An adaptive decentralized control scheme is proposed for real-time control of oscillatory dynamics and overall stability improvement of an interconnected power system. A standard framework of continuous-time (CT) infinite-horizon optimal control paradigm is defined and an extended online actor-critic (AC) algorithm based on policy iteration is used for its solution. The AC structure uses neural networks (NNs) whose weights are updated adaptively. The proof for the convergence of the scheme guarantees the system stability. The unobservable internal states of the synchronous machine are estimated using a numerically stable, swift and relatively accurate decentralized dynamic state estimator (DDSE) based on spherical-radial cubature rule. Applicability of the developed scheme has been demonstrated on a benchmark power system (IEEE 2.2, 16 machine 68 bus system) model via nonlinear time-domain simulations. Multi-processor technology based scaled laboratory setup was used for controller performance validation in real-time.

中文翻译：

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增强电力系统稳定性的分散非线性自适应优化控制方案

提出了一种自适应分散控制方案，用于实时控制互连电力系统的振荡动态和整体稳定性改进。定义了连续时间（CT）无限范围最优控制范式的标准框架，并使用基于策略迭代的扩展在线actor-critic（AC）算法对其进行求解。AC 结构使用权重自适应更新的神经网络 (NN)。方案收敛性的证明保证了系统的稳定性。同步电机不可观察的内部状态是使用基于球径向体积规则的数值稳定、快速且相对准确的分散动态状态估计器 (DDSE) 来估计的。已在基准电力系统（IEEE 2.2，16 机 68 总线系统）模型通过非线性时域仿真。基于多处理器技术的规模化实验室设置用于实时控制器性能验证。