Prediction of stroke probability occurrence based on fuzzy cognitive maps

Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%

PID Type Stabilizer Design for Multi Machine Power System Using IPSO Procedure

Abstract This paper presents a modified Iteration Particle Swarm Optimization (IPSO) algorithm to tune optimal gains of a Proportional Integral Derivative (PID) type multiple stabilizers and non-smooth nonlinear parameters (such as saturation limits) for multi machine power system, simultaneously. The problem of robustly tuning of PID based multiple stabilizer design is formulated as an optimization problem according to the time domain-based objective function which is solved by a modified strategy of PSO algorithm called IPSO technique that has a strong ability to find the most optimistic results. In the proposed algorithm, a new index named, Iteration Best, is incorporated in standard Particle Swarm Optimization (PSO) to enrich the searching behavior, solution quality and to avoid being trapped into local optimum. To demonstrate the effectiveness and robustness of the proposed stabilizers, the design process takes a wide range of operating conditions and system configuration into account. The effectiveness of the proposed stabilizer is demonstrated through nonlinear simulation studies and some performance indices on a four-machine two areas power system in comparison with the classical PSO and PSO with Time-Varying Acceleration Coefficients (PSO-TVAC) based optimized PID type stabilizers. The results of these studies show that the proposed IPSO based optimized PID type stabilizers have an excellent capability in damping power system inter-area oscillations and enhance greatly the dynamic stability of the power system for a wide range of loading condition. Also, it is superior that of the PSO and PSO-TVAC based tuned stabilizers in terms of accurateness, convergence and computational effort

Coordinated multi‐stage expansion planning of transmission system and integrated electrical, heating, and cooling distribution systems

Abstract The rapid development of different energy production and conversion technologies at the distribution system (DS) level facilitates the integration of electrical, heating, and cooling DSs. The planning and operation of integrated DSs will affect the asset management of the transmission system (TS), which in turn will change the electrical energy prices and investment decisions at the DS level. This paper addresses a tri‐level multi‐stage approach for coordinated expansion planning of TS and integrated electrical, heating and cooling DSs. The photo voltaic panels (PVs), wind turbines (WTs), combined heat and power generation (CHP) units, boilers, and electrical and absorption chillers are considered investment candidates for coupling the DSs. Moreover, the expansion planning of TS lines, electrical feeders,and heating and cooling pipelines are integrated into the proposed model. The discussed model is tested on the modified 6‐bus, 30‐bus, and 118 bus IEEE TS comprising electrical, heating, and cooling DSs. The numerical study proves that the distribution system operators (DSOs), connected to different nodes of the TS, reduce their operation and emissioncosts by investing in the energy resources. Moreover, the proposed scheme successfully reduces the total expected cost of the transmission system operator (TSO) and successfully postpones the expansion of the TS

Risk-based planning of the distribution network structure considering uncertainties in demand and cost of energy

Technical and financial uncertainties may put distribution system planning at risk. In this paper, a new risk-based planning method is proposed which pays more attention to low-probability and high con- sequences events in energy supplying systems. The proposed approach is adopted for determining the optimal structure of a Medium Voltage network where risk-based determination of the radial network structures is implemented through an uncertainty model of the system's variables based on discrete states, called scenarios. The cost of distribution system planning consists of investment cost, mainte- nance cost, power losses cost, reliability cost, and technical risk cost. In this paper, appropriate models are proposed to consider the monetary effects of technical risks. The proposed approach is applied to a test system consisting of 52 electric load points and two substations. It is observed that the proposed risk-based method for planning the optimal network structure can properly reduce the cost of extreme events, therefore reducing the concerns of distribution system operators about these possible situations