Chun-Feng Lu

Effect of battery energy storage system on load frequency control considering governor deadband and generation rate constraint

Since a battery energy storage system (BES) can provide fast active power compensation, it also can be used to improve the performance of load-frequency control. In this paper a new incremental model of a BES is presented and merged into the load-frequency control of a power system. A comprehensive digital computer model of a two-area interconnected power system including governor deadband and generation rate constraint is employed for a realistic response. Computer simulations show that the BES is very effective in damping the oscillations caused by load disturbances. The BES model is suitable for charging mode and discharging mode operations. Optimization of controller gains is obtained by the second method of Lyapunov. >

Power system load frequency control by evolutionary fuzzy PI controller

Power system load frequency control by fuzzy PI controller is proposed in this paper. During control, a fuzzy system is used to adaptively decide the PI controller gain according the area control error and its change. To ease the design effort and improve the performance of the controller, the fuzzy system is deigned by the hybrid of genetic algorithm and particle swarm optimization (HGAPSO). Simulations on a two-area interconnected power system with different kinds of perturbation are performed. The performance of the proposed approach is verified from simulations and comparisons.

Power system load frequency control by genetic fuzzy gain scheduling controller

Abstract A Genetic Algorithm (GA) based fuzzy gain scheduling approach for load frequency control is proposed in this paper. In this approach, a fuzzy system is used to adaptively decide the integral or PI controller gain according to the area control errors (ACE) and their changes. To reduce both the fuzzy system design effort and the number of fuzzy rules, the fuzzy system is designed automatically by genetic algorithms. To improve the design performance, a new genetic algorithm using elitist strategy combined with similarity measure on relatives between individuals is proposed. Based on similarity measure, mutated reproduction is adopted to increase the chance for reaching better solutions and to avoid premature phenomena. Simulations on a two‐area interconnected power system with different kinds of perturbation are performed. The superiority of the proposed method over existing ones is verified from simulations and comparisons.

Power system load frequency control with fuzzy gain scheduling designed by new genetic algorithms

A genetic algorithm (GA) based fuzzy gain scheduling approach for power system load frequency control is proposed in this paper. In this approach, a fuzzy system is used to adaptively decide the integral controller gain according to the area control errors (ACE) and their changes. To reduce the fuzzy system design effort and the number of fuzzy rules, the fuzzy system is designed automatically by GA. To improve the design performance, a new GA using elitist strategy combined with similarity measure is proposed. Simulations on a two-area interconnected power system with different kinds of perturbation are performed. The superiority of the proposed method over existing ones is verified from simulations and comparisons.

DAMPING TORSIONAL OSCILLATIONS BY A SUPERCONDUCTING MAGNETIC ENERGY STORAGE UNIT

ABSTRACT This paper investigates the effect of a superconducting magnetic energy storage (SMES) unit on the repressing of torsional oscillations in a capacitor compensated power system. The IEEE Second Benchmark Model is employed. To increase the damping of torsional modes, an auxiliary controller, proportional-integral-derivative (PID) or lead-lag, is employed to control the power modulation of the SMES unit according to generator speed deviation. The gains of the proposed auxiliary controller are determined by a pole assignment method where the eigenvalues of the torsional modes are placed at a prespecified position. Eigenvalue analysis and computer simulations of the studied system show that the SMES unit provides adequate damping torque over a wide range of operating conditions. Stability investigation shows that the PID controller is better than the lead-lag controller. The SMES unit also ensures a stable system when one of the parallel lines is out of service.

A Self-Evolving Interval Type-2 Fuzzy Neural Network for Nonlinear Systems Identification

Abstract This paper proposes a Self-Evolving Interval Type-2 Fuzzy Neural Network (SEIT2FNN) for nonlinear systems identification. The SEIT2FNN has both on-line structure and parameter learning abilities. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. An on-line clustering method is proposed to generate fuzzy rules which flexibly partition the input space. For parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm for high accuracy learning performance. The antecedent part parameters are learned by gradient descent algorithms. Comparisons with identification using other type-1 and type-2 fuzzy systems verify the performance of the SEIT2FNN.

Position estimation of a mobile robot by PSO algorithm using a laser range finder

This paper shows that a laser range finder and four artificial reflectors can be used to determine the position of a mobile robot in a three-dimensional (3D) working space provided that the four reflectors are not installed in the same plane. Moreover, a particle swarm optimization (PSO) algorithm is used to filter possible measuring errors. To show the feasibility and accuracy of the proposed method, experimental results are included for illustration.

Control of flexible AC transmission system by evolutionary fuzzy controller

A fuzzy controller designed by the hybrid of genetic algorithm and particle swarm optimization (F-HGAPSO) is employed for thyristor controlled series capacitor to improve the transient stability of flexible AC transmission system. The fuzzy controller outputs an approximate series capacitance to flexible AC transmission system according to both the deviation of rotation speed and its change with time. To design the fuzzy controller, a new design approach, the F-HGAPSO, is proposed. Simulations have demonstrated the effectiveness of the proposed F-HGAPSO.

Dynamic plant control using Recurrent Fuzzy Controller with ant colony optimization in real space

This paper proposes the design of a Takagi-Sugeno-Kang (TSK)-type Recurrent Fuzzy Network (TRFN) using ant colony optimization in real space (ACOR). The TRFN contains feedback loops in each rule. When the TRFN is applied to control a dynamic plant, no a priori knowledge of the plant order is necessary. Only the current state(s) and desired output(s) are fed as TRFN inputs. All of the free parameters in each recurrent rule are optimized using ACOR. The ACOR stores solutions in an archive and updates solutions using selection and Gaussian random sampling processes. The ACOR-designed TRFN is applied to control a dynamic plant for performance verification. Comparisons with other optimization algorithms verify the advantage of ACOR.

Fuzzy Controller Design by Hybrid Evolutionary Learning Algorithms

An evolutionary fuzzy system that automates the design of fuzzy systems by hybridizing multi-group genetic algorithm and particle swarm optimization, called F-MGAPSO, is proposed in this paper. By F-MGAPSO, we aim to simultaneously design the number of fuzzy rules and free parameters in a fuzzy system. In initial population, the number of rules encoded in each individual is randomly assigned, and the individuals with equal number of rules constitute the same group. Evolution of population consists of three major operations: group enhancement, variable-length individual crossover and mutation, where group enhancement is to enhance elites in each group by local version of particle swam optimization, respectively. To demonstrate the performance, F-MGAPSO is applied to fuzzy control of a nonlinear plant