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.

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.

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.

The most crucial event in a mining project is the selection of an appropriate mining method (MMS). Consequently, determining the optimal choice is critical because it impacts most of the other key decisions. This study provides a concise overview of the development of multiple selection methods using a cascade‐forward backpropagation neural network (CFBPNN). Numerous methods of multicriteria decision‐making (MCDM) are discussed and compared herein. The comparison includes several factors, such as applicability, subjectivity, qualitative and quantitative data, sensitivity, and validity. The application of artificial intelligence is presented and discussed using CFBPNN. The Chengchao iron mine was selected for this investigation to pick the optimum mining method. The results revealed that cut and fill stoping is the most appropriate mining method, followed by sublevel and shrinkage stoping methods. The least appropriate method is open‐pit mining, followed by room and pillar and longwall mining methods.

This paper proposes a photovoltaic mathematical model featuring reduction in mathematical solution and comparable accuracy. The proposed model mimics the characteristics of the practical model by solving two nonlinear equations only, thus providing low computational time. In this paper, the most important parameters, i.e. the short circuit current, open-circuit voltage, maximum power point of the PV module, are determined with best accuracy under different solar irradiance intensities and module temperatures. The best estimation of the most important parameters enhance the model's accuracy at any values of irradiance and temperature. The proposed procedure is used the Newton-Raphson method based on simplified method to calculate the parameters of a PV system. The initial values of these parameters are estimated by using the simplified method to prevent a bad starting point which can compromise the convergence of the Newton-Raphson's method. Also the proposed equations which are used to calculate these parameters of a PV system, allow one to calculate it's without relying on the experimental I-V curve to determine the parameters of a PV system as usually reported in literature. The effectiveness of the model is shown by comparing the accuracy of the proposed model with those of the available models.