptions used for this study.

This chapter presented the critical role played by induction motors in short-term voltage stability phenomenon, and emphasizes the need for including proper dynamic models of motor loads in voltage stability related planning studies. The chapter also sheds light on the need of a coordinated framework for reactive power planning considering both static and dynamic reactive control devices, which mitigates both post-contingency steady state as well transient voltage recovery and motor stalling problems. The mixed integer optimization based VAR planning algorithm is formulated to satisfy the minimum post-contingency steady state voltage stability margin and post-fault transient voltage recovery performance criteria under many critical contingencies simultaneously. The developed method was illustrated in largescale system with 16173 buses representing US Eastern Interconnection, wherein induction motor dynamic characteristics modeling and bus voltage magnitude limit enforcement were incorporated within the overall analysis and planning respectively. The results verify that the method works satisfactorily to plan an optimal mix of static and dynamic VAR devices under

1 There may be cases, when the required SVC to mitigate slow voltage recovery problems might be less, and they may still not satisfy the post-contingency steady state voltage stability margin requirements. For such cases, the MIP may

SVC peak output at bus 14071 is 290 MVar, and that of bus 14084 is 270 MVar.

**Figure 10.** Bus 14071 voltage profile under cont. 1 with final allocation

choose MSCs as an effective addition to the optimal solution.

Venkat Krishnan and James D. McCalley *Iowa State University, USA* 

### **7. References**


the Substation Protection Subcommittee of the IEEE Power System Relaying Committee, *IEEE Trans. on Power Delivery*, Vol. 10, No. 2, April 1995

**Chapter 7** 

© 2012 Anwari et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Anwari et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Optimization of Induction Motors Using Design** 

**of Experiments and Particle Swarm Optimization** 

Houssem Rafik El-Hana Bouchekara, Mohammed Simsim and Makbul Anwari

The level of prosperity of a community is related to its ability to produce goods and services. But producing goods and services is strongly related to the use of energy in an intelligent way. Energy can be exploited in several forms such as thermal, mechanical and electrical (Boldea & Nasar, 2002). Electrical energy, measured in kWh, represents more than 30% of all used energy and it is on the rise (Boldea & Nasar, 2002). The larger part of electrical energy is converted into mechanical energy in electric motors. Among electric motors, the induction motor is without doubt the most frequently used electrical motor and is a great energy consumer. About 70% of all industrial loads on a specific utility are represented by induction motors (Maljkovic, 2001). The vast majority of induction motor drives are used for heating, ventilation and air conditioning (Blanusa, 2010; Cunkas &

The design of an induction motor aims to determine the induction motor geometry and all data required for manufacturing to satisfy a vector of performance variables together with a set of constraints (Boldea & Nasar, 2002). Because induction motors are now a well developed technology, there is a wealth of practical knowledge, validated in industry, on the relationship between their performance constraints and their physical aspects. Moreover, mathematical modeling of induction motors using circuit, field or hybrid models provides formulas of performance and constraint variables as functions of design variables

The journey from given design variables to performance and constraints is called analysis, while the reverse path is called synthesis. Optimization design refers to ways of doing efficient synthesis by repeated analysis such that some single (or multiple) objective (performance) function is maximized and/or minimized while all constraints (or part of them) are fulfilled (Boldea & Nasar, 2002). The aim of this chapter is to present an optimal

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/38665

**1. Introduction** 

Akkaya 2006).

(Boldea & Nasar, 2002).


Available: http://www.wecc.biz/documents/library/procedures/ CriteriaMaster.pdf

