Morteza Sarailoo

A novel model predictive control scheme based on bees algorithm in a class of nonlinear systems

This paper proposes a novel algorithm for utilizing bees algorithm in a model predictive control (MPC) in order to control a class of nonlinear systems. The bees algorithm is utilized in order to solve the open loop optimization problem (OOP), and it is based on the foraging behavior of honey bees. The proposed algorithm makes use of the bees algorithm for minimizing a predefined cost function in order to find the best input signals subject to constraints and a model of the system. The class of systems considered in this paper includes autonomous nonlinear systems without delay and with continuous and discrete inputs. The proposed algorithm is validated by simulating a three tank system as a case study. A comparison between the proposed novel MPC with different predictive horizons and a conventional MPC demonstrates the potential advantages of the proposed method such as reduction in computation time, good convergence toward desired values and ability of control management. Simulations also show the simplicity of applying and efficiency of the proposed algorithm for designing an MPC based on the bees algorithm. A bees algorithm is used to solve the open loop optimization problem in a MPC.Application to nonlinear autonomous systems with continuous and discrete inputs.Main features of the method are computation time reduction and control management.The proposed method has been applied to a three tank system as a case study.Comparison with a traditional optimization method (MILP) has been provided.

A new PMU placement algorithm to meet a specified synchrophasor availability

As real-time monitoring and control are entering to smarten power grids, imposing data availability criterion on PMU placement into an existing (or a new) measurement network becomes ever more important. This criterion decreases the probability of a wrong action. Synchrophasor availability (SA) at a bus measures the fraction of time in the long run when its PMU output data are correct and accessible. The SA-based placement goal is to meet a prescribed SA profile across the power system with a minimum number of PMUs. This paper reports two new developments in the SA-based PMU placement. (1) Consideration of equipment faults, in addition to data communication interruptions. (2) An efficient iterative linear program is developed to solve the problem, and the solutions always meets the full static observability. The solution is demonstrated through PMU placement in several IEEE test systems under a variety of SA specifications and random event rates.

A novel hybrid approach using sms and ROCOF for islanding detection of inverter-based DGs

This paper proposes a novel hybrid approach for islanding detection of inverter-based distributed generations (DG) based on combination of the Slip mode frequency-shift (SMS) as an active and rate of change of frequency (ROCOF) relay and over/under frequency relay as passive methods. This approach is utilized to force the DG to lose its stable operation and drift the frequency out of the allowed range of the frequency threshold. Performance of the proposed approach is evaluated under the IEEE 1547, UL 1741 and multiple-DG operation. The simulation results demonstrate the effectiveness of the proposed approach for detection of islanding, especially for loads with high quality factor. It operates accurately under the condition of load switching and does not interfere with the power system operation during normal condition. In other words, not only it holds the benefits of both SMS and ROCOF, but also it removes their drawbacks by having less non-detection zone and faster response.

Study of sufficient number of optimal tilt angle adjustment to maximize residential solar panels yield

In this paper a comprehensive study is carried out on the optimal number of intervals for adjusting the optimal tilt angle of a solar panel. The tilt angle of solar panels is important for capturing solar radiation and it depends on the path of the sun in the location of the solar panel. A Bee Algorithm is used to compute the optimal tilt angle for a given period. The main goal is to show instead of using a tracking system, which is costly for residential usage, manual adjustment of the tilt angle of a solar panel for a certain number of times during a year is sufficient to receive most of the solar radiation. This study is performed for different locations across the US with different latitudes and longitudes. The optimal number of intervals for each location is computed and the effects of the changes in the longitude and latitude are investigated.

On the performance of forecasting models in the presence of input uncertainty

Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data. In this study, the performance of several commonly used forecasting methods in the presence of weather predictors with uncertainty is assessed and compared. Accordingly, both observed and forecasted weather data are collected, then the influential predictors for solar PV generation forecasting model are selected using several measures. Using observed and forecasted weather data, an analysis on the uncertainty of weather variables is represented by MAE and bootstrapping. The energy forecasting model is trained using observed weather data, and finally, the performance of several commonly used forecasting methods in solar energy forecasting is simulated and compared for a real case study.

Fuzzy predictive control of three-tank system based on a modeling framework of hybrid systems

In this article, a fuzzy predictive control scheme is proposed for controlling liquid level in a three-tank system in the presence of disturbances and uncertainties. The three-tank system is considered as a hybrid system, and a hybrid model of the system is obtained using the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized based on a piecewise affine linear method. Then, a model predictive control is designed based on the hybrid model and applied to the three-tank system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from the problem of bias in output in the presence of disturbance and uncertainty. In this article, a fuzzy supervisor is utilized to adjust the main predictive controller such that the effects of disturbance and uncertainties are degraded by using zero-offset tracking. The proposed fuzzy predictive control scheme has advantages of simplicity and efficiency in normal operation, and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method.

An algorithm for resilient sensor network upgrade with fewest PMUs

This paper reports a new development in relation to a recently published algorithm by the authors [1]. The algorithm iteratively searches for a minimum number of phasor measurement units to be added into a power system to meet a prescribed synchrophasor availability (SA). SA constraints impose a resilience requirement in the face of random interruptions in both power and data transmissions. Our earlier algorithm requires a sufficiently small step-size between iterations to achieve optimality. This paper offers an analytically calculated step-size that is just small enough to guarantee optimality. As a result, the number of buses in the system bounds the iteration number. The paper presents a separate algorithm to search among all feasible placements using branch and bound in case the iteration limit is reached before the SA specification is met. The new placement algorithm is applied to the IEEE 68-bus test system under a variety of conditions to quantify the resilience-cost tradeoff and effects of losing a single transmission line on the SA are investigated. In addition results of SA for the proposed method in this paper and the basic placement for static observability are compared.

An ellipsoidal expansion algorithm for estimating and representing regions of attraction for large power systems

This paper proposes an algorithm for efficient offline computation and representation of regions of attraction (RoA) to speed up assessing transient stability on-line of large lossy power systems. The RoAs are obtained for prescribed normal and post-fault circuit topologies in the form of unions of ellipsoids. The RoAs provide the basis of the decision support for a Secondary Protection (SP) scheme designed to recover from the Primary Protection (PP) misoperations. The algorithm iterates between a simulation step and an ellipsoidal expansion step. In the simulation step an electromechanical model of the system is utilized to generate state trajectories originating from randomly selected points on the surface of an ellipsoid, from which a new polyhedron is defined. In the expansion step, a convex optimization problem is solved to find the maximum volume inscribed ellipsoid on the polyhedron. The proposed algorithm is applied to the IEEE 68-bus test system for which the RoAs are obtained for all anticipated contingencies. One normal and forty- eight post-fault ellipsoidal RoAs are produced, with the estimated smallest critical clearing time at 131 milliseconds, sufficiently large for an SP to correct any of the anticipated cases of failure to trip or false trip by the PP.

Fault diagnosis based on partitioned power system models

Through partition of the model of a full power transmission network, this paper resolves the complexity issue arising from using a multiple model filtering (MMF) approach to transmission short-circuit fault diagnosis. The MMF approach to power system diagnosis was recently developed to mitigate cascading failures in power systems, caused by protection misoperations, through providing decision support for protective control actions. The partition is defined by the structure of a sensor network overlaid on the transmission network to support, among many functions, fault diagnosis. The ability to diagnose faults using the MMF approach relies on the observability of independent current and voltage variables of the transmission network from time-synchronized measurement samples acquired by the sensor network. The paper shows that such observability is retained in all partitioned networks, for which the complexity of real-time computation and communication is bounded despite the size of the full network. A set of rules for partitioning is stated in graph theoretic terms. The MMF approach to diagnosis of transmission line faults is applied to the IEEE 68-bus test system and the benefit in complexity reduction through partition is quantified.