Effect of ECS low‐pass filter timing on grid frequency dynamics of a power network considering wind energy penetration
2017; Institution of Engineering and Technology; Volume: 11; Issue: 9 Linguagem: Inglês
10.1049/iet-rpg.2016.0855
ISSN1752-1424
Autores Tópico(s)Wind Turbine Control Systems
ResumoIET Renewable Power GenerationVolume 11, Issue 9 p. 1194-1199 Research ArticleFree Access Effect of ECS low-pass filter timing on grid frequency dynamics of a power network considering wind energy penetration Kenneth E. Okedu, Corresponding Author Kenneth E. Okedu kenokedu@yahoo.com Department of Electrical and Electronic Engineering, Kitami Institute of Technology, Hokkaido, JapanSearch for more papers by this author Kenneth E. Okedu, Corresponding Author Kenneth E. Okedu kenokedu@yahoo.com Department of Electrical and Electronic Engineering, Kitami Institute of Technology, Hokkaido, JapanSearch for more papers by this author First published: 13 June 2017 https://doi.org/10.1049/iet-rpg.2016.0855Citations: 5AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This study investigates the effect of wind energy penetration on the frequency response of a multi-machine power network considering different time constants of a low-pass filter (LPF) in the DC chopper of an energy capacitor system (ECS). The power network is made up of wind farm composed of fixed speed induction generator interconnected to a steam and hydro synchronous power plants. Heavy and light loads were connected in the system. The ECS is connected at the terminal of the wind turbine. The LPF time constant parameter was varied for different cases considering the same wind speed for the wind turbine. Scenarios with and without considering the synergy of a series dynamic braking resistor (SDBR) connected to the stator of the wind turbine and the ECS were investigated. The results obtained were compared to a case where no frequency control was employed in the power network. Simulations were carried out in power system computer aided design and electromagnetic transient including DC. The results show that a higher time constant of the LPF effectively damp the oscillations of the grid variables and restores quickly the system during network disturbance. The SDBR was used to further enhance the performance of the ECS. 1 Introduction Owing to large disturbances in the power network, the control of power system is becoming complex by day. The change in the frequency of a power network could be as a result of load fluctuation or integration of renewable energy system such as wind energy, among others. In the literature, the control of load frequency of a power network using automatic load frequency control by variation of generation set points on the speed governor to maintain system frequency has been presented in [1, 2]. In a parallel AC–DC interconnected power transmission system [3, 4], the DC system can effectively cooperate with an AC system in improving frequency response. The use of supervisory fuzzy controller to stabilise frequency deviations in an AC–DC interconnected power system was reported in [1]. It was concluded that a better performance was achieved using the fuzzy controller, however, the system remains complex and is based on uncertainties. Since the main goals of the load frequency control are to maintain zero steady state error in a mixed generation system, the genetic algorithm approach was proposed in [5-7] to reduce the frequency transient deviations. However, the genetic algorithm approach produces oscillation in the frequency deviations and longer time to reach steady state for the power system variables. The adaptive notch filter was proposed to control and mitigate current and frequency distortions in [8-10]. However, notch filters gives better performance only when the frequency of the measured signal remains constant. The main challenges in employing active passive filters are the nature of the equipment, reference signal and the control strategy [11]. The battery energy system can effectively mitigate oscillations in power system, hence could be used for frequency regulation. Rechargeable energy battery system like the Redox flow batteries are recently been used in place of flexible AC transmission system such as the super magnetic energy storage system due to fast and better performance [12-15]. However, the life cycle of such batteries cannot be guaranteed based on constant usage for large power networks. Recently, wind farms are integrated into existing power systems made up of synchronous machines in order to minimise fuel consumption [16-19]. In this regard, the power system would operate in an unstable manner due to inherently intermittent and fluctuating nature of the wind energy. Consequently, the rate of frequency change is very high and lower values of frequency are easily achieved when the power system network is made up of only synchronous generators. Since the integration of renewable alternative sources to existing power network would lead to some challenges, it was necessary to implement grid codes or requirements to effectively run the grid system in order to avoid shut down of wind farms. The integrated renewable energy sources are often tied to the grid via inverters that are electronic in nature, which helps in frequency control and stability enhancement of the network. In [20], the additional power to achieve control of the network was provided by both internal and external energy storage devices. In [21], the energy capacitor system was used to stabilise grid voltage system, however, frequency responses were not considered and the use of different low-pass filter (LPF) time constants was not implemented. Since frequency stability is of great concern, tripping of the renewable energy generators like the wind turbines is most likely to avoid grid dynamics or transient disturbances [22]. This paper tends to proffer a solution to this based on the grid codes minimum frequency values stipulated in the operation of wind farms and power systems. It was reported in [23-26] that the series dynamic braking resistor (SDBR) could be used to improve the stability of large wind farms composed of induction generators. Since the SDBR can increase the output of the wind generator in addition to limiting its speed excursion during grid dynamics, the response of the wind generator speed would improve with less oscillation. This work uses the energy capacitor system (ECS) to achieve smoothing of the terminals of the distorted wind turbine variables in order to maintain steady supply despite the stochastic nature of the wind. A further step of designing and investigating the effect of varying the time constant parameter of the ECS DC chopper LPF was carried out. Different time scenarios were considered for the same wind speed of the wind generator. This is a simpler and cost effective way of achieving frequency stability of the power network. The ECS LPF parameter variation during grid network disturbance is not widely reported. The results obtained for various simulation scenarios in PSCAD/EMTDC platform [27] show that higher time constant of the LPF could effectively damp the oscillations of the wind turbine, the connected synchronous generators and the entire power system during the system dynamics. Another salient aspect of this paper is the synergy of the ECS and SDBR control strategy. When the SDBR is connected to the stator of the wind generator, the performance of the ECS system was further enhanced in stabilising the grid network. 2 Power system frequency control and renewable sources The balance between the power generated and demanded in a grid network is known as the frequency control of the system. This imbalance could be caused by small load variations, power plant outages or tripping of the lines. These scenarios can be avoided by implementing a frequency control mechanism in the power network as its dynamics has proven to significantly affect the design of control in power systems [28]. In the traditional power plants, there is usually installed primary frequency or droop controllers that regulates the volume of power flowing to the prime movers of the machine. There is filtering in between the droop control activation and the physical contribution with time constant of the turbine system in the range of several seconds [29, 30]. This could be termed the primary frequency control. During grid disturbances, in order to bring the frequency back to its original value, an integral controller needs to be implemented (secondary control) in the system in addition to the primary frequency control. A description of the frequency control mechanism is shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Classical frequency control From Fig. 1, the frequency control in a power system can be grouped into two sections. The first section consist of the frequency controllers of the power plants when they are yet to be activated and in this case, the plants will absorb or release their kinetic energy to contain the change in frequency. This section could be referred to as inertia response stage, considering the fact that the inertia dampens the frequency changes. In the second section, the system frequency is initially stabilised and then restored to its original frequency with the use of the primary control or governor mechanism and secondary control [31, 32]. The impact of integrating renewables on both sections in Fig. 1 is as follows. Based on the inertia response, the frequency variation, considering a significant generation-load imbalance is determined by [32]: (1) where, Pg is the generated power, Pl is the power demand, ωel is the electrical angular frequency and Jsystem is the inertia of the system. Equation (1) shows the derivative of the kinetic energy stored in all the generators of the power system. In a single generator shaft, the stored kinetic energy is often expressed proportional to its power rating and is referred to as the inertia constant expressed in [32]: (2) Where Sgen is the nominal apparent power of the generator ωel,0, is the nominal system frequency and p the number of pole pairs. The inertia constant is measured in seconds usually in the range of 2 to 9 s for large power plants [33]. Expressing (1) in per unit and combining with (2) leads to: (3) From (3), Hsystem is the initial constant of the system. Assuming , then the initial rate of frequency change could be expressed as: (4) From the above equations, the rate of frequency change depends on the magnitude of the power imbalance and the system inertia. Furthermore, the system inertia is determined by the number of operating generators in the network and the inertia of each of these generators. Due to the strong coupling between the rotational speed and electrical frequency exhibited by synchronous generators, they can contribute to this inertia. However, for the case of wind generators, because they electrically decouple the motion of the generator from the grid frequency, they do not have the capability to contribute to inertia response. Although, depending on the control strategy of the converters, they can contribute to the response and it is known as virtual inertia for variable speed wind turbines. In such scenario, the rotor speed could be decreased using the control systems faster than the frequency of the system [34-36]. Thus, a large increase in active power could be achieved from the wind generators in the initial moments. Consequently, augmenting or replacing traditional generation by wind generation would lead to lower system inertia, which in frequency event can lead to a high rate of frequency change. However, if the wind generator control includes virtual inertia, the power system frequency change in the case of power imbalance would not be much. In this work the fixed speed squirrel cage induction wind turbine type is used in the wind farm. Again, wind generators or renewable energy sources are basically poor in primary or secondary control [37] and because of the lower system inertia, the other synchronous generators in the grid would have less time to adapt to the changes in the network. Because the number of generators delivering primary control is reduced, the scenario of a lower minimum or higher maximum frequency would occur during grid disturbances. 3 Model system of study The system model used in this paper is shown in Fig. 2, with wind energy penetration into a multi-machine power system via an aggregated 100 MVA wind turbine system. The wind farm is made up of smaller wind turbines of 2.5 MVA capacity each. However, for simulation purpose, an aggregated wind turbine was considered. The ECS is connected at the terminals of the wind turbine in the wind farm as shown. Loads X and Y are connected between the bus bar connecting the wind farm and the other synchronous generators of steam and hydro turbine type. The line parameters of the system are also given in the figure. The power network is operating at 100 MVA, 66 kV and 50 Hz. The rating and parameters of the ECS are 30 MW, 3.5 kV/66 kV, with a pulse-width modulation carrier frequency of 1.05 kHz. Table 1 gives the parameters of the wind turbine and the synchronous generators, respectively [25]. Table 1. Parameters of the generators Generator type Synchronous generator (steam) Synchronous generator (hydro) Generator type Induction generator (wind turbine) MVA 250 250 MVA 100 ra, pu 0.003 0.003 r1 (pu) 0.01 xa, pu 0.102 0.130 x1, pu 0.1 Xd, pu 1.651 1.200 Xmu, pu 3.5 Xq, pu 1.590 0.700 r21, pu 0.035 X/d, pu 0.232 0.300 x21, pu 0.030 X/q, pu 0.380 r22, pu 0.014 X//d, pu 0.171 0.220 x22, pu 0.098 X//q, pu 0.171 0.250 H, s 1.5 T/do, s 5.900 5.000 T/qo, s 0.535 T//do, s 0.033 0.040 T//qo, s 0.078 0.050 H, s 3.000 2.500 Fig. 2Open in figure viewerPowerPoint System model 4 Control strategy of the power network Fig. 3 shows the control structure of the ECS, where a dq to abc transformation is done with an angle theta that is calculated from the phase lock loop of the system. The effective voltage value is compared with a reference value that is then passed through a proportional integral (PI) controller system. The signal is further compared with a signal generator to generate pulse reference signals which are used for switching the ECS system to achieve control of the wind farm system and the entire power network during grid disturbances. Fig. 3Open in figure viewerPowerPoint Control structures of the energy capacitor system and chopper Moreover in Fig. 3, the control structure and design for the inbuilt LPF is shown for the DC chopper. The reference signals are the grid active power and the wind turbine active power, respectively. The difference of the powers is fed through a PI system whose output is added to a constant gain value to generate signal that is compared with a triangular carrier signal generator. The output is thus used in the switching of the DC chopper circuit unit of the ECS system for charging and discharging limitations. Fig. 4 shows the SDBR control strategy for the wind turbine connected to its stator in the model system presented in Fig. 2. During grid disturbances, when the grid frequency is above or below the set standard of 50 Hz (± 0.5 Hz), the SDBR switch would activate to mitigate the effect. A small value of SDBR 0.05 p.u. increases the mechanical power extracted from the drive train, thus reducing its speed excursion. Also, since mechanical torque is proportional to the square of the stator voltage of the wind turbine, the effect would enhance the post dynamic recovery of the wind turbine during network disturbances. Fig. 4Open in figure viewerPowerPoint Series dynamic braking resistor (a) Structure, (b) Control 5 Simulation results and discussions Simulations were run for 600 s for dynamic analysis of the power network considering the stochastic practical wind speed data in Fig. 5a obtained in Hokkaido Island, Japan. The simulations for various cases of the ECS low-pass filter time constant were analysed. In addition, cases where no control of the ECS was employed and where SDBR was used were also investigated. In Fig. 5b, for no control scenario, the trend of the active power of the wind farm is same with the wind speed variation and the lowest and highest active power and wind speed appear at 375 and 490 s, respectively. With no ECS control, much oscillations of the active power were observed because the wind generators lack the capability to contribute to the inertia response, instead it decouples its motion electrically from the grid frequency. However, these oscillations die with the implementation of the ECS control for various low-pass filter time constants. A low-pass filter time of 30 s gives much oscillation than a low-pass filter time constant of 240 s. Since the rate of frequency change depends on the magnitude of the power imbalance and the system inertia, the response of the grid frequency for the scenario of no control in Fig. 5c follows the nature of the active power of the wind farm. For the scenario where no ECS control was implemented, the frequency deviation fall down to ∼48 Hz, which is not healthy for the power network. However, the frequency of the power network is been maintained at 50 Hz despite the nature of the wind speed considering ECS control strategy. The ECS help reduce the dynamics and depth of frequency drops and therefore allow the wind farm to participate in frequency control. Fig. 5Open in figure viewerPowerPoint Simulations were run for 600 s for dynamic analysis of the power network considering the stochastic practical wind speed data (a) Natural wind speed, (b) Wind farm active power, (c) System grid frequency A SDBR connected to the stator of the wind turbine based on the control strategy of Fig. 4 was used to further enhance the performance of the frequency of the system to ∼49.5 Hz. The grid frequency code [38] in Fig. 6 requires that the frequency of the power network should not go below 49 Hz during system disturbance. When compared to the standard frequency grid requirement, the use of ECS control with very high LPF time constant achieved the best response of the power network of the system frequency. Although, responses of the LPF for 60, 120 and 180 s show that the grid frequency of the system was recovered, but the variables were still having some oscillations. Fig. 7 shows the responses of the connected hydro and steam turbine synchronous generators of the power network. The trend of the active power of the synchronous generators is different from that of the wind generator because there is strong coupling between the rotational speed and electrical frequency of the synchronous generators, thus, they can contribute to the system inertia. The responses show that the low-pass filter time constant of the ECS has influence also on the synchronous machines in the power network. When no ECS control was implemented, the active powers of the synchronous machines were distorted and these oscillations were effectively damped by a higher value of low-pass filter time constant in the ECS DC chopper unit. Fig. 6Open in figure viewerPowerPoint Grid frequency requirement by E.ON NETZ GmbH Fig. 7Open in figure viewerPowerPoint Responses of the connected hydro and steam turbine synchronous generators of the power network (a) Responses of the steam synchronous generators, (b) Responses of the hydro synchronous generators 5.1 Synergy of the ECS and SDBR schemes Fig. 8 shows the synergy of the ECS and SDBR schemes in further enhancing the frequency stability of the power network during dynamics considering average time constant of the ECS DC chopper low-pass filter. The improved performance of the grid frequency using the synergy of the ECS and SDBR is based on the reasons presented in Section 4, on the SDBR dynamics during grid disturbances. Fig. 8Open in figure viewerPowerPoint Grid frequency dynamics for ECS and SDBR synergy 6 Conclusion With increasing power generation using renewable energy sources, it is imperative to analyse their impact on the control of the power network. This paper investigated the effect of wind farm penetration on an existing power network utilising an ECS control. A SDBR connected to the stator of the wind turbine was also used to mitigate the fluctuations of the grid frequency. Improved performance of the wind farm and the entire power network was achieved via the careful tuning of the low-pass filter time constant in the DC chopper unit of the ECS. The results show that a higher low-pass filter time constant damps the oscillation of the variables of the power network more effectively, with a faster recovery of the system during network disturbance due to wind energy penetration. Moreover, the synergy of the ECS and SDBR system was investigated. 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