This paper proposes the development of a two-axis sun tracking solar energy system using fuzzy logic as intelligent quality policy. To achieve maximum efficiency for solar panels, it is necessary to follow the sun’s path in the sky. Therefore, the architecture for the two-axis sun tracking solar energy system uses software to control the hardware. The hardware comprises (i) solar cells; (ii) lead-acid batteries; (iii) a gear box; (iv) a stepping motor; and (v) a light detection circuit, while the software comprises (i) a detection system; (ii) a fuzzy tracking controller; and (iii) a database system. A fuzzy logic controller is designed as the software architecture of the system to decide the timing for tracking the sun. The nearest position that results in receiving direct sunlight is obtained from the database. Our system is fully automatic in a changing environment and takes into account meteorological changes and the effects of the external environment arising from a malfunction. This approach reduces the number of starting motors and results in smaller energy loss in cloudy, cloud mask, or unstable weather conditions.

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.

This paper presents preventive security-constrained optimal power flow (PSCOPF) with hybrid genetic-ant colony optimization (HGACO) is presented The PSCOPF can be divided into three steps involving security analysis, severest event selection, and preventive algorithm. First, Novel security analysis will be conducted before fault occurred in the system by current-based power flow technique. Second, ranking method will be used to highlight the most severe event caused by the specific facility. And finally, a preventive algorithm will make use of the contingency information, and could be kept the operator system security and avoided congestion when fault occurred. The HGACO is integrated with genetic algorithm and ant colony optimization, and 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.

Purpose: This paper aims to present the analysis, design and implementation of functional link-based recurrent fuzzy neural network (FLRFNN) for the control of variable-speed switched reluctance generator (SRG). Design/methodology/approach: The node connecting weights of the FLRFNN are trained online by back-propagation (BP) algorithms. The proposed estimator requires less processing time than traditional methods and can be fully implemented using a low-cost digital signal processor (DSP) with MATLAB toolboxes. The DSP-based hybrid sensor presented in this paper can be applied to a wind energy-conversion system where the SRG is used as a variable-speed generator. The current transducer is used to monitor the energized current and proximity sensors for rotor salient. Findings: The authors have found that optimal based on FLRFNN with Grey controller can resolve the regulation of the system with uncertainty model and unknown disturbances. This technique can maintain the system stability and reach the desired performance even with parameter uncertainties. Originality/value: This design will improve the performance of SRG to operate more smoothly. This application is currently being studied because the SRG has well-known advantages such as robustness, low manufacturing cost and good size-to-power ratio. Performance of the proposed controller can offer better stability characteristics. Finally, the SRG has a very good efficiency in the whole operating range.

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.