Optimal planning of integration the Photovoltage Distributed Generation (PV-DG) and DSTATCOM is a crucial task due to the stochastic variations of PV output power and the load demand which are related to solar irradiance variations and the activities of the customers, respectively. In this article, the optimal planning problem of the PV-DG and DSTATCOM system is solved. The proposed model considers the uncertainties of the solar irradiance and the load demand for a multi-objective function, including the cost reduction, the voltage profile, and stability index improvement. Modified Ant Lion Optimizer (MALO) is proposed to enhance the basic ALO searching ability using two strategies. The first strategy is based on Levy Flight Distribution (LFD) to strengthen the exploration of the algorithm and avoid the premature of the basic ALO. In contrast, the second strategy is based on updating the solutions in a spiral orientation to improve the exploitation of the algorithm. The IEEE 69-bus and 118-bus radial distribution systems are used to demonstrate the effectiveness of the proposed method, and the yielded simulations are compared with the basic ALO and other well-known optimization techniques for power loss minimization under deterministic conditions. The simulation results demonstrate that the techno-economic benefits can be increased considerably by optimal inclusion of two PV-DGs and DSTATCOMs compared with a single system.

This paper proposes a new approach for rapid detection of islanding events in a microgrid (MG). The proposed approach is a two-step procedure in which the first step is to extract some valuable features from the voltage and current signals. Such signals are analyzed for finding the second harmonic by the discrete Fourier transform (DFT). Then, the symmetrical components of this second harmonic are calculated for voltage and current, resulting in six features; positive, negative and zero sequence components. In the second step, a novel deep learning classifier based on long short-term memory (LSTM) network to identify the islanding decision is applied. The LSTM is a new artificial intelligence technique which is a distinctive pattern of recurrent neural networks. To evaluate the performance of the proposed approach, simulated and practical voltage and current signals are used. The simulated signals are generated by simulating a MG consisting of inverter based wind DGs using Matlab Simulink, while the practical data are collected from an experimental model consisting of wind and PV DGs. Different intentional and unintentional islanding events are conducted and processed using the proposed approach. The results show that in comparison with other artificial intelligence algorithms such as decision tree (DT), support vector machine (SVM) and artificial neural network (ANN), the proposed approach is efficient and reliable in detecting the islanding with high accuracy, high dependability and small detection time.

Distributed generations (DGs) have been utilized in some electric power networks. Power loss reductions, voltage improvement, increasing reliability, postponement of system upgrading and environmental friendliness are some advantages of DG-unit applications. This paper presents a new optimization approach that employs a flower pollination algorithm (FPA) to determine the optimal DG-unit’s size and location in order to minimize the total system real power loss and improve the system buses voltage. The FPA is a new metaheuristic optimization technique and it is inspired by the reproduction strategy of the flow pollination process of flowering plants. To reveal the validity of the FPA algorithm, IEEE 33-bus, 69-bus and 136-bus radial distribution test systems are examined with different test cases of the objective function using the MATLAB. Furthermore, the results obtained by the proposed FPA algorithm are compared with other metaheuristic optimization techniques such as backtracking search optimization algorithm, artificial bee colony, and selection algorithm. The outcomes verify that the FPA algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems

This paper addresses the allocation of a hybrid system that includes PV-DG and DSTATCOM. The planning problem considers the variations of load demand and solar irradiance under deterministic and probabilistic conditions. An efficient optimization algorithm called MPA is implemented to assign the optimal placement and ratings of the hybrid PV-DG and DSTATCOM. The considered objective function is a multi-objective function that includes the annual cost reduction, improvement of voltage profiles, and system stability improvement. The assessment is accomplished with the inclusion of a single and two-hybrid system on a large 94-bus system. For validating the effectiveness of the MPA, the yielded results are compared with the PSO, which is considered a commonly used algorithm. In deterministic conditions, the hourly variations of the load demand and solar radiation are considered in four yearly seasons, while in probabilistic conditions, 3 years of hourly historical data of solar irradiance and load demand are utilized to describe the uncertainties of the load demand and solar irradiance. The simulation results demonstrate that the optimal inclusion of a single-hybrid PV-DG and DSTATCOM system can enhance the system's technical performance (the voltage profile, the voltage stability) and enhance the economic scheme (total annual cost reduction). In addition, the inclusion of two-hybrid systems is superior compared with the inclusion of a single-hybrid system in terms of the considered objective functions, as well as the proposed technique is more efficient for solving the allocation problem of the hybrid system.

In the last decade, the integration of distributed generators (DGs) in radial distribution systems has reached high penetration levels. Impacts of DGs on the active power loss and system voltage profile has become significant. Optimization algorithms are tools which are used to optimally find the site and size of DGs within specific limits and constraints. This paper presents an optimization algorithm that employs modified flower pollination algorithm (MFPA) to determine the optimal DGs allocation in order to minimize the system power losses. MFPA is a new metaheuristic population-based optimization algorithm, and has few tuning parameters. Therefore, the updating of these tuning parameters towards the most effective values has a higher reliability of success than in other competing metaheuristic methods. The performance of the proposed MFPA is investigated on three standard test systems; IEEE 33-bus, IEEE 69-bus and IEEE 136-bus. The results show that the effectiveness and validity of MFPA in determining the optimal allocation of DER units with minimum power losses. The superiority of the proposed MFPA is evaluated by comparing it with other metaheuristic optimization algorithms such as the discrete artificial bee colony algorithm (DABC) and standard flower pollination algorithm (FPA).