AI Enabled Control of Distributed Generation

用于可再生能源和存储的电力电子接口（逆变器）的控制必须设计为在具有广泛变化的操作条件的电力系统中稳定运行。随着惯性和传统发电越来越多地被基于逆变器的发电所取代，而且不同逆变器控制系统的互操作性无法保证，这变得更具挑战性。该项目将探索如何使用人工智能（AI）来在线调整和适应逆变器控制，方法是使用逆变器作为其正常控制的一部分进行测量来估计本地电网的状态。这些估计在异常操作条件下尤其重要，例如故障，其中级联故障可能发生在基于逆变器的发电中。人工智能将用于持续监控当地电网状况，并确定控制功能和模式的变化，特别是在系统发生故障时。将采用两种方法。第一个将使用AI进行电网状态估计，例如，基于来自本地测量的部分信息的系统范围阻抗。数据驱动的人工智能方法，例如，深度学习方法将被用作其确定性对应物的替代品，用于响应逆变器和电力系统控制，特别是对于传统技术失败的非线性操作区域。实验数据将从FlexElec实验室的典型逆变器系统中收集，并用于训练AI模型。第二种方法将利用模型驱动的AI方法，其中最先进的相位-幅度数学框架将被用作AI架构设计和实现的模型知识。相位振幅数学框架将通过与数学系的合作进一步开发，该框架将进行调整和定制，以帮助设计，培训和实施人工智能模型。通过将电力系统结构的知识结合到AI模型中，所提出的方法将改善本地连接的功率转换器的电网状态估计（并且固有地增强其操作）。这两种方法将在FlexElec实验室进行实验评估，并与传统的网格跟踪和网格形成技术进行比较。

The control of power electronic interfaces (inverters) for renewable energy and storage must be designed to operate stably in a power system with widely varying operating conditions. This is becoming more challenging as inertia and conventional generation are being increasingly replaced with inverter-based generation, and interoperability of different inverter control systems is not guaranteed. This project will explore how artificial intelligence (AI) can be used to tune and adapt inverter control on-line by estimating the state of the local grid using measurements made by an inverter as part of its normal control. These estimates are especially important during abnormal operating conditions eg faults, where cascade failures can occur with inverter based generation. AI will be used to continually monitor local grid conditions and determine changes to control functions and modes especially when system faults occur. Two approaches will be employed. The first will use AI for grid status estimation, e.g., system-wide impedance based on partial information from local measurements. Data-driven AI methods, e.g., deep learning methods, will be exploited as a replacement for its deterministic counterparts for responding to inverter and power system controls, especially for the non-linear operating regions where conventional techniques fail. Experimental data will be collected from typical inverter systems available in the FlexElec laboratory and used to train AI models.The second approach will exploit model-driven AI methods, where the state-of-the-art phase-amplitude mathematical framework will be exploited as the model knowledge for the design and implementation of the AI architecture. The phase-amplitude mathematical framework will be further developed through collaboration with Maths Department, and the framework will be adapted and tailored to aid the design, training and implementation of the AI model. By incorporating knowledge of the power system structure into the AI model, the proposed approach will improve the grid status estimation for locally connected power converters (and inherently enhance their operation). Both approaches will be evaluated experimentally in the FlexElec laboratory and compared to conventional grid following and grid forming technologies.