Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method which takes the advantage of a ML-based algorithm, namely SVM, in order to produce the results more efficiently. The results of the simulations based on the model and experimentally-measured parameters of a real-life practical PV plant, gives much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely grid fault emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with same kernel dimension and no custom hyper plane bordering. Discrimination between islanding and grid fault events is an essential dilemma which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously, detect the grid faults authentically. NDZs and DT are tested using two dimensions, namely the generated active energy from PV plant (0-110% of Pn), and distribution network voltage levels (± 10% of Un). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment and several tests are executed for several scenarios, Finally, comparisons with previously-reported techniques prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications.

This paper presents a new islanding detection strategy for low-voltage (LV) inverter-interfaced microgrids based on adaptive neuro-fuzzy inference system (ANFIS). The proposed islanding detection method exploits the pattern recognition capability of ANFIS and its nonlinear mapping of relation between inputs. The ANFIS monitors seven inputs measured at point of common coupling (PCC), namely root-mean square (RMS) of voltage and current (RMSU and RMSI), total harmonic distortion (THD) of voltage and current (THDU and THDI), frequency (f), and also active and reactive powers (P, Q) that are experimentally obtained based on practical measurement in a real-life microgrid. The proposed method is composed of passive monitoring of the mentioned inputs and therefore, does not influence power quality (PQ); but considerably decreases non detection zones (NDZs). In order to cover as much situations as possible, minimizing false tripping and still remaining selective, type and number of samples are introduced. Here, one of the main goals is reducing NDZ by still keeping PQ in order. Based on the sampled frequency and number of samples, we find that the proposed method has less detection time and better accuracy, compared to the reported methods. Simulations performed in MATLAB/Simulink software environment and several tests performed based on different active load conditions and multiple distributed generation (DG)s, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.

The cheap and reliable primal energy source for BESS refueling necessitates a special attention for combining RERs with PHEV charging stations in microgrids. Rapid charging is an operation mode of PHEV for drivers which demands fast recharging of BESSs of the electric cars. This charging mode manifests as low impedance short circuit at DC side, making power transient on power grid side. This paper presents a new anti-islanding protection scheme for LV VSC-based microgrids by exploiting SVMs. The proposed anti-islanding protection method exploits powerful classification capability of SVMs. The sensor monitors seven inputs measured at the PCC, namely RMS value of voltage and current (RMSV, RMSI), THD of voltage and current (THDV, THDI), frequency (f), and also active and reactive powers (P, Q). This approach is based on passive monitoring and therefore, it does not affect the PQ. In order to cover as many situations as possible, minimize false tripping and remain selective, training and detection procedures are simply introduced. Based on the presented sampling method and input model, the proposed method is tested under different conditions such as PHEV rapid charging, additional load change and multiple DGs at the same PCC. Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed based on different scenarios and compared with previously-reported techniques, this analysis proved the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard, and its superiority over other methods.

One of the most crucial problems in distribution networks (DNs) is islanding detection. This paper presents a new Gibbs phenomenon-based hybrid method for islanding detection in low voltage (LV) DNs, which is essentially based on a combination of active and passive methods of frequency rate of change at a given moment and measurement of constant total harmonic distortion (THD). The proposed method exploits Gibbs phenomenon occurring at the interpolation of sinusoidal functions. Beside identification of Gibbs phenomenon, root mean square (RMS) of the voltage is tracked in parallel with THDU for firm islanding detection. Finally, to prove the effectiveness of the proposed islanding detection strategy, offline digital time-domain simulation studies have been performed on a test microgrid including voltage-sourced converter (VSC)-based renewable/ distributed energy resources (RERs/DERs) in MATLAB/Simulink environment for different configurations and load conditions and the results have been verified by comparing with previously-reported methods. The results indicate that the proposed method performs fast and precise islanding detection in both low-power and high-power LV microgrids with reasonable impact on power quality (PQ) according to EN 50160 standard. By combining these fields as active and passive techniques, the proposed hybrid method gives non detection zones (NDZs) and detection time (DT) much smaller compared with frequency-and harmonic-based individual islanding detection methods.