Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

The penetration of PVs into the power grid is increasing day by day, as they are more economical and environment-friendly. However, due to the intrinsic intermittency in solar radiation and other meteorological factors, the generated power from PVs is uncertain and unstable. Therefore, accurate forecasting of power generation is considered one of the fundamental challenges in power system. In this paper, a deep-learning model based on two-stage attention mechanism over LSTM is proposed to forecast a day-ahead PV power. In addition, the Bayesian optimization algorithm is applied to obtain the optimal combination of hyper-parameters for the proposed deep-learning model. Various input features that can affect the PV power generation such as solar radiation, temperature, humidity, snowfall, albedo etc. are considered and their impact with respect to the attention mechanisms on the forecasted PV power is analyzed. The input consists of data from 21 PVs installed at different geographical locations in Germany. The proposed model is compared with state-of-the-art models such as LSTM-Attention, CNN-LSTM, and Ensemble model for day-ahead forecasting. The model is also compared with various single attention mechanisms such as Input-attention, SNAIL, Raffel, and Hierarchical attention etc. The proposed model outperforms the traditional methods in terms of accuracy, hence proving its efficiency. Forecasting Skill (FS) score of the proposed model is 0.4813 whereas 0.4427 is for the Ensemble model, which is the best among other state-of-the-art models. Root Mean Square (RMSE) and Mean Absolute Error (MAE) of the proposed model is 0.0638 and 0.0324 respectively, whereas those of the Ensemble model are 0.0685 and 0.0369 respectively.

An Intrusion Detection System (IDS) is a tool used primarily for security monitoring, which is one of the security strategies for Supervisory Control and Data Acquisition (SCADA) systems. Distributed Network Protocol version 3 (DNP3) is the predominant SCADA protocol in the energy sector. In this paper, we have developed an effective and flexible IDS for DNP3 networks, observing that most critical operations in DNP3 systems are utilized based on the function codes in DNP3 application messages, and that exploitation of those function codes enables attackers to manipulate the system operation. Our proposed anomaly-detection method deals with possible attacks that can bypass any rule-based deep packet inspection once attackers take over servers in the system. First, we generated datasets that reflected DNP3 traffic characteristics observed in real-world power grid substations for a reasonably long time. Next, we extracted input features that consisted of the occurrences of function codes per TCP connection, along with TCP characteristics. We then used an unsupervised deep learning model (Autoencoder) to learn the normal behavior of DNP3 traffic based on function code patterns. We called our approach FC-AE-IDS (Function Code Autoencoder IDS). The evaluation of the proposed method was carried out on three different datasets, to prove its accuracy and effectiveness. To evaluate the effectiveness of our proposed method, we performed various experiments that resulted in more than 95% detection accuracy for all considered attack scenarios that are mentioned in this study. We compared our approach to an IDS that is based on traditional features, to show the effectiveness of our approach.

Cyber attacks targeting the Supervisory Control and Data Acquisition (SCADA) systems are becoming more complex and more intelligent. Currently proposed security measures for the SCADA systems come under three categories: physical/logical network separation, communication message security, and security monitoring. However, the recent malwares which were used successfully to disrupt the critical systems show that these security strategies are necessary, but not sufficient to defend these malwares. The malware attacks on the SCADA system exploit weaknesses of host system software environment and take over the control of host processes in the SCADA system. In this paper, we explain how the malware interferes in the important process logics, and invades the SCADA host process by using Dynamic Link Library (DLL) Injection. As a security measure, we propose an algorithm to block DLL Injection efficiently, and show its effectiveness of defending real world malwares using DLL Injection technique by implementing as a library and testing against several DLL Injection scenarios. It is expected that this approach can prevent all the hosts in the SCADA system from being taken over by this kind of malicious attacks, consequently keeping its sanity all the time.

With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method.