Multi-machine Power System Environment Research Articles

Simple neuro-controller with a modified error function for a synchronous generator

A simple neuro-controller for a synchronous generator is presented in this paper. The controller consists of a single neuron trained on-line using the back propagation algorithm based on a modified error function. The modified error function uses the controlled variable and its derivatives. Based on the variables used in the modified error function, the controller performs the functions of terminal voltage control and power system stabilizer. Simulation studies for a single-machine and multi-machine power system environment are presented to demonstrate the effectiveness of using the simple neuro-controller.

TS-fuzzy controller for UPFC in a multimachine power system

The unified power flow controller is one of the most versatile flexible AC transmission system devices, which can be used to control the active and reactive power flows in a transmission line by injecting a variable voltage in series and reactive current in shunt. The application of Takagi- Sugeno type fuzzy logic controllers for UPFC voltage source inverter control in a multi-machine power system environment is proposed. This type of fuzzy controller provides a wide range of control gain variation by including both linear and non-linear terms in the consequent part of the fuzzy rule base. The TS fuzzy controllers are quite general in that they use arbitrary input fuzzy sets, any type of fuzzy logic, and the general defuzzifier. Several computer simulation tests on a multi-machine power system indicate the significant damping performance of this new TS fuzzy controller for the UPFC.

An optimized fuzzy controller for a synchronous generator in a multi-machine environment

In this paper, an optimized neuro-fuzzy power-system stabilizer (NF PSS) is proposed to improve the transient and dynamic stability of synchronous machines. The NF PSS employs a five-layer fuzzy-neural network (FNN). The learning scheme of this FNN is composed of three phases. The first phase uses a clustering algorithm for coarse identification of the initial membership functions of the fuzzy controller (FC). The second phase extracts the linguistic-fuzzy rules from the available training data. In the third phase, a multi-resolutional dynamic genetic algorithm (MRD-GA) is used to fine-tune and optimize the membership functions of the FC. Extensive simulation studies have been carried out to show the performance of the NF PSS and to compare it with a Conventional PSS (CPSS) in a multi-machine power-system environment.

Performance of a Fuzzy Logic Based PSS in a Multi-Machine Power System Environment

Effectiveness of the fuzzy logic based power system stabilizer in a multi-machine power system environment is investigated in this paper. The power system used to study the performance of the stabilizer exhibits multi-mode oscillation phenomena. Machine speed deviation and accelerating power as the plant's output are used as the inputs to the stabilizer. Results of simulation studies for different operating conditions and disturbances are given in the paper.

Experimental studies with power system stabilizers on a physical model of a multimachine power system

Results of experimental studies on a physical model of a multimachine power system with two different power system stabilizers are described in this paper. One stabilizer is based on an adaptive algorithm implemented on a single-board computer and the other is an analog model of a strong action automatic excitation regulator with relay forcing device commonly used in the Russian power stations. Studies demonstrate the efficacy of these two algorithms in the multimachine power system environment.

Application of fuzzy logic stabilisers in a multimachine power system environment

Application of fuzzy logic based power system stabilisers (FLPSS) in a multimachine environment is described. A five-machine power system representing two generation areas is used. The proposed FLPSS uses machine speed deviation and accelerating power as the inputs. The performance of the FLPSS when the system exhibits multimode oscillation phenomenon is illustrated. Simulation tests and results for different operating conditions and disturbances are discussed.

Artificial neural network power system stabilizers in multi-machine power system environment

The effectiveness of an artificial neural network (ANN), functioning as a power system stabilizer (PSS), in damping multi-mode oscillations in a five-machine power system environment is investigated in this paper. Accelerating power of the generating unit is used as the input to the ANN PSS. The proposed ANN PSS using a multilayer neural network with error-backpropagation training method was trained over the full working range of the generating unit with a large variety of disturbances. The ANN was trained to memorize the reverse input/output mapping of the synchronous machine. Results show that the proposed ANN PSS can provide good damping for both local and inter-area modes of oscillations. >