Freddy Milla

Hybrid predictive control strategy for a public transport system with uncertain demand

In this article, a hybrid predictive control (HPC) strategy is formulated for the real-time optimisation of a public transport system operation run using buses. For this problem, the hybrid predictive controller corresponds to the bus dispatcher, who dynamically provides the optimal control actions to the bus system to minimise users’ total travel time (on-vehicle ride time plus waiting time at stops). The HPC framework includes a dynamic objective function and a predictive model of the bus system, written in discrete time, where events are triggered when a bus arrives at a bus stop. Upon these events, the HPC controller makes decisions based on two well-known real-time transit control actions, holding and expressing. Additionally, the uncertain passenger demand is included in the model as a disturbance and then predicted based on both offline and online information of passenger behaviour. The resulting optimisation problem of the HPC strategy at every event is Np-hard and needs an efficient algorithm to solve it in terms of computation time and accuracy. We chose an ad hoc implementation of a Genetic Algorithm that permits the proper management of the trade-off between these two aspects. For real-time implementation, the design of this HPC strategy considers newly available transport technology such as the availability of automatic passenger counters (APCs) and automatic vehicle location (AVL) devices. Illustrative simulations at 2, 5 and 10 steps ahead are conducted, and promising results showing the advantages of the real-time control schemes are reported and discussed.

Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

This work presents a novel design and development of a fuzzy predictive supervisory controller, based on genetic algorithms (GA), for gas turbines of combined cycle units. The control design is based on an objective function that represents the economic and regulatory performance of a gas turbine by using a dynamic optimal set-point for the regulatory level. A fuzzy model is considered in order to characterize the nonlinear behavior of the gas turbine, which is used in two supervisory control systems. The first fuzzy supervisory control design includes a fuzzy model, where its parameters are held constant for the successive predictions. For the second fuzzy supervisory control design, its parameters are updated in each prediction and its nonlinear optimization problem is solved using GAs. The proposed fuzzy supervisory controllers are compared against a supervisory controller based on linear models and a regulatory controller with constant optimal set-points. Results indicate that the fuzzy GA predictive supervisory controller captures adequately the nonlinearities of the process, which, in turn, provides a promising approach to improve the performance of the combined cycle unit.

Bus-Stop Control Strategies Based on Fuzzy Rules for the Operation of a Public Transport System

In the daily operation of a bus system, the movement of vehicles is affected by uncertain conditions as the day progresses, such as traffic congestion, unexpected delays, randomness in passenger demand, irregular vehicle dispatching times, and incidents. In a real-time setting, researchers have devoted significant effort to developing flexible control strategies, depending on the specific features of public transport systems. In this paper, we propose a control scheme for the operation of a bus system running along a linear corridor, based on expert rules and fuzzy logic. The parameters of the fuzzy controllers were tuned through a particle swarm optimization (PSO) algorithm. That is, the control strategies aim at keeping regular headways between consecutive buses, with the objective of reducing the total waiting time of passengers. The proposed control systems rely on measures of the position of each bus, which are easy to obtain and implement by means of emerging automatic vehicle location devices through Global Positioning System (GPS) technology. The utilized strategies are holding, stop-skipping, and the integration of both. After tuning the controller parameters, we conducted several simulation tests, obtaining promising results in terms of savings in waiting times with the implementation of the proposed rules, noting that the best performance occurred when fuzzy rules are included. The methodology has great impact, and it is easy to implement due to its simplicity.

Comparison of fixed speed wind turbines models: A case study

This paper presents a model comparison of a fixed speed wind turbine (FSWT) operating on a real wind farm. By relying on real data obtained from a wind farm operating in the Chilean Interconnected System, three different models are identified and analyzed. First, a phenomenological model based on physical principles governing the production of electricity from wind power is considered. This model is fine-tuned in accordance with practical considerations, such as wind correction factors. Then, a linear model and a Takagi & Sugeno (T&S) fuzzy model are identified. From the experimental results, the linear model is the simplest one, but also the one that presents the worst performance indexes. The best prediction capability is obtained with the T&S model; however, in terms of interpretability, the phenomenological model outperforms the other two black-box models.

Predictive optimized adaptive PSS in a single machine infinite bus

Power System Stabilizer (PSS) devices are responsible for providing a damping torque component to generators for reducing fluctuations in the system caused by small perturbations. A Predictive Optimized Adaptive PSS (POA-PSS) to improve the oscillations in a Single Machine Infinite Bus (SMIB) power system is discussed in this paper. POA-PSS provides the optimal design parameters for the classic PSS using an optimization predictive algorithm, which adapts to changes in the inputs of the system. This approach is part of small signal stability analysis, which uses equations in an incremental form around an operating point. Simulation studies on the SMIB power system illustrate that the proposed POA-PSS approach has better performance than the classical PSS. In addition, the effort in the control action of the POA-PSS is much less than that of other approaches considered for comparison.

Hierarchical MPC Secondary Control for Electric Power System

Although in electric power systems (EPS) the regulatory level guarantees a bounded error between the reference and the corresponding system variables, to keep its availability in time, optimizing the system operation is required for operational reasons such as, economic and/or environmental. In order to do this, there are the following alternative solutions: first, replacing the regulatory system with an optimized control system or simply adding an optimized supervisory level, without modifying the regulatory level. However, due to the high cost associated with the modification of regulatory controllers, the industrial sector accepts more easily the second alternative. In addition, a hierarchical supervisory control system improves the regulatory level through a new optimal signal support, without any direct intervention in the already installed regulatory control system. This work presents a secondary frequency control scheme in an electric power system, through a hierarchical model predictive control (MPC). The regulatory level, corresponding to traditional primary and secondary control, will be maintained. An optimal additive signal is included, which is generated from a MPC algorithm, in order to optimize the behavior of the traditional secondary control system.

Hybrid predictive supervisory control based on genetic algorithms for a gas turbine of combined cycle power plants

This work considers the optimization of the gas turbine for a combined cycle power plant by using a supervisory level. The design of a hybrid predictive supervisory controller is based on state space discrete model including the switching behavior of PI control system. The control design is based on an objective function that represents the economic and regulatory performance of a gas turbine by using a dynamic optimal set-point for the regulatory level. The hybrid predictive supervisory control problem considers the hybrid behaviour using a mixed logical dynamical systems model within the optimization problem and solved by Genetic Algorithms. The proposed algorithms are applied to the gas turbine of a thermal power plant and successfully compared with the regulatory control strategy with constant optimal set-points.

Model predictive power stabilizer optimized by PSO

An electric power system (EPS) often exhibits low frequency electromechanical oscillations due to insufficient damping caused by adverse operating conditions. In this paper, a model predictive power stabilizer (MPPS) is proposed to improve the oscillations in a single machine infinite bus (SMIB) power system. This approach is part of a small signal stability analysis that uses equations in incremental form around an operating point. The MPPS provides the optimal control inputs and design parameters were optimized using particle swarm optimization (PSO). A conventional power system stabilizer (CPSS), with parameters also optimized with PSO, was used for comparison. A large number of dynamic system responses for different values of power and system parameters have been used to validate our proposals.