Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.

中文翻译：

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PGMAN：用于全色锐化的无监督生成多对抗网络


仔细思考电网的发展，可以看出其结构的根本性变化，其发展是在很长一段时间内逐渐发生的。最重要的发展之一是通信基础设施的扩展，这为系统的网络层带来了许多优势。点对点（P2P）能源交易的实际执行是其核心优势之一，但也可能导致网络攻击等系统性风险。因此，有必要形成一种有效的方法来应对这些挑战。本文重点研究虚假数据注入攻击（FDIA）的在线检测，该攻击试图破坏随机条件下最优点对点能源交易的趋势。此外，本文提出了一种有效的改进的基于强化学习的智能优先级选择（IPS-RL）方法，可以在最短的时间内检测并阻止恶意攻击，从而实现基于点对点结构的有效能源交易。将所提出的方法与支持向量机（SVM）、强化学习（RL）、粒子群优化（PSO）-RL和遗传算法（GA）-RL等其他方法进行比较，以验证该方法的功能。所提出的方法在三个互连的点对点结构形式的微电网上实施和检验，其中每个微电网具有各种代理，例如光伏（PV）、风力涡轮机、燃料电池、潮汐系统、存储单元等。最终，无迹变换（UT）用于不确定性分析并进行接近现实的模拟。