Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software.

Detection of unintentional islanding, defined as inadvertently separation of distributed generators (DGs) from the utility grid, is a major challenging issue for modern distribution networks. Islanding detection becomes problematic especially when the local generation matches or closely matches the local load. Therefore, there are strict requirements for accurate, fast, and reliable islanding detection of renewables and DG-based systems. Various islanding schemes have been proposed in the literature, which can be categorized as remote, local, and intelligent-classifier-based schemes. Recently, intelligent schemes have gained attention due to their superior properties and advantages relative to traditional approaches. This paper overviews the shift in research from traditional schemes to intelligent islanding schemes. It also highlights the major obstacles, challenges, advantages and disadvantages, and future research directions of intelligent schemes. In this study, the intelligent-classifier-based islanding detection schemes presented over the last decade are analyzed objectively and comprehensively from all aspects of islanding detection. This research further highlights feature selection schemes and the most common parameters used for islanding detection. Finally, based on a detailed and critical analysis, the findings and potential recommendations are presented.

With the increasing integration of electric vehicles (EVs) into the distributed energy resources (DER) system, the security of EV charging stations (EVCS) from cyber-attacks is paramount. Utilizing deep learning and recurrent neural networks (RNNs) presents promising advantages in anomaly detection within power systems. Bi-directional long-short-term memory (Bi-LSTM) emerges as a viable choice for anomaly detection, offering distinct advantages that learn from both the forward and backward sequences of the data compared to conventional deep neural networks, RNNs, and basic LSTMs. This study proposes data-driven anomaly detection (DDAD) techniques using a Bi-LSTM network. Seven statistical features are extracted from the passive parameters (voltage, current, frequency, and SoC). Then, the wrapper feature selection method is used to identify the most relevant features, enhancing the accuracy of the proposed DDAD model. We generate a dataset of normal events such as line faults, load switching, capacitor switching, and cyberattack events, including denial-of-service (DoS), spoofing, replay, and data manipulation attacks, using an extended API integrated with RT-LAB to automate the process. We demonstrated the DDAD model on a DER-integrated EVCS microgrid model on a Hardware-in-Loop (HIL)-based intelligent Cyber Physical System (iCPS) testbed environment. Comprehensive experiments are conducted to evaluate the performance of our proposed DDAD model's accuracy, precision, recall, and F1 score with the testing dataset. We compared our results against LSTM, multi-layer perception (MLP), support vector machine (SVM), and linear regression (LR) techniques. This study emphasizes the development of an efficient approach for detecting anomalies on EVCS, and our results underscore the effectiveness of our proposed methodology, achieving an average testing accuracy of 99.42%, thereby reinforcing the cyber-physical security of EVCS.

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s.

The integration of distributed generators has changed the paradigm of modern power transmission systems. To cope with energy demands, electrical networks emphasize the efficient utilization of power transmission. Thus, high-voltage DC (HVDC) and hybrid (AC/DC) transmission systems are also getting attention owing to their high efficiency in addition to the widely adopted high-voltage AC (HVAC) systems. Most faults in the bulk of transmission lines are temporary or intermittent. Auto-reclosing schemes can be used to prevent these faults. However, conventional auto-reclosing schemes based on constant dead time cannot recognize the fault nature within the assigned duration. Consequently, the accuracy of power grids can be compromised. Therefore, adaptive auto-reclosing schemes are convenient for overcoming the issues caused by the rapid restoration of faulty power lines. This can enhance system reliability and avoid power failures and blackouts. This study is based on a systematic, detailed, and thorough research review of the existing auto-reclosing schemes in all three power transmission lines, i.e., AC, DC, and hybrid (AC/DC). Subsequently, a critical analysis has been performed to assess the pros and cons of each existing adaptive auto-reclosing scheme. Finally, future recommendations are presented to improve adaptive auto-reclosing schemes in each medium.