Computational and mathematical models provide an important compliment to experimental studies in the development of solar energy engineering in case of electro-conductive magnetic micropolar polymers. Inspired by further understanding the complex fluid dynamics of these processes, we examine herein the non-linear steady, hydromagnetic micropolar flow with radiation and heat source/sink effects ...

A robust speed control technique for permanent-magnet synchronous motor (PMSM) drives is proposed in this paper for electric vehicle applications. The robust controller consists of a neural-network controller (NNC) in the speed feed-back loop in addition to an on-line trained neural-network model-following controller (NNMFC) in the feedforward loop. The adaptive neural-network model-following c...

In this paper, we develop a static electric power supply chain network equilibrium model with known demands and establish the equivalence between the model and a transportation network equilibrium model with fixed demands over an appropriately constructed supernetwork. This equivalence yields a new interpretation of electric power supply chain network equilibria in path flows. We then exploit t...

This paper proposes a new method for transmission loss allocation. The share of each bus in the transmission line losses is determined using transmission line loss equations with respect to bus-injected currents. Then, it is applied to the total network transmission lines. In the proposed method, comparing with other methods, a solution to remove the negative loss allocation has been introd...

In this paper, the effect of renewable energy resources (RERs), demand response (DR) programs and electric vehicles (EVs) is evaluated on the optimal operation of a smart distribution company (SDISCO) in the form of a new bi-level model. According to the existence of private electric vehicle parking lots (PLs) in the network, the aim of both levels is to maximize the profits of SDISCO and the P...

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

Abstract Accurate mid-long-term load forecasting is of great significance to power system planning and safe operation. Therefore, this paper proposed a prediction electrical based on PSO RBF neural networks. Based network, the model established, parameters network are iterated by obtain optimal parameter value, which key improving accuracy. The comparison calculation results shows that it can i...