Marcelo Favoretto Castoldi

Design of Power Systems Stabilizers for distributed synchronous generators using Linear Matrix Inequality solvers

The objective of this research is to evaluate the performance of a Power System Stabilizers (PSS), designed using a formulation based on Linear Matrix Inequalities (LMIs), in an Electric Power Distribution System (EPDS) with distributed synchronous generators operating under unbalanced three-phase. Under these conditions, the steady-state operation of the EPDS is not an equilibrium point, which introduces a new condition not covered by typical PSS design techniques. To overcome this problem, an EPDS modeling technique developed on a previous work by the authors research group was used. For the PSS design, a technique called V-K iteration was employed and the maximization of the minimum damping was set as the controller design objective. Furthermore, three LMI solvers were used (and compared) to assess design feasibility. A total of 880 operating points (for which the open loop minimum damping was smaller than 3%) were considered for the design. The results show that the proposed formulation is feasible and good controller performances can be achieved by the different PSSs designed using each of the tested LMI solvers.

Temperature influence analysis on parameter estimation of induction motors using differential evolution

Three-phase induction motors are widely used in industry due to their low cost and ruggedness compared with other electromechanical conversion machines, as the direct cur-rent motors, for example. Systems and control drives (DTC, FOC and V/f) are applied in industry, so it is important to know the electrical and mechanical parameters of the machine, such as, stator and rotor resistances, stator and rotor leakage inductance and magnetizing inductance, moment of inertia and friction coefficients. However, parameters like stator and rotor resistances suffer influences throughout its use due to external factors, such as temperature, which impairs the machine efficiency. In the search for a method to find these parameters, even with the changes provided by the temperature influence, this work proposes the use of differential evolution for the estimation of the parameters using as input the curve of a single phase stator current. Tests were realized using four different temperature values, and for each one it was assigned a corresponding input, in order to analyze the temperature influence on stator and rotor resistance. The results presented show that it is possible to estimate the motor electrical and mechanical parameters even with the changes provided by the temperature influences.

Simultaneous and coordinated tuning of PSSs and PODs using differential evolution

Tuning Electric Power System (EPS) controllers for small-signal enhancement is a very hard task. Nowadays, the most used techniques for this task involves a trial-and-error process. These techniques consider a set of operating conditions of the system and multiple modes of oscillation, making it very difficult for a human designer to keep track of so many variables and parameters. This results in a sequential approach, where one controller is tuned, placed in the system, and then the designer proceeds to tune the next controller. This paper proposes a method for simultaneously tuning controllers considering several operation conditions at once. The tuning is done in a coordinated way, avoiding interactions among the controllers. A differential evolution technique is used to perform the automatic tuning method proposed in this work. One important point to remark is that this algorithm can tune different types of controllers at once (PSSs and PODs in this case). Results show satisfactory performance of the tuned controllers, showing the effectiveness of the proposed technique. Furthermore, a significant productivity gain can be achieved if the engineer in charge of this design only supervises the automatic process, instead of performing all the calculations himself/herself. This is one of the main contributions of the present paper.

Metaheuristics optimization applied to PI controllers tuning of a DTC-SVM drive for three-phase induction motors

Abstract This paper presents the application of optimization metaheuristics in direct torque control with space vector modulation (DTC-SVM) of a three-phase induction motor. Two metaheuristic algorithms are considered: Ant Colony Optimization (ACO) and Differential Evolution (DE). These techniques are considered in order to achieve an optimized tuning of proportional-integral (PI) controllers in the DTC-SVM control loops, such as rotor speed, electromagnetic torque, stator flux linkage and estimation of the linkage stator flux. Hence, the paper aims to contribute to adjusting the PI controllers of DTC-SVM. All the optimization procedure is performed via computer simulation. Once the optimized gains are obtained, they are applied to the practical system developed herein. Simulation and experimental results are presented in order to validate the approach proposed.

Estimation of parameters and tuning of a speed PI of permanent magnet DC motor using differential evolution

In this work, a system of parameter estimation and tuning of a speed PI controller of a permanent magnet DC motor using differential evolution is presented. Initially the algorithm estimates permanent magnet DC motor parameters, they are, armature resistance, armature inductance, constant of torque, coefficient of friction and moment of inertia. Later, with the parameters of the plant estimated, the algorithm tuned the best set of parameters of the PI controller, using the performance index of controllers ITAE. Experimental results are presented to validate the proposed method.

Automatic Tuning of PSSs and PODs Using a Parallel Differential Evolution Algorithm

Since Electric Power Systems are constantly subjected by perturbations, it is necessary to insert controllers for damping electromechanical oscillations originally from these perturbations. The Power System Stabilizer (PSS) and Power Oscillation Damper (POD) are two of the most common damping controllers used by the industry. However, just the inclusion of these controllers does not guarantee a satisfactory damping of the system, being necessary a good tune of them. This paper proposes a method for simultaneously tuning different kind of controllers considering several operation conditions at once. A differential evolution technique is used to perform the automatic tuning method proposed, with the great advantage of the parallel computing, since modern computers have more than one core. Simulation results with the benchmark test system New England/New York show the satisfactory performance of the parallel algorithm in a short running time than its non-parallel structure.

Combining subpopulation tables, non-dominated solutions and Strength Pareto of MOEAs to treat service restoration problem in large-scale distribution systems

The network reconfiguration for service restoration (SR) in distribution systems is a combinatorial complex optimization problem since it involves multiple non-linear constraints and objectives. For large networks, no exact algorithm has found adequate SR plans in real-time. On the other hand, methods combining Multi-objective Evolutionary Algorithms (MOEAs) with the Node-depth encoding (NDE) have shown to be able to efficiently generate adequate SR plans for large distribution systems (with thousands of buses and switches). This paper presents a new method that combining NDE with three MOEAs: (i) NSGA-II; (iii) SPEA 2; and (iii) a MOEA based on subpopulation tables. The idea is to obtain a method that cannot-only obtain adequate SR plans for large scale distribution systems, but can also find plans for small or large networks with similar quality. The proposed method, called MEA2N-STR, explores the space of the objectives solutions better than the other MOEAs with NDE, approximating better the Pareto-optimal front. This statement has been demonstrated by several simulations with DSs ranging from 632 to 1,277 switches.

Parallel simultaneous and coordinated tuning of PSSs using Ant Colony Optimization

Power system controllers are typically designed using trial-and-error techniques, which may require large effort and time from the part of the designer to find a satisfactory solution. This work proposes an algorithm to perform a simultaneous and coordinated tuning of the controllers (PSSs) in an automatic form. To perform this tuning, the algorithm uses an optimization technique based on ant colony metaheuristic. In addition, a parallel structure for the algorithm is proposed to minimize the computation time. Results show satisfactory performance of the tuned controllers, evidencing the effectiveness of the proposed technique. Furthermore, a significant productivity gain can be achieved if the engineer in charge of this design only supervises the automatic process, instead of performing all calculations himself/herself.