• Energy Research

The paper examines parallel operation of two adjacent steam power plants (SPP) under coordinated reactive power (Q) - voltage control. A coordinated reactive power-voltage controller (CQVC) performs optimal coordination of the synchronous generators' Q outputs in order to maintain the voltage at a SPP HV busbars. When the plants are equipped with CQVC, they are both trying to control the same voltage, so the Q oscillations may occur due to high level of coupling. Before the commissioning of the second CQVC detailed laboratory testing was performed to determine under which conditions the oscillations may occur. The impact of all generators on HV busbars voltage is determined first. Then the worst case scenario is chosen for the most influential generators, i.e., when both CQVCs act simultaneously and independently. The laboratory test rig is developed for CQVC testing. The results showed that coordinated control of influential generators results in stable operation and ensures uniform Q share among involved generators which leads to better power system voltage support.

The paper presents the digital realization of a model of reactive power flow (QFM) in a steam power plant using a programmable logic controller (PLC). The steam power plant (SPP) model is developed for pre-commissioning validation testing of the coordinated reactive power-terminal voltage (Q-V) control system. The SPP QFM includes a model for a synchronous generator, an excitation system, a step-up transformer, and the generator's droop characteristic modeled through the automatic voltage regulator (AVR). A QFM synthesis is based on a series of experiments performed on site. The parameters of the generator and AVR are estimated from recorded generator voltage and current time responses to a step change in voltage reference of the AVR. To get a complete QFM, transformers and network reactances are also included. In order to calculate reactive power (Q) flows more accurately, the generator Q output is adjusted by taking into account its real power output. Standard PLC hardware, as industrial grade equipment appropriate for on site testing, is used for practical QFM implementation after discretization of the continuous mathematical model. The developed QFM response is verified through a series of experiments performed in the laboratory.

{"references": ["J. D. Hurley, L. N. Bize, C. R. Mummert, \"The Adverse Effects of\nExcitation System Var and Power Factor Controllers, IEEE\nTransactions on Energy Conversion, Vol. 14, No. 4, pp:1636 - 1645\nDec. 1999.", "P. Lagonotte, J. C. Sabonnadiere, J. Y. Leost, , J. P. Paul, \"Structural\nanalysis of the electrical system : application to the secondary voltage\ncontrol in France,\" IEEE Transactions on Power Systems, Vol. 2, pp.\n479-484, 1989.", "S. Corsi, M. Pozzi, C. Sabelli, A. Serrani, \" The Coordinated\nAutomatic Voltage Control of the Italian Transmission Grid, Part II :\nControl apparatuses and field performance of the consolidated\nhierarchical system\", IEEE Trans. on Power Systems, November\n2004, Volume 19, Number 4, pp 1733-1741.", "W. Yu, H. Lee, D. Hur, C. Lim, T. Kim, J. Shin, S. Nam, Hybrid\nIntelligent Voltage and Reactive Power Control System for Jeju\nPower System in Korea, 8th WSEAS International Conference on\nPower Systems, Santander, Cantabria, Spain, September 23-25, 2008.", "J. Machowski, J. W. Bialek, J. R. Bumby, Power System Dynamics:\nStability And Control, , John Wiley & Sons, Chichester, 2008, pp 113\n- 115.", "John Grainger, Jr.,William Stevenson, Power System Analysis,\nMcGraw-Hill, 1994, p 369.", "K. Iba, H. Suzuki, M. Egawa, and T. Watanabe, \"Calculation of\nCritical Loading Condition with Nose Curve Using Homotopy\nContinuation Method,\" IEEE Transactions on Power Systems, vol. 6,\n1991.", "Ajjarapu, Computational Techniques for Voltage Stability Assessment\nand Control, Iowa: Springer, 2006, pp 51 - 53."]} This paper discusses coordinated reactive power - voltage (Q-V) control in a multi machine steam power plant. The drawbacks of manual Q-V control are briefly listed, and the design requirements for coordinated Q-V controller are specified. Theoretical background and mathematical model of the new controller are presented next followed by validation of developed Matlab/Simulink model through comparison with recorded responses in real steam power plant and description of practical realisation of the controller. Finally, the performance of commissioned controller is illustrated on several examples of coordinated Q-V control in real steam power plant and compared with manual control.

The paper discusses reactive power-voltage (Q-V) control in a multi-machine steam power plant (SPP). After briefly introducing some of the basic characteristics of synchronous generator's voltage control through automatic voltage regulators, the practices of widely applied manual voltage references setting for Q-V control in SPP is illustrated through several examples of SPP response recorded at site. Further the case studies were developed with the emphasis on the drawbacks of manual intra-plant Q-V control by building a mathematical model of realistic SPP in Matlab-Simulink. Relying on noticed drawbacks the paper highlights the requirements for enhancing SPP reactive power response and voltage support to the power system by coordinated Q-V control at power plant level. Finally, the paper illustrates the achieved performances of coordinated Q-V controller in real SPP.

The rapid emergence of the smart industry hides numerous challenges that need to be addressed promptly. In the transition between two industrial eras (Industry 4.0 and Industry 5.0), hands-on applications of digital twins in intelligent manufacturing are pivotal in enhancing efficiency, optimizing operations, and ensuring sustainability. The paper presents the digital twin (DT) concept in a vertical Industrial Internet of Things (IIoT) framework powered by machine learning (ML) models for time series forecasting. According to DT needs and hierarchical data processing, as well as edge, fog, and cloud computing, the paper presents state-of-the-art ML models and algorithms. Real-time and low-latency requirements of smart edge devices and monitoring systems force the selection of DT models powered by ML models for time series processing and forecasting. Stronger computer resources characterize the IIoT fog level. At this level, DT models should be supported by techniques and methods for parameter selection, correlation analysis, and heatmap visualization that facilitates time series processing. Special attention is devoted to developing a novel multivariate-time-series prediction method. This method should enable parameter prediction which cannot be directly measured. The method was validated based on several real-time series.

This paper presents commissioning details of a coordinated reactive power-voltage controller (CQVC) installed in a multimachine steam power plant (SPP). The CQVC regulates reactive power and voltage of a 1992MVA multimachine SPP. After briefly introducing basic control principles and relevant details about the CQVC's implementation, CQVC parameterization and tuning are thoroughly discussed. Once commissioned,the CQVC performance under various operating conditions is assessed. It is shown that the CQVC successfully maintains an HV busbar voltage and allocates reactive power to participating generators in accordance to their capability. Numerous CQVC responses recorded at the site are presented to demonstrate controller's performance under normal and extreme plant and network operating conditions.

When more than one source, same or different type, are connected to same point of common in the power plant, it is necessary to determine that only in-service sources with automatic voltage regulation capabilities share reactive load equally as a percentage of their dynamic reactive capability. By doing this, for any system voltage disturbance, equalized responses and maximum reactive power support from power plant units and plant as a whole is achieved. In this paper the methods for determination of dynamic reactive capability according to different criteria of different sources connected to electrically same point are given. The advantages and disadvantages are analysed and the most advantageous method is suggested. This method maximizes dynamic reactive power support from more sources by securing simultaneous exhaustion of the control reserves and provides the basis for further optimizations and equal opportunities for fair participation in voltage service market.

In this paper the zone controller is presented which regulates the reactive power-voltage characteristic (Q-V) of the zone pilot node by altering the reactive power outputs of power plants within the zone. As far as the zone is self-sufficient regarding reactive power needs, the generation is adapted to fit consumption and quasi-independent operation is achieved. As soon as limits are reached the import/export of the zone has to be permitted and voltage is maintained along the defined Q-V characteristics while providing reactive support to surrounding zones. Reactive power is distributed among power plants according to equal reactive reserve margin so maximal voltage stability margin is maintained. The result is minimization of losses due to minimization of reactive power flows among the zones and thus across long distances. Finally simulation results of the proposed controller are given in order to present the controller response in the quasi-independent operation mode.