Distribution System Modeling and Analysis

Integration of distributed generation units (DGs) and capacitor banks (CBs) in distribution systems aim to enhance the system performance. This paper proposes water cycle algorithm (WCA) for optimal placement and sizing of DGs and CBs. The proposed method aims to achieve technical, economic, and environmental benefits. Different objective functions: minimizing power losses, voltage deviation, total electrical energy cost, total emissions produced by generation sources and improving the voltage stability index are considered. WCA emulates the water flow cycle from streams to rivers and from rivers to sea. Five different operational cases are considered to assess the performance of the proposed methodology. Simulations are carried out on three distribution systems, namely IEEE 33-bus, 69-bus test systems, and East Delta network, as a real part of Egyptian system. The simulated results demonstrate the effectiveness of the proposed method compared with other optimization algorithms. Also, the results demonstrate that the proposed WCA gives superior performance for the system and give distinguished improvements in both economic and environmental benefits. Moreover, the results give the flexible operation with controllable power factor DGs that is better than those using DGs at fixed power factor.

Optimal Placement and Sizing of Distributed Generation and Capacitor Banks in Distribution Systems Using Water Cycle Algorithm

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

https://www.mdpi.com/1424-8220/21/4/1038/pdf

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system In turn, the second design depends on the genetic algorithm-based artificial neural network Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency

https://www.mdpi.com/1424-8220/21/4/1244/pdf

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

In recent years, with the widespread use of non-linear loads power electronic devices associated with the penetration of various renewable energy sources, the distribution system is highly affected by harmonic distortion caused by these sources. Moreover, the inverter-based distributed generation units (DGs) (e.g., photovoltaic (PV) and wind turbine) that are integrated into the distribution systems, are considered as significant harmonic sources of severe harmful effects on the system power quality. To solve these issues, this paper proposes a harmonic mitigation method for improving the power quality problems in distribution systems. Specifically, the proposed optimal planning of the single tuned harmonic filters (STFs) in the presence of inverter-based DGs is developed by the recent Water Cycle Algorithm (WCA). The objectives of this planning problem aim to minimize the total harmonic distortion (THD), power loss, filter investment cost, and improvement of voltage profile considering different constraints to meet the IEEE 519 standard. Further, the impact of the inverter-based DGs on the system harmonics is studied. Two cases are considered to find the effect of the DGs harmonic spectrum on the system distortion and filter planning. The proposed method is tested on the IEEE 69-bus distribution system. The effectiveness of the proposed planning model is demonstrated where significant reductions in the harmonic distortion are accomplished.

Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation

The output generations of renewable energy sources (RES) depend basically on climatic conditions, which are the main reason for their uncertain nature. As a result, the performance and security of distribution systems can be significantly worsened with high RES penetration. To address these issues, an analytical study was carried out by considering different penetration strategies for RES in the radial distribution system. Moreover, a bi-stage procedure was proposed for optimal planning of RES penetration. The first stage was concerned with calculating the optimal RES locations and sites. This stage aimed to minimize the voltage variations in the distribution system. In turn, the second stage was concerned with obtaining the optimal setting of the voltage control devices to improve the voltage profile. The multi-objective cat swarm optimization (MO-CSO) algorithm was proposed to solve the bi-stages optimization problems for enhancing the distribution system performance. Furthermore, the impact of the RES penetration level and their uncertainty on a distribution system voltage were studied. The proposed method was tested on the IEEE 34-bus unbalanced distribution test system, which was analyzed using backward/forward sweep power flow for unbalanced radial distribution systems. The proposed method provided satisfactory results for increasing the penetration level of RES in unbalanced distribution networks.

https://www.mdpi.com/2227-9717/9/3/471/pdf

An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty

The penetration of renewable energy sources (RESs) in distribution systems faces many issues due to their output uncertainty resulted from climate conditions. The uncertainty impacts on the voltage fluctuations are reduced by using a proposed bi-stage method. At first, the system voltage is controlled by determining the optimal setting of voltage-regulating devices such as voltage regulators, transformer tap changers and static VAR compensator. Then, the dispatchable distributed generation (DDGs) units are accompanied by the voltage regulating devices to achieve more reduction in the voltage fluctuations. In this line, unbalanced backward–forward sweep load flow method is formulated to analyse the unbalanced operation of three-phase distribution systems. The main objectives of the proposed method are to reduce voltage fluctuations to maintain voltage profile within its permissible limits. In addition, the cat swarm optimiser (CSO) is implemented to obtain the optimal planning of voltage regulating devices and DDGs to achieve the lowest uncertainty influence on the voltage fluctuations. The proposed method is applied to a real unbalanced IEEE 34-bus distribution test system. The highest capability of CSO algorithm, i.e. CSO provides the highest reduction on the voltage fluctuations, is proven compared with particle swarm optimisation, harmony search and water cycle algorithms.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2018.5136

Minimisation of voltage fluctuation resulted from renewable energy sources uncertainty in distribution systems

This paper presents a proposed method to allocate the distributed generation (DG) units on distribution networks using cat swarm optimization algorithm. The proposed method finds the optimal placement and sizing of DG units. The objectives of the optimization problem are minimizing: total generation costs, total power losses, total emissions produced by the generation units and improving the voltage stability. The proposed method depends on cat swarm optimization (CSO) algorithm and parallel cat swarm optimization (PCSO) algorithm. The proposed optimization methods are tested on the IEEE 33-bus and IEEE 69-bus distribution systems. The results of these algorithms are compared to other previous methods that are reported in this field. The proposed optimization methods are considered effective and perfective method to find the placement and the sizing of the DG units on distribution systems.

Optimal placement and sizing of distributed generation units using different cat swarm optimization algorithms

Background: Spiritual leadership can guide leaders to help followers to be able to meet higher order needs. Purpose: Is to explore the relationship between spiritual leadership and organizational trust among nurses. Design: A descriptive correlational design was used in this study. Setting: the study was conducted at Menoufia university hospitals all 11 ICU units, all 6 operation room, all 21deparetments and 15 out patients clinics. Subjects: included all nursing managers were available 85 at the time of the study, 400 staff nurses were selected by simple random sample to participate in the study. Data collection Instruments: spiritual leadership and organizational trust questionnaire. Results: the highest percentage of nursing managers' perception was observed between moderate and high level of spiritual leadership. The highest percent of staff nurses had low levels of organizational trust dimensions while the lowest percent of staff nurses had high levels of organizational trust dimensions. Conclusion: there was positive statistically significant correlation between nursing managers’ altruistic love levels and staff nurses’ organization's trust dimension. Also there was positive statistically significant correlation between nursing managers’ membership levels and staff nurses’ nursing managers’ trust dimension and staff nurses’ organizational trust dimension and the medium score of staff nurses’ organizational trust levels increased with increase of nursing managers’ levels of spiritual leadership medium score. Recommendations: the nursing managers should use the application of innovative styles such as spiritual leadership to improving the organizational trust among staff nurses

https://menj.journals.ekb.eg/article_169050_f821a4bb9951bead93175ff27ae5026c.pdf

Eman S. Ali

Papers

Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation

An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty

Minimisation of voltage fluctuation resulted from renewable energy sources uncertainty in distribution systems

Optimal placement and sizing of distributed generation units using different cat swarm optimization algorithms

Spiritual Leadership and its Relation to Organizational Trust among Nurses at Menoufia University Hospitals