Sub‐synchronous interaction (SSI) phenomenon is one of the dynamic system problems that have an adverse influence on the safety and stability of the system.The use of supplementary damping controllers (SDCs) in the double‐fed induction generator (DFIG) converter controllers is quite promising due to their simplicity,low costs, effectiveness, and easiness of tuning. This paper presents a new effective input control signal, rotor voltage, to the SDC for damping SSI in a series‐compensated DFIG‐based wind farm. The proposed SDC is embedded into both the q‐axis and d‐axis of the rotor‐side converter inner current loops. Particle swarm optimization algorithm is used to identify the optimum parameters of the SDC which maintain the system stability at various operation conditions. Both eigenvalue analysis and time‐domain simulations have been carried out to demonstrate the capability of the proposed SDC for enhancing the system stability and damping SSI. Compared to the conventional SDC, the proposed SDC has the best performance where it can quickly and robustly damp the SSI at different compensation levels, different wind speeds, and sub‐synchronous control interaction.

The main aim of this work was the maximization of the energy saving of balanced and unbalanced distribution power systems via system reconfiguration and the optimum capacitor's bank choice, which were estimated by using a new algorithm: modified Tabu search and Harper sphere search (MTS-HSSA). The results demonstrated that the proposed method is appropriate for energy saving and improving performance compared with other methods reported in the literature for IEEE 33-bus adopted systems, including large scale systems such as IEEE 119 and the IEEE 123 unbalanced distribution system. Moreover, it can be used for unbalanced distribution systems distributed generators (DGs). The results demonstrated that the proposed method (the optimal choice of shunt capacitor(SC) banks and the optimal reconfiguration via the proposed algorithm) is appropriate for energy saving compared with different strategies for energy saving, which included distributed generation (DG) at different cost levels.

As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy-saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I-GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I-GWO has a movement strategy called dimension learning-based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I-GWO-TS) algorithm can solve mixed-integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high-power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors' location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system.

Decision-making is very important in many fields, such as mining engineering. In addition, there has been a growth of computer applications in all fields, especially mining operations. One of these application fields is mine design and the selection of suitable mining methods, and computer applications can help mine engineers to decide upon and choose more satisfactory methods. The selection of mining methods depends on the rock-layer specification. All rock characteristics should be classified in terms of technical and economic concerns related to mining rock specifications, such as mechanical and physical properties, and evaluated according to their weights and ratings. Methodologically, in this study, the criteria considered in the University of British Columbia (UBC) method were used as references to establish general criteria. These criteria consist of general shape, ore thickness, ore plunge, and grade distribution, in addition to the rock quality designation (ore zone, hanging wall, and foot wall) and rock substance strength (ore zone, hanging wall, and foot wall). The technique for order of preference by similarity to ideal solution (TOPSIS) was adopted, and an improved TOPSIS method was developed based on experimental testing and checked by means of the application of cascade forward backpropagation neural networks in mining method selection. The results provide indicators that decision makers can use to choose between different mining methods based on the total points given to all ore properties. The best mining method is cut and fill stopping, with a rank of 0.70, and the second is top slicing, with a rank of 0.67.

<p>This paper propose a new approach to determine a linear  mathematical model of a PV moduel based on an accurate nonlinear model  . In this study, electrical parameters at only one operating condition are  calculated based on  an accurate model. Then, first-order Taylor series approximations apply on the nonlinear model to estimate the proposed model at any operating conditionts. The proposed method determines the number of iteration times. This decreases calculation time and the speed of numerical convergence will be increased. And, it is observed that owing to this method, the system converged and the problem of failing to solve the system because of inappropriate initial values is eliminated. The proposed model is requested in order to allow photovoltaic plants simulations using low-cost computer platforms. The effectiveness of the proposed model is demonstrated for different temperature and irradiance values through conducting a comparison between result of the proposed model and experimental results obtained from the module data-sheet information.</p>