F. Daneshfar, E. Hosseini,
Volume 8, Issue 4 (12-2012)
Abstract
Recently several robust control designs have been proposed to the load-frequency control (LFC) problem. However, the importance and difficulties in the selection of weighting functions of these approaches and the pole-zero cancellation phenomenon associated with it produces closed loop poles. Also the order of robust controllers is as high as the plant. This gives rise to complex structure of such controllers and reduces their applicability in industry. In addition conventional LFC systems that use classical or trial-and-error approaches to tune the PI controller parameters are more difficult and time-consuming to design.
In this paper, a bisection search method is proposed to design well-tuned PI controller in a restructured power system based on the bilateral policy scheme. The bisection search is a very simple and rapidly converging method in mathematics. It is a root-finding approach which repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing.
The new optimized solution performance has been applied to a 3-area restructured power system with possible contracted scenarios under large load demand and area disturbances. The results evaluation shows the proposed method achieves good performance compared with a powerful robust ILMI-based controller. Moreover, this newly developed solution has a simple structure, and is fairly easy to implement in comparison to other controllers, which can be useful for the real world complex power systems.
A. Khoshsaadat , M. R. Mosavi, J. S. Moghani,
Volume 10, Issue 3 (9-2014)
Abstract
Static Synchronous Series Compensator (SSSC) is a series compensating Flexible AC Transmission System (FACTS) controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC) has been proposed for controlling of the SSSC-based damping system and applied to a Single Machine Infinite Bus (SMIB) power system. For implementation of the learning process in this controller, we use of the one approach of the learning ability that named as Forward Signal and Backward Error Back-Propagation (FSBEBP) method for improving of the system efficiency. This artificial intelligence-based control model leads to a controller with adaptive structure, improved correctness, high damping ability and dynamic performance. System implementation is easy and it requires 49 fuzzy rules for inference engine of the system. As compared with the other complex neuro-fuzzy systems, this controller has medium number of the fuzzy rules and low number of layers, but it has high accuracy. In order to demonstrate of the proposed controller ability, it is simulated and its output compared with that of classic Lead-Lag-based Controller (LLC) and PI controller.
M. Esmaili, H. A. Shayanfar, K. Gharani,
Volume 10, Issue 4 (12-2014)
Abstract
Phasor Measurement Units (PMUs) are in growing attention in recent power systems because of their paramount abilities in state estimation. PMUs are placed in existing power systems where there are already installed conventional measurements, which can be helpful if they are considered in PMU optimal placement. In this paper, a method is proposed for optimal placement of PMUs incorporating conventional measurements of zero injection buses and branch flow measurements using a permutation matrix. Furthermore, the effect of single branch outage and single PMU failure is included in the proposed method. When a branch with a flow measurement goes out, the network loses one observability path (the branch) and one conventional measurement (the flow measurement). The permutation matrix proposed here is able to model the outage of a branch equipped with a flow measurement or connected to a zero injection bus. Also, measurement redundancy, and consequently measurement reliability, is enhanced without increasing the number of PMUs this implies a more efficient usage of PMUs than previous methods. The PMU placement problem is formulated as a mixed-integer linear programming that results in the global optimal solution. Results obtained from testing the proposed method on four well-known test systems in diverse situations confirm its efficiency.
F. Azma, H. Rajabi-Mashhadi,
Volume 11, Issue 2 (6-2015)
Abstract
This paper develops an effective control framework for DC voltage control and power-sharing of multi-terminal DC (MTDC) grids based on an optimal power flow (OPF) procedure and the voltage-droop control. In the proposed approach, an OPF algorithm is executed at the secondary level to find optimal reference of DC voltages and active powers of all voltage-regulating converters. Then, the voltage droop characteristics of voltage-regulating converters, at the primary level, are tuned based on the OPF results such that the operating point of the MTDC grid lies on the voltage droop characteristics. Consequently, the optimally-tuned voltage droop controller leads to the optimal operation of the MTDC grid. In case of variation in load or generation of the grid, a new stable operating point is achieved based on the voltage droop characteristics. By execution of a new OPF, the voltage droop characteristics are re-tuned for optimal operation of the MTDC grid after the occurrence of the load or generation variations. The results of simulation on a grid inspired by CIGRE B4 DC grid test system demonstrate efficient grid performance under the proposed control strategy.
M. Heidari,
Volume 13, Issue 3 (9-2017)
Abstract
In this paper, a new type of multi-variable compensation control method for the wind energy conversion systems (WECS) is presented. Based on wind energy conversion systems, combining artificial neural network (ANN) control and PID, a new type of PID NN intelligent controller for steady state torque of the wind generator is designed, by which the steady state torque output is regulated to track the optimal curve of wind power factor and the blade pitch angle is regulated to keep the stable power output. Also, the LPV model of the WECS, LPV compensator for the wind generator is designed to effectively compensate output of the wind generator torque and the blade pitch angle. Finally, simulation models of the control system based on a realistic model of a 8kw wind turbines are built up based on the Dspace platform. The results show that the proposed method can reduce interferences caused by disturbed parameters of the WECS, mechanical shocks of the wind generator speed are reduced while capturing the largest wind energyfluctuation range of wind generator power output is reduced, and the working efficiency of the variable pitch servo system is improved.
H. Shayeghi, A. Younesi,
Volume 13, Issue 4 (12-2017)
Abstract
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO) algorithm and are fixed. The second one is a reinforcement learning (RL) based supplementary controller that has a flexible structure and improves the output of the first stage adaptively based on the system dynamical behavior. Due to the use of RL paradigm integrated with PID controller in this strategy, it is called RL-PID controller. The primary motivation for the integration of RL technique with PID controller is to make the existing local controllers in the industry compatible to reduce the control efforts and system costs. This novel control strategy combines the advantages of the PID controller with adaptive behavior of MA to achieve the desired level of robust performance under different kind of uncertainties caused by stochastically power generation of DERs, plant operational condition changes, and physical nonlinearities of the system. The suggested decentralized controller is composed of the autonomous intelligent agents, who learn the optimal control policy from interaction with the system. These agents update their knowledge about the system dynamics continuously to achieve a good frequency oscillation damping under various severe disturbances without any knowledge of them. It leads to an adaptive control structure to solve LFC problem in the multi-source power system with stochastic DERs. The results of RL-PID controller in comparison to the traditional PID and fuzzy-PID controllers is verified in a multi-area power system integrated with DERs through some performance indices.
S. Chikha,
Volume 14, Issue 3 (9-2018)
Abstract
In this paper we propose a new configuration of the wind farm connecting with an electrical grid. The proposed Wind Energy Conversion System (WECS) is based on a two stages six-leg matrix converter using to drive a two Doubly Fed Induction Machines operating at different wind speeds. Each Doubly Fed Induction Generator (DFIG) is controlled through the rotor currents using the Finite Set Model Predictive Model (FS-MBC). The proposed control method selects the optimal switching state of the converter that minimizes the cost function where it represents the desired behavior of the system. The optimal voltage vector is then applied to the output of the power converter. The most advantage of the proposed control is its simplicity in implementation, since the method avoids the use of any linear or nonlinear controllers except for the external speed loop and there is no need for any type of modulator such as in PWM or SVM modulation. A cost function is formulated according to desired performance such as regulation of the stator active and reactive powers of the DFIGs and reactive power in the filter side. The control algorithm selects and applies the optimal voltage vector to the DFIG rotor terminals. The supervision algorithm distributes the active and reactive power references in proportional way for each wind turbines. From a safety point, this algorithm provides each wind turbines still operate far from its limits. The performance of a six leg IMC in WECS chain is evaluated in term of a good tracking performance.
E. Heydari, M. Rafiee, M. Pichan,
Volume 14, Issue 4 (12-2018)
Abstract
Among a multitude of diverse control methods proposed for doubly fed induction generator (DFIG) based-wind energy conversion systems, direct power control (DPC) method has demonstrated superior dynamic performance and robustness in presence of disturbances. However, DPC is not a flawless method and shortcomings like necessity for high sampling frequency, high-speed sensors and less noise-affected sampling circuit need to be mitigated by utilizing fuzzy controllers. Parameter setting in a fuzzy controller plays a vital role, especially under non-ideal grid conditions. In this paper, a fuzzy-genetic algorithm-based direct power control (FGA-DPC) method is proposed for DFIG, while, the parameters of the fuzzy controller are optimized by genetic algorithm. The objective of the optimization is to minimize the stator active and reactive power errors to increase the precision of reference tracking. The objectives of the controller are also optimizing active power absorption based on the zone of operation and adjustment of reactive power according to grid requirements. The proposed method improves the overall precision and speed of transient response as well as significantly reducing power oscillations under non-ideal grid conditions. Finally, to demonstrate the effectiveness of the proposed method, extensive simulations are performed in Matlab/Simulink under different conditions.
