1. Introduction
The physical power system is divided into mechanical and electrical subsystems, as shown in
Figure 1 [
1]. The mechanical subsystem includes handling the fuel source, furnace, boiler, and turbine to generate and control steam flow. Using the fuel source, the furnace and boiler produce high-pressure steam, which the turbine converts into mechanical energy to drive the synchronous generator. The generator then converts this energy into electrical power. The governor control system regulates the generator’s speed using droop speed control, adjusting the fuel supply based on grid frequency. An exciter, a type of DC generator, controls the field voltage (Efd) and current (Ifd) by supplying DC current to the generator’s field winding to maintain a constant system voltage [
2]. Exciters typically include an excitation power unit, excitation regulator, automatic voltage regulator (AVR), and power system stabilizer (PSS). The excitation power unit provides current to the generator rotor, while the excitation regulator adjusts the output. The AVR and PSS enhance system stability during disturbances [
2].
For a generator exciter, certain parameters like limits, exciter time constant, voltage sensing time constant, and saturation value are fixed by the manufacturer. However, voltage regulator gains, time constants, reactive compensation, and limit level values are tunable by the operator. Similarly, in the governor control system, parameters like water starting time, no-load gate, full load gate, turbine power fractions, maximum power, dead band, and turbine damping are fixed in the design, while droop, time constant, gains, and rate limits are tunable by the plant operator [
3].
Before commissioning a new unit into the grid, power system planning and operational studies analyze the grid’s stability and reliability with the changes. These studies use detailed models of synchronous machines and their control systems to simulate real-world performance [
3]. Sometimes, hardware-in-the-loop simulations are used to model and pre-tune synchronous generator controls [
4]. During commissioning, extensive testing, including off-line, open-circuit, and online tests, tunes the governor and exciter parameters to ensure optimal performance and stability. Once set, these parameters maintain stable operation under various conditions, minimizing the need for frequent adjustments. Real-time manual adjustments to governor or exciter parameters are rare, typically occurring only in response to system disturbances or significant operational changes, with re-tuning performed during scheduled maintenance. However, specific oscillation events like low-frequency forced oscillations in nuclear or steam power plants require immediate attention for modal analysis and parameter fine-tuning.
Forced oscillations in power systems are typically triggered by external periodic disturbances from cyclic loads, equipment malfunctions, inadequate control design or parameter settings, mechanical oscillations of a generator under unusual conditions, or adverse interactions within the power system [
5,
6]. Growing forced oscillations within the low-frequency oscillations (LFO) or ultra-low-frequency oscillations (ULFO) range are increasingly serious and can damage equipment, restrict the ability to transfer power, and degrade the power quality. Additionally, resonance of a forced oscillation with weakly damped local or inter-area system modes can also result in high-magnitude oscillations. [
7]. There have been numerous forced oscillation events that lasted for minutes to hours that have occurred around the world [
8]. On 11 January 2019, the U.S. Eastern Interconnection experienced a forced oscillation event as a result of a defective input to a generator’s steam turbine controller. The forced oscillation occurred for 18 min at a frequency of 0.25 Hz [
9]. Bonneville Power Administration (BPA) reports several sustained oscillations due to interactions between the PSS and under-excitation limiter (UEL) of a hydropower plant. Also in 2016, active power erratic behavior was noticed due to problems with the interaction between a plant controller and the governor of a generating unit [
10]. Several oscillation events across the U.S. summarizing generator equipment failures and the improper setting of system components like the governor, PSS, exciter, etc. rather than inaccurate modeling are discussed in [
11,
12]. Detecting and identifying the source of forced oscillations within the sub-control level of a generator remains an unsolved problem. However, analyzing oscillation trends and adjusting sub-control system parameters are crucial to restoring system stability in a short timeframe.
In the literature, several studies focus on tuning the AVR and PSS of the excitation system, using online tuning algorithms to adjust gain and time constant parameters to maintain system stability in real time [
13,
14,
15,
16,
17,
18,
19,
20]. However, the obtained parameters are acceptable for steady-state and small transient disturbance events and sometimes require an engineer’s judgement for a few cases such as the wrong value of AVR gain, incorrect values of the PSS, bad turbine-governor data, wrong generator inertia and time constants, etc. [
21]. These optimization algorithms are not suitable for tuning parameters during sustained forced oscillation events. Also, the level of accuracy of the algorithms depends on the field measurement transducers, which might not be available in all locations or may have inaccuracies. The authors of [
22] studied the influence of excitation system PID control parameters of the power system using 5% step response dynamic characteristics. This study’s results showed the parameters’ impact on the transient response of the power system. In [
23], the fault analysis was performed to find the root causes of the rectifier bridge filter and the excitation system’s 24 V power supply failure causing two downtime accidents. These failures require a deep investigation of component analysis. But few oscillation events, which appear due to dynamic control model parameter inconsistencies in nuclear or steam plants, need immediate oscillation event re-creation and model parameters’ re-tuning. However, in the literature, less attention was drawn towards the exciter parameters’ tuning during large, forced oscillation events.
Similarly, most studies focus on tuning the PID parameters of a few governor models. Recent works [
24,
25,
26,
27,
28] summarize the latest optimal algorithms for PID controllers for a hydro-turbine-based governor control. The importance of hydro power plant controller settings for suppressing frequency oscillations in the Turkish power system is highlighted in [
29]. In [
30], the authors discuss optimal techniques for tuning diesel governor parameters in hybrid renewable power systems using an exhaustive search approach. However, the tuning governor parameters described in [
29,
30] are for islanded mode operations. In [
31], low-frequency oscillation events related to governor control are presented, including a special case scenario that analyzes how governor control parameters influence the damping characteristics of inter-area oscillations and introduce a special ultra-low-frequency model into the power system. A few case studies in [
32,
33,
34] examined the impact of hydro-turbine and governor parameters on power systems with low- and ultra-low- frequency oscillations, focusing on isolated grid and single machine infinite bus system analyses. The work in [
35] discusses the impact of governor and hydraulic model parameters on grid stability through eigenvalue analysis but does not address the influence of model parameters during fault and low-frequency oscillation scenarios.
The literature shows a scarcity of research on the impact of exciter and governor parameters on persistent forced oscillations. Few historical low-frequency oscillation events related to governor control have been studied, but there is limited focus on exciter parameters and their relation to oscillations. Moreover, the literature focuses on optimizing a few parameters of the control system all at a time for a new steady-state and/or small transient disturbance. The novelty of this paper lies in bridging this gap by analyzing how changes in tuning for each single governor and exciter parameter will affect the persisting forced oscillations on the system. The findings will equip power plant operators and planning engineers with the knowledge to adjust control system parameters effectively.
The major contributions of this paper are summarized as follows. It (1) considers the influence of tuning each excitor and governor model parameter one at a time on the forced oscillations for an oscillation frequency in the 0.25 Hz and 1.4 Hz modes using a PSS/E dynamic simulation, (2) identifies and ranks the most sensitive model parameters of the SCRX, ESST1A, and AC7B excitors models, (3) obtains and ranks the sensitive parameters of HYGOV, GAST, and GGOV1 models, and (4) examines the time domain and frequency domain analysis of the model parameters.
The rest of the paper is organized as follows: the process of forced oscillation injection and oscillation frequency modes are specified in
Section 2; chosen exciter models, their standard parameters, and their tested range are described in
Section 3 and
Appendix A; selected governor models and their parameters are specified in
Section 4 and
Appendix B; the simulation results of sensitive excitor and governor model parameters are discussed in
Section 5; and the conclusions of the study are drawn in
Section 6.
5. Results and Discussion
This section discusses the sensitive parameters of the selected exciter and governor models based on the influence of each parameter change on forced oscillations. For both the local oscillation frequency of 1.4 Hz and inter-area frequency of 0.25 Hz, the deviation of the forced oscillation magnitude, frequency response, change in active/reactive power magnitude from the fixed frequency, and magnitude were used to rank the sensitive parameters. The analysis of individual parameter influence on the active power magnitude in governors and the reactive power characteristics in the exciter helped to understand and find the dominant parameters to see how each of these can alter the sustained forced oscillations. The parameters of the models were tested over a wide range to see if they have an impact on the forced oscillations and may cause instability in the system response or not. The specific results of each model are discussed in the respective subsections below.
5.1. SCRX Exciter
According to each parameter change, the impact on the forced oscillations’ frequency, magnitude, reactive power output, the SCRX model parameters, and their respective ranks are shown in
Table 2 below. Among the exciter models, the exciter gain was the most important parameter. The SCRX exciter gain (K) was changed from 10 to 1000 value, and the corresponding frequency and voltage responses are shown in
Figure 9. The very low value of K = 10 caused instability in the system response. The value of K = 100 had around 10 MVAR of forced oscillation magnitude. Increasing the K value from 100 to 1000 in steps of 100 increased the oscillation magnitude of 10 MVAR to 21.436 MVAR and 86.422 MVAR for the 0.25 Hz and 1.4 Hz modes, respectively. Similarly, the gain reduction ratio (T
A/T
B) changed from 0.05 to 1.0 in steps of 0.1. The frequency and voltage responses of Gen-1 for the T
A/T
B change are shown in
Figure 10.
The increase in the T
A/T
B parameter value from 0.1 to 1.0 increased the oscillation magnitude to 20.986 MVAR and 74.193 MVAR for the 0.25 Hz and 1.4 Hz modes, respectively. In contrast, the time constants T
E and T
B values increased, reducing the oscillation magnitude for both oscillation modes, as shown in
Table 2. The FFT analyses of the SCRX model parameters are shown in
Figure 11. The increase in the K and T
A/T
B values increased the oscillation magnitude linearly for the 1.4 Hz local oscillation mode, and, for the low-frequency 0.25 Hz mode, a very small oscillation magnitude change was observed. The exciter time constant T
E significantly affected the system’s dynamic response. At lower K values (K = 10, 50), the system’s sensitivity to T
E ≥ 7 was higher, resulting in a noticeable change in the oscillation frequency to 0.11719 Hz.
5.2. ESST1A Exciter
The ESST1A exciter model plays a critical role in maintaining the stability of power systems by regulating voltage. To understand how changes in the ESST1A parameters influence forced oscillations, we conducted a comprehensive analysis by varying key parameters and measuring the resulting reactive power output (MVAR) at oscillation frequencies of 0.25 Hz and 1.4 Hz.
Our study focused on several parameters, including the voltage regulator gain (K
A), regulator time constant (T
A), voltage regulator time constant (T
C), filter time constant (T
R), and voltage regulator time constants (T
B and T
B1). The results, summarized in
Table 3, indicated that changes in these parameters significantly impact the oscillation magnitudes and frequencies.
The voltage regulator gain (K
A) parameter was changed from 10 to 1000 in steps of 100. The low value of K = 50 had around a 0.5 MVAR forced oscillation magnitude. As shown in
Table 3, increasing the exciter gain value from 50 to 1000 increased the oscillation magnitude to 20.012 MVAR and 117.101 MVAR for the 0.25 Hz and 1.4 Hz modes, respectively. The FFT response of K
A for the 1.4 Hz oscillation mode is shown in
Figure 12. The low values of K
A from 50 to 400 showed a linear increase and, from 500 to 1000, the oscillation magnitude remained constant. Tuning extremely low values of KA ≤ 50 caused instability in the system. The regulator time constant (T
A) value was changed from 0 to 1 in steps of 0.1. As shown in
Figure 13, for T
A values below 0.3 and above 0.5, the system appeared more stable with lower oscillation magnitudes. The middle range of T
A (0.3 to 0.5) led to higher oscillation magnitudes specifically for the 1.4 Hz mode, indicating potential instability with an oscillation frequency of 1.298 Hz in both modes.
Similarly, the rise in the voltage regulator time constant (T
C) parameter value increased the oscillation magnitude to 110.137 MVAR. For both oscillation modes, the increase in T
C > 8 caused the change in oscillation frequency to 13.33 Hz. Likewise, the filter time constant (T
R) increased the forced oscillation magnitude from 10 MVAR to 24 MVAR with a T
R value increase from 0 to 1. In contrast, the increase in voltage regulator time constants T
B and T
B1 values reduced the oscillation magnitude. As shown in
Figure 13, the time constants T
A, T
C, T
R, T
B, T
C1, and T
B1 had a large influence on the system stability for the 1.4 Hz mode. The ESST1A parameters K
LR, T
F, and T
C1 showed no impact on the forced oscillations. The low values of the parameters I
LR ≤ 2, K
A ≤ 40, V
AMAX ≤ 2, V
RMAX ≤ 2, V
IMAX ≤ 0, and V
REF ≤ 0.6 can make the system became unstable. The higher values of these parameters K
F ≥ 0.1, K
C ≥ 0.6, V
AMIN ≥ 0.2, V
RMIN ≥ 0.2, and V
IMIN ≥ 0.2 can also make the system become unstable. K
A, T
C, T
R, T
B1, and T
B were the sensitive parameters of the ESST1A exciter model.
5.3. AC7B Exciter
The sensitive parameters of the AC7B exciter model were identified based on the impact of the parameter change response on forced reactive power oscillations and were ranked, as shown in
Table 4, as follows.
The voltage regulator gain (K
PA) parameter was changed from 0 to 50 in steps of 5.0, as shown in
Figure 14. The low values of K
PA of 0 to 5 had around a 174 MVAR forced oscillation magnitude. Increasing the K
PA value from 10 to 50 maintained the oscillation magnitude to 10 MVAR for both oscillation frequency modes. For both the oscillation modes, the oscillation magnitude of the K
PA, K
PR, K
E, and T
R parameters from the FFT analysis is shown in
Figure 15.
Similarly, the rise in the excitor constant (K
E) parameter value from 0 to 1.4 decreased the oscillation magnitude to 2.51 MVAR and 5.68 MVAR for the 0.25 Hz and 1.4 Hz modes, respectively, as shown in
Figure 15. In contrast, the increased values of the potential circuit gain coefficient (K
P), regulator proportional gain (K
PR), and filter time constant (T
R) increased the forced oscillation magnitude, as shown in
Figure 15. The excitation control stabilizer gain K
F1 oscillation magnitude response from the FFT analysis is shown in
Figure 16. The K
F1, K
F2, and K
F3 values between 0.5 to 1.0 had a stable response. The higher values of the parameters K
E ≥ 1.6, K
F1 ≥ 1.5, K
F2 ≥ 2.5, and K
F3 ≥ 1.5 can cause the system to become unstable. The lower values of V
AMAX ≤ 0, V
RMAX ≤ 2, and VFE
MAX ≤ 2 can also make the system unstable. The AC7B exciter parameters Kc, K
IR, K
DR, K
IA, K
L, T
DR, T
F3, V
EMIN, V
PSS, E
1, E
2, S(E
1), S(E
2), V
R, and V
A did not show any influence on the forced oscillations. The regulator gains K
PA, K
PR, K
IR, K
E, K
P, and T
R and the excitation control system stabilizer gains K
F1 and K
F2 were the sensitive parameters of the AC7B exciter model.
5.4. GAST Governor
The sensitivity of the GAST governor parameters was analyzed based on their influence on the forced oscillation magnitude, oscillation frequency, and active power oscillations of Gen-1. A forced oscillation magnitude of 10 MW peak to peak was applied at the GAST governor’s P
REF input to assess these effects. The sensitive parameters of the GAST governor and their associated ranks are given in
Table 5, as follows.
The speed droop (R) parameter was changed from 0 to 1. The low value of R = 0 had distorted oscillations with an oscillation frequency of 2.695 Hz for both oscillation frequency modes. As shown in
Figure 17, increasing the speed droop value from 0.1 to 1 produced an oscillation magnitude of around 13.33 MW and 10.08 MW for the 0.25 Hz and 1.4 Hz modes, respectively. Likewise, the increase in time constants T
1 and T
2 values had a very small impact on the forced oscillation magnitude. However, the T
2 = 0 made the system become unstable. Its value should always be greater than 0, i.e., T
2 ≥ 0.1. The minimum turbine power V
MIN ≥ 2 made the system unstable. The graphs presented in
Figure 18 show the FFT response of the GAST model parameters R, T
1, T
2, and T
3 changes. Each graph depicts the oscillation magnitude for two different oscillation frequencies, 0.25 Hz and 1.4 Hz, as the respective parameters were varied. Notably, the 0.25 Hz oscillations tended to exhibit higher magnitudes compared to the 1.4 Hz oscillations across all parameter changes.
The GAST parameters R, T1, T2, VMAX, and VMIN were the sensitive parameters of the model. The speed droop R = 0 and minimum turbine power VMIN ≥ 2 had distorted oscillations. The turbine damping factor (Dturb), temperature limiter gain (KT), and the exhaust temperature time constant (T3) of the GAST governor model did not show any influence on the forced oscillations.
5.5. HYGOV Governor
The sensitivity of the HYGOV governor parameters was analyzed based on their influence on the forced oscillation magnitude, oscillation frequency, and active power oscillations of Gen-1. The sensitive parameters of the HYGOV governor model were identified and ranked, as shown in
Table 6, as follows.
The turbine gain (A
T) was the most sensitive parameter of the HYGOV model. The A
T values, A
T < 0 and A
T ≥ 1.5, made the system unstable and had around 390 MW oscillations. A
T values between 0 to 1 showed damped oscillations for both oscillation modes, as shown in
Figure 19. The temporary droop (r) parameter was changed from 0 to 2, and the corresponding frequency and voltage responses are shown in
Figure 20. The low value of r = 0 had high 105 MW oscillations for both frequency modes. Increasing the temporary droop value from 0.2 to 2 reduced the oscillation magnitude to 2.9 MW and 5.709 MW for the 0.25 Hz and 1.4 Hz modes, respectively.
Figure 21 presents the effect of varying four different parameters of the HYGOV model on the oscillation magnitude for two different oscillation frequencies (0.25 Hz and 1.4 Hz). Each subplot illustrates the oscillation magnitude in response to changes in specific parameters: R, temporary droop (r), T
G, and Tw. The speed droop R and T
G showed a significant increase in the oscillation magnitude as their values increased. Conversely, the temporary droop (r) parameter significantly reduced the oscillation magnitude with increasing values. The water inertia time constant (Tw) had a comparatively minor effect on the oscillation magnitude.
The higher values of permanent droop and no power flow had damped oscillations. The minimum and maximum gate limit values, GMIN ≥ 2 and GMAX ≤ 0, made the system unstable. The turbine gain was the most sensitive parameter, and AT < 0 and AT > 1.5 can make the system unstable. The permanent droop (R), temporary droop (r), servo time constant (TG), and water time constant (TW) were the sensitive parameters of the HYGOV model and had a more pronounced effect on the 0.25 Hz oscillation mode. Changing the maximum and minimum gate limits below or above these values of GMAX ≤ 0 and GMIN ≥ 2 can make the system become unstable.
5.6. GGOV1 Governor
The analysis focused on the sensitivity of various GGOV1 model parameters; specifically, their impacts on the reactive power output (MW) at two oscillation modes are discussed in this section. The parameters were ranked based on their influence on the system stability and oscillation magnitude, as summarized in
Table 7.
The turbine gain (K
turb) was the most sensitive parameter of the GGOV1 model. The K
turb change response from 0 to 4 in steps of 1 is shown in
Figure 22. The K
turb < 0 and Kturb ≥ 4 made the system unstable and had around 316 MW of oscillations. The K
turb values increasing from 1 to 3 increased the oscillation magnitude for both oscillation frequency modes. The permanent droop (R) parameter was changed from 0 to 1. The low value of R = 0 had damped oscillations for both frequency modes. Increasing the R value from 0.1 to 1 increased the oscillation magnitude to 116.04 MW and 157.059 MW for the 0.25 Hz and 1.4 Hz modes, respectively. The FFT response of R and actuator time constant (T
ACT) parameters are shown in
Figure 23. The high values of T
ACT and turbine lag time constant (T
B) parameters also impacted the forced oscillation magnitude. In contrast, the increase in the governor proportional gain (K
Pgov) value increased the oscillation magnitude. Likewise, the high values of the turbine lead time constant (T
C ≥ 1) and no-load fuel flow (W
FNL ≥ 1) can cause the system to become unstable.
The higher values of the parameters VMIN ≥1.2, Kturb ≥ 4, WFNL ≥ 1.0, and TC ≥ 1 made the system unstable. The permanent droop R > 0.1, KIgov, TDgov, Tpelec, and LDREF = 0 delayed the system’s response. The turbine gain (Kturb), permanent droop (R), turbine lead and lag time constants (TC and TB), actuator time constant (TACT), and no-load fuel flow (WFNL) were the sensitive parameters of the GGOV1 governor model.
6. Conclusions
This study utilized Kundur’s two-area system in PSS/E to investigate the impact of exciter and governor parameters on forced oscillations, presenting a comprehensive analysis that highlights the novelty of our approach. By examining widely used exciter models such as SCRX, ESST1A, and AC7B, alongside governor models like HYGOV, GAST, and GGOV1, we systematically injected forced oscillations and altered model parameters to assess their influence on frequency, voltage response, and oscillation magnitudes at 0.25 Hz and 1.4 Hz. This dual-frequency analysis encompasses both inter-area and local oscillation modes, providing a thorough understanding of parameter sensitivity.
The following key findings identify and rank the sensitive parameters of each model according to their impact on forced oscillations:
SCRX Model: Parameters K, TA/TB, TE, and TB are the sensitive parameters of the SCRX exciter model. The oscillation magnitude for the 1.4 Hz mode increases linearly with K and TA/TB values, while the 0.25 Hz mode remains largely unaffected. The exciter time constant TE significantly influences the oscillation frequency at lower values.
ESST1A Model: Parameters KA, TC, TR, TB1, and TB are highly sensitive, crucial for dynamic response and system stability. These parameters help to control forced oscillations. The parameters KLR, TF, and TC1 show no impact on the forced oscillations.
AC7B Model: Sensitive parameters of this model include regulator proportional gains KPA, KPR, KIR, KE, KP, TR, KF1, and KF2. Other parameters, such as Kc, KIR, KDR, KIA, KL, TDR, TF3, VEMIN, E1, E2, S(E1), S(E2), VR, and VA, do not show any influence on the forced oscillations’ stability.
GAST Model: The speed droop parameter R significantly affects oscillation magnitudes and system stability. Parameters T1, T2, VMAX, and VMIN also influence oscillation characteristics.
HYGOV Model: Sensitive parameters of this model include permanent droop R, temporary droop r, servo time constant TG, and water time constant TW, especially impacting the 0.25 Hz oscillation mode.
GGOV1 Model: The most sensitive parameters are turbine gain Kturb, permanent droop R, turbine lead and lag time constants TC and TB, actuator time constant TACT, and no-load fuel flow WFNL. The other parameters, R > 0.1, KIgov, TDgov, and Tpelec, delayed the response of the system.
The ranking and identification of these sensitive parameters provides a novel approach to understanding and mitigating forced oscillations in power systems. This research offers practical insights for plant operators, enabling them to fine-tune control settings and improve system stability, thereby contributing to more resilient and reliable power system operations.