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摘要: 自能源（We-energy，WE）作为能源互联网的子单元旨在实现能量间的双向传输及灵活转换.由于自能源在不同工况下运行特性存在很大差异，现有方法还不能对其参数精确地辨识.为了解决上述问题，本文根据自能源网络结构提出了一种基于GAN技术的数据——机理混合驱动方法对自能源模型参数辨识.将GAN（Generative adversarial networks）模型中训练数据与专家经验结合进行模糊分类，解决了自能源在不同运行工况下的模型切换问题.通过应用含策略梯度反馈的改进GAN技术对模型进行训练，解决了自能源中输出序列离散的问题.仿真结果表明，提出的模型具有较高的辨识精度和更好的推广性，能有效地拟合系统不同工况下各节点的状态变化.Abstract: As a sub-unit of the energy internet, we-energy (WE) aims at realizing bi-directional power transformation and flexible conversion between various types of energies. As the operating characteristics of WE have large difference under different working conditions, existing methods can not accurately identify its parameters. In order to solve this problem, a data-mechanism hybrid driving method based on generative adversarial networks (GAN) is proposed. In order to switch the WE model under different operating conditions, fuzzy theory is used to achieve fuzzy classification of training data of the GAN model by expertise. A modified GAN model containing policy gradient feedback is applied in training model, therefore solving the issue of discrete output sequence of WE. Simulation results validate that the proposed model is of high identification accuracy and has better generalization performance, and can effectively fit the state variation of each node of the whole system under different operation modes.1) 本文责任编委 谭营
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表 1 自能源系统设备参数
Table 1 Parameter of equipment in WE system
自能源系统 容量(kW) 功率下限(kW) 功率上限(kW) 光伏发电 40 0 12 风力发电 1×3 0 30 电储能 5×3 $-$10 10 微燃气轮机 80 20 80 燃气锅炉 40×2 20 80 电锅炉 5×4 0 20 热储能 5×2 $-$10 10 水泵 0.5×4 0.4 0.6 压缩机 0.3×2 0.25 0.35 
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表 2 自能源常规运行时模型参数辨识结果
Table 2 Parameter identification results in regular
参数 估值 参数 估值 参数 估值 ${\theta _{11}}$ 0.035 ${\theta _{23}}$ 0.213 ${\theta _{41}}$ $-$0.106 ${\theta _{12}}$ 0.136 ${\theta _{24}}$ $-$0.622 ${\theta _{42}}$ $-$0.127 ${\theta _{13}}$ 0.078 ${\theta _{31}}$ 0.296 ${\theta _{43}}$ 0.312 ${\theta _{14}}$ $-$0.235 ${\theta _{32}}$ 0.065 ${\theta _{44}}$ 0.225 ${\theta _{15}}$ 0.438 ${\theta _{33}}$ 0.386 ${\theta _{45}}$ 0.064 ${\theta _{21}}$ 0.164 ${\theta _{34}}$ 0.176 ${\theta _{46}}$ 0.133 ${\theta _{22}}$ 0.153 ${\theta _{35}}$ 0.217
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表 3 自能源在电压异常时模型参数辨识结果
Table 3 Parameter identification results of WE model in abnormal voltage
参数 估值 参数 估值 参数 估值 ${\theta _{11}}$ 0.014 ${\theta _{23}}$ 0.178 ${\theta _{41}}$ $-$0.157 ${\theta _{12}}$ 0.123 ${\theta _{24}}$ $-$0.534 ${\theta _{42}}$ $-$0.134 ${\theta _{13}}$ 0.081 ${\theta _{31}}$ 0.237 ${\theta _{43}}$ 0.247 ${\theta _{14}}$ $-$0.211 ${\theta _{32}}$ 0.049 ${\theta _{44}}$ 0.265 ${\theta _{15}}$ 0.369 ${\theta _{33}}$ 0.276 ${\theta _{45}}$ 0.067 ${\theta _{21}}$ 0.145 ${\theta _{34}}$ 0.198 ${\theta _{46}}$ 0.233 ${\theta _{22}}$ 0.147 ${\theta _{35}}$ 0.234
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表 4 自能源在液压异常时模型参数辨识结果
Table 4 Parameter identification results of WE model in abnormal fluid pressure
参数 估值 参数 估值 参数 估值 ${\theta _{11}}$ 0.041 ${\theta _{23}}$ 0.206 ${\theta _{41}}$ $-$0.067 ${\theta _{12}}$ 0.089 ${\theta _{24}}$ $-$0.598 ${\theta _{42}}$ $-$0.131 ${\theta _{13}}$ 0.196 ${\theta _{31}}$ 0.256 ${\theta _{43}}$ 0.276 ${\theta _{14}}$ $-$0.158 ${\theta _{32}}$ 0.124 ${\theta _{44}}$ 0.256 ${\theta _{15}}$ 0.367 ${\theta _{33}}$ 0.267 ${\theta _{45}}$ 0.065 ${\theta _{21}}$ 0.146 ${\theta _{34}}$ 0.203 ${\theta _{46}}$ 0.118 ${\theta _{22}}$ 0.145 ${\theta _{35}}$ 0.178
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表 5 自能源在气压异常时模型参数辨识结果
Table 5 Parameter identification results of WE model in abnormal gas pressure
参数 估值 参数 估值 参数 估值 ${\theta _{11}}$ 0.045 ${\theta _{23}}$ 0.157 ${\theta _{41}}$ $-$0.095 ${\theta _{12}}$ 0.246 ${\theta _{24}}$ $-$0.576 ${\theta _{42}}$ $-$0.108 ${\theta _{13}}$ 0.069 ${\theta _{31}}$ 0.146 ${\theta _{43}}$ 0.289 ${\theta _{14}}$ $-$0.246 ${\theta _{32}}$ 0.068 ${\theta _{44}}$ 0.227 ${\theta _{15}}$ 0.398 ${\theta _{33}}$ 0.356 ${\theta _{45}}$ 0.074 ${\theta _{21}}$ 0.148 ${\theta _{34}}$ 0.269 ${\theta _{46}}$ 0.145 ${\theta _{22}}$ 0.169 ${\theta _{35}}$ 0.235
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