MEV Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 Journal of Mechatronics, Electrical Power, and Vehicular Technology e-ISSN: 2088-6985 p-ISSN: 2087-3379 www.mevjournal.com https://dx.doi.org/10.14203/j.mev.2017.v8.76-84 2088-6985 / 2087-3379 ©2017 Research Centre for Electrical Power and Mechatronics - Indonesian Institute of Sciences (RCEPM LIPI). This is an open access article under the CC BY-NC-SA license (https://creativecommons.org/licenses/by-nc-sa/4.0/). Accreditation Number: (LIPI) 633/AU/P2MI-LIPI/03/2015 and (RISTEKDIKTI) 1/E/KPT/2015. Frequency stability improvement of micro hydro power system using hybrid SMES and CES based on Cuckoo search algorithm Muhammad Ruswandi Djalal a, *, Herlambang Setiadi b, Andi Imran c a Department of Mechanical Engineering, Ujung Pandang State Polytechnics Jl. Perintis Kemerdekaan 7 km. 10, Makassar, Indonesia b School of Information Technology & Electrical Engineering The University of Queensland Level 4 / General Purpose South Building (building 78) St. Lucia Campus, Brisbane, Australia c Department of Electrical Engineering, Sepuluh Nopember Institute of Technology Jl. Raya ITS, Surabaya 60117, Indonesia Received 16 March 2017; received in revised form 7 November 2017; accepted 9 November 2017 Published online 28 December 2017 Abstract Micro hydro has been chosen because it has advantages both economically, technically and as well as in terms of environmental friendliness. Micro hydro is suitable to be used in areas that difficult to be reached by the grid. Problems that often occur in the micro hydro system are not the constant rotation of the generator that caused by a change in load demand of the consumer. Thus causing frequency fluctuations in the system that can lead to damage both in the plant and in terms of consumer electrical appliances. The appropriate control technology should be taken to support the optimum performance of micro hydro. Therefore, this study will discuss a strategy of load frequency control by using Energy Storage. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are devices that can store energy in the form of a fast magnetic field in the superconducting coil. For the optimum performance, it is necessary to get the optimum tuning of SMES and CES parameters. The artificial intelligence methods, Cuckoo Search Algorithm (CSA) are used to obtain the optimum parameters in the micro hydro system. The simulation results show that the application of the CSA that use to tune the parameters of hybrid SMES-CES-PID can reduce overshoot oscillation of frequency response in micro hydro power plant. ©2017 Research Centre for Electrical Power and Mechatronics - Indonesian Institute of Sciences. This is an open access article under the CC BY-NC-SA license (https://creativecommons.org/licenses/by-nc-sa/4.0/). Keywords: Micro hydro; superconducting magnetic-capacitive energy storage; Cuckoo; overshoot. I. Introduction Development of micro hydro power plant is one of the government policies for improving the economic and social conditions of rural communities. In this case, the provision of electric power in the countryside is one of the solutions to enhance the social and economic conditions in the rural area. Therefore, it is necessary to develop and utilize new and renewable energy sources by sticking to the principle of economically profitable, technically feasible, socially acceptable culture, and not causing environmental damage. Hence, micro hydro is one of the power plants that can achieve such as requirements. One of the most important aspects of the power system is the frequency. The frequency has to be maintained according to the system requirement. The frequency generated by the micro hydro generator is greatly influenced by the rotational speed of the generator. Moreover, the rotational speed of the generator is affected by the load changing. At night (above 23:00), ninety percent of homes turn off the lights, that makes the burden of the micro hydro is decreased. As a result, the frequency of the system will increase significantly. If the deviation of frequency is not well maintained, it will damage the electronic devices. Therefore, it is necessary to control frequency and load demand automatically. This method can be called as load frequency control (LFC) [1]. The LFC of micro hydro can be done by arranging the wicket gate of micro hydro to control the water flow to the micro hydro. However, due to increasing load demand and uncertainty of it, LFC alone is not enough to handle the problems. Hence, utilizing * Corresponding Author. Tel: +62 852 5098 6419 E-mail: wandi@poliupg.ac.id https://dx.doi.org/10.14203/j.mev.2017.v8.76-84 http://u.lipi.go.id/1436264155 http://u.lipi.go.id/1434164106 http://mevjournal.com/index.php/mev/index https://dx.doi.org/10.14203/j.mev.2017.v8.76-84 https://creativecommons.org/licenses/by-nc-sa/4.0/ https://crossmark.crossref.org/dialog/?doi=10.14203/j.mev.2017.v8.76-84&domain=pdf https://creativecommons.org/licenses/by-nc-sa/4.0/ M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 77 energy storage as an additional device to increase the frequency stability of micro hydro power system is essential. The integration of energy storages has been increased significantly over the past few decade. There have been many application of energy storage on power sectors such as for voltage stability, small signal stability, and frequency stability. As reported by Hung et al., the battery energy storage is used to stabilize the voltage stability on distribution system by considering high penetration of uncertainty photovoltaic plant [2]. Impact of integration of battery energy storage system (BESS) on electromechanical oscillations on the power system is reported by Setiadi et al. [3]. In this research, the variation of BESS proportional gain controller could change the dynamic characteristic of the power system. The influence of the large-scale battery energy storage system in the small signal stability of power system is reported by Setiadi et al. [4]. In that research, battery energy storage has a significant influence on local and inter- area oscillation. The application of redox flow batteries to enhance the frequency performance of power system is reported by Shankar et al. [5]. This research reports that the RFB has a huge influence on stabilizing the frequency performance of the power system. The application of capacitor energy storage (CES) to stabilize the frequency performance of power systems is reported by Kumar et al. [6]. Moreover, the application of superconducting magnetic energy storage (SMES) for enhancing small disturbance angle stability of multi-machine power system is reported by Lastomo et al. [7]. However, very scant attention has been paid to integrate two different energy storages at the same time and assess the performance on frequency stability. Hence, it is important to conduct a deeper study on how the frequency performance of power system, especially in micro hydro power plant when two different energy storage is integrating at the same time. Other major issues are how to design the parameter of the energy storage and make it secure and reliable for providing active power to the system. Hence, the utilizing metaheuristic algorithm as optimization method can be a solution for designing energy storage parameter. Metaheuristic algorithm can be classified based on the inspiration. There are a socially based inspiration, a physically based inspiration, and a biological based inspiration. In recent years, the application of metaheuristic based on biological inspiration such as particle swarm optimization and ant colony optimization are increasing significantly [8, 9]. However, those algorithms still have several drawbacks including long computation process and stuck at local optimum [10]. Hence, the deployment of a new and optimum algorithm such as cuckoo search algorithm (CSA) is crucial [11]. Hence, the novelty of this paper are: Investigating the frequency performance of micro hydro power plant, enhancement of frequency stability of micro hydro power plant using hybrid superconducting magnetic energy storage (SMES) and capacitor energy storage (CES), and utilizing CSA as optimization method for designing SMES and CES. II. Fundamental theory A. Micro hydro power plant The working principle of micro hydro power plant is utilizing the waterfall flow of a river. Micro hydro turbine can generate the mechanical energy using water flow power. This mechanical energy will spin the generator to produce electricity. The mathematical representation of electric power that can be generated from micro hydro can be described as given in equation (1) [12]. 3 [ ] [ / ]. [ ]. [ / ] th P W Q m s H m k N kg (1) where Pth and Q are active power generated from micro hydro and the amount of water flow to the turbine. H and k corresponded to the high of the water flow and gravitational constant. Moreover, completed representation of active power from the systems considering turbine (turbine) and generator (gen) efficiency can be described using equation (2) [12]. 3 [ ] [ / ]. [ ]. [ / ]. . real turbin gen P W Q m s H m k N kg   (2) For frequency stability study, the micro hydro power plant modelled as linear system (Figure 1) 1 . 1 b T s  + _ + _ + _ _ . 1 Kg Tg s  . 1 s K Ts s  + _ 3 . 1 K T s  Water Discharge 0.01 Rate Limiter Servo Motor as Governor Error Detection Gain 1 Induction Generator Gain Turbine Load Excharge K2 K1 Scope Figure 1. Block diagram of micro hydro M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 78 consists of induction generator, turbine, and servomotor as the governor [12]. B. Superconducting magnetic energy storage Superconducting Magnetic Energy Storage (SMES) store energy in a magnetic field created by the DC current in superconducting coils cooled by cryogenic systems. SMES comprises of a superconducting coil, cryogenic cooling system, and a power conditioning system (PCS). PCS is referred as a power electronic interface between SMES coil and the grid. In principle, superconductors have losses almost zero at cold temperature. The cryogenic of SMES consist of liquid helium, which can maintain the temperature at 4 K. The PCS is used to transfer energy from the SMES coil towards the system. A dc link capacitor PCS uses to connect the source voltage of the SMES coil towards the system. The working principle of SMES is divided into three, charging mode, standby mode, and discharging mode [13]. Setting performance of SMES is carried out by adjusting the duty cycle (D) of the converter which in this case using the Gate Turn Off (GTO) thyristors [14]. Figure 2 shows a schematic diagram of SMES while the mathematical representation of SMES can be described as given in equations (3) to (7). * SM DC V D V (3) (1 ) * SM DC V D V   (4) 0 0 1 t SM DC SM t SM I V d I L   (5) SM SM SM P V I (6) 21 2 SM SM SM W L I (7) Equation (3) is SMES mode in charging mode, where VSM is Voltage in SMES Coils, VDC is Voltage in DC Link Capacitor and D is Duty Cycle. ISM0 is the initial current of the inductor. PSM is power stored or transmitted by SMES. WSM is the energy stored in the SMES coil. Then, equation (4) is a mathematical representation of SMES in discharging mode, while equation (5) is a representation of current SMES. Furthermore, equation (6) described energy from SMES, while equation (7) described the energy in SMES’s coil. Figure 3 shows SMES configuration. The parameters that are owned by the SMES is starting from the input side in the form of . After that, the signal will enter the washout block where there is a washout time constant from SMES. It is then amplified by the SMES constantly reinforcing on the loop gain block. In this block, there is also a TDC time delay constant from the SMES control device. The next step is to restrict the signal to the desired saturation conditions on the rate limiter. Next signal is forwarded to the transfer block function inductance SMES where there is a parameter of LSM. The LSM is then summed with Ido to produce the output. The resulting output, PSM, is used as input (compensation) on the generator while waiting for the governor work. SMES is placed at the bus terminal of the generator to control the balance of power in the generator effectively. The block diagram of SMES- PID can be made using several SMES equations from references, as shown in Figure 4 [14, 15]. DC link capacitor Transformer Voltage Source Converter using GTO DC-DC chopper Ism Bypass switch SMES Coil 3 phase AC (from terminal bus generator) Figure 3. SMES configuration w ( + + ) 1+ST I p d KS K sK S Δω 1 + C DC K ST 1 SM SL π+ Id0 PSM VSM ISM Figure 4. Block diagram of SMES-PID Figure 2. Schematic diagram of SMES M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 79 C. Capacitor energy storage Capacitor Energy Storage (CES) stores energy in the form of an electric field in the capacitor. A CES consists of a storage capacitor and PCS. Storage capacitor consists of several discrete capacitors connected in parallel with capacitance (C). Leaking losses and dielectric capacitor bank at CES modeled by a resistance (R) connected in parallel to the capacitor. Storage capacitor connected to the grid through the PCS 12-pulse. PCS consists of ac to dc rectifier and dc to ac inverter. Figure 5 shows the schematic diagram of CES [16, 17]. Thyristor bypass serves to provide a path for current flow (Id) when converter failure occurs. DC breaker allows current (Id) energy diverted to discharge energy of resistor RD if the converter fails. By ignoring losses, bridge voltage (Ed) is as given in equation (8) and (9) [16, 17]. 02 cos 2d d d DE E I R  (8) 2 2 1/ 2 max min 0 [ ] 2 d d d E E E   (9) In the case that perturbation occurs in the system, the capacitor voltage is too low and other disorders occur before the voltage back to normal values, the energy will be more withdrawn from the capacitor which can cause intermittent control. The limit for the capacitor voltage is 30% lower from the rating Ed0 value to solve this problem. Hence, the mathematical representation can be described using equation (10) [16, 17]. min 0 30 d d E E (10) The operating point of the capacitor is such that the total energy absorbed which is equal to the amount of energy depleted. Initially, the capacitor is charged to its set Ed0 value. The CES voltage must find the initial condition as soon as possible to maintain the performance of the system. Therefore, a negative feedback signal of capacitor voltage deviation is essential to achieve a fast response of CES. The block diagram of CES is depicted in Figure 6 [17, 18]. where the capacitor voltage deviation (ΔEd) can be described as given in equation (11) [17, 18]. 1 1d d E I sC R           (11) Moreover, the CES power output injected into the system can be presented in equation (12) [17, 18]. 0 ( ) CES d d d P E E I    (12) III. Design hybrid SMES and CES using Cuckoo search algorithm This section provides a dynamic model of the overall system and Cuckoo search algorithm. At the end of this section, the objective function of the simulation is presented and the objective function will be achieved by using CSA. A. Overall simulation Based on the equations (1) to (12), the overall dynamic model of the entire system can be expressed in Figures 7 to Figure 9. Figure 7 illustrates the test system (micro hydro for frequency stability) with SMES and CES installed in the system. Figure 8 shows the dynamic model of CES in SIMULINK, while Figure 9 depicts a dynamic representation of SMES in SIMULINK. In this research, all of the systems are expressed in the linear model. The parameter that will be optimized by CSA is the SMES and CES parameter. DC Breaker Dump Resistor R Storage Capasitor + - C Id Id 6-pulse bridge convertor 6-pulse bridge convertor Y PCS By Pass SCR Reversing switch arrangement S1 S2 S3 S 4 3p 3p 3p Figure 5. Schematic diagram of capacitor energy storage   - CESK DCsT1 1 VDK R sC 1 1  dI dI     dd EE 0 dE 0dE f CESP Figure 6. Block diagram CES M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 80 B. Cuckoo search algorithm The Cuckoo search algorithm is one of the metaheuristic algorithm developed by Xin She Yang et al. [19], inspired by the behavior of cuckoo bird in breeding. From all species of cuckoo, it is known that 59 of them are parasitic. They utilize breeding nests of other birds of different species to incubate their eggs. In fact, not infrequently cuckoo eggs were put on another cuckoo's nest [20, 21]. Several types of cuckoo throw eggs from the original parent at the nest to increase the likelihood of their eggs hatch. It may cause a conflict between the host and cuckoo birds when the cuckoo lays its eggs, so the bird hosts throw the cuckoo’s egg or leave their nests and then discard the new nest. Other parasitic behavior is when the cuckoo hatches, cuckoo eggs usually hatch earlier than the host bird eggs, the unhatched eggs were discharged from the cuckoo's nest for children to get more food [20, 21]. Figure 10 shows the Cuckoo algorithm process in finding the controller parameters. Starting from the initialization parameters, the optimization process, and finally the optimum parameter optimization results. The final rule can be simplified with the approach pa fraction of n nest replaced with a new nest (with new solutions at random). For maximizing problems, quality or fitness of a solution can be compared to the Figure 7. Simulink model of entire system Figure 8. Simulink model of SMES Figure 9. Simulink model of CES Figure 10. Cuckoo search algorithm process M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 81 value of the objective function. Other forms of fitness can be defined in the same manner as the fitness function in the genetic algorithm. For simplicity, it can use a simple representation that any eggs in the nest represent a solution, and the cuckoo egg represents a new solution, the aim is to use the potential of new and better solutions (cuckoos) to replace a solution that is not good on the nest. Then the eggs had to be evolved, the more eggs will replace other eggs as measured by fitness, like in GA [20, 21]. In a host nest, there can be two eggs, in other words, the nest can hold more than one solution only to simplify the problem, and a nest will only store one solution (eggs). Based on the three rules, the basic steps Cuckoo Search (CS) can be summarized as pseudo code below [20, 21]. Begin objective function f (x), x = (x 1, ..., x) T Initialize the population of the target bird nest n xi (i = 1,2, ..., n) While (t Fj) Replace cuckoo cuckoo j with i End If re Reset nests with the worst conditions (Pa) Save nests that survived sort and find the best solutions End While process results and visualization end when the generation of new solutions x (t + 1) for a i cuckoo, Levy flight is shown as follows: The mathematical representation to generate a new solution considering Levy flight can be described using equation (13) [20, 21].  )1( ( ) t ti ix x Levy      (13) In equation (13), α> 0 is a measure of the stages that should be related to the scale of the problem of interest. In the most cases, the value of α is 1. Furthermore, the mathematical representation of Levy flight (random walk) can be defined using equation (14) [20, 21].   ~ , 1 3Levy u t     (14) C. Objective function The Objective function that used is Integral Time Absolute Error (ITAE), where the CSA will be optimized all parameters by minimizing the frequency error of the micro hydro as described in equation (15). 0 ( ) t ITAE t t dt  (15) where Δω is the frequency deviation of the system while t is the period of the simulation. Figure 11 shows the flowchart of the CSA for optimizing SMES and CES parameter. The parameter of SMES that will be optimized by CSA is Ksmes, Tdc, Tw, Kp, Ki, and Kd, while the parameter of CES is Kces, Tdc, Kp, Ki and Kd. Table 1 shows the parameter of Cuckoo search algorithm (the parameter if Cuckoo search algorithm is a dimensionless parameter) on this paper while Table 2 shows the lower and upper limit of the SMES and CES parameter. The algorithm starts by initializing the micro hydro, SMES, CES, and CSA parameters followed by initializing the population of the host with the particular constraint. The next step is a random search of cuckoo by using Levy flight function. Evaluation of the objective function is done by using ITAE of the frequency micro hydro. The process is continued by finding the best nest by using random process. The cuckoo will remove the Pa from the worst nest and put that Pa to the new nest. If the criteria are not satisfied, then the algorithm will be back to the initializing process. The algorithm will loop the process until the criteria is satisfied, in this paper the criteria is the max generation. Table 1. Parameters of Cuckoo search algorithm. Parameter Value Number of nests 25 Discovery rate of alien eggs/solutions 0.25 Tolerance 1.0e-5 Max Generation 50 Number of Variable (nd) 11 Table 2. Constraints of CSA Parameters Lower Upper CES Kces 80 90 Tdc 0.03 0.06 Kp 10 15 Ki 0.1 0.5 Kd 0 1 SMES Tdc 0.01 0.03 Tw 15 30 Ksmes 70 90 Kp 35 40 Ki 0 1 Kd 0 0.1 M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 82 IV. Result and discussion In this paper, three sections are reported in an attempt to investigate the enhancement of frequency stability using the proposed method. A load frequency control model of the micro hydro power system is used in this study. The case study was carried on MATLAB/SIMULINK environmental. Table 3 shows the dynamic data of micro hydro [22], SMES, and CES used in this paper while Table 4 illustrates the optimized parameter of SMES and CES using CSA. A. Governor time domain response This section is focusing on analyzing the governor response of micro hydro under different scenarios due to the small load perturbation. Figure 12 illustrates the governor response of micro hydro under different scenarios. It can be seen that by adding additional devices such as SMES, and CES, the overshoot of the governor response was decreased. Table 5 shows the detailed featured of overshoot of the micro hydro governor under different scenarios. It was observed that the best response was performed by system with SMES-PID-CES-PID indicated by smallest overshoot compared to the other scenarios. Start Input Parameter of CSA : - Number of Nest (n) - Discovery Rate (pa) - Tolerance (Tol) - Lower Bounds & Upper Bound SMES, CES and PID (Lb,Ub) - Maximum Generation (MaxGen) - Number of Variable SMES-CES-PID (nd) - Micro-Hydro Parameter Cuckoo random search with Levy Flights End No Evaluation fitness function of cuckoo The owner put back the cuckoo's nest best quality at random Removing Pa From worst nest and make a new one with the levy flights Initialization Population of Host Nest Results Process, Visualization and Output Results of Tuning Parameters SMES, CES & PID Check criteria manufacture, Describe Best Solution / The Best Nest A B A B Yes Figure 11. Flowchart of the CSA for optimizing CES and SMES Table 4. Optimum parameter of SMES and CES Parameters CSA Result CES Kces 88.1472 Tdc 0.0572 Kp 10.6349 Ki 0.4654 Kd 0.6324 SMES Tdc 0.0120 Tw 19.1775 Ksmes 80.9376 Kp 39.7875 Ki 0.9649 Kd 0.0158 Table 3. Dynamic data of the test system [22] Parameter Value Tb 1 Kg 1 Tg 13,333 K1 5 K2 8,52 K3 0.004 T 0,02 Ts 0,1 Ks 2,5 Sg 40 pf 0,8 Vg 400/231 ω 1500 fg 50 M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 83 B. Frequency dynamic response The time domain response of the frequency micro hydro could be performed by giving step input of small load disturbance in the system as shown in Figure 13. It was found that the frequency response of the system was increased when SMES and CES were installed on the system. This condition can be happened due to additional active power from CES and SMES. It was also found that by adding PID controller on SMES and CES could also increase the frequency of the system. It could be happened due to additional control signals from PID that make SMES and CES able to give more detailed active power to the system when small perturbation occurs. Table 6 illustrates the detailed features of overshoot of the micro hydro frequency response under different scenarios. It was observed that system Table 6. Overshoot of governor Cases Overshoot Uncontrolled -0.00031811 PID -0.00021392 CES -0.00012623 CES-PID -0.00001604 SMES -0.00019384 SMES-PID -0.00004173 SMES-CES -0.00010412 SMES-CES-PID -0.00001581 SMES-PID-CES -0.00003552 SMES-PID-CES-PID -0.00000983 Table 5. Overshoot of governor Cases Overshoot Uncontrolled -0.00000453 PID -0.00000312 CES -0.00000181 CES-PID -0.00000023 SMES -0.00000281 SMES-PID -0.00000061 SMES-CES -0.00000154 SMES-CES-PID -0.00000013 SMES-PID-CES -0.00000052 SMES-PID-CES-PID -0.00000009 Figure 12. Governor response under different scenarios Figure 13. Frequency response under different scenarios 0 5 10 15 20 -5 -4 -3 -2 -1 0 1 x 10 -6 Time (s) G o v e rn o r R e s p o n s e Uncontrol SMES-CES SMES-CES-PID CES-PID CES SMES SMES-PID SMES-PID-CES PID SMES-PID-CES-PID 0 5 10 15 20 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 x 10 -4 Time (s) F re q u e n c y D e v ia ti o n ( p u ) Uncontrol SMES-CES SMES-CES-PID CES-PID CES SMES SMES-PID SMES-PID-CES PID SMES-PID-CES-PID M.R. Djalal et al. / Journal of Mechatronics, Electrical Power, and Vehicular Technology 8 (2017) 76–84 84 with hybrid SMES-PID and CES-PID experienced lower overshoot compared to the other scenarios. Moreover, SMES and CES could store and release active power from the system depending on the condition of the load. If the load was increased, then SMES and CES will release (discharging) active power to the system, so the burden of the system is decreased (the system will experience lower overshoot). In contrary, if the load was decreased, the SMES and CES will store (charging) surplus active power from the system. V. Conclusion This paper proposed a method to enhance the frequency performance of micro hydro power system using hybrid SMES and CES based on CSA. From the investigated case study, it is found that by adding SMES and CES can enhance the frequency performance of the micro hydro power systems. It is also observed that the best performance is shown by the system with proposed method (hybrid SMES and CES based on CSA) indicated by the smallest overshoot and fastest settling from all of the scenarios. Further research needs to be conducted to analyze the impact of integrating SMES – CES hybrid in larger system, such as load frequency control of two or more power systems. High penetration of renewable energy sources in frequency stability domain can be considered to analyze the significant impact of integrating SMES – CES hybrid. 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