This manuscript proposes an Internet of Things (IoT) platform for energy management (EM) in multi-microgrid (MMG) system to enhance the power quality with hybrid method. The proposed method is the consolidation of opposition based crow search optimizer (OCSO) and radial basis functional neural network (RBFNN), hence it called RBFNOCS technique. The main aim of this manuscript is to optimally managing the power and resources of distribution system (DS) by constantly track the data from IoT-based communication framework. In the proposed work, every devices of home is interfaced with data acquisition module (DAM) that is IoT object along unique IP address resultant in large mesh wireless network. Here, the IoT-based communication framework is used for facilitating the development of a demand response (DR) energy management system (EMS) for distribution system. The transmitted data is processed by RBFNOCS technique. By utilizing the RBFNOCS method, the active with reactive power processing for optimal capacity unbalance compensation smart VSIs share the obtainable neutral current (NC). Likewise, the DS IoT framework enhances these networks flexibility and gives feasible use of obtainable resources. Moreover, the RBFNOCS method is responsible for satisfying the total supply with energy demand. The proposed model is activated in MATLAB/Simulink site and the performance is compared with existing models, namely improved artificial bee colony, squirrel search algorithm and gravitational search algorithm based artificial neural network (SOGSNN), GOAPSNN, fruit fly optimization, and FORDF technique. When compared with the existing methods, the efficiency of the RBFNOCS method is 93.4501%.

中文翻译：

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多微电网系统能源管理物联网平台提升电能质量：ARBFNOCS技术

这份手稿提出了一个物联网 (IoT) 平台，用于多微电网 (MMG) 系统中的能源管理 (EM)，以通过混合方法提高电能质量。所提出的方法是基于反对派的乌鸦搜索优化器（OCSO）和径向基函数神经网络（RBFNN）的合并，因此称为RBFNOCS技术。本手稿的主要目的是通过不断跟踪来自基于物联网的通信框架的数据来优化管理配电系统 (DS) 的电源和资源。在拟议的工作中，家庭的每个设备都与数据采集模块 (DAM) 连接，数据采集模块 (DAM) 是沿着唯一 IP 地址的物联网对象，从而形成大型网状无线网络。这里，基于物联网的通信框架用于促进配电系统的需求响应 (DR) 能源管理系统 (EMS) 的开发。传输的数据由 RBFNOCS 技术处理。通过利用 RBFNOCS 方法，有功和无功功率处理以实现最佳容量不平衡补偿智能 VSI 共享可获得的中性线电流 (NC)。同样，DS IoT 框架增强了这些网络的灵活性，并提供了对可用资源的可行使用。此外，RBFNOCS 方法负责满足能源需求的总供给。在MATLAB/Simulink站点上激活所提出的模型，并与现有模型进行性能比较，即改进的人工蜂群、松鼠搜索算法和基于人工神经网络（SOGSNN）的引力搜索算法，GOAPSNN、果蝇优化和 FORDF 技术。与现有方法相比，RBFNOCS 方法的效率为 93.4501%。