This paper proposes a chaotic stochastic fractal search algorithm (CSFSA) method to solve the reconfiguration problem for minimizing the power loss and improving the voltage profile in distribution systems. The proposed method is a metaheuristic method developed for overcoming the weaknesses of the conventional SFSA with two processes of diffuse and update. In the first process, new points will be created from the initial points by the Gaussian walk. For the second one, SFSA will update better positions for the particles obtained in the diffusion process. In addition, this study has also integrated the chaos theory to improve the SFSA diffusion process as well as increase the rate of convergence and the ability to find the optimal solution. The effectiveness of the proposed CSFSA has been verified on the 33-bus, 84-bus, 119-bus, and 136-bus distribution systems. The obtained results from the test cases by CSFSA have been verified to those from other natural methods in the literature. The result comparison has indicated that the proposed method is more effective than many other methods for the test systems in terms of power loss reduction and voltage profile improvement. Therefore, the proposed CSFSA can be a very promising potential method for solving the reconfiguration problem in distribution systems.

/pdf/distribution-network-reconfiguration-for-power-loss-4gw611irzu.pdf

Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

. Pitch control is a practical technique for power regulation above the rated wind speed it is considered as the most efficient and popular power control method. As conventional pitch control usually use PI controller, the mathematical model of the system should be known well. This paper deals with the operation and the control of the direct driven permanent magnet synchronous generator (PMSG). Different conventional strategies of pitch angle control are described and validated through simulation results under Matlab\Simulink.

http://apc.aast.edu/ojs/index.php/RESD/article/download/01.1.081/16

Pitch Angle Control for Variable Speed Wind Turbines

In this paper, we describe an extended version of Voxento which is an interactive voice-based retrieval system for lifelogs that has been developed to participate in the fourth Lifelog Search Challenge LSC'21, at ACM ICMR'21. Voxento provides a spoken interface to the lifelog dataset, which facilitates a novice user to interact with a personal lifelog using a range of vocal commands and interactions. For the version presented here, Voxento has been enhanced with new retrieval features and better user interaction support. In this paper, we introduce these new features, which include dynamic result filtering, predefined interactive responses and the development of a new retrieval API. Although Voxento was proposed for wearable technologies such as Google Glass or interactive devices like smart TVs, the version of Voxento presented here uses a desktop computer in order to participate in the LSC'21 competition. In the current Voxento iteration, the user has the option to enable voice interaction or use standard text-based retrieval.

https://dl.acm.org/doi/pdf/10.1145/3379172.3391728

Voxento 2.0: A Prototype Voice-controlled Interactive Search Engine for Lifelogs

Nowadays, most hospitals have new problem that is lack of medical nurse due to the number of patient increas rapidly. The patient especially with physical disabilities are difficult to control the switch on electrical appliances in patient’s room. This research aims to develope voice recognition based home automation and being applied to patient room. A miniature of patient’s room are made to simulate this system. The patient's voice is received by the microphone and placed close to the patient to reduce the noise.V3 Voice recognition module is used to voice recognition process. Electrical bed of patient is represented by mini bed with utilising motor servo. The lighting of patient room is represented by small lamp with relay. And the help button to call the medical nurse is represented by buzzer. Arduino Uno is used to handle the controlling process. Six basic words with one syllable are used to command for this system. This system can be used after the patient's voice is recorded. This system can recognize voice commands with an accuracy 75%. The accuracy can be improved up to 85% by changing the voice command into two syllables with variations of vowels and identical intonation. Higher accuracy up to 95% can be reached by record all the subject’s voice.

Voice recognition system for controlling electrical appliances in smart hospital room

Editorial: 40th anniversary: contemporary library research

Power losses and voltage drop are existing problems in radial distribution networks. This power losses and voltage drop affect the voltage stability level. Reconfiguring the network is a form of approach to improve the quality of electrical power. The network reconfiguration aims to minimize power losses and voltage drop as well as decreasing the Voltage Stability Index (VSI). In this research, network reconfiguration uses binary particle swarm optimization algorithm and Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method to analyze the radial system power flow. This scheme was tested on the 33-bus IEEE radial distribution system 12.66 kV. The simulation results show that before reconfiguration, the active power loss is 202.7126 kW and the VSI is 0.20012. After reconfiguration, the active power loss and VSI decreased to 139.5697 kW and 0.14662, respectively. It has decreased the power loss for 31.3136% significantly while the VSI value is closer to zero.

https://beei.org/index.php/EEI/article/viewFile/821/872

Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index

Photovoltaic (PV) is a source of electrical energy derived from solar energy and has a poor level of efficiency. This efficiency is influenced by PV condition, weather, and equipments like Maximum Power Point Tracking (MPPT). MPPT control is widely used to improve PV efficiency because MPPT can produce optimal power in various weather conditions. In this paper, MPPT control is performed using the Fuzzy Logic-Particle Swarm Optimization (FL-PSO) method. This FL-PSO is used to get the Maximum Power Point (MPP) and minimize the output power oscillation from PV. From the simulation results using FL-PSO, the values of voltage, and output power from the boost converter are 183.6 V, and 637.7 W, respectively. The ripple of output power from PV with FL-PSO is 69.5 W. Then, the time required by FL-PSO reaches MPP is 0.354 s. Compared with MPPT control based on the PSO method, the MPPT technique using FL-PSO indicates better performance and faster than the PSO.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/20544/13134

An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system

PurposeThis study investigates the level of artificial intelligence (AI) awareness among library leaders, practitioners and scientists of Indonesian academic libraries to elucidate the benefits of AI implementation and its necessary infrastructure and challenges.Design/methodology/approachThe study adopted a purposive sampling technique to select the 38 participants and thematic analysis to analyze the data, identifying eight themes: understanding of AI, AI adoption, benefits of AI, competencies needed to support AI, facilities to support AI, factors supporting AI adoption, AI-inhibiting factors and expectations of AI.FindingsDifferent viewpoints provided full awareness among library stakeholders and sufficient information to begin AI initiatives in Indonesian libraries as leaders, practitioners and scientists had a favorable, open and encouraging outlook on AI.Research limitations/implicationsThe study does not investigate variations in perspectives between the participants, but it examines their understanding of AI and elaborates the results into the concept of an intelligent library. Moreover, this study only uses samples from academic libraries.Practical implicationsLibraries can take these results into consideration before implementing AI, especially in technology and facilities, librarian competency with regard to AI and leadership roles in AI projects.Social implicationsLibrary boards and library associations can use this research as a source to create guidelines about AI implementation in academic libraries.Originality/valueThe study addresses the gap in the research on university libraries' readiness and awareness to implement AI, especially in developing countries.

https://repository.unair.ac.id/120004/1/Leaders%2C%20practitioners%20and%20scientists%27%20awareness%20of%20artificial%20intelligence%20in%20libraries_%20a%20pilot%20study.pdf

Leaders, practitioners and scientists' awareness of artificial intelligence in libraries: a pilot study

Photovoltaic (PV) is a renewable electric energy generator that utilizes solar energy. PV is very suitable to be developed in Surabaya, Indonesia. Because Indonesia is located around the equator which has 2 seasons, namely the rainy season and the dry season. The dry season in Indonesia occurs in April to September. The power generated by PV is highly dependent on temperature and solar radiation. Therefore, accurate forecasting of short-term PV power is important for system reliability and large-scale PV development to overcome the power generated by intermittent PV. This paper proposes the Jordan recurrent neural network (JRNN) to predict short-term PV power based on temperature and solar radiation. JRNN is the development of artificial neural networks (ANN) that have feedback at each output of each layer. The samples of temperature and solar radiation were obtained from April until September in Surabaya. From the results of the training simulation, the mean square error (MSE) and mean absolute percentage error (MAPE) values were obtained at 1.3311 and 34.8820, respectively. The results of testing simulation, MSE and MAPE values were obtained at 0.9858 and 1.3311, with a time of 4.591204. The forecasting has minimized significant errors and short processing times.

Aji Akbar Firdaus

Papers

Voice recognition system for controlling electrical appliances in smart hospital room

Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index

An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system

Leaders, practitioners and scientists' awareness of artificial intelligence in libraries: a pilot study

Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya