Dynamic Stability Improvement of Decentralized Wind Farms by Effective Distribution Static Compensator

Muhammad Naveed Naz; Saqif Imtiaz; Muhammad Kamran Liaquat Bhatti; Waseem Qaiser Awan; Muhammad Siddique; Ashfaq Riaz

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Dynamic Stability Improvement of Decentralized Wind Farms by Effective Distribution Static Compensator PDF

- ORCID：
Muhammad Naveed Naz ¹
- ORCID：
Saqif Imtiaz ¹
- ORCID：
Muhammad Kamran Liaquat Bhatti ¹
- ORCID：
Waseem Qaiser Awan ²
- ORCID：
Muhammad Siddique ¹
- ORCID：
Ashfaq Riaz ¹

Department of Electrical Engineering, NFC Institute of Engineering & Technology, Multan, Pakistan； Multan Electric Power Company, Multan, Pakistan

Updated：2021-05-19

DOI：10.35833/MPCE.2018.00422

Abstract

Dynamic instability of decentralized wind energy farms is a major issue to deliver continuous green energy to electricity consumers. This instability is caused by variations of voltage and frequency parameters due to intermittencies in wind power. Previously, droop control and inverter-based schemes have been proposed to regulate the voltage by balancing reactive power, while inertial control, digital mapping technique of proportional-integral-differential (PID) controller and efficiency control strategy have been developed to regulate the frequency. In this paper, voltage stability is improved by a new joint strategy of distribution static compensator (DSTATCOM) six-pulse controller based reactive power management among decentralized wind turbines and controlled charging of capacitor bank. The frequency stability is ensured by a joint coordinated utilization of capacitor bank and distributed wind power turbines dispatching through a new DSTATCOM six-pulse controller scheme. In both strategies, power grid is contributed as a backup source with less priority. These new joint strategies for voltage and frequency stabilities will enhance the stable active power delivery to end users. A system test case is developed to verify the proposed joint strategies. The test results of the proposed new schemes are proved to be effective in terms of stability improvement of voltage, frequency and active power generation.

Keywords

Dynamic instability; decentralized wind energy farms; voltage instability; frequency instability; distribution static compensator (DSTATCOM); reactive power management; intermittency

I. INTRODUCTION

THE world community is focussing on incorporating renewable energy as much as possible [

1]. The wind energy is taken as renewable energy resource as it has less emission. Wind energy has uncertainties in nature due to the inconsistency of wind speed and has the highest intermittencies among all renewable energy resources. The mentioned uncertainties of wind energy cause the dynamic instabilities of interconnected wind farms as a microgrid [2]. Load variations and intermittencies of wind turbines make power system unstable. Dynamic instability of decentralized wind turbines is a greatly concerned issue for future power grid [3].

Intermittencies of hydral, thermal and nuclear power stations are low. The issues of voltage instabilities occur due to abrupt load change [

4]. However, these issues do not happen in power system continuously. Also, many techniques have been developed to sort out these instability issues such as inertial control method, droop control technique, and load efficiency method [5]. The instability issue become worse when renewable energy resources with intermittent nature supply energy to consumers. Due to regular issues of renewable energy, the interconnection to traditional power grid becomes difficult [6].

The reactive power control is essential to stabilize the voltage and power supply to existing power lines since a large number of decentralized intermittent wind power resources need a reactive power reserve to deliver stable power to load [

7]. The most efficient way to control the utilization and generation of reactive power is by flexible alternating current transmission (FACT) devices. Static synchronous compensator (STATCOM) is a commonly used device which can be incorporated in the power system consisting of decentralized intermittent wind energy resources [8]. The STATCOM inserted into the distribution system is termed as distribution STATCOM (DSTATCOM). Controller scheme is developed to regulate the voltage and frequency, and stablize the power generation from decentralized intermittent wind power reserves [9].

Decentralized wind farms are coupled with the existing grid. This causes the instability of power system due to intermittencies of wind energy, which makes it difficult to supply electricity to consumers. Transient stability, voltage and frequency regulation are essential for the stable operation. These mentioned problems have been analyzed and solved by various scientists and researchers in various research literatures as mentioned in Table I, in which DER stands for distributed energy resources, INT represents intermittencies, and SO and MO represent single objective and multi-objectives, respectively. For example, in [

10], principles of DSTATCOM are formulated to regulate the voltage. A filter capacitor is incorporated to each phase in order to remove spikes of high-frequency switching. The voltage is maintained to a nominal value by reactive power controller. In [11], an improved control algorithm is developed to remove the DC-side pulsation in voltage and AC-side spikes due to unbalanced currents and voltages. A small DC capacitor is enabled by modulation switching function. An inverter-based DSTATCOM hardware system is developed to regulate the voltage in [12]. In [13], two split DC capacitor-based improved DSTATCOM controllers are modelled to regulate the voltage and compensate the reactive power. A field programmable gate array (FPGA) controller-based synchronous reference frame is developed to extract reference current and to mitigate harmonics in [14]. In [15], a solar photovoltaic DSTATCOM model integrated to non-linear load is developed. An algorithm is proposed to control the voltage and extract maximum power from a photovoltaic array. A frequency adaptive disturbance methodology is proposed to remove the spikes and improve the power quality at the distribution side of non-linear load in [16]. In [17], a new DSTATCOM design is developed based on naive back propagation to improve the performance of different parameters. The mitigation of harmonics and correction of power factor are achieved through DSTATCOM gradient descent back propagation technique in [18]. In [19], the power quality of a photovoltaic feeding system is improved by using JAYA optimization algorithm for DSTATCOM. An environmentally friendly reactive power dispatching system is developed for high penetration of wind power in the power system in [20]. In [21], small signal stability is analyzed by integrating power-system-stabilizer and automatic voltage-regulator in the power system of doubly-fed induction generator based wind power turbines. Reference [22] discusses the low inertia and frequency instability challenges of electrical grid due to incorporation of intermittent wind power turbines. Voltage instability which happens due to load fluctuation is tried to be solved in [23].

Table I EXISTING RESEARCH ARTICLES RELATED TO SUBJECT

Reference No.	Traditional grid	Microgrid	DER		INT	Dynamic stability	Voltage regulation	Frequency regulation	Objective
Reference No.	Traditional grid	Microgrid	Wind	Solar	INT	Dynamic stability	Voltage regulation	Frequency regulation	SO	MO
[10]	√						√			√
[11]	√						√		√
[12]							√		√
[13]	√						√			√
[14]							√		√
[15]		√		√	√		√			√
[16]	√							√	√
[17]	√						√			√
[18]	√						√			√
[19]		√		√				√	√
This work	√		√		√	√	√	√		√

A new concept of DC microgrid (DC-MG) emerges during the development of renewable energy resources and power electronics. A DC-MG has the capability of integrating renewable energy resources (e.g., solar) and energy storage devices in a simple topology. In [

24], a voltage control strategy is developed by connecting super-capacitor with DC-MG via bi-directional boost converter. This control strategy is based on dynamic feed-back linearization. DC electric spring (DC-ES) method is formulated for voltage regulation in DC microgrids in [25]. In this paper, the primary level is controlled by droop control method for operational coordination of multiple DC-ESs, and the secondary-level control is based on consensus scheme by balancing the state of charging among DC-ESs. In [26], voltage regulation is achieved with inverter-based decentralized generators by balancing the reactive power. A system consisting of distributed generation (DG) achieves DC voltage stability by combined action of consistency strategy and equal micro-increment principle in [27]. An adaptive genetic optimization algorithm is proposed to solve interval voltage model for wind and solar intermittencies in [28]. Distributed wind turbines can help stabilize the voltage if they are coordinated with appropriate controllers. In [29], a new wind turbine generation algorithm is developed to regulate the voltage of distribution feeder with real-time measurements by phasor measurement units.

The frequency of microgrids containing wind power turbines is regulated by conventional strategies of temporary over-generation control and inertia control. However, the second dip of frequency will occur in both control schemes during the restoration of rotational speed. In [

30], a modified control strategy is developed to prevent the second dip in frequency by appropriately balancing the recovery of rotational speed. The droop control method is one of the popular method to control the steady-state and transient instability in conventional power generators. However, this method is not suitable in frequency control of wind power turbines due to variable speeds. In [31], efficiency control strategy is proposed to improve the frequency stability. The isolated microgrid with high penetration of renewable energies is faced with frequency instability and insecurity issues due to load change and short circuit. In [32], a discretization scheme based on digital mapping technique of proportional-integral-differential (PID) controller is developed to resolve the underlying problem. A multi-input and multi-output control scheme controller is designed to regulate the frequency and voltage at the output of distributed energy resources with the variations of load in [33]. The concept of vehicle-to-grid (V2G) also plays an important role in controlling the load frequency of isolated microgrid. Reference [34] develops an adaptive multi-objective fractional order fuzzy PID controller to control the load frequency of islanded microgrid using V2G concept. A coordinated centralized control strategy is proposed to stabilize the primary frequency by appropriately balancing the active power of microgrid between wind power turbines and plug-in hybrid electric vehicles in [35]. The local clock has significant impact on the frequency of microgrids. In [36], the power sharing and frequency regulation strategies of voltage source inverter (VSI)-based microgrids are reviewed, and prototype policies of clock drifts are re-organized. In [37], the droop control strategy is implemented to regulate the frequency for islanded multi-microgrids to autonomously share power.

In this paper, a scheme for improving the dynamic stability of decentralized wind farms by effective DSTATCOM has been developed. The DSTATCOM six-pulse controller scheme is developed effectively with the following factors simultaneously taken into account:

1) Voltage and frequency instabilities due to distributed wind power turbines.

2) Development of new strategies through DSTATCOM six-pulse controller for effective management of reactive power among highly intermittent diversified wind turbines.

In the proposed scheme, the voltage and frequency stability towards dynamic stability, which guarantees the regular supply of green energy, has been modeled as multi-objectives. The new DSTATCOM six-pulse controller scheme has been formulated, which not only improves the dynamic stability, but also regulates the voltage and frequency up to the specified level. The developed algorithms are applied to system test model to analyze the results for dynamic stability improvement of voltage and frequency. Reactive power management by developed DSTATCOM controller scheme is also visualized.

The rest of the paper is organized as follows. In Section II, the modeling and mathematical formulation of the proposed model for dynamic stability improvement of decentralized wind farms is presented, by intelligently involving DSTATCOM. The multiple objectives and constraints have been discussed in this section. In Section III, the proposed solution algorithms for voltage and frequency regulation are explained. Section IV describes the implementation model and simulation results of the multi-objectives. Conclusion and future research work are discussed in Section V.

II. SYSTEM MODELING AND MATHEMATICAL FORMULATION

The decentralized wind farms developed model for dynamic stability improvement by effectively involving DSTATCOM is shown in Fig. 1. The developed model consists of three distinct sections: DG, DSTATCOM controller and electrical load. In Fig. 1, E_dc is the voltage across the capacitor C; SW_i ( $i = 1,2, \dots, 6$ ) represents the switching of DSTATCOM six-pulse controller; R is the transformer and inverter losses during conduction; L is the transformer equivalent inductance; v_DGa, v_DGb, and v_DGc are the line voltages of DG; i_DGa, i_DGb, and i_DGc are the alternating currents of DSTATCOM installed at DG; and v_ioa, v_iob, and v_ioc are the output voltages of the three-phase inverter.

Fig. 1 System model for decentralized wind farm dynamic stability improvement by effective DSTATCOM.

The DG section consists of decentralized wind turbines which have naturally intermittent power generation. The inconsistency in its generation makes it impossible to continuously supply green energy to electricity consumers. The instability of one wind turbine may lead to the overload of other wind turbines. It may be possible that the whole microgrid will shutdown due to instability issues. The DG needs reactive power reservoir for stabilization.

DSTATCOM is a controlling device for reactive power compensation. It has the ability to provide reactive power in the control mode which not only removes the transient issues but also regulates the voltage and frequency within the prescribed limit. DSTATCOM efficiently fulfills the requirements of reactive power reserved by capacitor bank in a controlled manner.

The non-linearization of load is another major problem of power system, which makes it instable. Microgrid generation is limited. Variable loads mostly cause the transient instability in the power system consisting of naturally intermittent wind turbines. This may also cause voltage fluctuations which needs to be sorted out within specified time to supply continuous energy to consumers. The main contribution of this paper is summarized briefly as follows:

1) Voltage stability is improved by a new joint strategies of DSTATCOM controller based reactive power management and controlled charging of capacitor bank. The reactive power stored by capacitor bank is controlled while the priority is given to voltage stability. The distributed wind turbines whose voltage deviations do not lie within the specified range need to be stabilized. Voltage over the specified level is brought back to the specified range by controlled charging of capacitor bank by DSTATCOM six-pulse controller, and less priority is given to the dispatching of required wind power turbine(s). Voltage below this specified limit is regulated by either reactive power management of DSTATCOM six-pulse controller, or the utility power grid backup. More priority is given to reactive power management. When voltage deviation occurs within specified limit, power is transferred to load.

2) Frequency stability is ensured by a joint coordinated utilization of capacitor bank and distributed wind turbine dispatching through a developed DSTATCOM six-pulse controller scheme. In this proposed scheme, current frequency lower than the reference frequency is brought up to the required frequency level by coordinated action of reactive power of capacitor bank, as well as the utility power grid as a backup, which is given less priority. In case of current frequency higher than the reference frequency, the frequency is regulated by charging the capacitor bank (if charging is required) or dispatching intermittent wind turbines from the system. In this case, the priority is given to charging capacitor bank. As frequency lies within the specified range, power is supplied to load.

A. DSTATCOM Modeling

Decentralized wind turbines have intermittencies which create instabilities in terms of real power generation, voltage and frequency. The mentioned instabilities in different parameters, if not removed within the specified time interval, may deteriorate the decentralized wind turbines. The instability of one wind turbine may shut down the whole microgrid system. The case becomes more serious when the unstable DG is integrated into the conventional power grid. Thus, the proposed system model will not only improve the generation stability of naturally intermittent wind turbines, but also regulate the voltage and frequency which are necessarily constant to integrate into the conventional power grid.

The DSTATCOM six-pulse controller is joined through transformer to AC distribution network as shown in Fig. 1. The capacitor put in service in DSTATCOM structure is utilized as energy storage element. The following assumptions are made to formulate a useful mathematical model:

1) Three-phase balanced distribution network is considered.

2) Equivalent resistances represent whole losses and equivalent inductances represent transformers.

The differential equations by exerting electrical relationship in Fig. 1 are written in mathematical expression as:

\{\begin{array}{l} {\dot{i}}_{D G a} = \frac{1}{L} (v_{D G a} - v_{i o a}) - \frac{R}{L} i_{D G a} \\ {\dot{i}}_{D G b} = \frac{1}{L} (v_{D G b} - v_{i o b}) - \frac{R}{L} i_{D G b} \\ {\dot{i}}_{D G c} = \frac{1}{L} (v_{D G c} - v_{i o c}) - \frac{R}{L} i_{D G c} \end{array}

(1)

The balanced three-phase system is converted into a rotating dq-frame. Equation (1) is converted into dq-frame if the AC current and voltage vector is x, and (1) can be written in mathematical form as:

\{\begin{array}{l} {\dot{i}}_{D G d} = \frac{1}{L} (V_{D G d} - V_{i o d}) - \frac{R}{L} I_{D G d} + ω I_{D G q} \\ {\dot{i}}_{D G q} = \frac{1}{L} (V_{D G q} - V_{i o q}) - \frac{R}{L} I_{D G q} - ω I_{D G d} \end{array}

(2)

The DC-side and AC-side equation can be expressed as:

\{\begin{array}{l} V_{i o d} = M E_{d c} s i n β \\ V_{i o q} = M E_{d c} c o s β \end{array}

(3)

where $M$ and $β$ are control variables of DSTATCOM, which represent the modulation index and the firing angle, respectively. They can be written in mathematical form as:

\{\begin{array}{l} M = \frac{\sqrt[]{V_{i o d}^{2} + V_{i o q}^{2}}}{E_{d c}} \\ β = a r c t a n (\frac{V_{i o q}}{V_{i o d}}) \end{array}

(4)

B. Controller Design

Mathematically, the DSTATCOM model can be written as a multi-input and multi-output non-linear system.

\{\begin{array}{l} {\dot{X}}_{D G i n p u t} = f (X_{D G i n p u t}) + g_{1} (X_{D G i n p u t}) s_{1} + g_{2} (X_{D G i n p u t}) s_{2} \\ X_{D G o u t p u t 1} = h_{1} (X_{D G i n p u t}) \\ X_{D G o u t p u t 2} = h_{2} (X_{D G i n p u t}) \end{array}

(5)

where X_DGinput is the input variable state vector of distributed grid; X_DGoutput₁, X_DGoutput₂ are the output varibles of distributed grid; $f (\cdot)$ , $g_{1} (\cdot)$ , $g_{2} (\cdot)$ are the input functions; $h_{1} (\cdot)$ , $h_{2} (\cdot)$ are the output functions; and s₁ and s₂ are the control variables. Some of these variables are calculated by (6)-(11).

X_{D G i n p u t} = [\begin{matrix} I_{D G d} \\ I_{D G q} \\ E_{d c} \end{matrix}]

(6)

f (X_{D G i n p u t}) = [\begin{matrix} \frac{V_{D G d}}{L} - \frac{R}{L} I_{D G d} + ω I_{D G q} \\ - \frac{R}{L} I_{D G q} - ω I_{D G d} \\ 0 \end{matrix}]

(7)

g_{1} (X_{D G i n p u t}) = [\begin{matrix} - \frac{E_{d c}}{L} \\ 0 \\ \frac{I_{D G d}}{C} \end{matrix}]

(8)

g_{2} (X_{D G i n p u t}) = [\begin{matrix} 0 \\ - \frac{E_{d c}}{L} \\ \frac{I_{D G q}}{C} \end{matrix}]

(9)

The state vector $S$ and the output variables X_DGoutput are defined as:

S = [\begin{matrix} s_{1} \\ s_{2} \end{matrix}] = [\begin{matrix} M c o s β \\ M s i n β \end{matrix}]

(10)

X_{D G o u t p u t} = [\begin{matrix} X_{D G o u t p u t 1} \\ X_{D G o u t p u t 2} \end{matrix}] = [\begin{matrix} I_{D G q} \\ E_{d c} \end{matrix}]

(11)

The system described above for DSTATCOM is a non-linear system which will be converted into linear form by using coordinated feedback linearization method. Let us take the coordinated non-linear transformation as:

y = [\begin{matrix} h_{1} & h_{2} \\ L_{f} h_{1} & L_{f} h_{2} \\ ⋮ & ⋮ \\ L_{f}^{r_{1} - 1} h_{1} & L_{f}^{r_{2} - 1} h_{2} \end{matrix}]

(12)

where $L_{f} h_{i} (X_{D G i n p u t})$ , $i = 1, 2$ are the lie-derivatives of $h_{i} (X_{D G i n p u t})$ along $f (X_{D G i n p u t})$ which converts the non-linear system with the state vector $X_{D G i n p u t}$ into a linear dynamic-system with the state-vector $y$ for $n = r_{1} + r_{2}$ ; and $r_{1} (< n)$ and $r_{2} (< n)$ are two integers.

The transformation of a non-linear coordinated system can be described mathematically as:

\tilde{y} = \tilde{Ψ} (X_{D G i n p u t})

(13)

For DSTATCOM, we may take $\tilde{y}$ as:

\{\begin{array}{l} {\tilde{y}}_{1}^{} = {\tilde{Ψ}}_{1}^{} (X_{D G i n p u t}) = h_{1} (X_{D G i n p u t}) = I_{D G q} \\ {\tilde{y}}_{2}^{} = {\tilde{Ψ}}_{2}^{} (X_{D G i n p u t}) = h_{2} (X_{D G i n p u t}) = E_{d c} \end{array}

(14)

By utilizing the above transformation criteria, a linearized partial system will be developed as:

\{\begin{array}{l} {\dot{\tilde{y}}}_{1} = \frac{\partial h_{1}}{\partial X_{D G i n p u t}} {\dot{X}}_{D G i n p u t} = L_{f} h_{1} + L_{g 1} h_{1} s_{1} + L_{g 2} h_{1} s_{2} \\ {\dot{\tilde{y}}}_{2} = \frac{\partial h_{2}}{\partial X_{D G i n p u t}} {\dot{X}}_{D G i n p u t} = L_{f} h_{2} + L_{g 1} h_{2} s_{1} + L_{g 2} h_{2} s_{2} \end{array}

(15)

The following equations can be obtained:

\{\begin{array}{l} {\dot{\tilde{y}}}_{1} = - \frac{R}{L} I_{D G q} - ω I_{D G d} - \frac{E_{d c}}{L} s_{2} \\ {\dot{\tilde{y}}}_{2} = \frac{1}{C} I_{D G d} s_{1} + \frac{1}{C} I_{D G q} s_{2} \end{array}

(16)

The system described can be expressed in linear form as:

\{\begin{array}{l} {\dot{\tilde{y}}}_{1} = v_{1} \\ {\dot{\tilde{y}}}_{2} = v_{2} \end{array}

(17)

where the controlled linear inputs $v_{1}$ and $v_{2}$ are written as:

\{\begin{array}{l} v_{1} = - \frac{R}{L} I_{D G q} - ω I_{D G d} - \frac{E_{d c}}{L} s_{2} \\ v_{2} = \frac{1}{C} I_{D G d} s_{1} + \frac{1}{C} I_{D G q} s_{2} \end{array}

(18)

C. Voltage Regulation

The power equation will be written as:

\{\begin{array}{l} P = 1.5 (V_{i o d} I_{D G d} + V_{i o q} I_{D G q}) \\ Q = 1.5 (V_{i o d} I_{D G q} - V_{i o q} I_{D G d}) \end{array}

(19)

where P and Q are the active and reactive power, respectively. P and E_dc are calculated as:

P = E_{d c} C_{d c} \frac{d E_{d c}}{d t}

(20)

E_{d c}^{̇} = \frac{0.66 M}{C_{d c}} (I_{D G d} c o s β + I_{D G q} s i n β)

(21)

If $β$ is chosen to be 0, we have:

\{\begin{array}{l} V_{i o q} = 0 \\ Q = 1.5 V_{i o d} I_{D G q} \end{array}

(22)

Therefore, the reactive power $Q$ is controlled by the current $I_{D G q}$ , and the regulation of DC voltage $E_{d c}$ is essential to compensate the reactive power $Q$ .

D. Frequency Stability

Primary frequency irregulation arises due to small imbalance between the generation and demand of DG. This mostly occurs due to load fluctuations sorted out by secondary frequency regulation known as automatic generation control (AGC). As AGC scheme is not suitable for wind turbines due to the intermittencies, an alternate solution is required. Moreover, AGC is more suitable for small imbalance between load and generation. For large-scale imbalance, the primary frequency factor becomes more important for maintaining the frequency within the limits.

In the case of wind turbines, frequency is regulated by reactive power reserve to keep the operation of wind turbines. The balance between supply and generation is kept under controlled condition by utilizing reactive power reserve in control mode by DSTATCOM controller.

Due to the loss of wind turbine, the frequency of microgrid system will drop from its specified level, which is known as negative frequency deviation $Δ f$ . The reactive power reserve is then actively incorporated in the system by DSTATCOM to stabilize the frequency by $- D_{i} Δ f$ . This joint strategy of coordinated control will stabilize the frequency. Moreover, if reactive power reserve is not sufficient, then the control automatically shifts to wind energy dispatching to regulate the frequency within the limits. The load side may also be involved in frequency regulation. However, frequency regulation provided by load management is non-controllable and physically unsuitable.

The frequency stability for distributed wind unit $i$ is ensured by determining the amount of reactive power $R_{i t}$ which will be either provided to the distributed wind unit or released from the distributed wind unit depending upon the conditions, which can be written mathematically as:

R_{i t} = \{\begin{array}{l} D_{i} \cdot d f_{t} d f_{i t} \leq d f_{t} \leq 0, \forall i, t \\ g_{i t, m a x} - g_{i t, n o r} d f_{t} < d f_{i t}, \forall i, t \end{array}

(23)

where $d f_{t}$ is the minimum negative level of frequency deviation during time interval $t$ ; $d f_{i t}$ is the frequency deviation when wind turbine unit $i$ is within its peak generation during time interval $t$ ; $D_{i}$ is the frequency regulation factor which is the amount of reactive power which is provided to the system; $g_{i t, m a x}$ is the maximum level of distributed power generation of wind turbine unit $i$ during time interval $t$ ; and $g_{i t, n o r}$ is the normalized power generation of distributed wind turbine unit $i$ during time interval $t$ . Equation (23) actually determines the amount of reactive power that will be either absorbed or supplied to stabilize the frequency by DSTATCOM controller. The upper half of (23) shows that if $d f_{i t} \leq d f_{t} \leq 0$ , $d f_{t}$ is multiplied by $D_{i}$ to stabilize the frequency. The lower half of (23) shows that if $d f_{t} < d f_{i t}$ , the reactive power is absorbed by an amount equal to the difference between $m a x g_{i t}$ and $n o r g_{i t}$ to regulate the frequency.

III. PROPOSED ALGORITHM

The multi-objectives described above are solved by the proposed algorithms in Fig. 2 and Fig. 3. Decentralized wind turbines supply electricity to the electrical load and traditional power grid. The stability of frequency and voltage parameters is necessary to merge the power of distributed wind turbines to the existing grid. Two separate flow chart algorithms for voltage and frequency regulation of decentralized wind turbines have been formulated and discussed.

Fig. 2 Flow chart of system model for voltage stability.

Fig. 3 Flow chart of system model for frequency stability.

The proposed voltage stability algorithm shown in Fig. 2 is described as follows.

Step 1: read the system input data including line resistance, line inductance, capacitance, DC voltage, and reference system parameters.

Step 2: measure the voltage and check the voltage variations. If voltage variations are within the specified limits, the charging status of capacitor bank is checked. If current value of charging is less than the specified value (80 $%$ ), the charging takes place to achieve its specified value through DSTATCOM controller. Otherwise, power is delivered. After charging the capacitor bank up to the specified level, voltage is still checked. If voltage is not within the specified range, the process is repeated.

Step 3: regulate voltage by adopting one option out of two available options depending on the condition of voltage deviation. If voltage is less than the reference voltage, it will be checked whether reactive power is reserved by the capacitor bank or not. If reactive power is reserved by capacitor, it is made available by the reactive power management system through DSTATCOM six-pulse controller. Otherwise, utility power grid is used as a backup source to regulate the under-voltage.

Step 4: check the voltage whether capacitor can be charged or not, if the voltage is more than reference voltage. If the capacitor has the capacity to be charged, it is charged through DSTATCOM six-pulse controller. Otherwise, required decentralized wind turbine(s) is(are) dispatched to regulate the over-voltage.

Step 5: check the charging status of capacitor bank. If current value of charging is less than the specified level (80 $%$ ), the charging takes place to achieve the specified charging value. Otherwise, the power is delivered. After charging the capacitor bank up to the specified level through DSTATCOM controller, voltage is still checked. If voltage is still not within specified voltage range, the process is repeated from Step 2. Otherwise, power is delivered to load, which ends the proposed algorithm.

Now, the proposed frequency stability algorithm is described below and the flow is illustrated in Fig. 3.

Step 1: read the system input data which include negative frequency deviation, frequency regulation constant, minimum frequency deviation level, frequency deviation at peak power generation time, the maximum and minimum power generation levels of distributed wind turbines.

Step 2: measure the frequency and check the frequency variations. If frequency variations are not within the specified limits, it is regulated by using one of two options. Otherwise, power is supplied to the load.

Step 3: regulate frequency by checking the current frequency with the required reference frequency. If the current frequency is less than the reference frequency, the charging state of capacitor is checked. If it is greater than the minimum capacitor charging, the distributed power generation system will utilize the reactive power as a backup through DSTATCOM six-pulse controller. Otherwise, power grid is utilized as a backup to stabilize frequency.

Step 4: check the charging state of charging of capacitor, if current frequency is greater than the reference frequency. If the state of charging is less than the minimum capacitor charging, excessive power is utilized to charge the capacitor bank through DSTATCOM six-pulse controller. Otherwise, only required distributed power generation wind turbine(s) is(are) isolated from the rest of power system to regulate the frequency.

Step 5: check frequency again after Step 3 and Step 4. If it is still not regulated, the cycle will be repeated from Step 2 until frequency is regulated upto the specified level, which ends the proposed algorithm.

IV. SIMULATION RESULTS AND DISCUSSION

The new system model has been tested which guarantees the regular supply of green energy from decentralized wind turbines to electricity consumers. The analysis of system model performance is done on the basis of multi-objectives including the improvement of dynamic stability, voltage and frequency regulation. The number of decentralized wind turbines may vary but six distributed wind turbines have been taken in our system model as shown in Fig. 4, where six wind turbines are connected through a transformer (0.575 kV/25 kV), the loads are connected through a transformer (25 kV/0.48 kV), DSTATCOM is connected through a transformer (0.48 kV/25 kV), and utility power grid is coupled through a transformer (132 kV/25 kV) at the point of common coupling (PCC). The power of loads 1-5 are 4.272 MVA, 5.385 MVA, 3.551 MVA, 6.946 MVA and 2.236 MVA, respectively. For each wind turbine of squirrel cage induction generation type, the rated power is 2 MW, and the rated voltage is 0.575 kV. For the DSTATCOM, the rated power is 2 MVA, the set point for reactive power is 0 Mvar, and the rated voltage is 0.48 kV.

Fig. 4 Single-line diagram of test system.

The developed DG supplies energy to electrical load. All of these wind turbines are apart from each other through a certain distance. Their power generations depend on wind speed which varies continuously. Hence, it is unfavourable for decentralized wind turbines to turn on/off on regular basis with short-time intervals. Due to these mentioned intermittencies of wind turbines, it becomes hard to provide constant power to electrical linear load due to the fluctuations in voltage and frequency. If reactive power is not available to fulfill the requirement, the active power loss will increase. Therefore, optimum reserves for reactive power is needed to regulate the voltage and frequency. The new DSTATCOM reactive power management scheme is tested to regulate voltage and frequency, which are necessary parameters to integrate the intermittent DG to conventional grid.

In Fig. 5, active power generation variations have been shown due to the intermittencies of wind turbines. Wind turbines have large uncertainties due to inconsistency of wind flow rate. Therefore, there are a large number of fluctuations in active power generation. Therefore, reactive power is required to generate stable power generation from these intermittent turbines. As there are a large number of wind power turbines, reactive power management is required which has been done by DSTATCOM controller. Figure 5(a) shows the active power generation fluctuations due to intermittent decentralized wind turbines. The blue color shows the minimum active power which is constantly deliver to the load, while the red color shows the variable active power generation from decentralized wind farms. Stable active power delivery is up to 4 MW, and the power above 4 MW is unstable and needs to be stabilized for utilization. Otherwise, only 4 MW power can be utilized. Figure 5(b) represents the active power generation stability, which has been achieved by reactive power management among decentralized wind turbines within a few seconds (3-5 s). In this figure, blue color shows the stabilized active power which suddenly increases from 4 MW to more than 9 MW. With the addition of only 2 Mvar by the newly developed DSTATCOM scheme, the stable active power generation has increased.

Fig. 5 Comparison of active power generation with and without developed scheme. (a) Active power generation variations due to wind farm intermittencies. (b) Active power generation stability due to developed scheme.

In Fig. 6, voltage instability has been shown due to active power generation inconsistency of intermittent wind turbines, and voltage stability has also been presented due to reactive power management among decentralized wind turbines. For constant electrical load, if active power generation variates, voltage fluctuations occur. The voltage is regulated by reactive power compensation. As there are a large number of wind turbines, reactive power is managed among diversified wind turbines to regulate the voltage, which is a necessary parameter to integrate wind power into the conventional power grid. A joint strategy of reactive power management and capacitor bank charging will sort out the problem of voltage instability. Figure 6(a) shows the voltage fluctuations due to irregular active power generation from diversified wind turbines. In this scenario, if the value of voltage whose reference level is 25 kV lies within the range of [

23.75, 26.25]kV and maintains within this range, it is called stable voltage; otherwise, it will be unstable voltage. The voltage varies between 14 kV and 25.5 kV as highlighted in black color in the graphical representation, while the magenta color represents the minimum voltage level which is not within the specified range. Although voltage archives the specified range many times, it can not maintain this level. Figure 6(b) represents the voltage stability due to the proposed scheme within short-time interval (3-5 s). Voltage is stabilized by proposed joint coordinated strategy to 25 kV which is within the specified range of [23.75, 26.25]kV and remain at this level.

Fig. 6 Voltage instability and voltage stability with developed joint scheme for intermittent wind farms. (a) Voltage instability due to intermittent wind farm. (b) Voltage stability for intermittent wind farm due to joint scheme through DSTATCOM controller.

In Fig. 7, frequency instability has been shown due to intermittent nature of wind turbines, and frequency stability has also been presented due to developed joint strategy.

Fig. 7 Frequency instability and frequency stability due to joint action of developed DSTATCOM controller scheme. (a) Frequency instability without developed scheme. (b) Frequency stability due to joint action of developed DSTATCOM controller scheme.

With constant electrical load in operation, if the active power generation variates due to the intermittencies, frequency fluctuations occur. The frequency is then regulated by the newly developed algorithm.

Figure 7(a) shows the frequency fluctuations due to the intermittencies of diversified wind turbines. If the frequency value whose reference level is 60 Hz lies between 59.9 Hz and 60.3 Hz and maintains this level, it is called stable frequency. Otherwise, it will be unstable frequency. The frequency variates between 59.75 Hz and 60.65 Hz. This parameter stability up to specified level is necessary to integrate the decentralized wind farms. Figure 7(b) represents the frequency stability up to 60.3 Hz due to developed joint strategy through DSTATCOM scheme within short-time interval (3-5 s). Frequency regulation is achieved near to reference level of 60 Hz. Figure 8 represents the reactive power management among DG due to developed joint strategy through DSTATCOM controller.

Fig. 8 Reactive power management among decentralized wind turbines through DSTATCOM controller.

V. CONCLUSION AND FUTURE RESEARCH

Dynamic stability improvement of decentralized wind farms by effective DSTATCOM is developed as multi-objectives. Dynamic stability with intermittent wind energy resources is improved. The integration of decentralized wind farms into traditional power grid is another major issue because voltage and frequency vary due to wind intermittencies. Voltage and frequency are stabilized to sort out the problems of highly intermittent decentralized wind turbines. The voltage stability is improved by a new joint strategy of DSTATCOM six-pulse controller based reactive power management and controlled charging of capacitor bank. The frequency is stabilized by a joint coordinated utilization of capacitor bank and distributed wind turbine dispatching through a new DSTATCOM six-pulse controller scheme. In both strategies, utility power is used as a backup source with less priority. These new joint strategies for voltage and frequency stabilities have enhanced the stable active power delivery to end users. A system test case is developed to verify the proposed joint strategies. The test results of the proposed new schemes are proved to be effective in terms of stability improvement of voltage, frequency and active power generation.

In present work, dynamic stability is not possible without second backup such as utility power grid or battery backup. This study can be enhanced further to improve dynamic stability in islanding mode without any backup. In future research, the stability of microgrid is improved in islanding mode with cooperative scheme of capacitor bank without any second backup. Moreover, solar and wind have different nature of instabilities issues in islanding mode. These issues will be solved to stabilize the power system in normal mode as well as islanding mode. To make the DSTATCOM developed scheme cost- effective, optimization techniques can also be applied. The stability can further be improved and made more efficient by establishing the unified power flow controller scheme. Wind plant reconnection to the grid is an important issue and we will also consider it in our future research. In our possible proposed scheme, after wind plant isolation, the DSTATCOM controller scheme will monitor the voltage and frequency parameters of microgrid after certain time interval and reconnect it automatically when a certain range of parameter values are observed.

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