Kai-Hung Lu

Optimal location and capacity of STATCOM for voltage stability enhancement using ACO plus GA

Voltage security is a crucial issue in power systems especially under heavily loaded condition. In the new scheme of restructuring, voltage stability problem becomes even more serious. To solve the problem, we integrate reactive power compensation concept by Static Synchronous Compensator (STATCOM) with Equivalent - current Injection (ECI). We derive a new STATCOM with ECI model. This paper shows the application of Ant Colony Optimization (ACO) plus Genetic Algorithms (GA) for optimal capacity and location of a new STATCOM with ECI model in a power system. Finally simulation shows the optimal location and capacity of new STATCOM with ECI model to enhance power system voltage stability by using GACO. The proposed method demonstrates the improvement of voltage stability margin.

A novel power flow analysis for microgrid distribution system

A microgrid distribution system is proposed in the paper, consisting of solar power, wind power, microturbine power and a battery energy storage system (BESS). By using the proposed algorithm, a more efficient network configuration can be obtained. The problem is optimized in a stochastic searching manner similar to that of the evolutionary programming. A direct building algorithm for microgrid distribution power flow analysis is proposed in this paper. Simulation results show that the proposed algorithm has advantages than the earlier developed algorithms. The optimization strategy is general and can be used to solve other hybrid power system optimization problems as well.

A study for price-based unit commitment with carbon trading by DI&C simulation

In this paper, the Hybrid Genetic Algorithm-Ant Colony Optimization (GACO) approach is presented to solve the unit commitment problem, and comparison with the results obtained using literature methods by nuclear-grade Digital Instrumentation and Control (DI&C) simulation. Then this paper applied the ability of the Genetic Algorithm (GA) operated after Ant Colony Optimization (ACO) can promote the ACO efficiency. The objective of GA is to improve the searching quality of ants by optimizing themselves to generate a better result, because the ants produced randomly by pheromone process are not necessary better. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. The other objective of this paper is to investigate an influence of emission constraints on generation scheduling. The motivation for this objective comes from the efforts to reduce negative trends in a climate change. In this market structure, the nuclear power plants (NPPs) and independent power producers (IPPs) have to deal with several complex issues arising from uncertainties in spot market prices, and technical constraints which need to be considered while scheduling generation and trading for the next day. In addition to finding dispatch and unit commitment decisions while maximizing its profit, their scheduling models should include trading decisions like spot-market buy and sell. The model proposed in this paper build on the combined carbon finance and spot market formulation, and help generators in deciding on when these commitments could be beneficial.

A preventive control for contingencies security

This paper presents an advance stratagem to prevent contingencies and solve the optimal problem of economic and security dispatch. The dispatch takes outage and limitation of technical constraints into account. In order to ensure state steady stability for accidents, preventive control algorithm (PCA) based on the generalized reduced gradient method processing a preventive contingency set jointly. The technique determines the active power dispatch and voltage of each generating unit before contingent condition so as to minimize the energy re-dispatch cost subject to dispatch, network, and security constraints. A secure process is proposed to control the power system and provide operators with emergency suggestions in deregulated market.

Intelligent fuzzy logic controller for a solar charging system

This paper presents an intelligent solar charging system with fuzzy logic control method. With the scarce energy source and the worsening environmental pollution, how to create and use a clean and never exhausted energy is becoming very important day by day. This solar charging system is composed of a solar cell, a charger, batteries, a buck converter and a digital signal processor. In the meantime, it also combines the fuzzy logic method with the tactics of charging to improve the efficiency of charging, suppress the abnormal battery temperature rise, lengthen the batterys life, and reduce the waste used. Finally, experimental and simulation results are shown to demonstrate the effectiveness and validity of the system.

Dynamic characteristic analysis for FACTS using GACO-FNN

The static synchronous series compensator (SSSC) is a series controller of Flexible AC Transmission Systems (FACTS). It can be controlled by thyristors, it also has the ability of fast control adjustments and high frequency operation. Through impedance compensation, it is able to control the magnitude and directions of the real power flow in the transmission system. In order to achieve a fast and steady response for real power control in power systems, this paper proposed a unified intelligent controller, which consists of Fuzzy Neural Network (FNN) and Ant Colony Optimization plus Genetic Algorithms (GACO) for the SSSC to provide better control features for real power control in the dynamic operations of power systems. Finally, the simulation results of the proposed controllers are compared with the conventional proportional plus integral (PI) controllers to demonstrate the superiority and effectiveness of the unified intelligent controller.

Real power control design for SSSC via Fuzzy Neural Network based on Genetic Algorithms

The static synchronous series compensator (SSSC) is a series controller of Flexible AC Transmission Systems (FACTS). It can be controlled by thyristors, it also has the ability of fast control adjustments and high frequency operation. Through impedance compensation, it is able to control the magnitude and directions of the real power flow in the transmission system. In order to achieve a fast and steady response for real power control in power systems, this paper proposed a unified intelligent controller, which consists of Fuzzy Neural Network (FNN) and Genetic Algorithms (GA) for the SSSC to provide better control features for real power control in the dynamic operations of power systems. Finally, the simulation results of the proposed controllers are compared with the conventional proportional plus integral (PI) controllers to demonstrate the superiority and effectiveness of the unified intelligent controller.

An intelligent maximum power tracking control strategy for wind-driven IG system using MPSO algorithm

This paper presents the design of an on-line training fuzzy neural network (FNN) using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the induction generator (IG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the FNN to improve the learning capability. The proposed output maximization control is achieved without mechanical sensors such as the wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is designed on the basis of the sliding mode control theory.

Probabilistic neural network and Polynomial Fitting Approach used to determine radio field strength under power lines in radial network

A new model based on Probabilistic neural network (PNN) and Polynomial Fitting Approaches (PFA) for radio field strength prediction has been developed. This paper researches the radio field strength, related to the service of a radio system for the operation of set points in the radial networks. The service uses radio propagation to dispatch messages to set points. In order to estimate the radio field strength, we performed some realistic measurements related to set points. Then, the data was analyzed using a combination of Probabilistic Neural Network and Polynomial approximations to estimate the radio field strength, and to create a new optimal model specific to the needs of radial networks.