Research on the application of PLC technology in electrical automation engineering

With the development of society and economy, electrical automation has come to be widely used in many industries; from industries in which merely basic construction activity is involved to aerospace high-tech, there is no shortage of electrical automation, and it has found the most extensive application in heavy industry and power generation [1]. The improvement and effective use of electrical automation can comprehensively improve work quality and work efficiency, and maximise the economic benefits obtained by enterprises [2]. A series of standards has been formulated in China to ensure the better development of electrical automation and to improve the versatility of related equipment [3]. In the 21st century, electrical engineering and its automation technology are being continuously improved and updated, and the effective integration of computer information technology and electrical automation can comprehensively improve the quality and work efficiency of the entire electrical automation system, thus facilitating it to become one of the important guarantees for the long-term development of the Chinese economy [4].

In traditional electrical control, most components of the electrical system are controlled in the form of relay contact, but relay control has the disadvantages of slow response time, high average repair rate, complicated wiring, low reliability, high power consumption and poor flexibility [5]. Therefore, an automatic control system with a wide range of applications, powerful functions and convenient use is urgently needed. The programmable logic controller appeared in response to this requirement and has been widely used in the field of automatic control, which has greatly promoted an enhancement in the levels of effectiveness and efficiency for industries that have adopted this automation [6]. The working principle of PLC is fundamentally the same as that of a computer, and it also realises a series of task controls through the execution of certain user programs. In terms of time, the PLC controller is different from the relay control system when performing tasks [7]. In the work of PLC, the method of cyclic scanning is used. This method refers to sampling the signals in each process during the running time of the program, and then performing operations and processing on the signals. The results are sent to the relevant actuators [8]. During the operating cycle of the system, some of the input data do not change, and similarly, some output data remain fixed whereas others might or might not change. In the PLC, the cycle scanning method is used to continuously sample and output the input variables and output variables, so that the corresponding execution actions can be made when the conditions are met [9].

Electrical automation based on PLC technology is widely used in all walks of life. In the field of agriculture, through precise control of the supply of water and fertiliser to crops, the waste of water resources can be reduced and the level of agricultural modernisation can be improved [10], Accordingly, several studies are available in the literature describing the impact of application of this technology in agricultural automation tasks. Leedy et al. [11] designed a fertiliser distribution device for a sprinkler irrigation system to realise the integrated irrigation of water and fertiliser. Colaco and Molin [12] and Chandel et al. [13] equipped the fertiliser applicator with a PDA and a vehicle-mounted computer, respectively, combined with technologies such as sensors, cameras and GIS positioning, to achieve accurate measurement and control of orchard fertilisation decision-making information. Sarangapani et al. [14] used two-line decoding technology and cloud computing to realise the intelligent irrigation of the water and fertiliser integrated delivery machine. Dwinugroho et al. [15] designed and optimised the mixed fertiliser system of the three-channel bypass fertiliser applicator, and verified the stability of the whole machine structure through simulation and experiments. Boobalan et al. [16] combined fuzzy control and FPGA technology to design a greenhouse water and fertiliser integration system for online fertiliser mixing. The application results show that the system has good control performance. Yang and Cheng [17] developed an intelligent irrigation system controlled and monitored by the Android platform. In applying this system, any Android-powered mobile phone can be used to communicate with soil moisture and temperature sensors in real time to realise automatic irrigation of farmland and online analysis of optimal water volume or crop yield. Liu et al. [18] designed a soilless cultivation irrigation cloud system based on EC sensors, which drives the single-chip microcomputer to control the irrigation and fertilisation devices according to the fertiliser parameters collected by the wireless sensors. Ouberri et al. [19] designed a fuzzy controller for precision irrigation nonlinear systems, using the T-S model to achieve continuous management of multiple input variables. Rajkumar et al. [20] established an intelligent fuzzy inference model for the randomness and nonlinearity of automatic irrigation systems, which can effectively improve their accuracy. Yadav and Shevade [21] proposed using the WebGIS framework to connect discrete agricultural systems to the Internet, and use big data analysis, AI, drones and other technologies to achieve sustainable promotion of precision agriculture. Stavrakoudis et al. [22] used UAV remote sensing technology to predict the data of rice yield and fertiliser requirement, and established a nutrient content inversion model of rice growth period through multiple linear regression method to ensure accurate diagnosis of rice fertiliser application.

In the field of urban drainage automation, Chopade [22] studied the adaptability of automatic control systems under different environmental conditions, and used experimental tests to obtain the results; Nie and Xu [23] mainly designed and implemented BCI-based automatic control systems. Yang and Fu [24] focussed on research into the automatic display system of drainage systems under certain conditions, using a mobile liquid crystal display. After years of development, the automation level of the current sewage treatment industry has been greatly improved, and the related technologies of the control system have been gradually updated and improved [25]. At present, the three main control systems in the control field are PLC programmable controller system, FCS fieldbus control system and DCS distributed control system [26]. Among them, the structure of the DCS control system is divided into three levels, including the control station, the operation station and the instruments in the process. FCS realises the organic combination of field process equipment, PLC controller and upper computer monitoring part through the established communication network, and thus facilitates the automation of field-level industrial control. FCS system is a relatively popular development point in the current control field, and it is also a popular development direction [27]. The openness, dispersion, maintainability and operability of the FCS system are relatively good, and it is also simple and easy to implement in such a way as to achieve complete onsite control. It is being increasingly used in current industrial production applications [28]. As a result of the effective development of the development platform and configuration software, the PLC control system has a more friendly human–machine interface, and at the same time, the cost is reduced; thus, by opting for this system, users can economically use a control system that offers a greater capability for friendly (i.e. hassle-free) human–machine interaction. The improvement of the communication level also makes the security and stability of the control system develop by leaps and bounds. The openness characteristics of PLC systems are also generally developed based on the customised needs of users, allowing the integration of third-party software and hardware to make PLC more powerful and empowering its use in a wider range.

Based on the findings from the literature outlined above, it is observed that PLC technology is widely used in various fields of electrical automation, which greatly improves the efficiency of the control system and simplifies the installation, monitoring, management, maintenance and other procedures. However, it still has some shortcomings, such as the efficiency still not being very high, the response time being long and the control accuracy being low. Therefore, this paper studies the practical application of PLC technology in various fields of electrical automation engineering, analyses its working principle in depth and optimises the automatic control system based on the PLC principle.

In the electrical automation control system, the RL network is a common loop in the electrical system, in which the resistance R and the inductance L are constants, the voltage U is the input quantity, the current I is the output quantity and the differential equation expressions of the input quantity and the output quantity are such as indicated in Eqs (1)–(4): (1) ur=Ldidt+Ri {u_r} = L{{di} \over {dt}} + Ri (2) T=LR T = {L \over R} (3) K=1R K = {1 \over R} (4) Tdidt+i=Kur T{{di} \over {dt}} + i = K{u_r}

In addition, the RC network structure is also a common loop of the electrical system. The resistance R and the capacitance C are both constants, the voltage Ur is the input quantity and the voltage Uc is the output quantity. Further, according to the circuit theory, the differential equation expressions of the input and output quantities can be obtained as shown in Eqs (5) and (6): (5) ur=Ri+1C∫idt {u_r} = Ri + {1 \over C}\int idt (6) uc=1C∫idt {u_c} = {1 \over C}\int idt where I is the current passing through the capacitor C and the resistor R. After the equation transformation, the differential equation expression between the input and output quantities can be obtained as shown in Eqs (7) and (8): (7) RCducdt+uc=ur RC{{d{u_c}} \over {dt}} + {u_c} = {u_r} (8) Tducdt+uc=ur T{{d{u_c}} \over {dt}} + {u_c} = {u_r}

At the same time, the PLC uses the armature voltage to control the DC motor in the system, I is the excitation current, U_a and I_a are the armature voltage and current, M is the load torque on the motor, and are the displacement and rotational speed of the motor, respectively. Therefore, the equation expression of the armature circuit controlled by PLC can be obtained as shown in Eq. (9): (9) ua=iaRa+Ladiadt+ea {u_a} = {i_a}{R_a} + {L_a}{{d{i_a}} \over {dt}} + {e_a}

The back EMF is proportional to the motor speed, as shown in Eq. (10): (10) ea=Kadθdt {e_a} = {K_a}{{d\theta} \over {dt}}

The electromagnetic torque M generated by the motor is proportional to the armature current I, as shown in Eq. (11): (11) M=Cia M = C{i_a}

The torque balance formula on the motor is shown in Eq. (12): (12) M−ML=Jd2θdt2+fdθdt M - {M_L} = J{{{d^2}\theta} \over {d{t^2}}} + f{{d\theta} \over {dt}}

Eqs (9)–(12) are the dynamic and static correlations of the armature controlled by the PLC, which is one of the mathematical models of the electrification system. The four equations mentioned above are transformed by formulas to obtain the attitude equation between the input and output of the motor, as shown in Eq. (13): (13) JRad3θdt3+(fRa+fLa)d2θdt2+(fRa+CKa)dθdt=Cua−RaML−LadMLdt J{R_a}{{{d^3}\theta} \over {d{t^3}}} + \left({f{R_a} + f{L_a}} \right){{{d^2}\theta} \over {d{t^2}}} + \left({f{R_a} + C{K_a}} \right){{d\theta} \over {dt}} = C{u_a} - {R_a}{M_L} - {L_a}{{d{M_L}} \over {dt}}

Since the inductance of the motor has little influence on the system, Eq. (13) can be simplified to Eq. (14): (14) JRad2θdt2+(fRa+CKa)dθdt=Cua−RaML J{R_a}{{{d^2}\theta} \over {d{t^2}}} + \left({f{R_a} + C{K_a}} \right){{d\theta} \over {dt}} = C{u_a} - {R_a}{M_L}

At the same time, the rotational speed of the motor is known as the input quantity, and it can be simplified by using the following Eq. (15): (15) JRadθdt+(fRa+CKa)ω=Cua−RaML J{R_a}{{d\theta} \over {dt}} + \left({f{R_a} + C{K_a}} \right)\omega = C{u_a} - {R_a}{M_L}

At the same time, PLC is in the motor drive control, in which the servo motor drives the screw to rotate once, and the volume V of the object brought out is shown in Eq. (16): (16) V=FL=t(S−b)⋅π⋅d/cosα V = FL = t\left({S - b} \right) \cdot \pi \cdot d/\cos \alpha

Then, every time the screw rotates once, the mass M of the object brought out is shown in Eq. (17): (17) M=V⋅γ⋅n M = V \cdot \gamma \cdot n

For the power model of the PLC-controlled motor drive, the motor power balance equation can be written as shown in Eq. (18): (18) F=mv¨0+Crx˙0+F0 F = m{\ddot v_0} + {C_r}{\dot x_0} + {F_0}

The transmission force formula of PLC-controlled servo motor is given by Eq. (19): (19) F=k(v−v0) F = k(v - {v_0})

After Laplace transformation from Eqs (18) and (19), Eqs (20)–(22) can be obtained: (20) F=(mS2+CrS)v0(S)+F0 F = \left({m{S^2} + {C_r}S} \right){v_0}(S) + {F_0} (21) F=k[v(S)−v0(S)] F = k\left[ {v(S) - {v_0}(S)} \right] (22) v0(S)=kv(S)−F0mS2+CrS+k {v_0}(S) = {{kv(S) - {F_0}} \over {m{S^2} + {C_r}S + k}}

For the motor controlled by PLC, if the influence of the friction resistance of the screw is not considered, the transfer function of the power model of the mechanical transmission of the servo system can be written as shown in Eq. (23): (23) G(S)=kmS2+CrS+k G(S) = {k \over {m{S^2} + {C_r}S + k}}

By mathematical transformation, Eq. (23) can be simplified to Eq. (24): (24) GL(S)=x0(S)x(S)=ωn2S2+2ξωnS+ωn2 {G_L}(S) = {{{x_0}(S)} \over {x(S)}} = {{\omega _n^2} \over {{S^2} + 2\xi {\omega _n}S + \omega _n^2}}

The application of PLC technology can not only effectively improve various problems existing in the traditional electrical automation control system, but also play a role in controlling material loss and saving costs, which can also promote the production efficiency and economic benefits derived by electrical automation enterprises. The use of internal model PID control algorithm strengthens the adaptability of the controller to the changes of the electrical automation system model, better meets the control requirements of stability, accuracy and rapidity, and realises the system heat source that would enable effective and efficient operation corresponding to the least amount of resource consumption; however, the electrical automation system Fan PID parameters have not yet achieved self-matching and self-adaptive functions. Therefore, it is necessary to continue to carefully analyse a large amount of electrical automation data that have been collected, establish electrical automation system models under various working conditions, and combine fuzzy control rules, compile fuzzy programs and add them to existing programs. In this way, the electrical automation system can independently choose to change the motor PID parameters according to different working conditions, so that the entire electrical automation system can always maintain efficient operation.

In order to further improve the accuracy of the weighing system of the bag packaging machine based on the electrical automation system, this paper studies and analyses the PLC control system, combines the fuzzy control with the PID control and proposes the fuzzy control based on the traditional PID. The control of the fuzzy PID control electrical automation system in the packaging machine weighing system is realised through the powerful control effect and programming ability of PLC. The experimental results show that the improved electrical automation control system has good accuracy, the system overshoot is small and the weighing accuracy is controlled at about ±0.1%, which better meets the requirements of accurate measurement of electrical automation packaging machines.

The automatic control system of electrical equipment based on PLC technology is designed. The PID control algorithm is used in the system, and it is written into the PLC programmable electrical automation system controller in a self-programming way. The electrical automation system controller is used to realise electrical The device is automatically controlled. The simulation test results show that the PLC is less affected by the number of threads, and its operation is relatively stable. The spectrum efficiency curve of the electrical automation system coincides with the ideal curve, and it has better communication performance and good applicability.