Machine learning and deep learning techniques are widely used to assess intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. However, no previous research has examined the performance of the proposed model on a variety of publicly accessible datasets in detail. Due to the dynamic nature of the attack and its rapidly changing attack techniques, the publicly accessible intrusion datasets must be updated and benchmarked regularly. The deep neural network (DNN) and convolutional neural network (CNN) are examined in this article as types of deep learning models for developing a flexible and effective IDS capable of detecting and comparing them with the proposed model in detecting cyber-attacks. The constant development of network behavior and the fast growth of attacks need the development of IDS and the evaluation of many datasets produced over time through static and dynamic methods. This kind of research enables the identification of the most efficient algorithm for identifying future cyber-attacks. We proposed a novel two-stage deep learning technique hybridizing Long-Short Term Memory (LSTM) and Auto-Encoders (AE) for detecting attacks. The CICIDS2017 and CSE-CICDIS2018 datasets are used to determine the optimum network parameters for the proposed LSTM-AE. The experimental results show that the proposed hybrid model works well and is applicable for detecting attacks in modern scenarios.

The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase-amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%.

Nowadays, modern technologies in power systems have been attracting more attention, and households can supply a portion of or all of their electricity based on on-site generation at their location. This can be challenging for utilities in terms of monitoring and recording the data because the households’ facilities can generate or consume the energy without passing it through a meter, increasing the complexity of a distribution network. The speed of transferring data to utilities is another important concern. There is a necessity to send the smart meter (SM) data of each house to a distribution management system (DMS) for more analysis in the shortest possible time. This paper presents a novel deep learning framework collaborating with sequence-to-sequence (seq2seq), long short-term memory (LSTM), and stacked autoencoders (SAEs) to forecast residential load profiles considering the photovoltaic (PV), battery energy storage system (BESS), and electric vehicle (EV) loads with more capability based on pre-defined patterns. Experimental results show that the proposed method achieves outstanding performance in the forecasting process of residential load profiles in comparison with other algorithms. Also, a smart distribution transformer can help utilities to receive the data instantly via wireless communication, which can reduce the transfer duration to every minute and make the prediction and monitoring more manageable considering the different combinations of distributed energy resources (DERs) in residential locations.

Digital substations have adopted a high amount of information and communication technology (ICT) and cyber–physical systems (CPSs) for monitoring and control. As a result, cyber attacks on substations have been increasing and have become a major concern. An intrusion-detection system (IDS) could be a solution to detect and identify the abnormal behaviors of hackers. In this paper, a Deep Neural Network (DNN)-based IDS is proposed to detect malicious generic object-oriented substation event (GOOSE) communication over the process and station bus network, followed by the multiclassification of the cyber attacks. For training, both the abnormal and the normal substation networks are monitored, captured, and logged, and then the proposed algorithm is applied for distinguishing normal events from abnormal ones within the network communication packets. The designed system is implemented and tested with a real-time IEC 61850 GOOSE message dataset using two different approaches. The experimental results show that the proposed system can successfully detect intrusions with an accuracy of 98%. In addition, a comparison is performed in which the proposed IDS outperforms the support vector machine (SVM)-based IDS.

Ranging methods using received signal strength (RSS) information are widely used for indoor localization because they can be easily implemented without conducting site surveys. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, we develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and non-line-of-sight (NLOS) states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, we formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, we apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92 m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09% over the benchmark scenario.