Communications on Applied Nonlinear Analysis 

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Design of Hybrid System for Remote Area Electrification at Trishuli, 

Chhattisgarh, INDIA 

 

Payal Deshpande1*, Pragya Nema2 

1Research Scholar, Department of Electrical and Electronics Engineering, Oriental University, Indore (M.P.) 

2Supervisor, Department of Electrical and Electronics Engineering Oriental University, Indore (M.P.) 

*nenepayal14@gmail.com 

 

Article History: 

Received: 12-01-2025 

Revised: 15-02-2025 

Accepted: 01-03-2025 

Abstract:  

Rural development depends on having an abundance of reasonably priced energy, but 

50% of the world's population lacks power while 20% have limited availability. 

Inadequate power forces affluent people and companies to relocate to cities, leaving rural 

regions even more vulnerable, since 80% of the total population are living in low 

electrification of area. Although the effort is still difficult, governments, non-

governmental organizations, private businesses, all have undertaken extensive rural 

electrification projects. Standalone systems and grid expansion are common strategies. 

An emphasis on a hybrid model combining grid expansion with local renewable energy 

production, this research offers state-of-the-art methodologies for developing technically 

sound and economically viable solutions in a village named Trishuli in Chhattisgarh 

State. According to the simulation findings, in most cases, the hybrid solution is the least 

expensive method of action. It offers a sustainable, dependable, and profitable way to 

close rural energy shortages and promote long-term growth.  

Keywords: Homer Pro, PV, Battery, UltraCapacitor MIC, ANFIS Controller. 

 

Introduction  

In rural areas, even though electrical grids are available, the supply is insufficient [1]. As per Indian 

development survey 2005 data, 11 states had more than 10 hours of power outages per day on average. 

According to more recent survey covering 240 villages, and 1920 respondents spread across India 

following analysis nh6as been done and concluded that 36% of rural households receiving supply from 

grid receive an electrical supply for more than 20h per day, only 44% receive an electrical supply for 

more than 16h per day, and 30% receive an electrical supply for less than 12h per day [2]. Another 

survey conducted in rural areas in India over 2083 household [3], reported an average number of hours 

supply per day of less than 6, and close to five days a month without any electricity. More than 80% 

of people were reported to be very dissatisfied with both the number of hours supply and regularity of 

service.  

1.1 Remedial Solution to Overcome Power Outrages: 

Distribution grids are already stretched beyond their rated capacity. This is one of the reasons for the 

frequent power interruptions during peak hours and in general, of the improper distribution and 

reliability of electrical supply in rural areas [4][5]. Many villages are situated far from the grid or in 

mailto:*nenepayal14@gmail.com


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areas where they are difficult to access. The required investments in grid reinforcement or extension 

may be very high compared to the low consumption level of a newly electrified village. In addition, 

power thefts and low metering levels in rural areas have led to poor financial health for utilities that 

are not ready to invest in new infrastructures [6]. The International Energy Agency (IEA) predicted 

that mini-grids and off-grid systems will account for 70% of future rural electrification owing to the 

high operating and maintenance costs associated with grid extensions to remote locations [7]. 

Therefore, the easiest way to electrify rural areas is to extend the grid, which will solve all these 

concerns. Developing nations often employ centralized generation, extension of the main grid, or the 

development of standalone microgrids. 

2.1. Feasibility of Site Selection and its implementation: 

  The common steps taken into consideration in the normal procedure of rural electrification include: 

 The load is first calculated by looking at how much power other nearby electrified villages use, as 

well as by looking at the residents' individual demands and economic capabilities. 

The next step is to undertake a technical feasibility study to determine the viability of extending the 

main grid and the practicality of using local resources in a standalone solution.  

The third step is to determine which options are financially feasible by comparing their respective 

incentives and the main grid electricity tariff.  

Finally, the best course of action should enhance societal welfare while remaining practicable, cost-

effective, and efficient.  

Social considerations and implementation challenges are also a part of successful rural electrification 

strategies [8][9], with off-grid electrification [10] finding that local ownership and engagement are 

essential. 

2.2. Implementation at selected site:   

A robust demonstrating device for optimizing and simulating hybrid energy networks, HOMER Pro 

(Hybrid Optimization of Multiple Energy Resources) is appropriate for distributed generation and 

microgrid operations. It simulates the transfer of energy and interconnections across a variety of 

situations, allowing investigators to assess the technical and fiscal viability of numerous system 

designs. To provide resilient architecture pursuant to unpredictability, the tool uses a sensitivity 

evaluation to evaluate the effects of factors like fuel prices, load changes, and availability of assets. 

Finding the best and most economic alternatives is aided by economic indicators such as Levelized 

Cost of Energy (LCOE) and Net Present Cost (NPC).  

Above discussed method is used for the electrification at Trishuli village located in Balrampur district 

of Chhattisgarh State. The following diagram shows the necessary Hybrid PV/Hydro System designed 

in HOMER Pro to check its economic feasibility. Based on it optimized NPC and the annual cost of 

the system is mentioned in Table 1 and Table 2 respectively. 

 



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Fig. 1: - Hybrid PV/Hydro system diagram designed in HOMER Pro 

 

Table 1: Optimized Net Present Costs of PV/ Hydro 

Label 

 

Money Functionin

g 

Auxiliary Retrieve Asset

s 

Aggregate 

Generic 1kWh Lead 

Acid 

15610 6811 13980 -1874 0 34245 

Generic flat plate PV 26,955 1,497 0 0 0 28,465 

Generic Hydro 100kW 460847 178225 0 0 0 638,190 

System Converter 803.78 0 342.25 -64.35 0 1,179 

System 503393 185858 14300 -1925 0 701954 

 

Table 2: Annualized Costs of PV/Hydro System 

Label 

 

Money Functioning Auxiliary Retrieve Assets Aggregate 

Generic 1kWh Lead Acid 1211 515 1044 -144.24 0 2648 

Generic flat plate PV 2100 110.11 0 0 0 2200 

Generic Hydro 100kW 35651 13768 0 0 0 49366 

System Converter 62.32 0 26.55 -4.96 0 83.49 

System 38752 14353 1102 -149.50 0 54298 



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Fig 2: Graphical representation of Total Annualized Costs of PV/Hydro system 

 

3. System Modelling 

3.1 Converter Design 

Depending upon the load side requirement, a novel MIC working in buck boost mode is designed. 

Different operation modes of Buck-Boost converter for independent switching pulses are given in 

Fig.3. In Buck-Boost mode for t1, t2, t3, t4, t5, and t6 input voltages VPV, VBT, VUC and their combinations 

serve as input to the inductor in each case. For t7 negative voltage appears across the inductor. 

Waveform for voltage and current across inductor in Buck Boost mode of operation is shown in Fig.4 

& different working states are summarized in tabular form as shown below.  

Table 3. MIC Stages in Buck-Boost Mode of Operation 

 

 

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

Capital Operating Replacement Salvage Resource Total

Generic 1Kw Lead Acid

Generic flat plate PV

Generic Hydro 100kW

System Converter

System

Mod

e 

Conducting 

Switches 

Active Source Equation for VL Inductor Mode 

1 S1, S3, S5 VPV VPV – 0 Energy is stored 

2 S2, S3, S5 VBT VBT – 0 Energy is stored 

3 S2, S4, S5 VUC  VUC – 0 Energy is stored 

4 SS1, S3, S5 VPV +VBT VPV +VBT - 0 Energy is stored 

5 SS2, S2, S5 VBT +VUC  VBT+VUC - 0 Energy is stored 

6 SS1, SS2, S5 VPV + VBT + VUC  VPV +VBT +VUC -0 Energy is stored 

7 D1, D2 None -VO Energy is released 



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S1 S3

S2 S4

SS2SS1

 

S5vBT

vUC
C

L D2

D1
vPV

R

(a)

 

S1 S3

S2 S4

SS2SS1

 

S5vBT

vUC
C

L D2

D1
vPV

R

(b)

S1 S3

S2 S4

SS2SS1

vBT

vUC

L

D1
vPV

R

(c)

 

S1 S3

S2 S4

SS2SS1

vBT

vUC

L

D1
vPV

(d)

 

S1 S3

S2 S4

SS2SS1

vBT

vUC D1
vPV

R

(e)

S1 S3

S2 S4

SS2SS1

vBT

vUC

L

D1
vPV

(f)

 

L

 

 

S5
C

D2

 
S5

C

D2

 

S5
C

D2

R

R

 

S5
C

D2

S1 S3

S2 S4

SS2SS1

 

S5vBT

vUC
C

L D2

D1
vPV

R

(g)

 

 

Fig. 3: Different Working Sates MIC (MIC Buck Boost Mode) 

 

TON

TS

TOFF

CHARGE DISCHARGE

IL

VL

P1

t

t

t

t

t

t

t
TIME (Sec)

t1 t2 t3 t4 t5 t6

0

0

D1

D2

D3

D4

D5

D6

VL1

VL2

VL3
VL4

VL5

VL6

U1=VBT+VUC

U2=VPV+VBT

U3=VPV+VBT+VUC

VL7

P2

P3

P4

P5

P6

 

Fig. 4: Waveform of MIC in Buck-Boost mode for single switching cycle where, VL1=V_PV: 

VL2= V_UC: VL3= V_BT: VL4= U1: VL5= U2: VL6= U3: VL7 = -VO 

3.2 Performance analysis of Buck boost converter: 

The following methods are used to evaluate the values of parameters used in the circuit: 

Based on principle of volt-second balance, average inductor voltage value can be calculated as: 



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∫
𝑇𝑠

0

𝑣𝐿(𝑡)𝑑𝑡 = (𝑉𝑃𝑉)𝑡1 + (𝑉𝐵𝑇)𝑡2 + (𝑉𝑈𝐶)𝑡3 + (𝑈1)𝑡4 + (𝑈2)𝑡5 + (𝑈3)𝑡6 + (−𝑉𝑜)𝑡7 

𝑉𝑃𝑉(𝑡1 + 𝑡4 + 𝑡6) + 𝑉𝐵𝑇(𝑡2 + 𝑡5 + 𝑡6) + 𝑉𝑈𝐶(𝑡3 + 𝑡5 + 𝑡6) − 𝑉𝑜(𝑡7) = 0 

𝑉𝑜 =
𝑉𝑃𝑉𝐷𝑃𝑉+𝑉𝐵𝑇𝐷𝐵𝑇+𝑉𝑈𝐶𝐷𝑈𝐶

(1−𝐷𝑃𝑉−𝐷𝐵𝑇−𝐷𝑈𝐶)
                               …. (1) 

here,  𝑈1 = (𝑉𝑃𝑉 + 𝑉𝐵𝑇): 𝑈2 = (𝑉𝐵𝑇 + 𝑉𝑈𝐶): 𝑈3 = (𝑉𝑃𝑉 + 𝑉𝐵𝑇 + 𝑉𝑈𝐶) 

𝑇𝑠 = 𝑡1 + 𝑡2 + 𝑡3 + 𝑡4 + 𝑡5 + 𝑡6 + 𝑡7:  

 𝑡7 = 𝑡𝑂𝐹𝐹 

𝐷𝑢𝑡𝑦 𝑐𝑦𝑐𝑙𝑒 𝑜𝑓 𝑃𝑉, 𝐷𝑃𝑉 =
𝑃𝑉 𝑜𝑛 𝑡𝑖𝑚𝑒(𝑡1 + 𝑡4 + 𝑡6)

𝑇𝑠
:  

𝐷𝑢𝑡𝑦 𝑐𝑦𝑐𝑙𝑒 𝑜𝑓 𝐵𝑇, 𝐷𝐵𝑇 =
𝐵𝑇 𝑜𝑛 𝑡𝑖𝑚𝑒(𝑡2 + 𝑡5 + 𝑡6)

𝑇𝑠
: 

𝐷𝑢𝑡𝑦 𝑐𝑦𝑐𝑙𝑒 𝑜𝑓 𝑈𝐶, 𝐷𝑈𝐶 =
𝑈𝐶 𝑜𝑛 𝑡𝑖𝑚𝑒(𝑡3 + 𝑡5 + 𝑡6)

𝑇𝑠
 

𝑇ℎ𝑒𝑟𝑒𝑓𝑜𝑟𝑒
(𝑡7) 

𝑇𝑠
 =  (1 − 𝐷𝑃𝑉 − 𝐷𝐵𝑇 − 𝐷𝑈𝐶) 

∆𝑖𝐿waveform and output voltage ripple ∆𝑣𝑐   are used to calculate the parameter values to be connected 

in circuit. Therefore, ∆𝑖𝐿 can be given as, 

∆𝑖𝐿 = (
𝑈𝑃𝑉

𝐿
) 𝑡1 + (

𝑈𝐵𝑇

𝐿
) 𝑡2 + (

𝑈𝑈𝐶

𝐿
) 𝑡3 + (

𝑈1

𝐿
) 𝑡4 + (

𝑈2

𝐿
) 𝑡5 + (

𝑈3

𝐿
) 𝑡6 = − (

𝑉𝑜

𝐿
) 𝑡7 

here, 

𝑈𝑃𝑉 = 𝑉𝑃𝑉 − 𝑉𝑜: 𝑈𝐵𝑇 = 𝑉𝐵𝑇 − 𝑉𝑜: 𝑈𝑈𝐶 = 𝑉𝑈𝐶 − 𝑉𝑜 

Solution for ∆𝑖𝐿 yields 

∆𝑖𝐿 =
𝑉𝑜

𝐿𝑓𝑠
(1 − (𝐷𝑃𝑉 + 𝐷𝐵𝑇 + 𝐷𝑈𝐶))                                        … (2) 

Here, 𝑓𝑠 =
1

𝑇𝑠
 

Therefore,  

𝐿 =
𝑉𝑜

∆𝑖𝐿𝑓𝑠
(1 − (𝐷𝑃𝑉 + 𝐷𝐵𝑇 + 𝐷𝑈𝐶))                                                     … (3) 

The above equation gives the value of Inductor. 

Now for calculating the value of capacitors the capacitor charge balance method can be used  

Considering the above waveform as snh6own in fig.4, for time duration t1, t2 and t3: capacitor is 

charging and for time interval t4 it is discharging.  

So, the 𝑖𝑐 for time interval t1, t2 and t3 is given by 



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𝑖𝑐 = 𝐶
𝑑𝑣𝑐

𝑑𝑡
= 𝐼𝑜 

Therefore, the slope of capacitor voltage is, 

𝑑𝑣𝑐

𝑑𝑡
=

𝑖𝑐

𝐶
=

𝐼𝑜

𝐶
=

𝑉𝑜

𝑅𝐶
 

Similarly, for time interval t4, the slope is, 

𝑑𝑣𝑐

𝑑𝑡
=

𝑖𝑐

𝐶
=

𝐼𝐿

𝐶
−

𝑉𝑜

𝑅𝐶
 

Based on principle of capacitor charge balance  

∆𝑣𝑐 = 𝑠𝑙𝑜𝑝𝑒 × 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑠𝑙𝑜𝑝𝑒 

∆𝑣𝑐 =
𝑉𝑜

𝑅𝐶
× (𝑡𝑃𝑉 + 𝑡𝐵𝑇 + 𝑡𝑈𝐶) 

∆𝑣𝑐 =
𝑉𝑜

𝑅𝐶
× (𝐷𝑃𝑉 + 𝐷𝐵𝑇 + 𝐷𝑈𝐶)𝑇𝑠 

                                          𝑣𝑐 =
𝑉𝑜

𝑅𝐶𝑓𝑠
× (𝑡𝑃𝑉 + 𝑡𝐵𝑇 + 𝑡𝑈𝐶)                                              … (4) 

Here; ∆𝑣𝑐 = peak-to-peak voltage ripple 

                                     𝐶 =
𝑉𝑜

𝑅∆𝑣𝑐𝑓𝑠
× (𝑡𝑃𝑉 + 𝑡𝐵𝑇 + 𝑡𝑈𝐶)                                                 … (5) 

The above equation gives the value of Capacitor. 

 3.3. Structure of ANFIS Controller  

ANFIS is a mixed neuro fuzzy system that improves fuzzy inference systems (FIS) by adding the 

ability for neural networks to learn. This method modifies FIS MFs using input-output training data 

and learning algorithm. Thus, the ANFIS algorithm employs a mixed learning rule and manages 

complex nonlinear systems. The effectiveness of ANFIS in modifying FIS's membership functions has 

been broadly acknowledged.  

Figure 5.18 illustrates the architecture of ANFIS controllers. 

 

Fig. 5: Structure of the Back Propagation ANFIS 



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● Layer 1 is Fuzzification Layer: Here fuzzy membership functions (A1, A2, A3, A4, A5) are 

related to input variables (X, Y). 

● Layer 2 is Rule Layer where the fuzzy rule’ firing strength is considered by multiplying 

incoming signals. 

● Layer 3 is a normalization layer that measures regulated rule strength using weights (W1, 

W2). 

● Layer 4 is Defuzzification Layer, which forms fuzzy rules from input variables. 

● Layer 5 is a Summation Layer displaying the ANFIS controller's output. 

  4. Simulations & Result Analysis 

The proposed Novel- SRF Controller based Fuzzy ANFIS algorithm is compared with the standard 

algorithm used in ANFIS which has a fixed learning rate. Fig. 6 gives the initial learning rate for the 

required MFs. 

 

Fig. 6: - Time Convergence Graph 

 

Fig.7: - Switching Pulses in Buck-Boost Operating Mode & Fig. 8: - RMS Analysis of V, I & P 

of PV, Battery, UC and Load in buck-boost mode 



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Fig. 9 & 10: - 𝑉𝐿 & 𝐼𝐿  ;  𝑉0 & 𝐼0 Waveforms Buck-Boost Operating Mode 

 

 

Fig. 11: - Uncontrolled Load V & I waveforms without controller & Fig. 12: Output V & I 

waveforms with ANFIS Controller 

Different loads are applied at the simulation time of 0.1 sec., 0.2 sec., 0.3 sec. Fig.7 shows the input 

switching pulses in Buck-Boost mode. The loads are sequentially increased as can be observed by the 

waveform of current and RMS Analysis of V, I & P of PV, Battery, UC and Load can be seen in Fig. 

8.  Changes in inductor voltage and current and hence the changes in voltage and current across the 

load are observed as in Fig. 9 & 10. The last waveforms show the variation in final output voltage and 

current before the use of ANFIS controller and after the use of controller.   

5. Conclusion  

The results obtained from simulation work indicate the efficient working of the proposed control 

scheme. The above results show that for remote area electrification, the above discussed novel set up 

will prove to be a better option and can be further implemented for nonlinear load with discontinuous 

mode of conduction. 

6.Funding 

This research received no specific grant from any funding agency in the public, commercial, or non-

for-profit sectors. 

 



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7.Conflicts Of Interest  

The authors declare that there is no conflict of interest. 

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