Real-time Power System Control Research Articles

A Grouping-Based Frequency Support Scheme for Wind Farm Under Cyber Uncertainty

The injection of a significant amount of wind power tends to increase the difficulty of the grid frequency control. Therefore, wind farm (WF) has to have the ability to participate in system frequency support. However, during the frequency control process in a large WF, individual wind generators may prone to instability due to possible over-deceleration. In addition, the deeply-intertwined cyber physical power system (CPPS) has gradually replaced the traditional grid due to the development of information and communication technology. However, CPPS also introduces cyber uncertainties that have a significant negative impact on the real-time control of power system. To address above issues, this paper considering possible cyber uncertainty, and proposes a grouping-based hierarchical frequency support scheme for WF. In the proposed scheme, the variable speed wind turbines (VSWTs) are clustered into several groups, according to their wind profiles at first, by dispatching the same control commands to the VSWTs belonging to the same group. As a result, the number of the VSWTs and control variables reduces significantly. Then, a hierarchical control scheme based on virtual inertial control is proposed with consideration of possible cyber uncertainty. The top-layer controller determines whether cyber uncertainty exists, and calculates power increment based on frequency deviation and cyber uncertainty. The bottom-layer controller coordinates the power increment distribution on the basis of ensuring the safe operation of all VSWTs. By do this, it is expected to achieve not only optimal frequency response but also wind generator stability. The effectiveness of proposed scheme is verified on a simulation platform built using MATLAB.

A Multi-Objective Model Predictive Controller for Power Oscillation Damping and Voltage Control in Power System

Abstract This paper proposes a multi-objective model predictive control (MO-MPC) strategy for power oscillation damping (POD) and voltage control in a coordinating manner. In the objective function, a time-varying weight is imposed on the objective function to accommodate the continuous changes of the power system dynamics. With online-optimization, the MO-MPC searches for coordinated control actions to different AVR system and SVC in power system. In simulation tests, two single-objective MPC (SO-MPC) strategy are compared to the MO-MPC. The results demonstrate that, for different faults, the MO-MPC is capable of handling both oscillation and voltage drop problem. However, the control performance of each SO-MPC scenario is restricted. Moreover, the feasibility of implementing the proposed MO-MPC in realtime power system control is evaluated. The innovative “publisher-subscriber” framework is proposed as the communication architecture. Furthermore, the run time of MPC optimizer routine is also timed and it is in a feasible range for high-speed control.

Compressive Sensing-Based Optimal Reactive Power Control of a Multi-Area Power System

To deal with the increasing load demand and environmental effects of conventional power devices, power system has become an enlarged complex network with the integration of distributed generators. Real-time control of power system now needs a massive amount of information to be transmitted between each local device and the central controller. The transmission of a huge quantity of information data poses great challenge to the communication network. To deal with this issue in reactive power control, this paper proposes a novel real-time compressive sensing-based optimal reactive power control of a multi-area interconnected power system. The objective is to minimize the power loss, voltage deviation, and reactive power generation cost simultaneously. According to the proposed scheme, the measured data in each control area is compressed before being transmitted through the communication network, and then recovered accurately by the discrete central controller. Orthogonal matching pursuit algorithm is adopted to recover the compressed data, owing to its fast convergence speed. Simulation results demonstrate the effectiveness of the proposed compressive sensing-based approach by significantly reducing the data size of the transmitted data.

Computational Approaches for Bad Data Handling in Power System Synchrophasor Networks

Abstract The state estimators used in real-time power system control centers now process bad data as a standard routine. With the introduction and deployment of phasor measurement units (PMUs), it is possible to model power systems, even with their time-varying nature, in real-time. However, PMUs remain vulnerable to providing bad data for several reasons. In this paper, a new intelligent framework, the cellular computational netwo rk (CCN), is introduced for the decentralized predictive modeling and dynamic state estimation (DSE) of a power system with PMU data. The CCN-based DSE is resilient to interactions between multiple segments of bad data from one or more PMUs.

Real-time control of power systems using nodal prices

This article presents a novel control scheme for achieving optimal power balancing and congestion management in electrical power systems via nodal prices. We develop a dynamic controller that guarantees economically optimal steady-state operation while respecting all line flow constraints in steady-state. A benchmark example illustrates the effectiveness of the proposed control scheme.

A Fast Quadratic Programming Approach for Large-Scale Reactive Power Optimization

ABSTRACT A fast reactive power optimization method for large-scale power systems has been developed in this paper. The method is fast and robust. The large-scale power system is decomposed into subsystems. The subsystem solution is non-iterative and it provides a suboptimal solution when constraints are violated. Results of applying this method to the IEEE 118 bus system and a sample 200 bus system indicate it can be used in real-time power system control.

Optimal power rescheduling for system security using stochastic second-order load flow

This paper proposes a method for optimal corrective rescheduling of generation for system security applications in power system real-time control. Because of input data (load and generation) uncertainty due to measurement error, forecast in accuracy or demand variation, a full stochastic load flow model based on a second-order solution algorithm is used for system monitoring and a linear programming formulation based on sensitivity coefficients is used to obtain minimal-cost adjustments of generation patterns. The routines were developed on a minicomputer.

A New Approach for Detection and Identification of Multiple Bad Data in Power System State Estimation

This paper presents a new approach for detection and identification of ultiple bad data --- estimation-identification approach which has been developed in order to improve the performance of static-state estimators of power systems. It describes the important function of detection and identification of bad data which is of crucial importance in designing a static-state estimator as a basic element in power system real-time control; it also discusses tthe basic principle, method of solution and programing technique of so called "Estimation-Identificaition Approach" which is based on the better utilization of the resources of residues and the analysis of the property of its sensitivity matrix; and some test results of simulation on a model system and a practical searching system of doubtful data are given. Test results of identifying bad data is good, it is usually successful especially in the case of the occurence of multi-bad data. At the end of this paper comments concerning the advantages of this approach and its recommendation as a prospective practical approach in real-time applications are given.

Centralized real-time power system control using on-line computer

On-line computers can be used for real-time power system control. A simulation method for an optimal adjustment of the control parameters and experiences about accuracy and computing time are described.

Adaptive Short-Term Forecasting of Hourly Loads Using Weather Information

A comprehensive computer-oriented procedure for Probablistic forecasting of hourly power-system loads with lead times of 1 to 24 hours is described. The forecasts produced are based on both historical load data and information from the latest weather forecast. The methodology combines stochastic load models and adaptive weather- load models optimally to yield an adaptive forecasting procedure especially suited for real-time control of power systems.