Voltage Quality Improvement Using Solar Photovoltaic System

Solar power and wind are the most promising distributed energy sources. The photovoltaic system has attracted significant attention in recent years. Although their benefits are numerous, they are known to create power quality issues, because of the power electronic converters they use. This paper investigates if there is a possibility to improve power quality only by connecting a solar photovoltaic system to the grid, and then gives results of on-site power quality measurements in the grid before and after the connection of the solar photovoltaic system

Complete and versatile remote controller for PV systems

The promotion of the distributed generation and the photovoltaic (PV) energy great potential put this technology in the spotlight of investors. Thus, the optimization of its efficiency is made a priority. In this paper, a complete and versatile remote controller (C&VRC) for PV systems is presented, scalable to any kind and size of installations (with or without partial shading, with or without connection to the grid, housing, small plants or even large-scale solar plants) and valid for any weather conditions. The C&VRC involves the controller itself, which is constituted by a set of sensors and actuators connected to the converters Direct Current (DC)/DC and DC/Alternating Current (AC), if necessary, and a control unit communicated with them through the third-generation mobile telecommunications technology (3G) network. The control strategies can be changed according to the different weather conditions and installations characteristics, which guaranties the optimum performance of the system. The use of the 3G network as communication platform makes the C&VRC useful in remote and low accessibility places, because it is the most extended worldwide. The C&VRC is also based on free software, open hardware and low-cost electronic devices. Experimental results are presented in this paper to show the C&VRC optimum usability.This paper is framed in the project “Integral control system to optimize the microgrids energy demand” funded by the Spanish Ministry of Science and Innovation, call for Scientific and Technical Research and Innovation 2020-2023. Funding for open access charge: Universidad de Huelva / CBUA.This paper is framed in the project “Integral control system to optimize the microgrids energy demand” funded by the Spanish Ministry of Science and Innovation, call for Scientific and Technical Research and Innovation 2020-2023. Funding for open access charge: Universidad de Huelva / CBUA

Diagnosis kerosakan talian penghantaran sistem kuasa mengguakan kaedah inferens neural kabur ubah suai

Transmission line protection system is one of the most challenging issues in power system protection due to requirements of fast and accurate technique. Fault diagnosis on the transmission line conventionally is implemented either for detection and classification or detection and location of fault only. This leads to time consuming either to locate or classify fault. None of the researchers perform detection, classification and location of fault simultaneously due to huge data transactions and delays. To ensure the availability of supplies, the control centre requires fast and complete information data for immediate follow-up. Among typical intelligent techniques which are normally applied include Fuzzy Logic (LK), Back Propagation Neural Network (RNRB), Support Vector Machines. This thesis develops a fault diagnosis based on the hybrid intelligent algorithms for detection, classification and location of fault simultaneously. The proposed technique is a combination of RNRB and LK techniques known as Adaptive Neuro-Fuzzy Inference System (SINKUS). SINKUS algorithm has been proposed to enhance the integrated data training for detection, classification and location of faults in power system transmission lines. For each of the stated assignment, four different SINKUS structures were developed producing a total of twelve SINKUS structures, each with six inputs and fuzzified using triangular membership function. During fault detection the system detects the affected phase through four different SINKUS structures and feeds the information to the next stage for classification. At this stage, the system is able to classify the type of the fault in a detected phase, using another four different SINKUS structures. On successful classification, another group of four SINKUS structures can be able to position the fault location. The effectiveness of the proposed algorithm is demonstrated on 13-bus IEEE Test System, 118-bus IEEE Test System and the Riau Power System. The types of fault performed in the diagnosis are symmetrical and asymmetrical faults. The simulation results are compared with the LK and the RNRB techniques to verify its performance. From the observed results, it is found that the relay operating time using SINKUS technique is only within 0.11 to 0.16 seconds or has been reduced to about 20% to various scales of power systems with 99.95% accuracy. Hence, the proposed technique is able to provide complete, fast and accurate fault information and can be applied to large-scale power system