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2023 Электротехника, информационные технологии, системы управления № 45 Научная статья
DOI: 10.15593/2224-9397/2023.1.01 УДК 621.314.58
Х.М. Джассим, А.М. Зюзев
Уральский энергетический институт Уральского федерального университета им. первого Президента России Б.Н. Ельцина, Екатеринбург, Россия
НЕЧЕТКОЕ УПРАВЛЕНИЕ АВТОНОМНОЙ СИСТЕМОЙ ЭЛЕКТРОСНАБЖЕНИЯ НА ОСНОВЕ МНОГОУРОВНЕВОГО ИНВЕРТОРА С АКТИВНОЙ НЕЙТРАЛЬЮ
Использование возобновляемых источников энергии в распределительных сетях растет во всем мире из-за известных проблем традиционных сетей производства и передачи электроэнергии. Комбинируя возобновляемые источники энергии с аккумуляторными системами, можно добиться устойчивости энергосистемы при одновременном повышении ее надежности. Цель исследования: разработка специальных методов и алгоритмов управления для обеспечения эффективности работы энергосистемы при наличии нелинейных и несбалансированных трехфазных нагрузок. Методы: предлагается устойчивая автономная система электроснабжения с использованием четырехстоечного трехуровневого инвертора. Кроме того, работа интегрированной аккумуляторной системы поддерживается контроллером нечеткого управления, который устанавливает соответствие баланса мощности между генерацией и потреблением. При формулировании правил нечеткой системы использовался опыт проектирования, основанный на взаимодействии с пользователем, а выходные данные системы нечеткого управления представляют собой эталонные значения для регулирования тока батареи. Это обусловило гибкость предлагаемой системы управления для реализации на различных интегрированных устройствах или в различных условиях эксплуатации с минимальными изменениями. Кроме того, для регулирования инвертора, подключенного к нагрузке, использовалась комбинация пропорциональных резонансных регуляторов и обычных регуляторов тока. Это необходимо для обеспечения стабильности инвертора и достижения эксплуатационных целей. Затем был использован специальный тип ШИМ-генератора для генерации сигналов переключения транзисторов предлагаемого инвертора. Результаты: предлагаемые решения протестированы в компьютерной модели в сценариях колебаний генерируемой мощности, предельной нелинейности и асимметрии нагрузки. Результаты показали эффективность применения предлагаемого инвертора и системы управления для поддержания стабильности и надежности энергосистемы.
Ключевые слова: управление с нечеткой логикой, управление энергопотреблением, фотогальваническая система, автономное электроснабжение, несбалансированные нагрузки, пропорциональный резонансный контроллер.
Haider M. Jassim, Anatoliy Ziuzev
Ural Federal University named after the first President of Russia B.N. Yeltsin, Ural Energy Institute, Yekaterinburg, Russian Federation
FUZZY MANAGEMENT CONTROLLER FOR AUTONOMOUS POWER SUPPLY SYSTEM BASED ON ACTIVE NEUTRAL MULTILEVEL INVERTER
Renewable energy utilization in the distribution network is increasing globally to tackle problems of traditional power production and transmission networks. By combining renewable energy sources with battery systems, the sustainability of the power system can be achieved while increasing its reliability. Purpose: nonlinear and unbalanced three-phase loads require specialized techniques to maintain their operation without compromising power system efficiency. Methods: a sustainable autonomous power supply system that utilizes a four-leg three-level inverter is proposed to operate such loads under difficult situations. Moreover, the operation of the integrated battery system is maintained by a fuzzy management controller that corresponds to the power mismatch between generation and demand. A user experience-based design has been followed in formulating the fuzzy system rules, while the output of the fuzzy management system represents the reference values for the battery current controller. This prompted the flexibility of the proposed management system for being implemented on different integrated units or under different operating conditions with minimal change. Furthermore, a combination of proportional resonant controllers and current controllers has been employed in regulating the load-connected inverter. This is to ensure the stability of the inverter and the achievement of the operational goals. Then, a special type of PWM generator has been used to generate the switching pattern of the proposed inverter's internal transistors. Results: the design has been tested in a simulation scenario under difficult conditions of generated power fluctuations and demand extreme nonlinearity and asymmetricity. However, the results indicated the effectiveness of the proposed inverter and management system in maintaining the stability and reliability of the power system.
Keywords: Fuzzy logic control, Energy management, photovoltaic system, autonomous power supply APS, unbalanced loads, Proportional resonate controller.
Introduction
The increased demand, fluctuation, deregulation of the energy market, and the substantial cost of power transmission system operation and maintenance have encouraged the shift from the classical centralized power system to the modern decentralized structure. The integration of renewable distributed generators to the local electricity grid has unwrapped many opportunities especially for operating certain parts of the electricity grid autonomously as standalone systems [1, 2]. Such operation is required for the electrification of critical infrastructural systems like water pumping and telecommunication [3]. Furthermore, these autonomously operated power systems are direly needed in remote and isolated communities where the power distribution network is inaccessible due to economic or geographical reasons [4].
However, these renewable distributed generators (RDGs) like wind and solar energy tend to be intermittent in nature which undermines the sustaina-bility of autonomous power supply (APS) systems [5]. Therefore, energy storage systems are essential for the continuous and uninterruptable operation of loads and to act as a source of power compensation when the quality of the generated power experience degradation. Battery systems can be used in these situations owing to their fast response time, large power density, and customizable power capacity. The interaction between these two systems can be facilitated using energy management systems that are claimed to improve power system efficiency giving the same level of component fatigue [6]. Another barrier to the application of APS systems is the unbalanced and nonlinear loads which significantly affect the quality of the power system. The complexity of interconnected power converters, nonlinear characteristics of some industrial loads, and wear and faults of power components due to internal and environmental factors can cause an imbalance in three-phase systems [7]. This is a problematic concern for power system designers as such phenomena result in high harmonics and vibration in the mechanical parts of electrical loads. Thus, these issues are required to be thoroughly and jointly studied for practical considerations.
There is a substantial magnitude of research that addresses the problem of intelligent energy management for autonomous and grid-tide power systems. An optimized adaptive fuzzy logic PID controller was developed for load frequency regulation problems in autonomous power generation systems [8]. The APS system, in this case, was integrated into various energy generation and energy storage units while the fuzzy PID controller was employed to maintain the load frequency during generation/demand mismatch situations. A modified wheal optimization algorithm had to be used for tuning the membership functions of the fuzzy logic system and for enhancing the controller performance. A similar system structure was proposed for the same objective, but with a sine-cosine optimization algorithm which was utilized for further enhancement of overall system stability [9]. A comparison between the classical PI-like fuzzy controller and the interval type-2 fuzzy controller has been conducted in [10] based on a Microgrid model that integrated different resources and technologies. The aim of that research was to address the effect of modelled parameters variations on the quality of the generated and consumed power by different units and the overall load frequency response of the grid. However, the load frequency
regulation problem may be considered an oversimplification of the power system since the utilized model is based on a basic representation of power components. Moreover, the objective of such controllers is usually only to maintain the nominal frequency of the load while ignoring other variables. For filtering current harmonics induced by a wind turbine in a grid-tide power system, dual adaptive neural fuzzy inference systems were introduced and deployed on the DC resources of the studied power system [11]. The aim of the dual controller was to regulate the voltage and frequency deviations of the common AC bus by managing the power-sharing between the AC and DC sides of the grid. Another dual-droop controller was proposed to manage the power-sharing between different DC and AC resources integrated in a Microgrid while maintaining the operation of loads [12]. An optimization algorithm was implemented on the controller to enhance the voltage and frequency responses especially when the Microgrid is disconnected from the main grid. Other studies were more concerned with managing the stored energy in energy storage devices for power regulation using intelligent methods to achieve sustainability of the APS systems [3, 4, 6, 13-15]. In a Microgrid situation, a fuzzy logic controller has been implemented to control a voltage sources converter VSC which interconnects the AC side of the grid with the DC side [13]. The battery controller reacted to the changes in the DC voltages while the fuzzy controller regulates both the voltage and frequency of the AC load. A short and long terms energy storage architecture was considered to regulate the demand and generation of hybrid APS systems [6]. The fuzzy controllers operated directly on the DC bus by managing the injected and consumed power of the two energy storage units. Another hybrid energy storage system was developed to increase the reliability of APS systems and decrease the cost of operation and maintenance [4]. The proposed management system consisted of two fuzzy logic systems controlling the shared power by supercapacitor and hydrogen-based storage devices. A practical study was conducted on sustaining a metallurgical site in Algeria [3]. The employed management system ensured the uninterruptable operation of the industrial site by utilizing various generators and energy storage units. The controllers were designed to manipulate the contribution of sub-generator units to the main DC bus.
The previously reviewed articles focused on the energy management issue for balanced linear loads, except for one research where the unbalanced conditions were regulated using an unspecialized power converter [14]. Due
to the increased complexity of power systems, unbalanced and nonlinear three-phase loads have become the common representation of industrial and domestic loads. To achieve proper performance, these loads are required to be operated by a specialized power inverter called a four-wire converter [16]. The configuration of these converters allows them to compensate for the im-perfectness generated by the asymmetricity and nonlinearity of loads. These converters are viable candidates for integrating RDGs due to their capability in handling power quality degradation especially when the power system is operated in standalone mode. A linear robust H™ controller was been introduced to control a four-leg three-phase inverter [17]. The objective of the control algorithm is to reduce the current harmonics induced by the DC bus by controlling the neutral line current and preventing it from passing high-frequency components. Furthermore, the authors stated that four-wire active neutral architecture is preferred over the split capacitor uncontrolled inverters because of their ability to regulate the zero-sequence current carried on the neutral line and compensate the fundamental frequencies of the loads. A specialized nine switches four-leg inverter was employed to control two groups of loads under balanced and unbalanced conditions [7]. The authors proposed a separate controller to regulate the current at the fourth leg and achieve the required performance by their studied particular application. On the other hand, a four-level inverter was suggested to achieve better switching performance by reducing switching stress and losses [18]. Half H-bridge structure was used to stabilize the common neutral point which ensured minimal utilization of power components.
In this research, a standalone APS system based on a four-leg multilevel converter is proposed. The converter can handle nonlinear and unbalanced loads by engaging an active neutral control method which is formulated based on proportional resonant compensators [19]. The multilevel architecture is proposed due to the advantages mentioned earlier and the extensive usage of such converters in practical situations [17, 18]. The sustain-ability of the power system is accomplished using photovoltaic (PV) solar panels and a battery system. The PV solar panels charge the battery system and maintain the operation of the power components and loads, while the battery system discharge the stored energy when the operation of loads is compromised as a consequence of supply fluctuation or outages during nighttime. The reference value of the injected/absorbed battery current is tuned by a fuzzy management controller that calculates the required current level. The contribution of this article is as follows:
• This article combines the topics of intelligent power management systems and regulation and operation of nonlinear unbalanced loads. To the extent of our knowledge, there has not been such an attempt in the currently available and reviewed literature.
• The fuzzy management system is designed based on user experience with a simple decision-making model and used to provide the reference values for charge/discharge battery current. This is different from other research in the field as they use complicated PID-based fuzzy controllers to directly control the battery converter or even the interlinking power converter. The employment of a fuzzy logic management system, in our case, is to mitigate the uncertainties and observation errors generated by demand prediction algorithms and power sensors.
• A three-level four-leg converter is considered due to its promising applications. Higher-level converters are in high demand due to their advantages while four-wire converters can provide better performance for both balanced and unbalanced load operations. The switching pattern of the power transistors is generated by the level-shift PWM generation method.
The rest of this article will be organized as follows: the APS system design will be presented in section (II), while the utilized control schemes will be developed in section (III). Section (IV) is the results and discussion, while section (V) is the conclusion and future work.
1. Autonomous Power Supply System Design
The proposed system embodies four main entities. The first one is the photovoltaic solar panels which generate the power required by the load and other power system components. The generated power depends on the irradiation level and the working temperature of the panels. Since these values are not constant and vary according to environmental factors, a battery system unit is employed to power the system when a drop in the generation is experienced. Furthermore, the battery system can provide power regulation when fluctuation on the DC line occurs or change in the demanded power by the load. This load is assumed to be nonlinear and unbalanced in nature which represents a wide verity of industrial and domestic applications these days. This unbalanced operation of the load is required to be regulated by the main control system utilized in this research. The most essential part of
the system, however, is the power converter which interconnects the DC resources to the load. The converter is chosen to be four-legs three-levels to cope with the proposed load operation.
A. Four-Leg Three-Level Inverter. Four-wire inverters have been extensively used in the industry to maintain a stable operation of special loads where power quality is a detrimental issue. There are several configurations of these inverters including the most famous splitting capacitor structure. Nevertheless, the four-leg inverter is considered an efficient choice due to the lower cost, the composing design that does not require bulky capacitors, and the simplicity of the applied control system. The fourth leg of the inverter acts as a regulator for the neutral point current which resulted in the inverter being named an active neutral point inverter. The fourth leg is to inject and absorb currents from the neutral wire to balance the operation of the three-phase system. The injected or absorbed current is referred to as zero sequence current due to its function in canceling the variations in the three other voltage lines. In this research, a three-level design is considered to reduce the switching voltages and, ergo, reduce the switching losses. In this case, a larger number of switches must be installed but with a lower voltage rating. A diode-clamped design is implemented since it does not involve capacitor balancing issues. The configuration of the four-leg three-level inverter is demonstrated in figure (1).
Fig. 1. Four-leg Thee-Level Inverter
B. PV Solar System. As illustrated before, the PV solar system is entitled with energizing the entire power system and the related components. Consequently, the size of the solar panels and the configuration of the connected cells are carefully selected to produce (2.5) times more power than the consumed power by the load. The solar panel installation is assumed to have a (100.5 kW) maximum power generation capability. This power varies according to the irradiation levels at a constant temperature as can be seen in figure (2).
Fig. 2. Change of PV output power with solar irradiation
Then, the produced power by the solar array is supplied to a boost converter which is responsible for controlling the output voltage and current of the system. The converter is constructed as shown in figure (3).
Fig. 3. PV solar panel with the boost converter
The gate voltage of the power MOSFET is controlled by the Maximum Power Point Tracking (MPPT) algorithm which manipulates the output of the boost circuit to extract the maximum allowable power from the solar panels. The algorithm performs a reference voltage-based control by changing the switching pattern of the power transistor using a PWM signal. The capacitor (C2) and the inductor (Z1) are designed according to the following equations [20]:
Q _ I out (Yout in)
1 _ fsw • A V • V0 u t ' ( )
£ _ vin(yout~vin) ^2)
fsw'&I'Vout
where fsw is the switching frequency of the boost power MOSFET, Vout is the output voltage, is the expected minimum input voltage of the converter, is the voltage ripple of the output, and is the allowable current ripple. It should be mentioned that the switching frequency of the converter is set by the carrier signal which is modulated with the control signal to produce the switching pattern of the power transistor.
C. Battery System. The battery system is incorporated into the APS to sustain the load requirements when the generation unit encounters a drop in the produced power due owing to environmental changes. Furthermore, the designed battery system must support the system requirements in periods of total blackout when the PV solar panels are disconnected or turned off during nighttime. The capacity of the battery system is evaluated according to the following equation [14]:
p _ Edemand /ox
^ bM ~ Vbat DoD' (3)
where is the demand power, is the nominal battery voltage
which in this research is set to 360 V, while DoD is the depth of discharge and is selected to be (0,6). Thus, the battery capacity utilized by the proposed system is found to be (200 Ah). On the other hand, the battery system is integrated into the APS using a bidirectional buck-boost converter which regulates the charge/discharge voltage and current of the battery. The converter is developed as demonstrated in figure (4). The architecture of such a converter is composed of two power MOSFET switches with an opposite operation to prevent short
circuits. The converter is connected to the common DC bus on one side, while it is connected to battery terminals through a filter circuit on the other side. The filter parameters are designed according to the following equations [21]:
required output voltage
D =
2Max output voltage ' 'rip-D-Ts
C1 = L1 =
" rip
Vl-D-TS
I
rip
(4)
(5)
(6)
where Ts is the switching period of the power transistors, Vrip is the voltage ripple of the converter, Irip is the allowable current ripple of the converter, and VL is the voltage drop across the inductor.
Fig. 4. Battery system with the buck-boost converter
2. Control Schemes
The control schemes utilized in this research are illustrated in figure (5). The four-leg three-level inverter is controlled by a combination of proportional resonance and current controller algorithms. The output signals of the controller are supplied to a PWM generator which is specially designed to produce the switching patterns of the proposed inverter. The PV boost converter, on the other hand, is controlled internally using the MPPT algo-
rithm which depends only on the local measurements of voltage and current of the solar panels. The battery controller is based on a fuzzy management system that generates the reference values of a current controller that regulates the charge/discharge levels of the battery.
A. Inverter Controllers. The output terminals of the power inverter are connected to the load through an LC filter to isolate harmonics from being injected back to the DC side of the inverter. The mathematical relations between the control signals and the output voltage and current are [16, 19]:
Vr
VY = Cyn — L— ¡y , (7)
г \Ir]
r uyn — L — dt h
Cbn UB\
\Irl] vR]
Iyl d — c — dt Vy
Ubl\ yBi
(8)
Three proportional resonance PR controllers are assignment with regulating the load voltages on the three phases and generating the reference values for the current controller.
(9)
where V£, Vy, and Vg are the reference voltage values for the three phases that are usually required to be identical sinusoidal signals with a phase shift between them. The G P R ( s ) represents the proportional resonant controller in the S-domain. The transfer function of this controller is described as follows [22]:
Kt s
Г/* 1 lrl Vr -Vr
I* 'yl = Gpr(s) Vy -Vy
г Jbl. Vb -Vb
GPR(S) - KP +
s2+KiKf
(10)
where , , and are the controller parameters.
Then, the current controller obtains the reference signals form the PR controller and generates the control signals.
(11)
In this research, the K(s) current controller is chosen to be a proportional plus integral controller. The gain values of the PR controller and current controller can be found by breaking up the mathematical description of
Г С 1 / lrl -Ir + Irl \VR
r uyn Г 1yl ~ly + Wl ВД + Vy
pbn_ I* Jbl -h + Ibl \Ув
each phase and using MatLab control system design tuner tool to locate the values that correspond to best response. The more detailed procedures was formulated by [19, 22] research articles have been followed in this work.
Fig. 5. The general design of the proposed APS system and the control schemes
The zero-sequence current controller Cz is synthesized using the following equation [23]:
CC z — 0 '5 m b.X^CCyy^' CCy^' Cj^^ 0 '5 m 1 n ^CCyy^' CCy^' Cj^^ ■ (12)
Furthermore, the PWM generator is constructed according to the following specialized equations that correspond to the multi-level architecture proposed in this design.
PWM( 1,5, ,9,11) = sgn{ carrier
upper
CZ1
c7
PWM(2,6,10,14) = sgn{ carrieriower
r C
urn
r
uyn
Cbn ■ Cz
C —C
^rn z
c — c
^yn z
(VDC/2)' (13)
Q
bn
C7
c7
?DC/2\ (14)
PWM(3,7,11,IS) = 1 - PWM( 1,5, ,9,11), P WM ( 4' 8' 12' 16) — 1- P WM ( 2' 6, 10' 14) ,
(15)
(16)
where and are the upper and lower shifted carrier
signals respectively.
B. MPPT algorithm. This algorithm is deployed to extract the maximum allowable power out of the solar panels and follows the power/irradiation graph demonstrated in figure (2). The algorithm accepts the voltage and current outputs of the solar panel as input and produces a change in the boost converter reference voltage as an output [24]. These changes must adhere to the scheme illustrated in figure (6).
Fig. 6. MPPT algorithm implementation
C. Battery Controller. This controller is modeled as a fuzzy logic management agent and PI current controller fused in one coherent structure. The fuzzy logic input ports are supplied with the estimation of the load demanded power and the measurements of the PV solar system output power. However, it is assumed that the power estimation algorithm is not completely accurate and contains some uncertainties. In addition, the measured generated power is polluted with noise and affected by sensor accuracy. The fuzzy logic mechanism is built upon the principle of dealing with such imperfect situations. Therefore, the utilization of a fuzzy management controller has been well-established for this design. The inputs are projected on the membership functions shown in figure (7), while the inference mechanism evaluates the
proper fuzzy rule to fire. The employed fuzzy system used in this controller design is the Sugeno type which means that the outputs of the fuzzy inference system are crisp values instead of membership projections as in the Mamdani type [25]. The surface demonstrated in figure (7) clearly demonstrates the relation between input and output variables according to the provided rules.
i
5
O 0.5
Û
0
1
2
O 0.5 O
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 09 1
Demand
20 S o •20 1
Fig. 7. Fuzzy management system input membership functions and generated surface behavior
The fuzzy rules employed in the fuzzy inference system are demonstrated in the table (1). These rules are written based on user experience and can easily be modified if the system requirements change. This functionality makes this design more fixable than other work in this field since the management system does not depend on the nature of the generation unit or the load only their power representation.
Table (1)
Fuzzy system rules
Demand / generation Zero Low Medium High
Zero 0 10 20 30
Low -10 0 10 20
Medium -20 -10 0 10
High -30 -20 -10 0
0.1 0.2 0.3 04 0.5 0.6 0.7 0.8 09 1 Generation
Demand Generation
The output reference current is then supplied to the battery current controller which controls the two power MOSFETS of the buck-boost converter. The complete structure of the fuzzy management controller is exhibited in figure (8).
Fig. 8. Battery current control system with fuzzy management controller
3. Results and Discussion
In this section, the proposed design is simulated using the MatLab/Simulink program to validate the effectiveness and efficiency of the various integrated units. The tested load configuration and the utilized parameter values for each component can be found in appendix A and appendix B respectively. The load is arranged such that a diode bridge is connected to each phase to induce nonlinearity, while the configuration of each phase and power demand are noticeably different. The simulated scenario assumes that the irradiation level is drastically changing, and the generated power fluctuated in a severe manner. This can be observed in figure (10), where the generated power is intentionally corrupted with high noise to illustrate the effectiveness of our design even with unrealistic conditions.
On the other hand, the demand power is also oscillating in response to changes in the consumed currents at each cycle. Although the demand power reaches more than (40 kW) in parts of the response cycle, in other parts, the demand power is reduced to less than (6 kW) in correspondence to variation
in the unbalanced load's current demand. This is where the proposed management system demonstrates its highest value since the battery system will provide only the sufficient required power to operate the load when the PV-generated power drops to less than the demand power. The battery reference current shown in figure (10) is the actual output of the fuzzy management system in response to the variations in generation and demand.
Figure (11) exhibits the response of the battery system to the variation in the APS system.
x104
time(sec)
x104
- 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
time(sec)
Fig. 10. Changes in Power system during the simulated scenario
time(sec)
Fig. 11. Battery system response
The battery state of charge SoC starts growing when the generated power by the PV panel is more than adequate to operate the power system and the load. The SoC then changes and starts to decrease when the APS system requires the assistance of the battery system to operate the load during shading periods. The associated control signals are demonstrated in figure (12).
10
0.5 1 1.5 time(sec)
0.5 1 1.5 time(sec)
0.5 1 1.5 time(sec)
0.5 1 1.5 2 time(sec)
Fig. 12. Inverter control signals
These signals are generated by the PR plus the current controllers on each phase. These signals are scaled and delivered to the special PWM generator to generate the switching patterns of the four-leg three-level inverter according to equations (13)—(16).
On the other hand, the neutral point controller corresponds to the zero-sequence current controller described in equation (12). Furthermore, the output currents of each phase are displayed in figure (13).
The previously described behavior of the demand side can be obviously observed in the figure where the current consumption of each phase is different from the other. Additionally, the effect of nonlinear elements is appearing in each phase where even the shapes of the current signals are non-identical. As illustrated before, we employed this imperfect behavior in minimizing the injected power by the battery system during generation power degradation periods. This will result in more savings to the stored power resources and a slower discharge rate.
Fig. 13. Consumed currents by each phase
Although the currents of the three-phase system are asymmetrical and non-identical, the voltage response of each phase is well-maintained and well-shaped. This is due to the employed control system that acts rapidly to mitigate the effect of the nonlinear unbalanced loads. There is hardly any change to the voltage peak value of the phase voltages demonstrated in figure (14).
Fig. 14. Voltage response of the three-phase system
555555555555555555555555555555555555555555655555O55
On the other hand, figure (15) demonstrates the most essential results obtained by the employed four-leg three-level inverter. The phase-to-phase voltages measured on the output terminals of the inverter are demonstrated in the top figure. The three levels are a characteristic behavior of such configuration and confirm that lower switching voltages have been achieved and, hence, lower switching losses. The bottom part of the figure shows the neutral current that acts according to the regulation of the fourth leg to minimize the variations caused by the unbalanced loads.
0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 time(sec.)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 time(sec.)
Fig. 15. Characteristic behavior of the proposed inverter
Conclusion
A four-leg three-level inverter has been proposed to control the power-sharing of the APS system. The APS system was equipped with PV solar panel and battery system to induce sustainability and reliability in the operation of the power system. This research has considered the performance of intelligent-based power management controllers when dealing with nonlinear and unbalanced loads. The designed fuzzy logic management system relied only on the power estimation of the load and power measurements of the PV panel to generate a decision about the operation of the battery system. The validity of the proposed design has been demonstrated using a simulation scenario where the management system and the proposed inverter have been subjected to difficult situations. Yet the results showed that the effectiveness and reliability of the system are maintained.
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8. Sivalingam R., Chinnamuthu S., Dash S.S. A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems // Automatika. Taylor & Francis. - 2017. - Vol. 58, № 4. - P. 410-421.
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11. Effective grid interfaced renewable sources with power quality improvement using dynamic active power filter / S. Kasa [et al.] // Int. J. Electr. Power Energy Syst. Elsevier. - 2016. - Vol. 82. - P. 150-160.
12. Jassim H.M., Ziuzev A. Dual Droop-Based Controllers for Hybrid Microgrid with Photovoltaic and Wind Turbine Distributed Generators // 2022 International Ural Conference on Electrical Power Engineering (UralCon). - IEEE, 2022. - P. 417-422.
13. Vigneysh T., Kumarappan N. Autonomous operation and control of photovoltaic/solid oxide fuel cell/battery energy storage based microgrid using fuzzy logic controller // Int. J. Hydrogen Energy. Elsevier. - 2016. -Vol. 41, № 3. - P. 1877-1891.
14. Enriching the stability of solar/wind DC microgrids using battery and superconducting magnetic energy storage based fuzzy logic control / K.M. Kotb [et al.] // J. Energy Storage. Elsevier. - 2022. - Vol. 45. -P. 103751.
15. The concept of autonomous power supply system fed with renewable energy sources / W. Fedak [et al.] // J. Sustain. Dev. Energy, Water Environ. Syst. - 2017. - Vol. 5, № 4. - P. 579-589.
16. Aboelsaud R., Ibrahim A., Garganeev A.G. Review of three-phase inverters control for unbalanced load compensation // Int. J. Power Electron. Drive Syst. - IAES Institute of Advanced Engineering and Science, 2019. - Vol. 10, № 1. - P. 242.
17. $ HA infty $ Control of the Neutral Point in Four-Wire Three-Phase DC-AC Converters / Q.-C. Zhong [et al.] // IEEE Trans. Ind. Electron. - IEEE, 2006. - Vol. 53, № 5. - P. 1594-1602.
18. A simplified structure for three phase 4 level inverter employing fundamental frequency switching technique / A. Masaoud [et al.] // IET Power Electron. - Wiley Online Library, 2017. - Vol. 10, № 14. -P.1870-1877.
19. Garganeev A.G., Aboelsaud R., Ibrahim A. Voltage control of autonomous three-phase four-leg VSI based on scalar PR controllers // 2019 20th International Conference of Young Specialists on Micro/Nanotech-nologies and Electron Devices (EDM). - IEEE, 2019. - P. 558-564.
20. Verma A.K., Singh B., Kaushik S.C. An isolated solar power generation using boost converter and boost inverter // Int. J. Eng. Inf. Technol. - 2010. - Vol. 2, № 2. - P. 101-108.
21. Ummadisingu M.V., Kumar G.V.N., Rafi V. Design and Analysis of a Neural Networks Based Parallel Buck-Boost Converter to Improve Stability in DC Micro-Grid // 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES). - IEEE, 2021. - P. 1-6.
22. Pereira L.F.A., Bazanella A.S. Tuning rules for proportional resonant controllers // IEEE Trans. Control Syst. Technol. - IEEE, 2015. -Vol. 23, № 5. - P. 2010-2017.
23. Carrier-based PWM scheme for three-phase four-leg inverters / D.A. Fernandes [et al.] // IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society. - IEEE, 2013. - P. 3353-3358.
24. Obukhov S., Ibrahim A., Aboelsaud R. Maximum power point tracking of partially shading PV system using particle swarm optimization // Proceedings of the 4th International Conference on Frontiers of Educational Technologies. - 2018. - P. 161-165.
25. Blej M., Azizi M. Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for fuzzy real time scheduling // Int. J. Appl. Eng. Res. - 2016. - Vol. 11, № 22. - P. 11071-11075.
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6. Gerlach L., Bocklisch T. Experts versus algorithms? Optimized fuzzy logic energy management of autonomous PV hybrid systems with battery and H2 storage. Energies. MDPI, 2021, vol. 14, no. 6, 1777 p.
7. Wang R. et al. Control strategy for four-leg nine-switch inverter under unbalanced loads. IEEE Access. IEEE, 2020, vol. 8, pp. 50377-50389.
8. Sivalingam R., Chinnamuthu S., Dash S.S. A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems. Automatika. Taylor & Francis, 2017, vol. 58, no. 4, pp. 410-421.
9. Rajesh K.S., Dash S.S. Load frequency control of autonomous power system using adaptive fuzzy based PID controller optimized on improved sine cosine algorithm. J. Ambient Intell. Humaniz. Comput. Springer, 2019, vol. 10, no. 6, pp. 2361-2373.
10. Jassim H.M., Ziuzev A. Optimized-Fuzzy Droop Controller for Load Frequency Control of a Microgrid with Weak Grid Connection and Disturbances. 29th International Workshop on Electric Drives: Advances in Power Electronics for Electric Drives (IWED). IEEE, 2022, pp. 1-7.
11. Kasa S. et al. Effective grid interfaced renewable sources with power quality improvement using dynamic active power filter. Int. J. Electr. Power Energy Syst. Elsevier, 2016, vol. 82, pp. 150-160.
12. Jassim H.M., Ziuzev A. Dual Droop-Based Controllers for Hybrid Microgrid with Photovoltaic and Wind Turbine Distributed Generators. 2022 International Ural Conference on Electrical Power Engineering (UralCon). IEEE, 2022, pp. 417-422.
13. Vigneysh T., Kumarappan N. Autonomous operation and control of photovoltaic/solid oxide fuel cell/battery energy storage based microgrid using fuzzy logic controller. Int. J. Hydrogen Energy. Elsevier, 2016, vol. 41, no. 3, pp. 1877-1891.
14. Kotb K.M. et al. Enriching the stability of solar/wind DC microgrids using battery and superconducting magnetic energy storage based fuzzy logic control. J. Energy Storage. Elsevier, 2022, vol. 45, 103751 p.
15. Fedak W. et al. The concept of autonomous power supply system fed with renewable energy sources. J. Sustain. Dev. Energy, Water Environ. Syst., 2017, vol. 5, no. 4, pp. 579-589.
16. Aboelsaud R., Ibrahim A., Garganeev A.G. Review of three-phase inverters control for unbalanced load compensation. Int. J. Power Electron. Drive Syst. IAES Institute of Advanced Engineering and Science, 2019, vol. 10, no. 1, 242 p.
17. Zhong Q.-C. et al. $ HA infty $ Control of the Neutral Point in Four-Wire Three-Phase DC-AC Converters. IEEE Trans. Ind. Electron. IEEE, 2006, vol. 53, no. 5, pp. 1594-1602.
18. Masaoud A. et al. A simplified structure for three phase 4 level inverter employing fundamental frequency switching technique. IET Power Electron. Wiley Online Library, 2017, vol. 10, no. 14, pp. 1870-1877.
19. Garganeev A.G., Aboelsaud R., Ibrahim A. Voltage control of autonomous three-phase four-leg VSI based on scalar PR controllers. 2019 20th International Conference of Young Specialists on MicroNanotech-nologies and Electron Devices (EDM). IEEE, 2019, pp. 558-564.
20. Verma A.K., Singh B., Kaushik S.C. An isolated solar power generation using boost converter and boost inverter. Int. J. Eng. Inf. Technol, 2010, vol. 2, no. 2, pp. 101-108.
21. Ummadisingu M.V., Kumar G.V.N., Rafi V. Design and Analysis of a Neural Networks Based Parallel Buck-Boost Converter to Improve Stability in DC Micro-Grid. 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES). IEEE, 2021, pp. 1-6.
22. Pereira L.F.A., Bazanella A.S. Tuning rules for proportional resonant controllers. IEEE Trans. Control Syst. Technol. IEEE, 2015, vol. 23, no. 5, pp. 2010-2017.
23. Fernandes D.A. et al. Carrier-based PWM scheme for three-phase four-leg inverters. IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2013, pp. 3353-3358.
24. Obukhov S., Ibrahim A., Aboelsaud R. Maximum power point tracking of partially shading PV system using particle swarm optimization. Proceedings of the 4th International Conference on Frontiers of Educational Technologies, 2018, pp. 161-165.
25. Blej M., Azizi M. Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for fuzzy real time scheduling. Int. J. Appl. Eng. Res., 2016, vol. 11, no. 22, pp. 11071-11075.
Appendix A: Nonlinear Unbalanced Load Configuration
Appendix B: Parameters of the utilized modules
Module Parameter Name Parameter Value
PV solar system Li 1,7 mH
C2 2148 ^f
PV open circuit voltage 367,2 V
Converter switching frequency 5 kHz
Inverter parameters L 2,5 mH
C 80 ^f
Inverter switching frequency 10 kHz
Proportional resonant and current controller Kp 0,3
Ki 150
Kf 640
K (KP, Ki) [38, 0,3]
Battery current controller Kp 0,05
Ki 10
Converter switching frequency 5 kHz
Load configuration Rr 10 Q
Lr 50 mH
Ryi 5 Q
Ry2 60 Q
Cy 3 mf
Lb 20 mH
Rb 35 Q
Cb 5 mf
About the authors
Haider M. Jassim (Yekaterinburg, Russian Federation) - Graduate Student of the Department "Electric Drive and Automation of Industrial Installations" of the Ural Energy Institute Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russian Federation (620002, Yekaterinburg, 19, str. Mira, e-mail: khdzhassim@urfu.ru).
Anatoly M. Zyuzev (Yekaterinburg, Russian Federation) - Doctor of Technical Sciences, Associate Professor of the Department of "Electric Drive and Automation of Industrial Installations" Ural Energy Institute Ural Federal University named after the first President of Russia B.N. Yeltsin (620002, Yekaterinburg, 19, Mira str., e-mail: a.m.zyuzev@urfu.ru).
Сведения об авторах
Джассим Хайдер Майтам (Екатеринбург, Россия) - аспирант кафедры «Электропривод и автоматизация промышленных установок» Уральского энергетического института Уральского федерального университета имени первого Президента России Б.Н. Ельцина (620002, Екатеринбург, ул. Мира, 19, e-mail: khdzhassim@urfu.ru).
Зюзев Анатолий Михайлович (Екатеринбург, Россия) - доктор технических наук, доцент кафедры «Электропривод и автоматизация промышленных установок» Уральского энергетического института Уральского федерального университета имени первого Президента России Б.Н. Ельцина (620002, Екатеринбург, ул. Мира, 19, e-mail: a.m.zyuzev@urfu.ru).
Поступила: 12.12.2022. Одобрена: 25.01.2023. Принята к публикации: 01.04.2023.
Финансирование. Исследование не имело спонсорской поддержки.
Конфликт интересов. Авторы заявляют об отсутствии конфликта интересов по отношению к статье.
Вклад авторов. Все авторы сделали эквивалентный вклад в подготовку публикации.
Просьба ссылаться на эту статью в русскоязычных источниках следующим образом:
Джассим, Х.М. Нечеткое управление автономной системой электроснабжения на основе многоуровневого инвертора с активной нейтралью / Х.М. Джассим, А.М. Зюзев // Вестник Пермского национального исследовательского политехнического университета. Электротехника, информационные технологии, системы управления. - 2023. - № 45. -С. 5-30. DOI: 10.15593/2224-9397/2023.1.01
Please cite this article in English as:
Jassim H.M., Ziuzev A.M. Fuzzy management controller for autonomous power supply system based on active neutral multilevel inverter. Perm National Research Polytechnic University Bulletin. Electrotechnics, information technologies, control systems, 2023, no. 45, pp. 5-30. DOI: 10.15593/2224-9397/2023.1.01
