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      Global Energy Interconnection

      Volume 4, Issue 2, Apr 2021, Pages 214-224

      Real-time controller hardware-in-the-loop co-simulation testbed for cooperative control strategy for cyber-physical power system

      Zhenyu Wang ,Donglian Qi ,Jingcheng Mei ,Zhenming Li ,Keting Wan ,Jianliang Zhang
      ( College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, P.R.China )


      Various distributed cooperative control schemes have been widely utilized for cyber-physical power system (CPPS), which only require local communications among geographic neighbors to fulfill certain goals.However, the process of evaluating the performance of an algorithm for a CPPS can be affected by the physical target characteristics and real communication conditions.To address this potential problem, a testbed with controller hardware-in-the-loop (CHIL) is proposed in this paper.On the basis of a power grid simulation conducted using the real-time simulator RT-LAB developed by the company OPAL-RT, along with a communication network simulation developed with OPNET, multiple distributed controllers were developed with hardware devices to directly collect the real-time operating data of the power system model in RT-LAB and provide local control.Furthermore, the communication between neighboring controllers was realized using the cyber system model in OPNET with an Ethernet interface.The hardware controllers produced a real-world control behavior instead of a digital simulation, and precisely simulated the dynamic features of a CPPS with high speed.A classic cooperative control case for active power output was studied to explain the integrated simulation process and validate the effectiveness of the co-simulation testbed.

      0 Introduction

      In the field of modern power systems, the emerging concept of a cyber-physical power system (CPPS) emphasizes the heterogenous integration of the physical systems of the power network infrastructure and cyber systems for information sensing, processing, intelligence, and control [1-3].With this integration, a smart grid is expected to greatly enhance the efficiency, reliability, and flexibility of power production and consumption, along with the access to renewable energy resources, the demand response, and distributed intelligence [4-6].

      In order to achieve this goal, various cooperative control schemes have been introduced to meet the complicated control objectives of a CPPS.With a properly designed observation network, each control unit can monitor the behaviors of all its neighbors, and execute control instructions under uncertain communication conditions [7].For example, event-triggered cooperative control strategies that considered packet loss and cyber-attacks have been proposed to improve the voltages and frequency stability of distributed energy resources [8], [9].In order to regulate the active power from a cluster of distributed generators, an attack-resilient cooperative control strategy was proposed that considered the possibility if a communication failure or cyber-attack [10] [11].However, the advantages of these distributed algorithms were all verified in an all-digital simulation environment, commonly a MATLAB software environment.A Simulink module was used to model, simulate, and analyze the power system, and the algorithms were often embedded in the form of an S-function.Moreover, the simulation of uncertain communication conditions was done simply by deploying different adjacency matrices.Although the convenience of this modeling method makes simulation easier, it also obviously overlooks many real-world constraints.

      In order to overcome this drawback, co-simulation testbeds that coordinate cross-platform simulations of the dynamic power system and discrete event-based cyber system have been proposed [12].Instead of a fully digital simulation, the controlled targets can be built in a reallife simulator such as a real-time digital simulator (RTDS) [13], DIgSILENT Power Factory [14], or RT-LAB [15].Then, the communication network model is simulated using a cyber simulator such as OPNET [16].Thus, the co-simulation testbed is converted from a fully digital environment to a hardware-in-the-loop (HIL) environment.A comprehensive overview of the various tools that have been applied to power and communication networks and the characteristics of these tools were provided in reference [17], which introduced the latest grid co-simulation methods in detail from the perspectives of their use cases, architectures, and research examples.The impact of real-life components on the system performance was evaluated and analyzed in reference [18].Compared with the testbed for an all-digital simulation, an HIL testbed can provide more accurate performances for the equipment and simulated system, and is closer to the actual system.

      However, the HIL testbed frameworks mentioned above all focused on access to real-life electrical targets or communication system simulators.The control strategy was still embedded in the simulation of the physical side, or the control panel was set up separately as a master station that only provided the control application setting [18], [19].As an important part of the CPPS control architecture, the application of real-life control terminals to collect and transmit data and execute control commands has been neglected.Therefore, this study developed a novel cosimulation testbed for CPPS simulation.In this testbed, the distributed hardware controllers were flexibly placed in the co-simulation loop.It overcame the current challenges of analyzing the performance of the algorithm and impact of cyber events on real-life control terminals.On the basis of the co-simulation testbed proposed in reference [20], multiple hardware-controllers developed by the advanced digital signal processing (DSP) for the distributed control terminals were installed between the power system simulator and the network simulator.An attack-resilient cooperative control strategy for multiple distributed generators [10] was validated using the testbed, which produced an accurate real-time simulation and verified the effectiveness of the cooperative control strategy under an authentic CPPS environment.The major contributions of this paper are summarized as follows.

      (1) Compared with other similar testbeds, the multiple distributed hardware controllers were designed for cyberphysical co-simulation.The power system simulation was conducted in RT-LAB, and the communication network was modeled in OPNET with diversified cyber simulation scenarios.Multiple hardware controllers loaded with the cooperative control strategy were set up to provide real-life control terminal participation.

      (2) A simulation framework for cooperative control based on the co-simulation testbed was proposed.The hardware controllers perceived the state of the controlled target in a distributed manner through an analoguedigital converter (ADC) module, and returned the control command with pulse-width modulation (PWM) signals.The data interaction between controllers was carried out through OPNET based on the Transmission Control Protocol/Internet Protocol (TCP/IP) and socket format.

      (3) The influences of a time delay and change in the communication topology on the co-simulation were analyzed, which made it possible to conduct a simulation with constraints that were close to the real-world constraints.

      The rest of the paper is organized as follows.Section 2 introduces the cooperative control scenarios for the CPPS.Section 3 specifies the structure of the proposed CHIL testbed.Section 4 reports the results of a simulation for an output power cooperative control case.Finally, section 5 presents the conclusions.

      1 Cooperative control scenarios of CPPS

      1.1 Cooperative control characteristic of CPPS

      Fig.1 Framework of cyber-physical power system

      As shown in Fig.1, the physical power grid, sensors, computing units, and control system of a CPPS are closely integrated through the information flows [21].The measured values of the power grid, such as the active power, reactive power, voltage, and current are collected by sensors, forwarded by routers at the cyber layer, and finally gathered at the control center of the decision-making layer.This bottom-up scheme allows the central control center to collect information from all parties and calculate the corresponding control strategy.Then, commands are sent to each controlled terminal device to perform corresponding operations.This closed-loop control characteristic of the system has undergone essential changes compared to the traditional power grid.

      This centralized scheme enjoys the advantage of simplicity in design and can accomplish complicated control objectives.However, with the increasing access to distributed renewable energy resources and the electricity market enabling load-side interaction, an increasing number of scenarios require distributed controlled objects to communicate with the central controller.Obviously, a central scheme will no longer be able to adapt for the large geographical distribution and real-time control demands because of the high bandwidth of the communication and non-plug-and-play characteristic of the devices.

      Motivated by the intrinsic distributed nature of distributed generation, a networked control system based on a distributed cooperative control scheme was introduced for the control architecture of a CPPS [22].This cooperative scheme only requires local communications among neighbors to fulfill specific goals, with advantages that include a low operating cost, fewer system requirements, robustness to communication interruption, and flexible scalability.Thus, it has the following new control characteristics that differentiate it from a centralized scheme: (1) a more local scope for information collection; (2) a more flexible communication network with shorter distances; (3) directly connected control terminals and more cyber security issues that need to be considered.

      1.2 Cooperative control applications of cosimulation testbed

      Depending on the above characteristics, the cosimulation testbed should map the cooperative control characteristics of a CPPS into the simulation network.The co-simulation of a cooperative control strategy aims at verifying the effectiveness of the control algorithm under a CPPS environment.At the level of the controlled targets, the cooperative control applications of the cosimulation roughly include the strategy for an independent system [23], the control scheme for specific distributed resources [10], and the control scheme for multiple power electronics equipment [24].In addition, a security analysis of the applications under cooperative control is always considered.These three main applications are discussed in the following.

      (1) For an entire CPPS system, the cyber and physical layers are co-simulated simultaneously, where the cooperative control is validated to improve the stability of system operation, such as the stability of the system voltage and frequency [9].The cyber and physical side models are built using their respective simulation software or hardware, which are connected to each other through a flexible interface.Therefore, the real-time capability is particularly important, which means the power simulator must be able to support the real-time simulation of a large-scale network, with the cyber simulator simultaneously realizing the physical and logical construction of a communication network.

      (2) Aiming at distributed resources, the cooperative control applications focus on the state variable control of specific units.For example, the active or reactive power of each distributed energy resource is cooperatively controlled to reach a consensus utilization ration of its maximal available power [10].This means that in the simulation, each control terminal model must be directly connected to the controlled object, with a distributed relationship between them.

      (3) The use of power electronics equipment in the CPPS greatly improves the system intelligence, efficiency, and dynamic characteristics.Each device can be represented by a node in a communication graph that models the information flow.The cooperative control scheme in this situation enjoys structural modularity and satisfactory dynamic performance.However, in order to achieve plugand-play capability for the devices, the fault tolerances in both the cyber and physical domains and dynamic performance of the proposed cooperative control scheme must be verified.

      In summary, to establish a co-simulation testbed for a cooperative control strategy, it is necessary that it ensures real-time performance, includes distributed terminals, and has cyber security analysis capability.In addition, real-life hardware controllers were used in this study to increase the authenticity.A structure diagram of the simulation platform is shown in Fig.2, in which the control terminals developed based on the DSP could communicate directly and execute the control instructions.

      2 Components of CHIL testbed

      2.1 Power system simulation

      Fig.2 Structure diagram of simulation platform

      The power system was modeled in the real-time simulator RT-LAB developed by the company OPAL-RT [25].RT-LAB, which is fully integrated with MATLAB Simulink, includes a test application system for rapid control prototyping and hardware in the loop.With a small step size operation, RT-LAB can perform in real time and provide digital and analog signal input/output ports for highspeed data exchange with real-life equipment.The power system model could be changed using Simulink in the host computer.RT-LAB split this model into three subsystems: the SM module, SS module, and SC module.A typical segmented power system model is shown in Fig.3.The SM module contained all the electrical models and electrical network topologies.The SS module was a secondary subsystem that could interact with the SM module frequently, and the two modules were run synchronously.The SC module was a host computer module, which could monitor each physical quantity in the system.Then, the different computing nodes of the RT-LAB target machine performed parallel calculations after loading the subsystems to ensure the accuracy and speed of the simulation.

      Fig.3 Power system model in RT-LAB

      In the process of using RT-LAB to simulate the power system, the segmentation of the model was the most important procedure.In this study, the primary equipment was included in the SM module, and the secondary control equipment was included in the SS module.This approach separated the control units from the simulation environment, and helped to form the CHIL framework for developing or using mature controllers.Compared with the traditional method of incorporating both primary and secondary equipment into the fully digital simulation environment, RT-LAB only simulated the primary side equipment.The real-life secondary control equipment interacted with the power system model in RT-LAB through the data interface to achieve the same control effect as an actual object.

      2.2 Cyber system simulation

      The communication system simulation utilized a network simulation server (high-performance computer) with the OPNET software [26] and multiple network interface cards (NIC).The communication model of the CPPS was built in the network simulation server and used to set up the corresponding communication nodes, including virtual nodes and actual nodes.

      The virtual nodes were some virtual simulated communication nodes corresponding to various computing or communication equipment in the actual cyber system, such as communication servers, switches, and information transceiver nodes.It simulated nodes that were not involved in the power system simulation model and simultaneously participated in the simulation of the entire communication network.The actual nodes were the communication nodes of the controlled objects in the physical model, that is, the corresponding communication node of each controller.The OPNET network simulation diagram is shown in Fig.4.The four actual nodes in Fig.4 are the actual NICs in the OPNET host computer, which were respectively connected to each hardware controller through the system-in-the-loop (SITL) module [27].The SITL module provided an interface between the physical hardware and virtual network.Ethernet packets were transmitted via this interface.

      In the cyber system simulation, the communication characteristics were investigated by setting the parameters for different scenarios.Abnormal behaviors such as communication delay, packet loss, and malicious attacks could be simulated using changes in the programming process.The addition of the real-life hardware controllers made the communication conditions much closer to reality considering the hardware latency and processing time, and better verified the effectiveness of the cooperative control strategy.

      Fig.4 Communication model in OPNET network simulation

      2.3 Hardware controller terminal

      Combining the new control characteristics presented in subsection 2.1, the hardware controller application was developed based on the TMS320F28377D chip from Texas Instruments, which has a 200 MHz main frequency and dual-core 32-bit floating-point microcontroller unit [28].The relationship between the hardware controller and the cyber-physical system simulation is shown in Fig.5.

      One single hardware controller was composed of a monitoring unit, control module, communication module, and so on.For the monitoring unit, the real-time operating data in RT-LAB were collected using the ADC module of the hardware controller, where the signals were converted from analog to digital.Each hardware controller had 20 (12-bit) ADC channels.Thus, a single controller could measure 20 analog values and discretize them at the internal clock frequency to complete the analog-to-digital conversion.The cooperative control algorithm was programmed into the control module, and the control instructions were output as typical PWM signals.Each controller had 16 PWM channels, which was enough to meet the requirements.The communication module in the controller was developed based on the Ethernet w5300 chip [29].Using the TCP/IP protocol, every controller was connected to the SITL interface of the cyber simulation in OPNET to realize the CHIL participation.Meanwhile, by configuring different IP addresses, the directed data packets flowed through the real-life control module and virtual communication model, which realized the distributed interactions between multiple controllers.

      Fig.5 Hardware controllers in framework of co-simulation testbed

      3 Case study

      3.1 Cooperative control strategy for multiple DGs

      To verify the need for the hardware controllers and their influence on the power and cyber system simulation, a classical cooperative control strategy for multiple distributed photovoltaic (PV) generators connected to a grid was tested on the proposed testbed.Using the system model shown in Fig.6, the cooperative strategy was designed to make the four PV generators converge and operate at a certain (or the same) ratio of available power.Each hardware control unit only collected the real-time power information of the corresponding controlled unit.The expected output voltage value of each PV array was calculated and represented by the duty cycle of the PWM signals.Then, the hardware controller sent PWM signals to DC/DC converters [30].

      In each DC/DC converter of the PV array, the output of the boost converter was connected to a common DC bus of 500 V.With the reference voltage value calculated by the hardware controller, the output voltage of the PV array was regulated at the input of the boost to control the PV power output.A three-phase voltage source converter (VSC) was used to connect the DC bus and convert the 500 VDC to 260 VAC.The VSC converter was represented by equivalent voltage sources generating the AC voltage averaged over one cycle of the switching frequency [31].

      Fig.6 Cooperative control strategy for PV arrays connected to grid

      Fig.7 Output voltage of single PV array with changing duty cycle

      In order to verify the validity of the grid-connected PV array model, the operational control of one single PV array photovoltaic model was first tested.Fig.7 shows the output voltage curve of a single PV array when the duty cycle is changed from 0.3 to 0.6 at 6 s.The output voltage dropped from 350 V to 200 V at 6 s, and the voltage was equal to 500 V × (1-duty cycle).In this process, the environmental temperature and irradiance were assumed to be constant, and the photovoltaic current remained at a relatively fixed value.Therefore, the output power of each PV could be controlled according to the set value.The performance of a single PV array system was validated.

      For any PV array i, the output power is In addition, the controlled voltage value of each PV array can be obtained according to the expected output power, Pi, divided by the known current.Ratio αi is controlled as follows:

      where the maximum output of the PV array is defined as P1max,P2max,..., Pimax, and parameter k is a sufficient proportional gain controlling the convergence rate.Ni is the collection of neighboring communications from the PV generators.

      In order to achieve a balance between the supply and demand, a certain leader PV array l must track the output ratio of the system:

      where Pload is the total load, and the system output ration meets 0≤≤1.More details about the algorithm proof can be seen in reference [10].

      3.2 Simulation results

      To investigate the feasibility and effectiveness of the proposed testbed, three representative simulation cases were carried out with the same physical side power system model and different communication scenarios.The initial output ratios of four arrays were set at 0.3, 0.4, 0.5, and 0.6, and their maximum output power values were 2 kW, 4 kW, 6 kW, and 7 kW.Pload was set at 10 kW.ki was set at 0.1, and k/was set at 0.9.

      (1) Case 1

      The availability of the proposed testbed was verified in this case.The power system model was loaded in the RTLAB target, and the cyber communication network was emulated in the OPNET host.The hardware controllers collected analog signals from RT-LAB and fed back control commands in the form of PWM signals.To keep the algorithms in the distributed hardware controllers running simultaneously, each controller was activated by a key-press on the leader hardware controller of PV array 1.The communication system between multiple DSPs ran normally.The comparison results are shown in Fig.8.The output power ratios of the PV arrays converged to the same value (0.5263) within a limited number of iterations.PV array 1, as the leader agent, followed system output ration αs from the initial value of 0.3.

      Fig.8 Comparison of results between experimental testbed
      and MATLAB simulation

      Compared with the simulation results in MATLAB, the convergence speed of αi on the testbed was much slower.As shown in Fig.8(a), the values of α2, α3, and α4 showed larger decreases during the first ten iterations than those obtained in the fully digital simulation environment.This was because the neighboring PV array’s information could not be received at the beginning of the iterations.In order to detect the existence of this inherent delay, a time delay process was artificially added to the full digital simulation.In the first ten iterations, PV arrays 2, 3, and 4 could not communicate with their neighbors because of the delays.The convergence curves of αi in this situation are shown in Fig.9.Compared with the results in Fig.8(a), the decrease in αi also appeared to be greater.The values of αi started to rise at the tenth iteration, and now the αi information for a neighboring node was received for the first time.This indicated that the inherent delay caused by real communication could affect the performance of the cooperative control strategy.

      (2) Case 2

      Fig.9 Convergence curves of αi in MATLAB with delay

      Fig.10 Convergence curves of αi on testbed for case 2

      In order to solve the time delay in case 1, in this case a system interrupt program was used in each hardware controller.The algorithm program was suspended until the neighbor information was received.The results are shown in Fig.10.Compared to the results in case 1, a faster convergence speed for αi was obtained, but it was still slightly slower than the simulation results in the fully digital environment.

      The results of the power system simulation in RTLAB are shown in Fig.11.At the beginning, each PV array operated at the initial output ratio, and the output power values were 0.6 kW, 1.6 kW, 3 kW, and 4.2 kW.The cooperative control algorithm was activated at t = 5 s.The inverter model in RTLAB accepted the control commands from the hardware controller, and the output voltage was controlled to achieve the desired output power according to the ratios.After the period of rising or falling, the output power of each PV array tended to stabilize and reach the set ratio.

      Fig.11 Output power of PV arrays on testbed for case 2

      (3) Case 3

      On the basis of case 2, a communication failure was simulated to analyze the effects of faults in the cyber network on the distributed control of the output power ratio.The communication link between PV array 1 and PV array 2 was set to fail in OPNET, which caused the communication topology to change from a ring to a single line.From the results shown in Fig.12, it can be seen that αi could still converge to the same value under the condition that one communication link was broken.However, compared with the ring topology, the convergence speed of the algorithm was slower with a single line topology.It took approximately 200 iterations to converge to the same ratio compared with the 100 iterations required in case 2.In particular, PV array 4 could only accept the output power ratio information from PV array 3, without being able to directly obtain the leader information from PV array 1.The change process for the output power is shown in Fig.13.Just like the results in case 2, the output power followed the change in αi, but a longer time was required to reach the target value because of the slower convergence speed of αi.

      Fig.12 Convergence curves of αi on testbed for case 3

      Fig.13 Output power of PV arrays on testbed for case 3

      4 Conclusion

      In order to determine the cooperative control characteristics of a CPPS, this study investigated a cosimulation testbed with controller hardware-in-the-loop to overcome the shortcomings of a fully digital simulation.The testbed based on the RT-LAB real-time simulation system and OPNET discrete-event simulation tool had the capability of accurately modeling the physical power system and cyber system in real time.The hardware controllers acted as the sensors and actuators of the cooperative control algorithm, replacing the code used in a fully digital simulation, which provided a co-simulation environment for researchers to verify the performance of a cooperative control strategy for a CPPS.Simulation cases were used to validate the effectiveness of the testbed and analyze the impact of a time delay and change in the communication topology.Future work will consider cyber system models and application scenarios for the testbed that include more realistic conditions such as cyber-attacks.


      This work was supported by the National Key Research and Development Program of China (Basic Research Class) (No.2017YFB0903000), and the National Natural Science Foundation of China (No.U1909201).

      Declaration of Competing Interest

      We declare that we have no conflict of interest.


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      Fund Information

      supported by the National Key Research and Development Program of China (Basic Research Class) (No.2017YFB0903000); the National Natural Science Foundation of China (No.U1909201);

      supported by the National Key Research and Development Program of China (Basic Research Class) (No.2017YFB0903000); the National Natural Science Foundation of China (No.U1909201);


      • Zhenyu Wang

        Zhenyu Wang received B.S degree from Jiangnan University, Wuxi, China, in 2017.He is currently pursuing the Ph.D.degree with the College of Electrical Engineering, Zhejiang University, Hangzhou, China.His current research interests include power system state estimation and cyber-physical security with application in distribution network.

      • Donglian Qi

        Donglian Qi received the Ph.D.degree in control theory and control engineering from Zhejiang University, Hangzhou, China, in March 2002.Since then, she has been with the College of Electrical Engineering, Zhejiang University where she is currently a Professor.Her current research interests include the basic theory and application of cyber physical power system (CPPS), digital image processing, artificial intelligence, and electric operation and maintenance robots.She is an Editor for the Clean Energy, the IET Energy Conversion and Economics, and the Journal of Robotics, Networking and Artificial Life.

      • Jingcheng Mei

        Jingcheng Mei received M.S degree from the College of Electrical Engineering at Wuhan University in 2014.He is currently pursuing the Ph.D.degree with the College of Electrical Engineering, Zhejiang University, Hangzhou, China.His current research interests include discrete optimization, distributed optimization, with applications to energy/power systems.

      • Zhenming Li

        Zhenming Li received B.E.E degree in Zhejiang University, Hangzhou, China, in 2017.She is currently pursuing the Ph.D degree with the College of Electrical Engineering, Zhejiang University, Hangzhou, China.Her current research interests include voltage optimization and control of renewable energy and cyberphysical security with application in smart grid.

      • Keting Wan

        Keting Wan received B.E.E degree at Xian Jiaotong University, Xi’an, China, in 2019.He is currently pursuing the Ph.D.degree with the College of Electrical Engineering, Zhejiang University, Hangzhou, China.His current research interests include distributed cooperative control of DC microgrid and safe operation of power cyber-physical system.

      • Jianliang Zhang

        Jianliang Zhang received his Ph.D degree in control theory and control engineering from Zhejiang University, Hangzhou, China, in June 2014.Since then, he has been working with College of Electrical Engineering, Zhejiang University.He was a visiting scholar at Hongkong Polytechnic University(PolyU) (2016-2017).His current research interests include distributed optimization, with applications to energy/power systems, and cyber-physical security with application in smart grid, etc.

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      Reference: Zhenyu Wang,Donglian Qi,Jingcheng Mei,et al.(2021) Real-time controller hardware-in-the-loop co-simulation testbed for cooperative control strategy for cyber-physical power system.Global Energy Interconnection,4(2):214-224.

      (Editor Dawei Wang)
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