Decentralised load-frequency controller design based on structured singular values

Decentralised power system load frequency control beyond the limit of diagonal dominance

The design of decentralized robust load frequency control for interconnected power systems is studied in this paper. It is shown that, subject to a condition based on structured singular values (SSVs), each local area load-frequency controller can be designed independently. The robust stability condition for the overall system can be easily stated to achieve a sufficient interaction margin (introduced in this paper), and a sufficient gain and phase margin defined in classical feedback theory during each independent design. Within this general framework, each local area controller design is based on a new linear quadratic regulator design approach.

Three-phase voltage source converter (VSC) is widely used for interfacing distributed energy resource (DER) units with the ac utility grid. Current vector control in synchronous reference frame is the common approach for these applications. In this frame, system model represents a multi-input multi-output (MIMO) control system. However, decoupling terms are used to split the system into two single-input single-output (SISO) subsystems. Moreover, feed-forward terms are also employed to achieve soft start. In this paper, it is shown that when the grid is weak, the feed-forward terms have stabilizing effect in addition to the soft-start advantage. Nevertheless, the decoupling terms have a destabilizing effect. The paper concludes that, if proportional-integrating (PI) compensators are used (which is a common practice), the decoupling terms can be avoided. However, the feed-forward terms must be taken from the phase-locked loop to have an improved stability. Detailed stability analysis of a grid-connected VSC considering the PLL dynamics and grid impedance is carried out and verified by means of simulations.

A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a supervision module for the local controller, ILC can improve the tracking performance of the closed-loop system along the batch direction. In this study, an ILC-based P-type controller is proposed for multi-input multi-output (MIMO) linear batch processes, where a P-type controller is used to design the control signal directly and an ILC module is used to update the set-point for the P-type controller. Under the proposed ILC-based P-type controller, the closed-loop system can be transformed to a 2-dimensional (2D) Roesser's system. Based on the 2D system framework, a sufficient condition for asymptotic stability of the closed-loop system is derived in this paper. In terms of the average tracking error (ATE), the closed-loop control performance under the proposed algorithm can be improved from batch to batch, even though there are repetitive disturbances. A numerical example is used to validate the proposed results.

This paper presents a neural-network-based predictive control (NPC) method for a class of discrete-time multi-input multi-output (MIMO) systems. A discrete-time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real-time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31fromTexasInstruments.Twoexamples,includingthecontrolofa MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes.

Traditional machinery for manufacturing processes are characterised by actuators powered and co-ordinated by mechanical linkages driven from a central drive. Increasingly, these linkages are replaced by independent electrical drives, each performs a different task and follows a different motion profile, co-ordinated by computers. A design methodology for the servo control of high speed multi-axis machinery is proposed, based on the concept of a highly adaptable generic machine model. In addition to the dynamics of the drives and the loads, the model includes the inherent interactions between the motion axes and thus provides a Multi-Input Multi-Output (MIMO) description. In general, inherent interactions such as structural couplings between groups of motion axes are undesirable and needed to be compensated. On the other hand, imposed interactions such as the synchronisation of different groups of axes are often required. It is recognised that a suitable MIMO controller can simultaneously achieve these objectives and reconciles their potential conflicts. Both analytical and numerical methods for the design of MIMO controllers are investigated. At present, it is not possible to implement high order MIMO controllers for practical reasons. Based on simulations of the generic machine model under full MIMO control, however, it is possible to determine a suitable topology for a blockwise decentralised control scheme. The Block Relative Gain array (BRG) is used to compare the relative strength of closed loop interactions between sub-systems. A number of approaches to the design of the smaller decentralised MIMO controllers for these sub-systems has been investigated. For the purpose of illustration, a benchmark problem based on a 3 axes test rig has been carried through the design cycle to demonstrate the working of the design methodology.

In this paper, a robust decentralized controller is designed for automatic voltage regulator (AVR) and load frequency control (LFC) systems of a generator in an islanded microgrid with multiple distributed generation systems. This control strategy can be readily applied to an islanded microgrid which consists of renewable distributed generation and energy storage systems. The control strategy proposed in this paper includes multiple local droop controllers, as well as robust decentralized controller that are designed and optimized independently by using the μ-synthesis technique. These controllers account for the dynamic coupling among the areas of the islanded microgrid without any communication links required among the local controllers. Moreover, these controllers are designed to be robust to the variations of different parameters of the microgrid, and hence improve the voltage and frequency control performance significantly. Simulation results of a small microgrid with a generator and a renewable energy resource confirm the effectiveness of the controller design approach proposed in this paper.

Advanced control of large-scale wind turbines: Structural load reduction and lifetime control

AIn this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems.

FRF 评估者 multi-inputmulti-output (MIMO ) 基于 errors-in-variables (EV ) 当模特儿系统被介绍减少 FRF H1 的偏爱错误评估者。FRF H1estimator 被噪音在系统的输入影响并且产生 under-estimationof 真 FRF。FRF 评估者基于 EV 模型考虑错误在两个都，茶碱系统放并且输出并且将导致更多的精确 FRF 评价。FRF 评估者在 6-DOF (degree-of-freedom ) 上基于 EV 模型被用于波形复制水力的颤动桌子。结果证明改进 MIMO 颤动控制系统的控制精确是有利的。

[formula omitted] sampled-data load frequency control with time-varying delays considering delay compensation

Numerous control methods based on modern control theory have been proposed to improve the load frequency control (LFC) characteristics of power systems. These control methods generally regard the load frequency control system as a linear system model. Furthermore, complicated mathematical equations have to be solved to determine control laws. However, realistic power systems are nonlinear, i.e., the nonlinearities of tie‐lines connecting between the control areas and of generation rate constraints of power plants exist. Consequently, it is considered that the design of controllers is difficult.In this paper, the application of fuzzy control to the load frequency control system is proposed to solve the forementioned problems. The proposed control first, to satisfy the load frequency control requirements, estimates power demand variations of the control areas and uses them as the output set‐point values of power plants, instead of using an integral‐type controller. Next, to improve stability and optimality of the nonlinear load frequency control system using the fore‐mentioned output set‐point control method, the proportional‐type fuzzy control considering a computation time‐delay is implemented. Parameters of the proposed fuzzy controller are determined by minimizing the integral square of the area control error (ACE) to ensure optimality of the system.

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments.

One of the most important features of a unified power flow controller (UPFC) is its multiple control functions. These control functions have been suggested to be implemented by multiple controllers. However, examples presented in the paper demonstrate the existence of the dynamic interactions among different UPFC controllers and it is suggested that the multiple functional UPFC is designed to be a multi-input multi-output (MIMO) controller. It is demonstrated in the paper that a single multivariable controller can be designed successfully for the UPFC to perform the multiple control functions which takes full account of the control interactions in the MIMO UPFC system and guarantees the closed-loop system stability.

This chapter covers two issues (along many others) relating to complex systems control. The main theme is connected with control of Multi-Input Multi-Output (MIMO) systems. If the controlled system has more inputs than outputs (further labelled as MI+MO) there exist many combinations of the inputs for one combination of the outputs. We are able to reach desired system outputs (the main control aim) with many input combinations. This situation is very interesting from practical point of view. Usually optimal inputs combination exists – from some point of view. This combination leads for example to minimum energy consumption, maximum production efficiency or minimum machinery load etc. In practice the set of possible inputs combination is reduced because of constrains and the best feasible combination lies very often on constrain. It would be suitable to extend the controller design to include supplementary demand simultaneously with the fulfilment of the main control aim – to ensure best feasible input combination, too. The common advanced controller like LQ controller has no problem with MIMO system which has different number of inputs and outputs in contrary to standard controllers designed as decentralized control. However the constrains respecting within the standard LQ controller design is not possible. Another advanced controller – Model Predictive Controller (MPC) allows constrains handling (Camacho & Bordons, 1999), (Maciejowski, 2002), (Rossiter, 2003) but the standard controller design doesn’t solve which combination of inputs will occur in the steady-state in case of system with more inputs than outputs. One possibility how to achieve optimal inputs combination is to formulate one term of the cost function connected with inputs penalization as a deviation from ideal inputs combination. This approach is used e.g. in (Novak, 2009) but according to the opinion of authors this approach isn’t as universal as following proposal. We have suggested adding another term into the cost function of predictive controller – terminal state in a form of the deviation from desired terminal state. The desired terminal state is chosen that it corresponds to feasible optimal inputs combination and a value of the set-point at the end of the control horizon. Authors call this technique Steady-state optimization because the influence of the terminal state deviation comes to light namely in steady-state when the main control aim (desired output combination) is or has been already fulfilled see also (Dusek & Honc, 2009). The controller ensures both main and supplementary control aims – achievement of desired outputs and inputs moving to an optimal combination. An incorporation of the terminal state into the cost function has also another advantage. The