I-Corps: Approximate Dynamic Programming and Artificial Neural Network Control for Microgrids

这个I-Corps项目更广泛的影响/商业潜力是作为分布式发电和微电网行业增长的催化剂。这种基于人工智能的控制系统将潜在地提供一种电网，该电网通过以令人难以置信的快速双向功率流减少停电和恢复成本而可靠，通过真实的时间诊断、自我修复和自适应能力而安全，并且通过减少设备故障和最小化功率损耗而更经济。该产品潜在的三个广阔的市场，包括公用事业，分布式发电和消费。该解决方案将增强可再生能源的发电能力，提高微电网的效率、可靠性、稳定性和电能质量，并为传统电力系统增加智能控制。逆变器能力目前是集成分布式发电源的重大挑战。该项目为微电网的控制和管理开发了一种神经网络控制技术。微电网是将可再生能源和分布式发电资源整合到电网中的一种途径，通常可以支持未来的智能电网。 微电网采用的一个关键挑战是对功率逆变器的充分控制。问题包括连接或断开电源时的高振荡、波动的电压和频率、故障和可靠性、逆变器之间的竞争控制以及高谐波失真。所提出的创新使用自适应动态规划和人工神经网络来实现微电网控制。它集成到一个控制器的传统控制方法，包括最优控制，比例积分控制，预测控制和滑模控制的优点。提出的创新有可能克服传统控制技术的局限性，更好地满足客户的需求和要求。

The broader impact/commercial potential of this I-Corps project is to act as a catalyst in the growth of distributed generation and microgrid industries. This artificial intelligence based control system will potentially provide an electrical network that is reliable by reducing outages and restoration costs with incredibly fast bidirectional power flow, secured with real time diagnostics, self-healing and adaptive capabilities, and more economical by reducing equipment failures and minimizing power losses. The product potentially three broad markets, including utilities, distributed generation and consumer. The solution will enhance energy generation from renewables, improve microgrid efficiency, reliability, stability and power quality, and add intelligent control to conventional power systems. Inverter capabilities are presently a significant challenge for integrating distributed generation sources. The proposed innovation would potentially provide an appropriate solution to address this challenge.This I-Corps project develops a neural network control technology for microgrid control and management. Microgrids are one path for integrating renewable and distributed generation sources into the grid and can generally support a future smart electricity grid. A key challenge in microgrid adoption is adequate control of power inverters. Problems include high oscillations when connecting or disconnecting an energy source, fluctuating voltage and frequency, malfunctions and reliability, competing control between inverters, and high harmonic distortions. The proposed innovation uses adaptive dynamic programming and artificial neural networks to implement microgrid control. It integrates into one controller the advantages of conventional control methods, including optimal control, proportional integral control, predictive control, and sliding mode control. The proposed innovation has the potential to overcome the limitations of the conventional control technologies and better meet customer demands and requirements.