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A novel power aware smart agriculture management system based on RNN-LSTM 基于 RNN-LSTM 的新型电力感知智能农业管理系统
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-16 DOI: 10.1007/s00202-024-02640-0
Anburaj Balasubramanian, Srie Vidhya Janani Elangeswaran

In the realm of economics, agriculture holds supreme importance. The Internet of Things (IoT) is now pivotal in agriculture, aiding farmers in monitoring crop yield. Smart meters and control methods streamline agricultural operations, managing intelligent equipment, bidirectional communication, and user interaction. Data from sensors capturing soil and environmental parameters like moisture, humidity and temperature are integrated into Neural Networks for predictive analysis. Water scarcity, irrigation, and electrical power utilization creates impact on global crop growth and quality. This paper introduces an IoT-enabled product for coconut farming, enabling real-time monitoring and control of irrigation, energy usage, and power quality. The Smart Agriculture Irrigation Management System (AIMS) monitors valves, pumps, water levels, soil, and environmental conditions autonomously. Users can implement automated or manual decision-making processes. Additionally, a Smart Agriculture Energy Management System with integrated Smart Agriculture Energy Meter monitors power consumption, Power Quality, anomalies, and disturbances, notifying farmers via cloud services with predicted values. Implemented in a coconut farm in Sirumalai, Tamil Nadu, India, the system aims to reduce manual stress, enhancing productivity, yield, and water saving by over 30%. Predicted energy consumption patterns and tariffs help farmers avoid excessive costs, resulting in around 40% energy savings, facilitated by the superior performance of RNN-LSTM model over traditional methods.

在经济领域,农业具有至高无上的重要性。物联网(IoT)如今在农业领域举足轻重,可帮助农民监测作物产量。智能仪表和控制方法可简化农业操作,管理智能设备、双向通信和用户互动。传感器采集的土壤和环境参数(如湿度、湿度和温度)数据被整合到神经网络中进行预测分析。水资源短缺、灌溉和电力利用对全球作物生长和质量造成了影响。本文介绍了一种用于椰子种植的物联网产品,可对灌溉、能源使用和电能质量进行实时监测和控制。智能农业灌溉管理系统(AIMS)可自主监控阀门、水泵、水位、土壤和环境条件。用户可以实施自动或手动决策过程。此外,智能农业能源管理系统还集成了智能农业能源计量表,可监控耗电量、电能质量、异常情况和干扰,并通过云服务将预测值通知农民。该系统在印度泰米尔纳德邦 Sirumalai 的一个椰子农场实施,旨在减少人工压力,提高生产率和产量,并节水 30% 以上。由于 RNN-LSTM 模型的性能优于传统方法,预测的能源消耗模式和费率可帮助农民避免过高的成本,从而节省约 40% 的能源。
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引用次数: 0
Wind power forecasting using a GRU attention model for efficient energy management systems 利用 GRU 注意力模型预测风力发电量,实现高效能源管理系统
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-15 DOI: 10.1007/s00202-024-02590-7
Lakhdar Nadjib Boucetta, Youssouf Amrane, Saliha Arezki

Modern energy management systems play a crucial role in integrating multiple renewable energy sources into electricity grids, enabling a balanced supply–demand relationship while promoting eco-friendly energy consumption. Among these renewables, wind energy, with its environmental and economic advantages, poses challenges due to its inherent variability, demanding accurate prediction models for seamless integration. This paper presents an innovative hybrid deep learning model that integrates a gated recurrent unit (GRU)-based attention mechanism neural network for wind power generation forecast. The developed model’s performance is compared against six other models, comprising four deep learning approaches—long short-term memory (LSTM), 1D convolutional neural network, convolutional neural short-term memory (CNN-LSTM), and convolutional long short-term memory (ConvLSTM)—as well as two machine learning models—random forest and support vector regression. The proposed GRU-based attention model demonstrates superior performance, particularly in 1-step to 3-step ahead predictions, with mean absolute error values of 59.45, 114.95, and 176.06, root mean square error values of 109.03, 201.83, and 296.55, normalized root mean square error values of 0.080, 0.148, and 0.218, and coefficient of determination (R2) values of 0.992, 0.975, and 0.948, for forecast horizons of 10, 20, and 30 min, respectively. These results underscore the robust predictive capability of the proposed algorithm. Significantly, this research constitutes the first application of the hybrid GRU-based attention model to the Yalova wind turbine dataset, achieving better accuracy when compared to prior studies utilizing the same data.

现代能源管理系统在将多种可再生能源并入电网方面发挥着至关重要的作用,在促进生态友好型能源消费的同时,实现了供需平衡。在这些可再生能源中,风能具有环境和经济优势,但由于其固有的可变性,需要精确的预测模型才能实现无缝集成,因此带来了挑战。本文提出了一种创新的混合深度学习模型,该模型集成了基于门控递归单元(GRU)的注意力机制神经网络,用于风力发电预测。该模型的性能与其他六种模型进行了比较,包括四种深度学习方法--长短期记忆(LSTM)、一维卷积神经网络、卷积神经短期记忆(CNN-LSTM)和卷积长短期记忆(ConvLSTM),以及两种机器学习模型--随机森林和支持向量回归。所提出的基于 GRU 的注意力模型表现出了卓越的性能,尤其是在 1 步到 3 步的超前预测中,平均绝对误差值分别为 59.45、114.95 和 176.06,均方根误差值分别为 109.03、201.83 和 296.55,归一化均方根误差值分别为 0.080、0.148 和 0.218,决定系数 (R2) 分别为 0.992、0.975 和 0.948,预测时间跨度分别为 10、20 和 30 分钟。这些结果凸显了拟议算法的强大预测能力。值得注意的是,这项研究是基于 GRU 的混合注意力模型在 Yalova 风力涡轮机数据集上的首次应用,与之前利用相同数据进行的研究相比,精度更高。
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引用次数: 0
Cascading fault prevention and control strategy based on economic dispatch of AC/DC systems 基于交直流系统经济调度的级联故障预防和控制策略
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-13 DOI: 10.1007/s00202-024-02649-5
Huiqiong Deng, Pan Xie, Hongyu Huang, Junfu Shen, Dengwei Pan

To effectively prevent cascading failures during the economic dispatch of AC/DC systems, this paper proposes a preventive control strategy that considers both safety and economic factors and establishes a nonlinear bilayer optimization model. First, based on the action characteristics of relay protection, a mathematical method for determining cascading trips is presented. Second, using the active power output of units as a control variable, a static security margin index that measures the safety level of AC/DC systems is established. An economic indicator is then given by comprehensively considering network loss, unit power supply cost, and environmental governance cost. To address the issues of the sparrow search algorithm falling into local optima and insufficient convergence accuracy, an improved sparrow search algorithm is proposed by integrating improved Circle chaos, spiral search, the Levy flight strategy, and mutation perturbation. Standard test functions are used for comparative analysis with other algorithms to demonstrate the effectiveness of the proposed algorithm. Next, this paper employs this algorithm to solve the aforementioned bilayer preventive control model using a Jacobian matrix preconditioning method combined with sparse storage technology in power flow calculations to improve computational efficiency. Finally, the improved IEEE 39-bus system is used for simulation analysis of the proposed algorithm and model, verifying the feasibility of the proposed strategy.

为有效防止交直流系统经济调度期间的级联故障,本文提出了一种同时考虑安全和经济因素的预防性控制策略,并建立了非线性双层优化模型。首先,根据继电保护的动作特性,提出了确定级联跳闸的数学方法。其次,利用机组有功功率输出作为控制变量,建立了衡量交直流系统安全水平的静态安全裕度指标。然后,综合考虑网络损耗、机组供电成本和环境治理成本,给出了经济指标。针对麻雀搜索算法陷入局部最优和收敛精度不足的问题,提出了一种改进的麻雀搜索算法,将改进的环形混沌、螺旋搜索、利维飞行策略和突变扰动结合在一起。本文使用标准测试函数与其他算法进行对比分析,以证明所提算法的有效性。接下来,本文采用该算法求解上述双层预防控制模型,在功率流计算中使用雅各布矩阵预处理方法结合稀疏存储技术,以提高计算效率。最后,利用改进后的 IEEE 39 总线系统对所提算法和模型进行仿真分析,验证了所提策略的可行性。
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引用次数: 0
A novel coil array compensation structure design with high-misalignment tolerance for UAV-enable WPT system 适用于无人机 WPT 系统的新型线圈阵列补偿结构设计,具有高偏差容限
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-13 DOI: 10.1007/s00202-024-02660-w
Cancan Rong, Jin Chang, Yachao Liu, Zhi Ling, Yunpeng Xu, Qibiao Lu, Yujie Liu, Hailin Zhao, Haoyang Wang, Chenyang Xia

The problem of charging unmanned aerial vehicles (UAVs) is an important application area for wireless power transmission (WPT). However, the flight characteristics of UAVs make it difficult for the traditional single-coil coupling mechanism to ensure alignment between the transmitting and receiving ends. It also has poor anti-skewing performance and a cumbersome optimization process, which ultimately decreases the efficiency of charging UAVs. To address this problem, this paper takes the 2 × 2 square coil array as research object, analyzes the distribution characteristics of the axial magnetic field at a specific height, proposes a "cross" compensation structure, and adopts genetic algorithm to optimize the side length and turns of the compensation coil. The goal is to significantly improve the axial magnetic field uniformity in the charging area and reduce the degree of magnetic field leakage. Finally, the effectiveness and superiority of the proposed "crossover" compensation structure and its optimization method are demonstrated through the construction and testing of the system experimental platform, which significantly improves the anti-offset performance and transmission efficiency of the WPT system.

无人飞行器(UAV)充电问题是无线电力传输(WPT)的一个重要应用领域。然而,由于无人飞行器的飞行特性,传统的单线圈耦合机构很难保证发射端和接收端的对准。同时,它的抗倾斜性能较差,优化过程繁琐,最终降低了无人机的充电效率。针对这一问题,本文以 2 × 2 方形线圈阵列为研究对象,分析了特定高度轴向磁场的分布特征,提出了 "十字 "补偿结构,并采用遗传算法对补偿线圈的边长和匝数进行了优化。其目的是显著改善充电区域的轴向磁场均匀性,降低磁场泄漏程度。最后,通过系统实验平台的搭建和测试,证明了所提出的 "交叉 "补偿结构及其优化方法的有效性和优越性,显著提高了 WPT 系统的抗偏移性能和传输效率。
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引用次数: 0
Traveling wave fault location for AC transmission lines: an approach based on the application of EMD and Teager energy operator 交流输电线路的行波故障定位:基于 EMD 和 Teager 能量算子应用的方法
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-13 DOI: 10.1007/s00202-024-02663-7
Andressa Oliveira, Fernando Moreira, Alessandra Picanço, Felipe Vasconcellos

This paper proposes an approach for the single-ended and the double-ended traveling wave-based fault location algorithm using the empirical mode decomposition associated with the Teager energy operator to extract characteristic data from the faulted voltage signals of an overhead transmission line. The simulation of the power system uses the JMarti line model, with an ideally transposed transmission line, and it was carried out using the alternative transients program (ATP) software. Subsequently, the MATLAB software was used for extracting the traveling wave arrival times and to perform the single-ended and the double-ended fault location algorithms for all simulated scenarios in ATP. The numerical and graphical results prove that the proposed methodology with the Teager energy operator and the double-ended analysis can better extract the characteristic data of the voltage signals and estimate the fault location with good accuracy, with percentage error of 0.034% for the best results, depending only on the fault type and the sampling rate adopted.

本文提出了一种基于单端和双端行波的故障定位算法,使用与 Teager 能量算子相关的经验模式分解,从架空输电线路的故障电压信号中提取特征数据。电力系统仿真采用 JMarti 线路模型,输电线路为理想换位,仿真使用替代瞬态程序 (ATP) 软件进行。随后,使用 MATLAB 软件提取行波到达时间,并在 ATP 中对所有模拟场景执行单端和双端故障定位算法。数值和图形结果证明,采用 Teager 能量算子和双端分析的建议方法能更好地提取电压信号的特征数据,并能准确估计故障位置,最佳结果的百分比误差为 0.034%,仅取决于故障类型和采用的采样率。
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引用次数: 0
Artificial intelligence modeling for power system planning 用于电力系统规划的人工智能模型
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-11 DOI: 10.1007/s00202-024-02652-w
Sonja Knežević, Mileta Žarković

The increasing complexity of modern power systems due to the integration of prosumers, renewable energy sources, and energy storage, has significantly complicated system organization and planning. Traditional centralized power plants are being replaced by decentralized structures, making the power flow more complex to predict. As a result, alternative methodologies for power system planning are imminent. This paper introduces a novel approach using a combination of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for forecasting system states. Here, ANN model predicts energy consumption, while the ANFIS model forecasts thermal and hydro power plant production as well as CO2 emissions. The accuracy of these models results from leveraging the collective expertise of power system planning professionals, utilizing extensive databases containing hourly data from measurements in Serbian power systems. These datasets encompass hourly production data from various energy sources, energy consumption patterns, and relevant environmental parameters (such as temperature, wind speed, and solar irradiation). To underscore the effectiveness of the proposed ANN model, predictions of power consumption from ANN are compared with predictions from ARIMA (autoregressive integrated moving average) model. The developed forecasting models are employed to predict annual and daily energy consumption, seasonal variations in thermal and hydro production, and annual CO2 emissions. The dependencies between power consumption/production and ambient parameters are visually depicted in three-dimensional representations. Model accuracy is evaluated through graphical, numerical, and error-based analyses across four distinct error metrics. By utilizing historical data and expert insights from previous production scheduling, these models enhance the precision of future production scheduling decisions. This approach minimizes human error, maximizes the utilization of human expertise, and establishes a framework for effectively planning large-scale power systems. The primary contribution of this research lies in the integration of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies. This combined approach minimizes the errors inherent in each individual methodology while leveraging their respective advantages. Specifically, the consumption prediction error achieved is 5.64%. When ANFIS is utilized with a training database based on ANN consumption prediction, the prediction error for CO2 emissions is 1.27%.

由于整合了用户、可再生能源和储能,现代电力系统的复杂性日益增加,使系统的组织和规划变得更加复杂。传统的集中式发电厂正在被分散式结构所取代,这使得电力流的预测变得更加复杂。因此,电力系统规划的替代方法迫在眉睫。本文介绍了一种结合使用人工神经网络(ANN)和自适应神经模糊推理系统(ANFIS)模型预测系统状态的新方法。其中,ANN 模型预测能源消耗,而 ANFIS 模型预测火力发电厂和水力发电厂的产量以及二氧化碳排放量。这些模型的准确性得益于电力系统规划专业人员的集体专业知识,并利用了包含塞尔维亚电力系统每小时测量数据的庞大数据库。这些数据集包括各种能源的每小时生产数据、能源消耗模式以及相关环境参数(如温度、风速和太阳辐照度)。为了强调所提议的 ANN 模型的有效性,将 ANN 预测的电力消耗量与 ARIMA(自回归综合移动平均)模型的预测结果进行了比较。所开发的预测模型可用于预测每年和每天的能源消耗、火力和水力发电量的季节性变化以及每年的二氧化碳排放量。电力消耗/生产与环境参数之间的依赖关系通过三维图表直观地描述出来。通过对四个不同误差指标进行图形、数值和误差分析,对模型的准确性进行评估。通过利用以往生产调度的历史数据和专家见解,这些模型提高了未来生产调度决策的精确度。这种方法最大限度地减少了人为误差,最大限度地利用了人类的专业知识,并建立了有效规划大规模电力系统的框架。这项研究的主要贡献在于整合了人工神经网络(ANN)和自适应神经模糊推理系统(ANFIS)方法。这种组合方法最大限度地减少了每种方法固有的误差,同时充分利用了它们各自的优势。具体来说,消耗量预测误差为 5.64%。当 ANFIS 与基于 ANN 消费预测的训练数据库一起使用时,二氧化碳排放量的预测误差为 1.27%。
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引用次数: 0
Sensitivity analysis of crosstalk in transmission lines to geometric uncertainties 输电线路串扰对几何不确定性的敏感性分析
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-07 DOI: 10.1007/s00202-024-02662-8
Abdelali Allal, Boubakeur Ahmed

This paper explores the impact of geometric parameter uncertainties on transmission line crosstalk, crucial to electromagnetic compatibility for modern electronic systems. It reviews existing EMC research and methods for quantifying uncertainty, focusing on a two-conductor transmission line affected by uncertainties in height (h), conductor radius (rw), and spacing (d). Sensitivity analysis employs the Monte Carlo Method, Unscented Transform Method, and Stochastic Collocation Method, coupled with Finite Difference Time Domain simulations. Results show that the Unscented Transform Method is more suitable for this problem due to nonlinearity. In general, the radius (rw) has less impact, and the height (h) has more impact than the spacing (d), but for fast transitions, d has more impact than h. Additionally, a similarity exists between the standard deviation of crosstalk (NEXT and FEXT) and the time derivatives of the crosstalk means and the source signal. These findings highlight the implications for signal integrity in high-speed systems.

本文探讨了几何参数不确定性对传输线串扰的影响,这对现代电子系统的电磁兼容性至关重要。它回顾了现有的电磁兼容研究和量化不确定性的方法,重点是受高度 (h)、导体半径 (rw) 和间距 (d) 不确定性影响的双导体传输线。灵敏度分析采用蒙特卡罗法、无痕变换法和随机定位法,并结合有限差分时域模拟。结果表明,由于非线性,无痕变换法更适合这一问题。一般来说,半径(rw)的影响比间距(d)小,高度(h)的影响比间距(d)大,但对于快速转换,d 的影响比 h 大。这些发现强调了高速系统中信号完整性的影响。
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引用次数: 0
Energy management system optimized for profit maximization of a photovoltaic plant with batteries applied to the short-term energy market 应用于短期能源市场的优化能源管理系统,实现带电池光伏电站的利润最大化
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-07 DOI: 10.1007/s00202-024-02633-z
Liane Marques de Oliveira, Camila Seibel Gehrke, Micael Praxedes de Lucena, Lucas Haas, Sidnéia Lira Cavalcante, Lucas Vinícius Hartmann, Flávio da Silva Vitorino Gomes, Italo Roger Ferreira Moreno Pinheiro da Silva

Photovoltaic (PV) generation plants, due to the intermittent nature of their output power, can benefit from the integration of Battery Energy Storage Systems (BESSs). In this context, this work proposes an optimized energy management system (EMS) for a joint operation of BESS in utility-scale PV plants (PV/BESS) aiming to profit maximization. The optimization of the BESS operation was achieved from the genetic algorithm, and for the problem formulation, the following cost functions were defined in the objective function: the energy pricing system; the costs associated with degradation and total loss of the batteries; the penalty related to exceeding the maximum power contracted. The proposed EMS defines the reference for a BESS to be integrated into a PV plant located in Coremas, Brazil. The results were obtained from a simulation architecture similar to the PV plant and demonstrated positive financial gains, compared to the operation of the PV plant without BESS. In the simulation of a whole year of operation, it was possible to achieve an additional daily revenue gain of up to 11%.

光伏(PV)发电厂由于其输出功率的间歇性,可以从电池储能系统(BESS)的集成中获益。在此背景下,本研究提出了一种优化的能源管理系统(EMS),用于公用事业规模光伏电站(PV/BESS)中电池储能系统的联合运行,以实现利润最大化。BESS 运行的优化是通过遗传算法实现的,为解决问题,在目标函数中定义了以下成本函数:能源定价系统;与电池退化和完全损耗相关的成本;与超过合同最大功率相关的惩罚。建议的 EMS 为将 BESS 集成到位于巴西 Coremas 的光伏电站中提供了参考。模拟架构与光伏电站类似,结果表明,与不使用 BESS 的光伏电站相比,BESS 可带来积极的经济收益。在模拟全年运行的情况下,每天的额外收益可达 11%。
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引用次数: 0
Hybrid binarized neural network for high-accuracy classification of power quality disturbances 用于高精度电能质量干扰分类的混合二值化神经网络
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-07 DOI: 10.1007/s00202-024-02650-y
Hui Li, Changhao Zhu, Xiao Liu, Lijuan Li, Hongzhi Liu

Binarized Neural Network (BNN) is a technique for reducing computational complexity and memory requirements by constraining weights and activations to binary values, enabling deployment on lightweight platforms. However, the current BNNs confront a problem of limited accuracy due to significant information loss, thereby failing to deal with in complex tasks, especially in power quality disturbance (PQD) classification. To solve this problem, we propose a hybrid binarized neural network (HBNN) model that reintroduces full-precision convolutional layers. This allows for the retention of more details and features from the original data, thereby enhancing the network’s representation of the data. HBNN enhances the nonlinear expressive capability by incorporating a full-precision convolutional layer as the input layer, while the subsequent layers maintain the binarized layer to reduce model complexity, enabling the network to better adapt to lightweight platforms. We validate the proposed method and the alternative baselines for classifying 16 types of power quality disturbances. Experiments demonstrate that HBNN improves accuracy by 9.13% compared to BNN.

二值化神经网络(Binarized Neural Network,BNN)是一种通过将权值和激活值限制为二进制值来降低计算复杂度和内存需求的技术,可部署在轻量级平台上。然而,目前的 BNN 面临着一个问题,即由于信息丢失严重,准确性有限,因此无法应对复杂的任务,尤其是电能质量干扰(PQD)分类。为了解决这个问题,我们提出了一种混合二值化神经网络(HBNN)模型,重新引入了全精度卷积层。这样就能保留原始数据的更多细节和特征,从而增强网络对数据的表征能力。HBNN 通过将全精度卷积层作为输入层来增强非线性表达能力,而后续层则保留二值化层来降低模型复杂度,从而使网络更好地适应轻量级平台。我们对所提出的方法和其他基准进行了验证,以对 16 种电能质量干扰进行分类。实验证明,与 BNN 相比,HBNN 的准确率提高了 9.13%。
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引用次数: 0
Multi-objective planning for optimal location, sizing, and power factor of distributed generators with capacitor banks in unbalanced power distribution networks 不平衡配电网络中带有电容器组的分布式发电机的最佳位置、规模和功率因数的多目标规划
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-05 DOI: 10.1007/s00202-024-02612-4
Pappu Kumar Saurav, Swapna Mansani, Partha Kayal

Allocating local real and reactive power effectively within distribution networks is crucial for meeting customer demand and upholding the quality of electrical energy. Optimal placement of distributed generators (DGs) and capacitor banks (CBs) is paramount in enhancing the efficiency of the power distribution system. However, in unbalanced radial distribution networks, achieving proper DG unit allocation remains challenging due to phase imbalances in loading and network structure, hindering the utilization of their full capacity. This article proposes a method for determining the suitable locations, sizes, and power factors of DG with CB units, considering the inherently unbalanced operation of the distribution network. The objective function aims to minimize power loss, improve multi-phase voltage stability, and enhance voltage balance among phases within the system. To address this complex multi-objective optimization problem, a fast and flexible radial power flow (FFRPF) technique is integrated, and an adaptive weighted aggregation method utilizing particle swarm optimization (PSO) is employed for the solution. The proposed algorithm’s performance is evaluated on unbalanced radial distribution networks (URDNs) consisting of 19, 25, 34, and 123 buses under various scenarios. Investigation of the simulation’s output reveals significant enhancements in power distribution efficiency across all tested URDNs.

在配电网络中有效分配本地实际功率和无功功率对于满足客户需求和保证电能质量至关重要。分布式发电机(DG)和电容器组(CB)的优化布置对于提高配电系统的效率至关重要。然而,在不平衡的径向配电网络中,由于负载和网络结构的相位不平衡,要实现分布式发电机组的合理配置仍然具有挑战性,这阻碍了其全部容量的利用。考虑到配电网络固有的不平衡运行,本文提出了一种方法,用于确定带 CB 单元的 DG 的合适位置、大小和功率因数。目标函数旨在最大限度地减少功率损耗,提高多相电压稳定性,并加强系统内各相电压的平衡。为了解决这个复杂的多目标优化问题,我们采用了快速灵活的径向功率流(FFRPF)技术,并利用粒子群优化(PSO)的自适应加权聚合法来求解。在由 19、25、34 和 123 个总线组成的不平衡径向配电网络(URDN)上,对所提出算法在各种情况下的性能进行了评估。对模拟输出的调查显示,所有测试的 URDN 均显著提高了配电效率。
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引用次数: 0
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