Carlos Alberto Favarin Murari

Transmission Network Expansion Planning Considering Uncertainty in Demand

This paper presents two mathematical models and one methodology to solve a transmission network expansion planning problem considering uncertainty in demand. The first model analyzed the uncertainty in the system as a whole; then, this model considers the uncertainty in the total demand of the power system. The second one analyzed the uncertainty in each load bus individually. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The models presented are solved using a specialized genetic algorithm. The results obtained for several known systems from literature show that cheaper plans can be found satisfying the uncertainty in demand

A Hybrid State Estimator: Solving Normal Equations By Orthogonal Transformations

The standard approach to the solution of the weighted least square (WLS) state estimation in power system is the iterative normal equations method. Occasional ill-conditioning has been experienced with this method. Recently alternative solution approaches based on orthogonal transformations have been proposed. Sparsity generally suffers in these methods. In this paper, the network condition which causes ill- conditioning is studied. A hybrid approach which enjoys the same numerical robustness of the orthogonal methods with sparsity close to the standard WLS method is presented. The modification needed to implement the hybrid method on an existing standard WLS state estimation program is quite small.

Transmission network expansion planning considering uncertainness in demand

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem considering uncertainness in demand. The methodology allows us to find an optimal transmission network expansion plan that allows the power system operates adequately in an environment with uncertainness. The model presented is solved using a genetic algorithm designed to solve the expansion planning with uncertainness in demand in an efficient way. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

Genetic algorithm of Chu and Beasley for static and multistage transmission expansion planning

In this paper the genetic algorithm of Chu and Beasley (GACB) is applied to solve the static and multistage transmission expansion planning problem. The characteristics of the GACB, and some modifications that were done, to efficiently solve the problem described above are also presented. Results using some known systems show that the GACB is very efficient. To validate the GACB, we compare the results achieved using it with the results using other meta-heuristics like tabu-search, simulated annealing, extended genetic algorithm and hybrid algorithms

Transmission network expansion planning in full open market considering security constraints

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n -1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

A fuzzy heuristic algorithm for distribution systems' service restoration

This paper describes a fuzzy heuristic algorithm for the service restoration of distribution systems. Its main objectives are: (a) to supply energy to the largest possible number of customers, (b) to minimize the number of switching operations, (c) to represent the operators knowledge and (d) to reduce the computational effort. This fuzzy algorithm, along with a fuzzy load estimator method, provided very good results, showing to be an efficient restoration tool.

Analysis of the Impact of Induction Generators on Distribution Systems Voltage Sags Due to Unbalanced Faults

This paper presents an analysis of the impact of induction generators on voltage sag in distribution systems caused by unbalanced faults. It was analyzed single line-to-ground faults (SLGFs) and line-to-line faults (LLFs). The analysis is conducted through electromagnetic transient simulations. Moreover, the impact of different connections of the transformer between the generator and the power system was also investigated. The results show that the voltage sags in different system buses are affected due to the presence of induction generators.

Comparison of compensation methods applied to the analysis of topological changes in electrical networks

This paper focuses on compensation methods used in the analysis of topological changes in power systems. The objectives are twofold: (a) listing the main methods available in the literature and (b) comparing them. Methods based on the inverse matrix modification lemma, diakoptics and sensitivities are implemented and tested. The comparison is based on memory storage requirements, precision of results, number of operations (mult/div and sum/sub) and processing times. It is shown that there is a best compensation method to be used depending on the adopted network power flow equations. It is also shown that the compensation method based on sensitivities applied to changes in the Jacobian matrix has the best performance among all other methods. The results of simulations using those methods applied to a number of small and large power systems are presented.

Didactic videos about basic concepts on alternating current circuits

This work emphasizes the importance of the formulation and application of didactic videos mainly in subjects where the classes are exclusively theoretical, as in the case of the subject ET016 — Electrotechnics, taught at the School of Electrical and Computer Engineering — UNICAMP. It is mandatory and it is offered to the students of the following Colleges: Food Engineering, Agricultural Engineering, Control and Automation Engineering, Mechanical Engineering and Chemical Engineering. In this subject, basic concepts of electricity are taught: single-phase and three-phase circuits; electrical energy production, transmission and application; physical principles and main application of transformers; electrical machinery of induction, asynchronous and direct current; and basic concepts of electric installations and protection devices. Upon elaborating and making these videos available, we aim at arousing a larger interest on the students, causing them to complement and reinforce the content taught at the attended theoretical classes.

A Practical Method for Estimation of Fault Ride-Through Capability of Wind Power Farms Based on Squirrel-Cage Rotor Induction Generators

This paper presents a practical method for estimation of fault ride-through capability of wind power farms composed by squirrel-cage rotor induction generators. The proposed method is based on the steady-state equivalent circuit of the induction generator and on the concepts of stable and unstable electrical-mechanical equilibrium points, which are calculated in the electrical and mechanical torque versus rotor speed space. The method has been validated by comparing the results obtained by the analytical method with those determined by dynamic simulation results.