As the penetration rate of new energies, energy storage devices and electric vehicles increases continuously in power grid, power grid faces strong random disturbances, as well as the issues of downfall in frequency control ability induced by the factors such as system inertia reduction and frequency control under-capacity in traditional power units. Therefore, an algorithm of automatic generation control with DQ(σ, δ) is proposed for the multi-area interconnected power grid. By integrating the eligibility trace, DQ(σ, δ) adopts the principle of double Q learning to unify double Q(δ) and double Expected-Sarsa(δ), thereby the over-estimation of Q in the algorithm and the phenomenon of over-fitting can be avoided. The simulations are provided to demonstrate the effectiveness of the proposed algorithm, where the improved IEEE standard two-area load-frequency control model and the model of the multi-area interconnected power grid based on Central China Power Grid are adopted. The results show that the phenomena of random disturbances and frequency instability can be eliminated in multi-area interconnected power grid. The convergence is faster and the dynamic performance is better in the proposed algorithm compared with the traditional algorithms.

The large-scale integration of renewable energy sources has led to a significant increase in the number of harmonic sources within distribution networks. Concurrently, the altered supply modes introduced by renewable integration have caused dynamic changes in the network topology. Therefore, this paper proposes a three-phase distribution network harmonic source location method based on a physics informed topology adaptive graph convolutional network. Firstly, a harmonic pseudo-measurement method based on emission characteristics modelling is developed to address the shortage of harmonic measurements. Secondly, considering the dynamic changes in the distribution network topology, a multi-layer perception topology adaptive graph convolutional neural network is proposed, which is used to establish the mapping relationship between harmonic measurements and state variables. Meanwhile, the harmonic transfer equation and system parameters are embedded into the neural network training as physical constraints to ensure the results conform to physical properties. Then, a harmonic source identification criterion is established, and the long-term statistical index derived from the estimated harmonic injection current is used to locate harmonic sources in the distribution network. Finally, the effectiveness of the proposed method was verified in the IEEE 37-bus system and the actual system.