An effective hybrid model is proposed to forecast the short-term wind speed.A new data preprocessing method is put forward.The fuzzy neural network is modified.Three comparative experiments are performed to prove the validity of the hybrid model. Wind speed forecasting plays a pivotal role in power dispatching and normal operations of power grids. However, it is both a difficult and challenging problem to achieve high-precision forecasting for the wind speed because the original sequence includes many nonlinear stochastic signals. The current conventional forecasting methods are more suitable for capturing linear trends, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a denoising method with a dynamic fuzzy neural network to address the problems above. Singular spectrum analysis optimized by brain storm optimization is applied to preprocess the original wind speed data to obtain a smoother sequence, and a generalized dynamic fuzzy neural network is utilized to perform the forecasting. With a smaller and simpler structure of the neural network, the model can effectively achieve a rapid learning rate and accurate forecasting. Three experimental results, which cover 10-min, 30-min and 60-min interval wind speed time series data, demonstrate that the model can both satisfactorily approximates the actual value and be used as an effective and simple tool for the planning of smart grids.

A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting

This paper presents a robust fuzzy $H_{\infty }$ control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering. Since vehicle lateral dynamics possesses inherent nonlinearities, the main objective is dedicated to deal with the nonlinear challenge in vehicle lateral dynamics by applying Takagi-Sugeno (T-S) fuzzy modeling approach. First, the nonlinear Brush tire dynamics and the nonlinear functions of longitudinal velocity are represented via a T-S fuzzy modeling technique, and vehicle parametric uncertainties are handled by the norm-bounded uncertainties. An uncertain nonlinear vehicle lateral dynamic T-S fuzzy model is then obtained with multi-fuzzy-rules. The resulting robust fuzzy $H_{\infty }$ state-feedback controller is designed with the parallel-distributed compensation strategy and premise variables, and solved via a set of linear matrix inequalities derived from Lyapunov asymptotic stability and quadratic $H_{\infty }$ performance. Simulations for two different maneuvers are implemented with a high-fidelity, CarSimⓇ, full-vehicle model to verify the effectiveness of the developed approach. It is confirmed from the results that the proposed controller can effectively preserve vehicle lateral stability and enhance yaw handling performance.

Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust Takagi-Sugeno Fuzzy Control

Optimal power flow (OPF) is one of the most important tools in power system operation and control, which determines the minimum operating cost and retains the control variables in their secure boundaries. This paper takes into account several unbridled practical constraints in the OPF problem, three of which – that is – valve-point effect, multi-fuel option, and, above all, prohibited operating zone are the most conspicuous ones. Further, the flexible alternating current transmission systems (FACTS) devices are considered, as well, which have several merits such as decreasing the active power transmission loss, controlling the power flow, and improving the voltage stability/profile, to name but a few. Accordingly, thyristor controlled series capacitor (TCSC) – the most popular and common component of the FACTS equipment’s category – is utilized in this study. As a result, the OPF problem integrated with such practical constraints referred to above as well as FACTS devices becomes a highly nonlinear-nonconvex optimization problem and to solve it, a reliable and efficient evolutionary algorithm such as fuzzy-based improved comprehensive-learning particle swarm optimization (FBICLPSO) algorithm is introduced. The proposed approach is scrutinized on IEEE 30-bus test system, which is a commonly used test system for solving the non-smooth and non-convex versions of the OPF problem. Comparing the obtained results by the proposed algorithm with the available alternatives in the literature corroborate the potential and effectiveness of the proposed approach.

An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices

In this paper, first the travel and delay times in a road ending in a traffic light are determined under different traffic flows and traffic light cycles using a microscopic traffic simulator. The obtained results are analyzed and compared with the results of other models reported in the literature. In addition, the optimal traffic light cycle times under different loads are determined for one road and a method is shown to obtain the optimal green period ratios of a traffic light as well as the cycle time in an intersection. Finally, the generation of a fuzzy model is presented and a methodology is suggested, where the fuzzy system is applied as a surrogate model for the determination of the optimal green period ratios and traffic light cycle times.

Surrogate Model based Optimization of Traffic Lights Cycles and Green Period Ratios using Microscopic Simulation and Fuzzy Rule Interpolation

Real-time experimental results: wheel slip λ versus time for the TSK fuzzy model 3 after optimization by the SA algorithm and after optimization by the PSO algorithm considering the validation data set. New Takagi-Sugeno fuzzy models dedicated to Anti-lock Braking Systems (ABSs) are proposed.A set of three optimal fuzzy models is derived by an original fuzzy modeling approach.The approach involves Simulated Annealing or Particle Swarm Optimization algorithms.The models are tested and compared against experimental data on ABS laboratory equipment. This paper suggests a synergy of fuzzy logic and nature-inspired optimization in terms of the nature-inspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs). A set of TSK fuzzy models is proposed by a novel fuzzy modeling approach for ABSs. The fuzzy modeling approach starts with the derivation of a set of local state-space models of the nonlinear ABS process by the linearization of the first-principle process model at ten operating points. The TSK fuzzy model structure and the initial TSK fuzzy models are obtained by the modal equivalence principle in terms of placing the local state-space models in the rule consequents of the TSK fuzzy models. An operating point selection algorithm to guide modeling is proposed, formulated on the basis of ranking the operating points according to their importance factors, and inserted in the third step of the fuzzy modeling approach. The optimization problems are defined such that to minimize the objective functions expressed as the average of squared modeling errors over the time horizon, and the variables of these functions are a part of the parameters of the input membership functions. Two representative nature-inspired algorithms, namely a Simulated Annealing (SA) algorithm and a Particle Swarm Optimization (PSO) algorithm, are implemented to solve the optimization problems and to obtain optimal TSK fuzzy models. The validation and the comparison of SA and PSO and of the new TSK fuzzy models are carried out for an ABS laboratory equipment. The real-time experimental results highlight that the optimized TSK fuzzy models are simple and consistent with both training data and validation data and that these models outperform the initial TSK fuzzy models.

Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems

This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.

Marius-Csaba Sabau

Papers

Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems