Microsoft Word - ETASR_V13_N3_pp10652-10658 Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10652-10658 10652 www.etasr.com Dhouib et al.: Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of … Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of Wind Farms during Fault Conditions Bilel Dhouib CEM Laboratory, Engineering School of Sfax, Tunisia bilel.dhouib@enis.tn (corresponding author) Mohamed Ali Zdiri CEM Laboratory, Engineering School of Sfax, Tunisia mohamed-ali.zdiri@enis.tn Zuhair Alaas Department of Electrical Engineering, Faculty of Engineering, Jazan University, Saudi Arabia zalaas@jazanu.edu.sa Hsan Hadj Abdallah CEM Laboratory, Engineering School of Sfax, Tunisia hsan.hajabdallah@enis.tn Received: 7 March 2023 | Revised: 19 March 2023 | Accepted: 22 March 2023 Licensed under a CC-BY 4.0 license | Copyright (c) by the authors | DOI: https://doi.org/10.48084/etasr.5838 ABSTRACT As the demand for renewable energy continues to increase, wind power has emerged as a prominent source of clean energy. However, incorporating wind energy into the power generation system at a high level can significantly impact the dynamic performance of the power system, resulting in increased uncertainties during operation. This study investigated the effectiveness of the Multi-Band Power System Stabilizer (MBPSS), a new power system stabilizer, in suppressing dynamic oscillations in a multi-machine power system connected to a wind farm. This research focused on analyzing the transient stability of a nine-bus network, commonly known as the Western System Coordinating Council (WSCC), integrated with a Doubly Fed Induction Generator (DFIG) using MATLAB/Simulink. The study evaluated the dynamic performance of the proposed system under fault conditions, including Line-to-Line-to-Line-to-Ground (LLLG) faults. Simulation results showed that MBPSS effectively dampened oscillations and improved the stability of the power system, even in the presence of severe faults and high-level integration of wind farms. Keywords-wind farm; DFIG; MBPSS; LLLG fault; multimachine power system; transit stability I. INTRODUCTION Wind is a highly promising source of renewable energy, thanks to its environmental-friendly characteristics and widespread availability [1]. However, the current rate of integration of wind energy into the electricity grid remains relatively low. The large-scale integration of wind power sources presents potential technical challenges due to their intermittent nature, which must be carefully examined and addressed as part of the development of a sustainable energy system for the future [2-4]. The 9-bus system has been extensively used in power system analyses. In [5], a probabilistic load flow analysis was presented for the 9-bus WSCC system, using a stochastic approach to validate the effectiveness of probabilistic analysis techniques to gain a more comprehensive understanding of power systems compared to deterministic analysis. The transient stability analysis of the IEEE-9 bus system under multiple contingencies was investigated in [6]. This study compared and employed both the Euler and Runga methods to examine changes in the frequency and rotor angle of the system under various fault conditions. The simulation results showed that the Runga method had a faster response. In [7], transient stability analysis was conducted on the IEEE 9-bus system, taking into account the integration of DFIG- and SCIG-based wind turbines. The simulation results showed that the DFIG- based wind turbine had a lower peak value of relative power angle than the SCIG-based one, but the results for settling time Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10652-10658 10653 www.etasr.com Dhouib et al.: Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of … were reversed. In [8], the transient stability assessment of an IEEE 9-Bus system integrated with a wind farm was investigated to evaluate its stability with DFIG integration. Two techniques are used to improve oscillation damping and ensure system stability: Power System Stabilizers (PSSs) and FACTS systems have effects extensively documented [9- 11]. However, while PSS is effective in damping local modes, it may not sufficiently address inter-zonal modes. Hence, various design approaches have been proposed to enhance PSS efficiency in damping diverse oscillation modes, such as the optimization of performance criteria to achieve optimal control [12], the use of neural networks in smart stabilizers [13], robust control [14], and fuzzy logic [15]. In summary, these controllers have been proven effective in addressing stability issues. This motivates the proposal of a novel power system stabilizer that integrates wind power generation for multi- machine systems. The implementation of MBPSS can significantly improve the primary frequency response of a power system. This is exemplified by the use of MBPSS at Hydro-Quebec (HQ) for several years [16]. Several studies, such as [17-19], highlighted the favorable effects of certain techniques on power systems. These techniques were shown to improve frequency response, global and electromechanical mode damping, and voltage stability. The improvements in the HQ transmission system can be attributed, at least in part, to its unique characteristics. HQ is isolated from the rest of the Eastern Interconnection and is characterized by a long radial structure, with power flow patterns established predominantly in the north-to-south direction. This study investigated the integration of a wind farm that utilized DFIG as variable speed generators directly connected to the grid into a 9-bus network at a high level. The primary objective was to analyze the impact of wind farm integration on the transient stability of the power system, focusing on evaluating the effects of the MBPSS stabilizer. The evaluation of the system's transient state was based on analyzing the power angles, angular speeds, and frequencies of the synchronous generators. Additionally, fault scenarios, such as the LLLG fault, were considered to assess the system's dynamic performance. The main contributions of the current paper are:  This study explored the effectiveness of an MBPSS that featured a three-frequency band structure (i.e. low-, intermediate-, and high-frequencies) for analyzing the system's transient stability.  High-level integration of the wind farm was taken into account to improve the robustness of the MBPSS.  The suggested system was exposed to high-level perturbation scenarios, including the LLLG fault, and the robustness of the controller was demonstrated to facilitate swift system recovery from these faults. II. WIND FARM MODEL A. Wind Speed Model Accurate modeling of wind turbines is highly dependent on the wind speed model. While wind is often viewed as a random phenomenon, its variability can be modeled using various approaches [20]. One way to represent the wind speed is to decompose it into a sum of harmonics, as demonstrated by [21]: �� ��� = 10 + 0.55�sin�0.0393��− 0.875 sin�0.1178��+ 0.75 sin�0.1963��− 0.625 sin�0.3927��+ 0.5 sin�1.178��+ 0.25 sin�1.9634��+ 0.125 sin�3.9269��� (1) B. Wind Turbine Model A wind turbine aims to utilize wind energy and convert it into torque that rotates the rotor blades. The amount of wind energy converted into mechanical energy by the rotor is determined by three factors: density of the air, swept area of the rotor, and wind speed. Air density and wind speed are unique to each location and are considered climatic parameters [22]. The mathematical expression for the mechanical power output of a wind turbine is given by [23]: �� = �� ��, �� !" �#$ (2) The mechanical power harvested by the wind turbine and transferred to the rotor is represented by Pm (W). The turbine power coefficient is denoted by Cp, while p (kg/m 3 ) represents the density of the air, A (m 2 ) indicates the area swept by the rotor, Vω (m/s) represents the wind speed, � stands for the speed ratio, and β (°) is the blade angle of inclination. Each wind turbine has its distinct power coefficient characteristics. As an example, the power coefficient for a 1.5MW wind turbine can be estimated through measurements and be approximated using [24]: �� ��, �� = 0.5176 %&&'() − 0.4� − 5* + ,"& ()⁄ + 0.0068 � (3) The value of the λi parameter is determined by: &() = &(/0.012 − 0.0$324/& (4) C. DFIG Model The utilization of dynamic modeling for the DFIG in the rotating d-q reference frame yields the following equations [23- 25]: ⎩⎪⎪ ⎨ ⎪⎪⎧ 9:; = <; =:; − >;?@; + :ABC:D 9@; = <; =@; + >;?:; + :AEC:D 9:F = ; − >F �?@F + :ABG:D9@F = ; − >F �?:F + :AEG:D (5) where: ⎩⎨ ⎧?:; = H; =:; + H� =:F?@; = H; =@; + H� =@F ?:F = HF =:F + H� =:;?@F = HF =@F + H� =@; Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10652-10658 10654 www.etasr.com Dhouib et al.: Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of … The variables uds, uqs, udr, and uqr denote the d-q axis stator and rotor voltages, while ids, iqs, idr, and iqr represent the stator and rotor currents on the same axis. Additionally, ψds, ψqs, ψdr, and ψqr indicate the stator and rotor flux components on the d-q axis. The stator and rotor resistances per phase are denoted by Rs and Rr. The stator and rotor inductances are represented by Ls and Lr, respectively, and Lm denotes the magnetizing inductance. III. SYSTEM MODEL A. Synchronous Generator Model The equations governing the two-axis models of the i-th synchronous machine in a multiple-machine network are [26]: ⎩⎪ ⎪⎪ ⎪⎪ ⎪⎨ ⎪⎪ ⎪⎪ ⎪⎪ ⎧ I:0J KL@J = −K@J − M N: − N: J −OBPQQOBPQ RBQQRBQ �N: − N:J �S =: + KT: I:0JJ KL@JJ = −K@JJ + K@J − M N: J − N:JJ +OBPQQOBPQ RBQQRBQ �N: − N:J �S =: + KT: I@0J KL:J = −K:J − UN@ − N@J − OEPQQOEPQ REQQREQ VN@ − N@J WX =@ I@0JJ KL:JJ = −K:JJ + K:J − YN@ − N@J + OEP QQ OEPQ REQQREQVN@ − N@J W Z =@ [L = \] �> − 1� ^>L = V�� − �_ − `�> − 1�W (6) The equation includes multiple terms. K:,@J and K:,@JJ indicate the machine's transient and subtransient voltages along the d-q axis per unit (pu), KT: represents the machine's excitation voltage (pu), =:,@ signifies the current on the d-q axis (pu), N:,@ , N:,@J , and N:,@JJ , represent the synchronous, transient, and subtransient reactances along the d-q axis (pu), I:0,@0J and I:0,@0JJ express the machine's transient and subtransient time constants along the d-q axis (s), δ denotes the machine's rotor angle (rad), \] represents the machine's synchronous base speed (rad/s), ω signifies the machine's rotor speed (pu), M refers to the machine's inertia coefficient (pu.s 2 /rad), D stands for the machine's damping coefficient (pu.s/rad), �� indicates the mechanical power applied to the machine shaft (pu), and �_ denotes the electrical power generated by the machine (pu). B. MBPSS model The Hydro-Québec-developed MB-PSS [27-28] differs from traditional stabilizers that usually use a series of stacked filters (high-pass and phase-lead-lag). Instead, the MB-PSS consists of three separate stages that work autonomously at low, medium, and high frequencies, making it more robust in different circumstances. The process of selecting the optimum values for Multi-Band Power System Stabilizer (MBPSS) parameters involves a combination of theoretical analysis, simulation studies, and field testing. By utilizing the Genetic Algorithm as an optimization technique, the optimal MBPSS parameters can be ascertained [29]. Based on the simulation studies, the MBPSS parameters are tuned to optimize the controller's performance. The tuning process involves adjusting the gain and time constants of the controller's different bands to achieve the desired damping ratio and bandwidth. The simplified structure comprises three gains KL, KI, and KH, which are categorized as low, intermediate, and high, and three frequencies FL, FI, and FH that correspond to low, intermediate, and high frequencies measured in Hz. IV. RESULTS AND SIMULATION A. 9-Bus Network A 9-bus test system was employed for all simulations, as shown in Figure 1. The system consists of three voltage levels, namely 16.5, 18, and 13.8KV, and includes three generators with a 567.5MVA total power output, six transmission lines, and three load nodes with 315MW combined consumption. The generator parameters are specified in the Appendix. Fig. 1. Nine bus network. Fig. 2. Nine-bus network with a wind farm. B. Modified Nine-Bus Network Multiple strategies can improve the effectiveness of the electrical grid across various stages of the power supply chain, including power generation, transmission, and distribution [30- Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10652-10658 10655 www.etasr.com Dhouib et al.: Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of … 32]. Various tests were conducted to determine the optimal wind turbine integration rate and the results showed that bus 7 was the best location for the wind farm. As a result, this location was selected to install a farm consisting of 74 turbines, each generating 1.5MW, for a total capacity of 111MW. The turbine parameters are shown in the Appendix. Figure 2 shows an updated illustration of the grid structure. C. Applying a Fault in the Proposed System At time t=1s, there was a fault with a duration of 0.1s on the transmission line between nodes 5 and 7. The fault was located in the middle of the line and was classified as an LLLG fault. The modified 9-node WSCC network simulation model was implemented using MATLAB/Simulink. A random wind profile was used to simulate realistic wind conditions, with wind speeds fluctuating between 8 and 14m/s for 10s. Figure 3 shows this time-varying profile which was the basis of the case study. Fig. 3. Wind speed profile. As mentioned above, the presence of a defect combined with a high level of wind energy integration (35%) results in changes to the power angle and angular speed differences of generators 2 and 3, as well as the frequency at each synchronous generator. To visualize these changes, the Figures below show the evolution of these parameters for two scenarios: (1) without MBPSS, and (2) with MBPSS. The simulation results show that the implementation of MB-PSS in a high wind power integration scenario results in reduced overshoot and faster convergence to the stable equilibrium point, compared to the system without MBPSS. Table I shows a comparison of the damping duration periods for the two scenarios. TABLE I. DURATION OF DAMPING Command Duration of damping Without MBPSS >10s With MBPSS 3s These data highlight the effectiveness of MBPSS in significantly reducing the damping period of the system. Figures 4 and 5 depict the power angle differences of generators 2 and 3. The power angles δ21 and δ31 undergo an increase to approximately 58.22° and 33.07°, respectively, followed by subsequent declines to 38.34° and 31.07° for generators 2 and 3. It can be noted that the inclusion of MBPSS results in a reduction of power angle, compared to the system without it, indicates system stability without any oscillations. The effectiveness of MBPSS in damping oscillations across all frequency ranges is demonstrated in Figures 6 and 7. Specifically, when subjected to an LLLG fault and a 35% wind turbine integration, the system with MBPSS outperforms the one without it. This can be attributed to its multi-stage structure and the presence of three distinct bands, which enable it to efficiently mitigate low-, intermediate-, and high-frequency oscillations. Fig. 4. Power angle variation δ21. Fig. 5. Power angle variation δ31. Fig. 6. Angular speed variation ω21. Figures 8-10 illustrate a significant increase in generator frequencies leading to system instability before the implementation of the MBPSS power regulator. Implementing MB-PSS, the generator frequencies were stabilized and consistently maintained the nominal frequency of 50Hz. Following a severe disturbance (LLLG fault), the simulation results for a 35 % wind turbine integration scenario with the MBPSS regulator indicate a faster response time and shorter stabilization period, compared to the same scenario without it. Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10652-10658 10656 www.etasr.com Dhouib et al.: Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of … Fig. 7. Angular speed variation ω31. Fig. 8. Frequency profile generator 1. Fig. 9. Frequency profile generator 2. Fig. 10. Frequency profile generator 3. V. CONCLUSION This study extensively investigated the modeling and simulation of a high-level wind farm system connected to an electrical network. Dynamic simulations were conducted on a 9-bus multi-machine network that included a wind farm to evaluate the effectiveness and robustness of the MBPSS stabilizer. A wind farm with a capacity of 111MW was installed at bus 7. Using MATLAB/Simulink, this study analyzed the impact of MBPSS on the transient stability of the power system under failure scenarios, including LLLG faults, and high-level integration of the wind farm. The simulation results indicate a direct correlation between wind farm capacity and power system transient stability. Integrating wind generation at higher levels led to an increase in transient instability. Moreover, the MBPSS was highly effective in enhancing power system stability during high-level integration of wind energy and severe faults and provided adequate damping to all oscillation modes as it consisted of multiple stages with three distinct frequency bands, enabling it to effectively dampen low-, intermediate-, and high-frequency oscillations. APPENDIX Table II displays the generator parameters in 'per unit' based on the nominal MVA and KV values. Tables III-V outline the parameters for the wind turbine linked with a DFIG. Tables VI and VII present the MBPSS data for the simplified settings mode and the detailed settings mode, respectively. TABLE II. GENERATOR PARAMETERS Generator No: #1 #2 #3 Rated MVA 247.5 192 128 ab 0.1460 0.8958 1.3125 abJ 0.0608 0.1198 0.1813 abJJ 0.0483 0.0891 0.1072 cbdJ 8.96 6 5.89 cbdJJ 0.04 0.033 0.033 ae 0.0969 0.8645 1.2587 aeJ 0.0969 0.1198 0.1813 aeJJ 0.0483 0.0891 0.1072 cedJ 0.31 0.535 0.6 cedJJ 0.060 0.08 0.07 fg 0.0006 0.0013 0.0032 ah 0.0336 0.0521 0.0742 i(sec) 23.64 6.4 3.01 TABLE III. DFIG DATA Parameters Value jklm(VA) 1.5e6/0.9 nklm(V) 575 o(Hz) 50 fp(pu) 0.00706 qrp(pu) 0.171 fsJ (pu) 0.005 qrsJ (pu) 0.156 qm(pu) 2.9 i(s) 5.04 t 3 TABLE IV. TURBINE DATA Parameters Value uméw_klm (MW) 1.5 yt 500 z (deg) 50 Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10652-10658 10657 www.etasr.com Dhouib et al.: Analyzing the Effects of MBPSS on the Transit Stability and High-Level Integration of … TABLE V. CONVERTERS DATA Parameters Value umg{ (pu) 0.5 [ q f ] (pu) [0.15 0.0015] [rq �|}� t~_rq ����� ] [0 90] nbw(V) 1200 � (F) 10000e-6 TABLE VI. MBPSS DATA FOR SIMPLIFIED SETTINGS MODE Parameters Value Low frequency band: [oq (Hz) yq] [0.2 20] Intermediate frequency band: [or (Hz) yr] [0.9 25] High frequency band: [oi (Hz) yi] [12 145] Signal limits [nqmg{ nrmg{ nimg{ njmg{] [0.75 0.15 0.15 0.15] TABLE VII. 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