Optimized control of a multi-source hybrid standalone microgrid for reliable power delivery

Abstract

The paper presents a multi-source hybrid standalone microgrid model incorporating wind, photovoltaic, and diesel generator components. It is designed to achieve continuous quality power with multiple intermittent energy renewable energy sources under varying weather and load conditions. The scheme uses an advanced variable-size perturb-and-observe method to enhance wind turbine performance, eliminating the need for wind speed sensors. Simultaneously, it aims to minimize oscillations near the maximum power point for photovoltaic arrays for stable operation. The distributed generation system is employed solely as a backup energy source for this microgrid system. The synchronization control of a diesel generator at the point of common coupling is achieved using in-phase and quadrature unit templates. Additionally, an economical LC filter is employed to reduce harmonics at the output.

Keywords

photovoltaics wind diesel generator standalone microgrid LC filter synchronization distributed generation harmonics

Introduction

Today, incorporating renewable energy sources like solar and wind into Battery Energy Storage Systems (BESS) is of great importance. In order to exploit renewable energy resources dependably and optimally, individual renewable energy sources could be integrated to function as one single electric power generator through the operation of microgrids as controlled entities. Microgrid’s primary features are the control of system parameters and energy balance. The effective integration of renewable energy sources into power systems is a critical challenge that can be addressed by developing advanced microgrid control schemes. Microgrids offer a versatile and coordinated solution to facilitate the integration of renewable energy sources in modern distribution grids (Rezkallah, 2016; Wang et al., 2019).

Power balance management and system frequency control are manageable as a microgrid manages its control scheme and oscillates between grid-tied and self-sufficient modes of operation (Kusakana and Vermaak, 2014). In self-sufficient microgrids, there exists active and reactive power equilibrium, which is maintained via active flow control amongst the system components such as renewable energy supplies, energy stores, and dispatchable supply systems are discussed in (Guan et al., 2010; Hassan et al., 2023; Hossain et al., 2010). Renewable energy sources are required to regulate grid voltage in addition to operating under power control mode explained in (Nisha and Jamuna, 2018). In grid-tied microgrids, excess power is exported to the main grid, while power deficits are balanced by importing energy, ensuring system stability (Huang et al., 2011; Li and Nejabtkhah, 2014). The IEEE standard for connecting dispersed energy sources is discussed (IEEE, 2003). The key system variables that regulate microgrid operation are voltage, frequency, and real and reactive power addressed in (Mahrouch and Ouassaid, 2019). The integration of renewable energy sources introduces specific challenges related to efficient operation and protection systems, particularly in low-voltage DC microgrids, as explained in (Zhao et al., 2023).

The microgrid system’s BESS capacity is still underutilized despite its advantages (Qiu et al., 2018). The power electronic control of integrated renewable systems is treated in (Atawi et al., 2023; Fatima et al., 2020). A detailed review of the governance of power electronic converters in microgrids is presented in (Rocabert et al., 2012). Controlling system voltage and maintaining power balance are the two most complex problems in standalone microgrid control. NABC and other control strategies employed for single-phase SEIG are analyzed in (Al-Manfi et al., 2022; Kalla et al., 2013). A comprehensive comparison between the various maximum tracking methods for solar arrays has been dealt with in (Prasanth Ram, Sudhakar Babu and Rajasekar, 2017). In this research, the design and implementation of an ASMC algorithm-based single-phase microgrid system is proposed (Kalla et al., 2018). Description is presented for a solar photovoltaic system (SPVA) standalone configuration with DC bus and DC-DC boost converter integrated with the PV system (Semwal and Badoni, 2020).

A battery can be linked straight to the DC bus, which eliminates the use of an independent power converter. In paper (Nisha and Jamuna, 2022a), the authors dealt with power converters that serve to connect both the battery and the standalone PV system to the DC bus. In real-world scenarios, implementing this kind of configuration is cumbersome and expensive. Additionally, it is highly challenging to manage multiple power converters with varying switching frequencies that are connected to a common DC bus. These scenarios cause excessive battery wear while deteriorating system stability. In (Gnanam and Kamaraj, 2023) simpler strategies involving a linear proportional-integral (PI) control scheme are deployed to manage the DC-DC boost and buck-boost converters. These controllers are not robust and cannot function properly under wide variations in the system characteristics or loading circumstances. Sliding mode control has been proposed in (Utkin and Lee, 2006) as a solution to these challenges and can obtain high performance. Regretfully, the chattering issue is not considered in (Zuo, 2015).

The authors (Yu et al., 2021) proposed that high-order SMC and terminal SMC are more robust. The experimental findings obtained provide a satisfactory performance that is free from chattering issues. Moreover, the need for advanced computational capabilities limits the practicality of high-order and terminal sliding mode controllers in real-time systems, particularly in environments where rapid computational responses are critical. While research continues to establish advances in sliding mode control systems, the trade-off between sophisticated control strategies and the accompanying computational demands necessitates careful consideration when selecting appropriate control methods for dynamic environments (Jha and Shaik, 2023).

Standalone microgrids using PV systems and diesel generators rely on power electronic converters to manage voltage, frequency, and power quality at the point of common coupling (PCC) (Li et al., 2023). Numerous control strategies have been developed to address harmonics and balance source current, enabling real-time monitoring and control of power flows to maintain stability and reduce distortions. Filtering techniques, such as LC filters, have been employed to further enhance power quality by removing undesirable frequency components. While conventional proportional-integral (PI) controllers are often used to monitor errors in the voltage source converter’s control loops (Neeraj et al., 2024), it may require modifications for optimal regulation, especially in systems needing multiple PI controllers, where saturation issues can arise (Bitoleanu et al., 2010). Rakhtala addressed these limitations by combining a PI controller with an adaptive fuzzy inference system, but real-time implementation remains challenging on simpler microprocessors due to processing constraints discussed in (Monfared et al., 2014).

Other authors (Rakhtala and Roudbari, 2016) have explored various control methods for load frequency control (LFC) and automatic voltage regulation (AVR) in microgrids. For example, LFC-based AVR models have been developed for interlinked systems (Wooding et al., 2023), and adaptive optimization-based frequency controllers have been designed to improve stability in standalone microgrids (Alghamdi and Cañizares, 2020). Further studies (Baros et al., 2021; Gupta et al., 2021; Gondaliya and Arora, 2015) have optimized PID controller gain parameters, implementing specialized algorithms to maintain frequency and power quality. Classical automatic generation control approaches (Gondaliya and Arora, 2015) have been extended with fractional-order PID-based controllers in deregulated systems (Prakash et al., 2020). Moreover, renewable energy penetration, particularly from wind sources, has necessitated efficient multi-area control strategies to ensure frequency stability (Khamies et al., 2020). Robust control strategies such as PID controllers based on linear quadratic Gaussian techniques have been proposed to enhance frequency stability in renewable-rich systems (Khamies et al., 2021).

Evolutionary computation and swarm-based optimization approaches have shown significant potential in addressing nonlinear behaviors in multi-area power systems. Techniques such as wind-driven optimization (Alhelou et al., 2018) and the flower pollination algorithm (Jagatheesan et al., 2017) have been successfully employed to enhance load frequency regulation. Moreover, hybrid strategies that integrate bacteria foraging with swarm intelligence methods have been investigated for automatic generation control, offering improved robustness and convergence properties (Panwar et al., 2019; Kouba et al., 2014).

In recent years, advanced control frameworks have emerged that combine classical and intelligent approaches. For instance, fuzzy logic controllers tuned via Artificial Bee Colony optimization have been introduced for islanded systems (Kumar et al., 2022), while terminal sliding mode controllers enhanced with intelligent algorithms have been applied to strengthen the stability of microgrids (Bagheri et al., 2021). Novel metaheuristic techniques, such as the sea horse optimizer, are also being explored to manage renewable-integrated power systems (Andic et al., n.d). Similarly, firefly algorithm-based controllers have been proposed for hybrid PV–thermal systems (Abd-Elazim and Ali, 2018a), and hybrid firefly-swarm optimized fuzzy PID structures have been reported to improve transient stability performance (Ray et al., 2019).

Metaheuristic algorithms continue to dominate load frequency control (LFC) research due to their ability to handle nonlinearities and optimize parameter tuning. Firefly algorithm-based designs have been widely implemented for PV–thermal hybrid grids, where improved controller performance has been demonstrated (Abd-Elazim and Ali, 2018b). Particle swarm optimization (PSO) has also gained prominence, with applications ranging from conventional PID tuning in single-area systems (Paliwal et al., 2020) to fuzzy logic-based LFC in multi-area networks with renewable and storage integration such as redox flow batteries and solar parks (Shafei et al., 2022). Beyond controller tuning, optimization techniques have been applied to system sizing strategies; for example, a genetic algorithm combined with an energy filter approach was proposed to determine optimal configurations in solar–wind–battery hybrid systems (Mahesh and Sandhu, 2020).

Hybrid metaheuristics are also being explored, such as the HGA–PSO framework for dynamic modeling in power system reliability studies (Fahimi et al., 2022). Alternative swarm-inspired methods like the Rao algorithm have been employed to optimize fractional PID controllers for frequency stabilization (Sony et al., 2022), while data-centric applications of optimization have emerged, such as vector quantization for multimedia data reduction (Kavitha et al., 2022). Furthermore, the Harris hawks optimization (HHO) algorithm and its variants have shown versatility across power system and engineering applications, providing efficient solutions in complex nonlinear environments (Shehab et al., 2022). Complementing these techniques, advanced control paradigms have also been developed; adaptive sliding mode controllers for renewable-driven standalone microgrids (Kalla et al., 2017) and stochastic non-integer controllers for microgrid LFC (Khooban et al., 2017) highlight the growing convergence of robust control theory with metaheuristic optimization.

The author (Han et al., 2017) introduced a centralized power management framework for photovoltaic (PV)-based DC microgrids, demonstrating how power tracking can enhance operational efficiency. Similarly, Yi et al., 2017 proposed a unified control and power management scheme for PV–battery hybrid microgrids, ensuring reliable performance in both grid-connected and islanded modes. These studies highlight the growing importance of coordinated management in distributed energy systems.

Soft computing approaches have also gained prominence in addressing frequency control challenges. Dashtdar et al. (2022a) investigated frequency regulation in islanded microgrids using energy storage and soft computing methods, showing improved resilience under dynamic load variations. Classical tuning techniques continue to be relevant; the authors (Mallesham et al., 2011) applied Ziegler–Nichols-based tuning for load frequency control, while (Maneesh, 2015) demonstrated the applicability of PI controllers for microgrid stability. Although conventional methods provide simplicity, they often lack adaptability under nonlinear conditions, motivating the adoption of advanced controllers.

Recent research has explored the use of fractional-order and predictive control to improve dynamic performance. Oshnoei et al. (2023) developed a fractional-order cascade controller for islanded microgrids, offering superior frequency stability compared to traditional designs. Model predictive control (MPC) frameworks have also been widely applied. For example, the author (Kerdphol et al., 2017) implemented a virtual inertia-based MPC to stabilize microgrids with high renewable penetration, while Sesha and Kesanakurthy (2018) applied MPC for simultaneous frequency and voltage control in standalone systems. Extending this line of work, Dashtdar et al., 2022b combined MPC with particle swarm optimization (PSO) to further enhance frequency control accuracy in renewable-integrated microgrids.

The role of energy storage in frequency regulation has been further emphasized in recent studies. In paper (Rangi et al., 2022) highlighted the use of optimal controllers in multi-area hydro–hydropower systems under deregulated environments, demonstrating that storage devices play a crucial role in stabilizing system dynamics. Collectively, these studies underline the transition from conventional PI-based tuning methods toward intelligent, predictive, and hybrid approaches that can ensure stability in increasingly complex renewable-integrated microgrids. Advanced control techniques such as sliding mode control (SMC), H-infinity, and event-triggered SMC have proven effective for robust frequency regulation in microgrids (Dev et al., 2023). For a deeper understanding of recent advancements in AGC and LFC for both conventional and renewable energy systems, a comprehensive review is provided in (Peddakapu et al., 2022). These innovations highlight the ongoing evolution of robust control strategies in ensuring stable, efficient, and sustainable energy systems for future microgrid applications.

In (Rafiee et al., 2024) paper presents a control method for managing load frequency in microgrids with uncertain system conditions. A virtual inertia concept and a PI controller are used to regulate frequency and control battery charging and discharging. The result of this paper shows that the method improves stability and reduces the need for large energy storage. The authors (Nisha and Jamuna, 2023) designed a control method to stabilize voltage and frequency in hybrid and standalone solar microgrid systems under varying conditions (Zheng et al., 2021). PID controllers tuned by Ziegler–Nichols and adaptive sliding mode controllers (SMC) are used to regulate power converters and maintain system stability. Simulation and real-time results show that the proposed controllers effectively improve performance in different operating scenarios. Conventional primary control in islanded AC microgrids uses droop and cascaded linear controls but suffers from complexity and slow dynamic response. A model predictive control-based virtual synchronous generator to simplify the structure, improve stability, and enhance dynamic performance is discussed in (Hassan et al., 2023). A poly-generation optimization approach combining solar and wind was discussed by (Saleem and Abas, 2024). Multi-agent systems for real-time energy management in hybrid low-voltage microgrids were analyzed by (El Hafiane et al., 2024). Islanded multi-microgrids have also been explored for real-time frequency stabilization (Gnanam and Kamaraj, 2023). New robust control strategies for MPPT and torque ripple minimization in wind energy systems were presented by (Brahmi et al., 2024). A comparative study of MPPT techniques for wind systems was carried out by (Hannachi et al., 2021).

The studies of SMC (sliding mode control) frequency regulation in microgrids (MGs) and maximum power extraction are advancing with many research works highlighting its benefits, like faster convergence, greater robustness, and ease of implementation proposed in Feng et al. (2013). In a typical MG system, frequency control involves two control loops: primary control and secondary control. Primary control employs a droop control method, also known as the fixed gain method. While droop control is effective, it struggles to stabilize frequency when there are fluctuations in load and irregular energy sources. Secondary control methods, including SMC, are implemented to overcome these challenges to improve system robustness and stability in the face of uncertainties and disturbances.

This study focuses on implementing a multi-source standalone microgrid that integrates photovoltaic systems, permanent magnet brushless DC generators, and synchronous reluctance generators as the primary energy sources to enhance reliability and efficiency. The diesel generator operates within a specific power range to optimize overall system efficiency. A voltage source converter is used to integrate both DC and AC sources, which improves power quality and allows for bidirectional power flow. The system also includes an SMC-based MPPT algorithm for the solar and wind systems, ensuring that each energy source operates optimally through dedicated converters designed for their unique characteristics. Additionally, the inclusion of a battery energy storage system helps stabilize the DC bus voltage and mitigates power fluctuations, further enhancing the system’s performance.

The primary contribution of this study is the integration of SMC with MPPT for PV and wind systems, ensuring efficient power extraction and system stability. A secondary contribution is the application of SMC for current control in the Voltage Source Inverter (VSI) and synchronization of the diesel generator, further enhancing overall system performance. The main objective is to improve robustness, dynamic response, and stability by effectively managing environmental variations. Key novel aspects include the development of a distributed control strategy for the seamless operation of solar, wind, battery, and diesel systems, integrating MPPT and current control. This approach significantly enhances resilience, modular scalability, optimal power distribution, and overall efficiency in hybrid energy systems.

The paper is structured as follows: the Microgrid Hybrid System with Multiple Generators (MHSMG) is described first. This is followed by the implementation of controllers for PV systems, wind turbines, diesel generators, and voltage source converters. The subsequent section presents the results and discussion, and finally, the conclusions of the study are provided.

System description

The multi-source hybrid standalone microgrid (MHSMG) shown in Figure 1 consists of integrated PV panels, wind turbines, DG, batteries, and AC loads. An SMC-MPPT strategy optimizes the effectiveness of PV and wind energy-based systems. Power from the increase in DC generated is transferred to the inverter, which supplies voltage to the AC loads. The AC voltage provided by the DG is controlled with an AVR to stabilize the output voltage. The battery bank under the control of the battery energy storage system (BESS) is protected from overcharging by a DC dump load. Also, a filter introduced before the point of common coupling PCC reduces harmonics for the system.

Figure 1.

System architecture of a multi-source standalone microgrid system.

Controller implementation for a standalone multi-source microgrid system

This section focuses on the development and implementation of control strategies for the converters within the system. The optimal performance of the proposed hybrid standalone microgrid is achieved using multiple distributed controllers. Such controllers are targeted at achieving optimal power output from the energy sources available. Additionally, the battery charging and discharging operations are managed effectively by generating appropriate PWM pulses for the energy storage systems. This approach ensures precise control over energy flow, enhancing the overall stability and efficiency of the microgrid.

Control strategy for wind turbines

The stator of the PMSG is directly connected to a constant DC link voltage via a DC-DC converter and a three-phase rectifier. This configuration removes the necessity for wind speed and rotor sensors, which are typically required in conventional wind turbine systems. In this setup, the setup, the DC link voltage serves as an indicator for the BESS (Rafiee et al., 2024). The BESS monitors this voltage to identify fluctuations in wind speed, which are directly linked to wind turbine rotor speed changes, as illustrated in Figure 2. To achieve MPPT, both the DC (I_WT) and voltage (V_rotorwind turbine are crucial. When the DC voltage fluctuates, the BESS adapts its operations dynamically. This corresponds to a change in the PMSG output brought on by variations in wind conditions. The relationship between mechanical torque and generated voltage primarily determines the electric characteristics of the PMSG. Consequently, it determines the electrical processes flow for maximum power retrieval from the wind turbine. The PMSG torque, Te, which is the electromagnetic torque resulting from the rotor’s magnetic field interacting with the stator current, directly affects the DC link voltage and enables the BESS to follow its strategic management control algorithm.

Figure 2.

Controller implementation in the wind turbine system.

The instantaneous power of the wind turbine at the (n-1)^th time step is given by (1),

P_{W T} = V_{W T (n - 1)} i_{W T (n - 1)}

(1)

The difference in instantaneous power at the time step is given by (2),

∆ P_{W T} = P_{W T (n)} - P_{W T (n - 1)}

(2)

The variable power ratio is expressed (3),

S_{W T (n)} = (P_{W T m a x} - P_{W T (n)} / P_{W T (n)}

(3)

SMC implementation for Wind MPPT Controller

Based on equation (3), the control law for SMC in the wind system

∆ d_{W T} (n) = U_{W T} * S i g n (S_{W T} (n))

(4)

{\begin{cases} U = 1 if ∆ P_{WT (n)} < 1 \\ U = - 1 if ∆ P_{WT (n)} > 1 \end{cases}

(5)

Here, U_WT is the SMC gain for wind perturbation.

The duty ratio of the wind MPPT algorithm is given as follows (6)

d_{W T (n + 1)} = d_{W T (n)} + U ∆ d_{W T} (n)

(6)

The control action depends on the sliding surface direction, $U_{W T}$ is a tuning parameter. Equation (6) states that the control signal of the PWM pulses flowing through the S_WT IGBT switch of a wind boost converter.

Control strategy for solar photovoltaic array

To implement MPPT on a photovoltaic (PV) system using the SMC algorithm, the total power is determined from the voltage and current output of the PV array. This is illustrated in Figure 3. This total power is calculated using equation (7).

P_{PV (n)} = V_{PV (n)} I_{PV (n)}

(7)

Equation (8) represents the previous instantaneous output power of the PV array in the system,

P_{PV (n - 1)} = V_{PV (n - 1)} I_{PV (n - 1)}

(8)

The instantaneous change in power is expressed as (9),

U {∆ d}_{PV (n)} = S_{PV (n)} ∆ T_{PV}

(9)

As indicated in equation (10), the gain is influenced by the instantaneous power variation of the PV array,

{\begin{cases} U = 1 if ∆ P_{PV (n)} < 1 \\ U = - 1 if ∆ P_{PV (n)} > 1 \end{cases}

(10)

The power ratio output is expressed in (11),

S_{PV (n)} = \frac{P_{PVmax} - P_{PV (n)}}{P_{PV (n)}}

(11)

Figure 3.

Controller implementation in the PV system.

SMC implementation for PV MPPT controller

From the equation (11) sliding surface for the PV MPPT controller drives the control law for SMC given as (12).

∆ d_{P V (n) = U_{P V} * S i g n (S_{P V} (n))}

(12)

Here, U_pv indicates the SMC gain value for PV Perturbation.

The duty ratio for the MPPT algorithm is expressed in (13)

d_{P V (n + 1)} = d_{P V (n)} + ∆ d_{P V (n)}

(13)

The sign of S_pv decides whether the duty cycle is incremented or decremented. The U_PV is a small positive constant controlling the size of the perturbation. Therefore, the IGBT switch in the boost converter is controlled by PWM pulses, which are generated based on the control signal provided by the SMC-MPPT algorithm.

Control of diesel generator synchronizer switch

Figure 4 shows the DG synchronizer switch’s control arrangement. The model consists of mechanical components such as a speed regulator, actuator, and engine, and electrical components such as an AVR, synchronous generator, and exciter. The required details are mentioned in equation (13), and the DG must be applied. As observed in Table 1, this architecture has five operational modes in which the distributed generation acts as an auxiliary energy source and energizes only when the power from the WT and the PV array exceeds the load power demand and the SOC of the BESS is less than 50%. At this moment, DG simultaneously powers the load and charges the BESS. They outline the scenarios in which each control algorithm would activate, and in regard to PCC, DG will not start synchronizing at the PCC unless these requirements are satisfied

Figure 4.

Block diagram for a diesel generator and synchronizer switch.

Table 1.

Operating modes of a multi-source hybrid standalone microgrid system.

Modes	Conditions	Sources	State of BESS
1	SOC >50% ; Ppv + PWT>PL	PV, WT, and BESS	BESS is charging
2	SOC >50% ; Ppv + PWT< PL	PV, WT, and BESS	BESS is discharging
3	SOC <50% ; Ppv + PWT>PL	PV, WT, and BESS	BESS is charging
4	SOC = 100%; Ppv + PWT>PL	PV, WT, and DC dump load is turned ON	Charging stopped
5	SOC = 50%; Ppv + PWT<PL	PV, WT, BESS, and DG are turned ON	BESS is charging

{\begin{cases} S o C % < 50 % \\ P_{P V} + P_{W T} < P_{L} \\ θ_{D G} = θ_{L} \\ V_{D G P} = V_{L P} \end{cases}}

(14)

Phase-locked loop (PLL) is based on in-phase and quadrature unit templates expressed in (15), and it is utilized to obtain the DG terminal voltage at PCC.

{\begin{cases} U_{D G a p} = \frac{V_{D G a}}{V_{D G P}} \\ U_{D G b p} = \frac{V_{D G b}}{V_{D G P}} \\ U_{D G c p} = \frac{V_{D G c}}{V_{D G P}} \end{cases}}

(15)

{\begin{cases} U_{D G a q} = (\frac{1}{1.732}) (- U_{D G b p} + U_{D G c p}) \\ U_{D G b q} = (\frac{1.732}{2}) U_{D G a p} + (\frac{1}{3.464}) (U_{D G b p} - U_{D G c p}) \\ U_{D G c q} = - (0.268) U_{D G a p} + (\frac{1}{3.464}) (U_{D G b p} - U_{D G c p}) \end{cases}}

(16)

VDGP is the amplitude of the voltage of the synchronous generator expressed in (16), and synchronous generator power is expressed as (17),

V_{D G P} = \sqrt{0.667 * (V_{D G a} + V_{D G b} + V_{D G c})}

(17)

Cosθ_L and sinθ_L are estimated at PCC using this method. If the criteria within (18) are satisfied, then the measured rotor speed is compared with the reference rotor speed, which is calculated as ω_ref multiplied by 1 and denoted as ω_r. The PI controller receives the obtained error signal, and the actuator receives the PI controller’s output. Fuel flow is represented by the actuator’s output, which is converted to mechanical torque. DG only supplies power to PCC when the synchronization requirements are met.

{\begin{cases} {∆ V}_{p} = V_{L P} - V_{D G P} e q u a l t o 0 \\ ∆ θ = θ_{L} - θ_{D G} e q u a l t o o \end{cases}}

(18)

Control structure of three-phase voltage source inverter

Active power is controlled by regulating voltage and frequency using an SMC controller enhancing power quality at the point of common coupling (PCC). The hierarchy consists of outer and inner loops; the former manages voltage while the latter controls current for the inverter. This dual-loop approach significantly improves the overall system performance. The SMC effectively addresses saturation issues and system nonlinearities while also ensuring precise management of filter currents, load currents, and reference voltage. The LC filter’s transfer function, combined with the park transformation, enables accurate control and reliable performance across various operating conditions, as detailed in equations (19) to (20). This integrated approach ensures stable and efficient operation of the system under varying conditions.

{\begin{cases} L_{f} \frac{d i_{f d}}{d t} + R_{f} i_{f d} = v_{f d} - v_{c f d} - j ω L_{f} i_{f q} \\ L_{f} \frac{d i_{f q}}{d t} + R_{f} i_{f q} = v_{f q} - v_{c f q} + j ω L_{f} i_{f q} \end{cases}}

(19)

{\begin{cases} C_{f} \frac{d v_{c f d}}{d t} = i_{f d} - i_{c f d} - j ω C_{f} v_{f q} \\ C_{f} \frac{d v_{c f q}}{d t} = i_{f q} - i_{c f q} + j ω C_{f} v_{f q} \end{cases}}

(20)

where C_f stands for the LC filter’s capacitor. At PCC, the supply frequency is set at 50 Hz.

K_{r} = R_{D} L_{f}

(21)

SMC implementation in VSI for current controller:

The d-axis sliding surface for VSI is expressed as (22):

S_{i d} (t) = i_{f d} (t) - i_{f d}^{r e f} (t)

(22)

For the q-axis sliding surface for VSI is expressed as (23):

S_{i q} (t) = i_{f q} (t) - i_{f q}^{r e f} (t)

(23)

For the d-axis voltage control law of the SMC is given by (24):

U_{f d} (t) = U_{c f d} (t) + R_{f i f d} (t) + L_{f} \frac{d_{i f d} (t)}{d t} - j ω L_{f} i_{f q} (t) - K_{d} S i g n (S_{i d} (t))

(24)

For the q-axis voltage control law expressed in (25):

U_{f q} (t) = U_{c f q} (t) + R_{f i f q} (t) + L_{f} \frac{d_{i f q} (t)}{d t} - j ω L_{f} i_{f d} (t) - K_{q} S i g n (S_{i q} (t))

(25)

Sliding surfaces ensure that the inverter currents, i_fd and i_fq track their references perfectly, overcoming disturbances like load changes. To minimize a large voltage, drop in the VSC, the K_r values are set as high as possible. However, it is crucial to avoid setting Kr excessively high, as this could also lead to undesirable voltage drops. According to Rocabert et al. (2012), Kr should be maintained within the range of nine to 27. For this investigation, “Kr is selected as 17. The algorithmic control strategy encompasses two control strategies: the inner and the outer control loops within the dq-frame. In the outer control loop, the signal from the Average Weighted Participation Index controllers is seen as the filter capacitor currents (i_Cqd), is combined with the detected load currents (i_Ldq) to determine the reference VSC currents in the dq-frame (i_fdq). To achieve decoupling in the dq-frame, the terms C_fvCfq and C_fvCfd are utilized. To ensure system stability without increasing losses, the output of the inner control loops in the dq-frame, which processes the VSC current errors Delta i_fdq, is fed into the SMC. These errors are subtracted from the terms i_cdqcdot K_r, which represent active damping. These damping terms are derived from equation (20) by multiplying the estimated filter capacitor current, iCdq, by the constant, K_r. Additionally, the terms omega L_fifq and omega L_fifd are incorporated into the inner control loops further to decouple the dynamics of the d-axis and q-axis. The inner control loops generate the output voltages of the VSC. Using equation (21), the reference PCC voltage in the dq-frame V_Ldqref is calculated. Finally, after applying the inverse park transformation, these three-phase PCC voltages are fed into a PWM block to control the gating signals for the VSI IGBT switches s1-s6. This approach ensures precise control and stable operation of the system.

Results and discussion

A comprehensive simulation has been carried out in MATLAB/SIMULINK to validate the performance of the sliding mode controller for a microgrid system integrated with a diesel generator. The proposed controller’s robustness and reliability are demonstrated in multiple simulation scenarios. A steady-state analysis has been carried out by doing the step load changes in the system, and a transient analysis is performed by applying sudden changes in loads. In addition to this, the system’s behavior is observed for the frequency variation during the input changes.

Case 1: Steady state analysis

The system consists of 100 kW of solar power and 40 kW of wind power. Which supplies both linear RL loads (130 kW) and nonlinear (150 kVAR) resistive loads under balanced conditions. The VSI output voltage and frequency are observed and presented in Figures 5 and 6. The reference phase voltage and frequency are fixed as 430 V and 50 Hz. The output voltage and current waveform, when presented in three phases, demonstrated sinusoidal behavior with low harmonic distortion.

Figure 5.

Voltage and frequency variation under linear RL load conditions.

Figure 6.

Voltage and frequency variation under RL nonlinear load conditions.

The power generation fluctuations under RL linear and nonlinear load scenarios are displayed in Figures 7 and 8.

Figure 7.

Power and load variations under RL linear condition.

Figure 8.

Power and load variations under RL load nonlinear Condition.

Case 2: Load transient analysis

The system comprises a 33 Ω resistor and a 7 mH inductor. Figure 9 illustrates the transient response when the load switches between full capacity and zero capacity. At t = 1 s, the entire load disconnects from the network and then reconnects at full capacity. Similarly, at t = 11 s, the load disconnects and reconnects at maximum capacity. During these transitions, both the system’s current output and voltage exhibit variations in response to the load changes. A brief distortion in the output voltage is observed, but it quickly stabilizes. Notably, at t = 1 second, disconnecting the load from the AC bus causes a voltage spike, reaching approximately 441 V.

Figure 9.

System response to a sudden step change in load demonstrates load transient behavior.

Figure 10 depicts the power and load variations under this step-change condition. The system complies with the EN 50160 standard, which mandates that network voltage and frequency deviations remain within ±10% and ±1%, respectively. Figure 11 highlights the frequency fluctuations during transient behavior, demonstrating that the frequency is well-maintained within the range of 50.2 Hz to 49.8 Hz.

Figure 10.

Step-change responses of both input power and load variation.

Figure 11.

Frequency response for the step load changing condition.

Additionally, the voltage remains effectively regulated within the ±10% threshold, even during sudden load shifts, ensuring stable and reliable operation. The frequency fluctuations during transient behavior are depicted in Figure 11, which shows that the frequency stays well-controlled between 49.8 Hz and 50.2 Hz. In addition, even when the load changes suddenly, the voltage stays constant within a ±10% margin.

Case 3: Source variant analysis

Solar power-off condition: SMC and AVR are turned on simultaneously under the PV system is down, but the system ensures the conditions supply to meet the load. The diesel generator supplied 70 kW, and the wind system provided 30 kW to meet the 100-kW load demand from t = 0 to 6 s. During this period, the PV system and batteries were offline. At t = 6 s, the load increased to 130 kW, with the DG supplying 100 kW and the wind system continuing to produce 30 kW. Figures 12 –14 illustrate the output voltage and current of the MHSMG, as well as the system’s overall power generation profile.

Figure 12.

Power contribution when PV OFF condition.

Figure 13.

Output voltage and current increase in the load condition at the PV off condition.

Figure 14.

Frequency response graph at the 130 kW load without PV integration.

Wind power-off condition: When there is no power supply from the wind source, the PV and DG remain operational, accommodating load variations. In this scenario, the AVR is activated, and SMC confirms an uninterrupted supply to the load. The SMC maintains the output AC supply, effectively handling load variations from 0 to 130 kW. In this situation, the PV system supplies 40 kW, while the DG provides the remaining 90 kW, as depicted in Figures 15 and 16. The frequency response of the wind system off with 130 kW loading condition is shown in Figure 17.

Figure 15.

Power contribution when wind-off condition.

Figure 16.

Output voltage and current waveform for different loads varying with wind-off condition.

Figure 17.

Frequency response of wind-off condition.

The interaction between solar PV and wind systems in an MHSMG demonstrates effective resource coordination to maintain reliability during fluctuating scenarios. Complementary generation profiles of PV and wind, managed through SMC and Automatic Voltage Regulation, ensure stable voltage, frequency, and power delivery despite intermittent resource availability. During solar power-off, wind and diesel generators adjusted dynamically to meet varying loads, while during wind power-off, PV compensated alongside the diesel generator, maintaining seamless operation. These scenarios highlight the control strategies’ robustness in balancing power and ensuring stability, with the potential for further enhancement through battery integration and predictive resource management.

Case 4: OPAL-RT results with different operating conditions

The MHSMG system has been designed and implemented utilizing MATLAB/SIMULINK, and it has been verified within an OPAL-RT real-time framework. The validation process integrated the testing of the system with constant load scenarios, step changes in load, and operation with a 130-kW load driven by a diesel generator in the absence of renewable energy sources. The results of these tests are illustrated in Figure 18 shows the real-time implementation in software-in-loop condition. Figure 19 demonstrates the system’s response to a constant load condition (130 kW load), revealing an RMS output voltage of 304.01 V and an RMS output current of 246.71 A. The three-phase output voltage and current of the sudden increasing and decreasing load conditions under SIL are shown in Figure 19. The Sliding Mode Controller ensures stable operation by maintaining a constant frequency of 50 Hz. In Figure 20, the real-time three-phase output voltage and current profiles are displayed, highlighting the system’s performance during abrupt load changes, specifically when the load shifts from 130 kW to 150 kW and then back to 130 kW. These results validate the system’s ability to handle dynamic load variations while maintaining stable and reliable operation.

Figure 18.

Real-time validation for MHSMG under software-in-loop condition.

Figure 19.

Voltage current output waveform under constant load condition (130 kW).

Figure 20.

Three-phase output voltage and current for real-time output of sudden increasing and decreasing load conditions.

Figure 21 displays the output voltage and current profiles during the diesel generator’s constant load conditions, with offline PV and wind sources. These graphs collectively demonstrate the robustness and effectiveness of the SMC controller in the MHSMG system under various operational conditions, confirming its capability to manage load variations and ensure a stable power supply using the implemented control strategies. While the SMC approach was successfully tested in OPAL-RT for smaller systems, practical challenges arise when considering larger-scale implementations. These include computational complexity, where more processing power is needed for real-time control, hardware constraints such as limited memory and processing speed, and scalability issues when expanding to larger networks or more inverters. Future work may focus on optimizing the control algorithms, upgrading hardware, or exploring decentralized control to address these challenges and improve system performance in real-world applications.

Figure 21.

Real-time three-phase output voltage and current with constant load condition under PV and wind-off conditions.

Case 5-Stability analysis for MHSMG

The thorough evaluation of system stability using Bode and Nyquist plots for the stability analysis of sliding mode LFC on an MG system is comprehensive. The system demonstrates the ability to tolerate a significant increase in gain before entering an unstable state, as evidenced by the Bode plot’s gain margin of 18.7 dB at the system’s response frequency of 16.5 rad/sec, illustrated in Figure 22. In addition, Nyquist plots indicate there are no open-loop poles or encirclements about the critical point (1, 0) in the complex s-plane: they show gaps in the right half s-plane, thus confirming stability. Closed-loop system stability is validated in Figure 23, where it is revealed that no closed-loop poles exist in the right half of the s-plane. Besides validating the sliding mode LFC’s stable operation within the MG system, the joint use of Bode and Nyquist plots substantiates that the LFC can maintain stability under diverse frequency and operational conditions.

Figure 22.

Stability analysis using Bode plot.

Figure 23.

Stability analysis using the Nyquist plot.

Comparison between conventional controllers versus sliding mode controllers

Finally, the proposed structure is compared to conventional P, PI, and PID controllers, which were tuned using the Ziegler–Nichols method. The comparison, conducted under identical system parameters, evaluates performance amidst uncertainties and disturbances (such as source and load variations). Figure 24 presents the dynamic response of different controllers—P, PI, PID, and SMC—as well as their Ziegler–Nichols-tuned variants (P-ZN, PI-ZN, and PID-ZN)—in terms of frequency deviation (Δf) over time. The SMC controller outperforms all classical controllers, offering the fastest settling time (∼1.4 s), minimal overshoot (∼0.2 Hz), and negligible steady-state error. In contrast, the P controller shows a large overshoot (∼1.4 Hz), prolonged settling time (∼8 s), and steady-state error (∼0.05 Hz), indicating its limited damping capability. Among the conventional controllers, PID-ZN demonstrates improved performance over PI-ZN and P-ZN, with a quicker settling time (∼2.8 s) and reduced overshoot (∼0.6 Hz). However, it still exhibits oscillations before stabilizing. PI-ZN and PI controllers both reach stability more slowly (∼4 s), with slightly higher overshoot and visible steady-state error. The PID controller (manually tuned) performs better than its ZN-based counterpart in terms of damping but takes longer to settle (∼3.2 s). The ZN-tuned controllers were implemented using the classical Ziegler–Nichols frequency response method [1], chosen for its simplicity and widespread use in industry. Overall, the proposed SMC approach delivers improved transient response and faster frequency stabilization under source and load disturbances.

Figure 24.

Frequency deviation comparison for P, PI, PID, and SMC controllers.

The SMC approach has been compared with other cutting-edge control techniques used in hybrid microgrid systems, including model predictive control (MPC), adaptive fuzzy logic control (FLC), biomimetic optimization techniques (specifically particle swarm and Artificial Bee Colony algorithms), H-infinity control, and linear quadratic regulators. While MPC offers excellent prediction-based performance, it requires intensive computation and detailed system models, making real-time implementation more complex. FLC and bio-inspired methods provide flexibility and adaptability, but they often require high tuning efforts and exhibit less deterministic performance under extreme disturbances. H∞ control and LQR/LQG techniques ensure robust stability but typically rely on accurate system modeling and linearization, which may limit their effectiveness under highly nonlinear and variable conditions. In contrast, SMC demonstrates superior robustness against uncertainties and disturbances, a faster dynamic response, and a simpler implementation that does not require exhaustive system modeling. Thus, SMC offers a practical and reliable solution for real-time control of autonomous hybrid microgrids.

Conclusion

The paper demonstrates the potential of a multi-source hybrid standalone microgrid system that effectively integrates wind, photovoltaic, and diesel generator sources to deliver a reliable and high-quality power supply. The integration of a Battery Energy Storage System (BESS), photovoltaic systems, Permanent Magnet Synchronous Generators (PMSG), and diesel generators has proven effective in managing the complexities associated with varying loads and unpredictable weather conditions. The proposed Sliding Mode Control (SMC) has been thoroughly validated through extensive simulations in the OPAL-RT environment under software-in-the-loop conditions, covering a wide range of operational scenarios, including steady-state and transient analyses, as well as situations requiring rapid load changes. The results confirm that SMC significantly reduces steady-state errors and minimizes harmonic distortion, ensuring the system maintains a stable output voltage and frequency within permissible limits, even during fluctuations in load and generation. Furthermore, the analysis of various load conditions, balanced, unbalanced, and nonlinear, demonstrates the system’s resilience and adaptability.

The performance of the microgrid in the absence of renewable energy sources managed through coordination between the diesel generator and wind power underscores the effectiveness of the SMC strategy in providing an uninterrupted power supply while adhering to international voltage and frequency standards. However, this study has certain limitations. The implementation is based on simulation, and software-in-loop-based real-time experimental validation has been conducted, which may limit the direct applicability of results in practical deployments. Additionally, while the SMC controller exhibits strong robustness and dynamic performance, its implementation complexity, sensitivity to model uncertainties, and requirement for accurate system parameters could present technical challenges in real-world applications. This research offers valuable insights into renewable energy management and microgrid applications, representing a significant step toward more sustainable energy solutions. The implications of this work go beyond theoretical advancements, providing practical pathways for enhancing the efficiency and stability of hybrid energy systems in real-world applications. Future research should focus on extending this work through hardware-based testing and real-time implementation to validate the system under actual field conditions. Moreover, analyzing economic feasibility and system scalability in larger or grid-connected hybrid systems would broaden the practical impact and contribute to the deployment of more resilient and sustainable microgrids.

Footnotes

Acknowledgment

The authors would like to express their sincere gratitude to the Smart Grid Lab, Vellore Institute of Technology, Chennai, for providing the necessary resources and support for this research work.

ORCID iDs

Nisha Gnanam

Jamuna Kamaraj

Author contributions

Nisha G: Conceptualization, methodology, software, and writing—original draft. Jamuna K: Investigation, supervision, and review editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix

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