基于Transformer嵌入式深度确定性策略梯度法的MB-PSS稳定性控制  

Stability control of multiband power system stabilizer based on Transformer-embedded deep deterministic policy gradient method

作　　者：殷林飞 赵镱然 YIN Linfei;ZHAO Yiran(School of Electrical Engineering,Guangxi University,Nanning 530004,China)

机构地区：[1]广西大学电气工程学院,南宁530004

出　　处：《综合智慧能源》2024年第12期1-9,共9页Integrated Intelligent Energy

基　　金：国家自然科学基金项目(62463001)。

摘　　要：新能源发电出力具有波动性、不确定性特征,会破坏电力系统电压和频率的动态平衡,引发潮流分布的改变,进而造成电网解列,威胁电网的整体稳定与供电安全。多频段电力系统稳定器(MB-PSS)的稳定控制能够提高电力系统电能传输时的可靠性和稳定性。为保证MB-PSS的稳定性,结合深度强化学习中的深度确定性策略梯度(DDPG)法以及生成式预训练模型(GPT)中的Transformer机制,提出了一种Transformer嵌入式DDPG(TDDPG)的多频段电力系统稳定器稳定性控制方法,将Transformer机制融入DDPG法的2个神经网络中,通过编码器和译码器来提高输入参数的维度,以解决DDPG法中2组神经网络输入过少的问题。三相接地故障和两相接地故障仿真结果表明,基于TDDPG法的多频段电力系统稳定器稳定性控制方法训练效果好,具有更高的控制精度。The renewable energy generation has volatility and uncertainty characteristics,which can disrupt the dynamic balance of voltage and frequency in power systems,alter power flow distribution,and consequently cause grid splitting,threatening overall grid stability and power supply security.The stability control of the multiband power system stabilizer(MB-PSS)can enhance the reliability and stability of power transmission within power systems.To ensure MB-PSS stability,a Transformer-embedded deep deterministic policy gradient(TDDPG)method was proposed by combining the deep deterministic policy gradient(DDPG)method in deep reinforcement learning with the Transformer mechanism from generative pre-trained models(GPT).The Transformer mechanism was integrated into two neural networks of the DDPG method,utilizing encoders and decoders to increase the dimensionality of input parameters,addressing the issue of insufficient input in the two neural networks of the DDPG method.Simulation results of three-phase and two-phase grounding faults demonstrate that the multi-band power system stabilizer stability control method based on the TDDPG method achieves excellent training results with higher control precision.

关 键 词：新能源 多频段电力系统稳定器 深度强化学习 生成式预训练模型 深度确定性策略梯度法 TRANSFORMER 神经网络 

分 类 号：TK712[动力工程及工程热物理—流体机械及工程] TM712[电气工程—电力系统及自动化]