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Automatic Load Frequency control of Distributed Generations in a Microgrid 3

Automatic Load Frequency Control of Distributed Generations ...

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3rd IEEE International conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Kay, Pravat Kumar Puhan, Pratap Sekhar Priyadarshini, Priyanka Natl Inst Technol Dept Elect Engn Rourkela India Sreenidhi Inst Sci & Technol Dept Elect & Elect Engn Hyderabad India Natl Inst Technol Dept Elect Engn Rourkelam India

ISBN: (纸本)9781665425360

With rapid growth in the demand of electrical energy across the globe, has increased the use of renewable sources for generation of power. The renewable sources can act as standalone system but their operation is not reliable because of their intermittent nature. This causes the load frequency to deviate in addition to load deviation caused by load change. But interconnection of renewable sources with conventional sources like thermal generation reduces the load frequency deviation to a greater extent. In this work Load Frequency control (LFC) of distributed system involving thermal, wind and solar generations has been presented. For controlling the frequency deviations by tuning of PI controllers, intelligent techniques are proposed like BFO and PSO. These optimizing techniques are very powerful techniques which are gaining popularity in industry. This paper also discusses the comparative study between BFO and PSO algorithms to determine the optimum parameter of PID controller for automatic load frequency control. A System involving a thermal plant, wind generation system and solar generation is modelled and simulated.

关键词： Optimal control Particle Swarm optimization Bacterial Foraging optimization Renewable Sources

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optimization of Power Distribution network Dispatching Auxiliary Decision-Making and Intelligent Ticketing System Based on PPO Algorithm

Optimization of Power Distribution Network Dispatching Auxil...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Zejian Qiu Weitao Shao Zhanvuan Han Aoqi Mei Zhengqing Wang Guangdong Power Grid Corp Dongguan Power Supply Bureau Dongguan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The existing intelligent ticketing system has problems such as insufficient automation, weak dynamic adaptability, and limited ability to cope with complex fault scenarios. Based on the PPO algorithm, this paper designs and implements an improved intelligent ticketing system. This paper introduces a graph neural network (GNN) to model the power grid topology, so that the system can dynamically generate operation tickets based on the multi-dimensional state of the power grid. Combined with the exploratory and adaptive nature of the PPO algorithm, it autonomously learns and optimizes the process of generating operation tickets under a changing dispatching environment to reduce the risk of misoperation. In the load experiment, the dispatch success rate of the PPO algorithm under low and medium loads reached 96.5% and 95.3% respectively, maintaining a fast response; under high load, the success rate dropped slightly to 91.8%, but it can still meet the real-time requirements. The fault experiment shows that the success rate of PPO is 98.2% in single-point failures and slightly decreased to 92.5% in multi-point failures, but it has strong robustness. The system achieves a balance between efficiency and safety in complex dispatching tasks.

关键词： network topology Heuristic algorithms Distribution networks Dispatching Power grids Graph neural networks Stability analysis Topology Voltage control optimization

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Fault Localization of Distribution network Based on Reverse-Local Learning Based Particle Swarm optimization Algorithm

Fault Localization of Distribution Network Based on Reverse-...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Wei Zhang Yangjun Zhou Chenying Yi Shan Li Bin Feng Weixiang Huang Juntao Pan Yuanchao Zhou Lifang Wu Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Nanning China Guangxi Power Grid Co. Ltd. Nanning China Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd. Nanning China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

With the large-scale access of distributed generation, the power flow characteristics of distribution network have been greatly changed. The conventional fault localization methods for distribution networks are no longer applicable. To deal with the problems induced by large-scale access of distributed generation, a novel fault localization method based on improved particle swarm optimization algorithm is proposed in this paper. Aiming at the issues of potential misjudge, an objective function/ fitness value correction method is proposed to adapt to bidirectional power flow induced by distributed generation. Aiming at the issues of sluggish convergence and poor robustness in existing models, this paper introduces the Reverse-Local Learning based Particle Swarm optimization algorithm to improve the optimization efficiency and algorithm stability. Case study is performed on the IEEE-33 bus distribution network to demonstrate the accuracy and efficiency of the proposed fault localization method.

关键词： Location awareness Accuracy Distribution networks Power system stability Robustness Stability analysis Distributed power generation Smart grids Particle swarm optimization Convergence

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Prediction and Modeling of Desalination Performance of Nanofiltration Membranes Based on Machine Learning

Prediction and Modeling of Desalination Performance of Nanof...

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International conference on Artificial Intelligence and Computer Information Technology (AICIT)

作者： BoWen Zhang Xiaojian Xu ZengZeng Zhang Lei Yao School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

In the process of desalination, the optimization of nanofiltration membrane is very important to improve the removal rate of sodium chloride. The traditional method is often unstable when the operating conditions change, and it is difficult to adapt to different water quality, which limits its application in desalination processes. In this paper, particle swarm optimization algorithm is used to optimize the backpropagation artificial neural network, and the desalting performance of nanofiltration membrane is predicted and modeled successfully. The accuracy of prediction in the test stage reached 99.38%. This model is conducive to allocating the energy, resources and time of the experiment more rationally. It provides a valuable reference for improving the desalination efficiency of nanofiltration membrane in industry. In addition, this study provides a new perspective on water treatment technology in resource-limited areas, which helps to improve the sustainable use of water resources and the quality of life of residents.

关键词： Machine learning algorithms Filtration Desalination Water quality Machine learning Predictive models Prediction algorithms Particle swarm optimization Information technology optimization

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Optimal Power Consumption on Distributed Edge Services Under Non-Uniform Traffic with Dual Threshold Sleep/Active control 3

Optimal Power Consumption on Distributed Edge Services Under...

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3rd Novel Intelligent and Leading Emerging Sciences conference, NILES 2021

作者： Amer, Amira A. Talkhan, Ihab E. Ismail, Tawfik Nile University Giza Egypt Cairo University Faculty of Engineering Department of Computer Engineering Egypt Department of EAL National Institute of Laser Enhanced Sciences Cairo University Giza Egypt

ISBN: (纸本)9781665421577

Mobile edge computing (MEC) is a key enabling technology for supporting high-speed and low latency services in the fifth generation (5G) and beyond networks. MEC paradigm moves computational resources from centralized cloud servers towards the edge of the network, nearer to the users. However, edge computation resources increase the power consumption of the network. Moreover, the non-uniform traffic load on the edge servers causes resources to be underutilized and decrease the system's power efficiency. To achieve the green networking concept encouraged in 5G and beyond networks, unused MEC resources should be switched to sleep mode to reduce the power consumption. The policy controlling sleep/active switching needs should optimize the power consumption and maintain an acceptable quality of service. The control policy should also eliminate frequent mode switches that cause system instability and degrade the system performance. This paper introduces a dual-threshold switching control to reduce power consumption and provide system stability. We also propose using the gaining and sharing knowledge (GSK) optimization algorithm for resource optimization and determining the optimum thresholds to be used by the system. Results showed that dual-threshold switching successfully reduced the power consumed in a single time slot by more than 10% per MEC server. Also, dual-threshold control displayed more stability compared to the single threshold control. © 2021 IEEE.

关键词： Electric power utilization

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optimization of Clustering, and QOS Routing in Wireless Multicast Ad-Hoc networks with Topology control

Optimization of Clustering, and QOS Routing in Wireless Mult...

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Smart Generation Computing, Communication and networking (SMART GENCON), International conference on

作者： Sukhman Ghumman Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In this study, we provide a novel approach to multicast wireless ad hoc network routing with cluster QoS optimization. The notion of topology compression is used to quantify the effect of multicast heterogeneity on scheduling by use of a compressed multiplex topology matrix. Topology matrices are defined by first identifying all feasible multicast pathways, including network-coded paths. In a conflict-free environment provided by careful scheduling, a selection of these pathways with optimal rates is selected to optimize feasible throughput and capacity per sum of transmission powers along the route. As a result, this optimization yields not one but two improvements: one in the form of performance enhancement due to planning on a condensed network topology, and another in the form of mixed route selection optimizations that strike a fine balance between multicast variety and network coding. As an example, we provide the numerical findings for a basic wireless butterfly network. If throughput is the only metric of importance, then either a network coded design or simple routing may be optimal, depending on the specifics of the system. However, a collection of simple routing pathways is optimal if throughput is prioritized above power consumption. In this study, we examine the problem of energy-efficient topology management in ad hoc wireless connections. We create an Energy Effective Topology controller method that incorporates a pertinent QoS Routing system in order to identify a network architecture that has maximum network lifespan fulfill QoS criteria, which includes traffic needs, hop bound, and bandwidth. The combined analytical along with numerical findings show that, when compared to algorithms that are solely concerned with reducing energy consumption, this optimal method can achieve the optimum network lifespan, which is a genuine period of time and more important and reasonable than merely reducing energy consumption.

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Energy-Efficient Resource Allocation in Wireless networks Using Federated-Double-Deep Q-network 3

Energy-Efficient Resource Allocation in Wireless Networks Us...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Anuradha, P. Fallahhusein, Muhamed Subramaniam, Sakthivel Srinivas, T. Aditya Sai Buckshumiyan, A. Chaitanya Bharathi Institute of Technology Department of Electronics and Communication Engineering Hyderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Computer Science and Engineering Salem India Jayaprakash Narayan College of Engineering Department of Artificial Intelligence and Machine Learning Mahbubnagar India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

Now a days, the resource allocation manages the resource networks such as bandwidth, power usage for ensuring Quality of Service (QoS) without overloading. However, the optimization fails to adapt traffic loads due to its dependance on static resource allocation that cannot adjust to user demands and power control. To overcome this limitation in this research resource allocation method Federated-Double-Deep Q-network (F-DDQN) is proposed for maximizing throughput and achieve minimum QoS requirements for all active Device-to-Device communication(D2D) pairs and Cellular Users (CUE). whereas Federated Learning enhances adaptability to dynamically communication environments. Initially, the data is collected from D2D-enabled heterogeneous cellular networks (HetNet) dataset and 3-tier HetNet system model are designed. Further, for resource management the F-DDQN enhances the energy-efficient by decentralizing action states and minimizes communication overhead and power consumption by sharing model updates instead of raw data, and DDQN effectively improves the energy efficiency in resource allocation by estimating Q-values and optimizes resource allocation through selecting the most energy-efficient transmitting power and channels using target estimation. From the results the proposed model achieved better results to evaluate its effectiveness in increasing system throughput with 58.90% compared with both CUE and D2D Users Equipment (DUE) with existing method a Deep Q-network-based Reinforcement Learning algorithm (DRL). © 2025 IEEE.

关键词： network performance

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Power Quality Monitoring Point Configuration of Distribution network based on Dung Beetle optimization Algorithm Considering Panoramic Perception

Power Quality Monitoring Point Configuration of Distribution...

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Electrical Engineering and Mechatronics Technology (ICEEMT), IEEE International conference on

作者： Shaoan Shou Hairong Luo Renhe Zhang Tao Ji Quan Liu Liqian Su State Grid Ningxia Electric Power Co. Ltd Wuzhong Power Supply Company State Grid Ningxia Electric Power Co. Ltd Electric Power Research Institute State Grid Ningxia Electric Power Co. Ltd Shanghai Jiao Tong University

In recent years, a large number of distributed power sources have been connected to the power grid, and harmonic pollution has become increasingly serious. The premise of harmonic control is to monitor harmonics. Reducing costs and improving accuracy while ensuring that the system is fully observable has become a new challenge. In this paper, considering the observability and redundancy of harmonic state estimation, combined with the judgment basis of the measurement device, the optimal configuration model of the measurement device is established, and the measurement configuration scheme is obtained by using the dung beetle optimization algorithm. The scheme with the highest redundancy is selected for error analysis, which proves the feasibility and effectiveness of the scheme.

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Topology Identification for Distribution network Driven by Diverse Measurement Data 3

Topology Identification for Distribution Network Driven by D...

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3rd International conference on Electrical Engineering and control Technologies, CEECT 2021

作者： Wang, Zijiing Wang, Keyou Xu, Jin Shanghai Jiao Tong University Department of Electrical Engineering Shanghai China

ISBN: (纸本)9781665440417

The analysis, optimization and control of the power grid becomes more complicated as the scale of renewable energy continues to expand. There are two factors causing the scarcity of accurate distribution network topology and parameters. Firstly, with lots of distributed equipment attached, the distribution network changes topology frequently. Secondly, the amount of monitoring equipment is limited. However, inferring the correct network topology through historical operating data becomes possible crediting to data-driven technology. Current identification methods require full sets of measurements. Unfortunately, actual measurements are generally incomplete and are from various sources. Data sets with this property are defined as diverse measurement data in this paper. Driven by diverse measurement data, a distribution network topology identification algorithm is proposed. Regression method and the branch power flow equation are employed to perform the identification. The analysis of IEEE 7-node and 33-node networks shows that accurate topology identification results are achieved, which indicates the algorithm proposed is robust and suitable for a variety of identification scenarios. © 2021 IEEE.

关键词： Electric power transmission networks

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Performance Analysis of the AVR Using An Artificial Neural network and Genetic Algorithm optimization Technique

Performance Analysis of the AVR Using An Artificial Neural N...

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International conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Niloy Goswami Md. Redowan Habib Abu Hena Shatil Kazi Firoz Ahmed Electrical and Electronics Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

The Automatic Voltage Regulator (AVR) is required to maintain a steady output voltage from the generator, and it relies heavily on the Proportional Integral Derivative (PID) controller. For the function of controlling industrial loops, a controller known as the PID controller is frequently used on account of its straightforward architecture, uncomplicated implementation, and excellent dependability. Traditional approaches to tuning the PID controller have their limits, but those limits may be overcome by incorporating more sophisticated tuning approaches. The main aim of this study is to provide the ideal design for tuning a PID controller using a Genetic Algorithm (GA) and an Artificial Neural network (ANN) in order to further improve the PID-based AVR system. The performance of the suggested approach is afterward compared with one another. The results of a simulation carried out in MATLAB show that GA tuning techniques give better performance.

关键词： Regulators Signal processing algorithms Artificial neural networks Signal processing control systems Automatic voltage control Time-domain analysis

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