WEB-ресурс научно-практических конференций

Chaikovskaya E.E., candidate of the technical sciences

Odessa

Inductive modeling of energetic systems self-organization control

Introduction

The existing control systems including expert diagnostic systems on the decision making level that monitor power consumption in order to control power production do not predict energy-saving energetic system development as they do not evaluate their dynamics activity.

Energetic systems qualities are revealed as synergetic i.e. capable of self-organization. This openness of the energy systems, changing with surrounding ambience by material, energy and information. This their not balance and not linearity, when at achievement by system of the threshold to stability, conditions to bifurcations before she is opened several, more then one possible way of the development.

Proved the possibility of systems self-organization control on the basis of evaluated information as a measure of power production and consumption in correlation in a common information space [ 1-3 ] .

1 Theoretical Part

For this purpose architecture of expert system based on synergetic principle is offered (fig.1) [ 1-3 ] .

Fig. 1. Architecture of the expert systems

1 – dynamic subsystem; 2 – diagnostics block; 3 – efficiency block; 4 – reliability block

Expert system is based on dynamic subsystem, which mathematical model was developed utilizing the principles of not balance Thermodynamics, reflects dynamic features of energetic system. Other modules of the expert system – diagnostics, efficiency, reliability blocks with corresponding mathematical description and their further increase.

So, for instance, mathematical model of the dynamic subsystem as bases of the expert system in the manner of transmission function on channel of the change accumulation capacities of the internal flow material on base of the change of its temperature when change the consumption of the external flow material has such type:

(1)

where C - heat capacitivity , kJ/kgК; G - consumption material , kg/s; S - parameter of Laplas transformation ; T _i, , T _m - constant time, characterizing heat accumulation ability internal flow material, metal, , s; g - specific mass material , kg/m; h - specific surface , m ² /m; m - coefficient to dependencies of the coefficient of the heat return from consumption material; z - coordinate of the length, m ; a - coefficient of the heat return , кW/m ² К; q , s - temperature separating walls, external flow , К. Indexes : i - internal flow ; m - the metallic wall; e - external flow.

Logic model method of dynamic subsystem causal - effect links as a basis of expert system for control information reception on decision-making level and identification new functioning conditions developed (fig.2) [ 1-3 ] .

Fig. 2. Logic model of dynamic subsystem causal - effect links.

CT - checking of the event ; Z - logical relations ; ST – identification of the event; Indexes: 1 – influences ; 2- internal diagnosed parameters ; 3- coefficients of the dynamic equations; 4 – essential diagnosed parameters; 5 – dynamic parameters ; c - checking workability; s – state.

So, block of the checking CT ₁ gets the reports on change the start conditions of the operati on the energy system with appearance outraging influences. This information, which enters from block of the checking CT ₁ , is a reason of the reception to information from block of the checking CT ₂ about change internal diagnosed parameter of the energy system. Information got from block CT ₂ , about change internal diagnosed parameter, is due to receptions of previous information, which enters from block of the checking CT ₁ . The information message from block of the checking CT ₂ , about change internal diagnosed parameter is a reason of the reception to information from block of the checking CT ₃ about change coefficients of the dynamic model i.e. this information from block CT ₃ is due to previous information message from block of the checking CT ₂ , . The information message from block of the checking CT ₃ about change coefficients of the dynamic model is a reason of the reception to information from block of the checking CT ₄ about condition essential diagnosed parameter (does not change because of charge or in charge accumulation capacities).Got information from block of the checking CT ₅ about change dynamic characteristic essential diagnosed parameter is due to receptions of previous information. Information from block of the checking CT ₅ is a reason for reception of resulting information from block of the checking CT _c for taking corresponding to decisions - in charge or charge accumulation capacities of the energy system, change modes conditions i.e. increase or reduction its internal energy, as well as presents the possibility function to value change to efficiency of the energy system and efficiency decision making.

Inductive modeling of the energy system control terminates the identifications its new condition after taking corresponding to decisions with use the identification part to logical model of the causal - effect links . According to logical model is realized identification parameter, controlled according to the first part of logical model, new condition of the operating the energy system.

Logical relations between dynamic subsystem and other modules of the expert system with use of confirming messages end entering the energy system into new functioning conditions [ 1-3 ] .

So, logical modeling inside dynamic subsystem of the expert system (the rice 2) can provide the reception of such resulting information:

(2)

or such information:

(3)

where ac - accumulation capacities; ? - time, s. Indexes: st. max.level - standard of the maximum operation level; st . .level - standard of the operation level; st. min. level - standard of the minimum operation level.

In this case on base of the coordinated interaction of the dynamic subsystem and diagnostics block in composition of the expert system with use of resulting information (2) possible to control the charge of the energy system and realize the identification of the charge (fig.3). With use of information (3) possible the decision making on the discharge of the energy system with realization of the identifications of the discharge (fig.3).

Fig. 3. Support of the dynamic balance

Logical modeling inside dynamic subsystem of the expert system can provide the reception of such resulting information:

(4)

or such information:

(5)

where index: st. min. level - standard of the minimum operation level.

In this case on base of the coordinated interaction of the dynamic subsystem and block to reliability in composition of the expert system possible to realize and identify change the code conditions of the operating the energy system with use of resulting information (4) for increase the internal energy of the system or with use of information (5) for its reduction. The coordinated interaction of the dynamic subsystem and block to efficiency in composition of the expert system on base the resulting information from dynamic subsystem about change accumulation capacities allows to value as change to functional efficiency of the energy system, so and efficiency decision making [ 3 ] .

Presented inductive modeling control allows on base s elf -organization and decision making to predict the way of the development of the energ etic systems. It is installed that discharge of the energy system and reception to resulting information (2) for its charge is indicative of moving the system from smaller accumulation to greater. Checking the event about discharge of the energy system and reception to resulting information (3) for its discharge is indicative of moving the system from greater accumulation to smaller. ( fig . 3).Checking the event about charge of the energy system and reception to resulting information (3) for its discharge is indicative of moving the system from greater accumulation to smaller. Checking the event about charge of the energy system and reception to resulting information (2) for its charge is indicative of moving the system from smaller accumulation to greater (fig. 3).

Energetic systems self-organization control:

where ES - expert system; D - dynamic subsystem; P - properties of expert system elements; x - impacts; f - diagnosed parameters; K - coefficients of the mathematical description; y - output parameters; d - dynamic parameters; Z , R - a logical relations in D and ES ; ? - time, s. Indexes: i - number of expert system elements; 0, 1, 2 - start stationary mode, external, internal type of influence.

2 Conclusion

Self-organization energetic systems control on the level of decision-making has power-saving efficiency, giving the opportunity of setting up energetic systems power-saving modes with the use of accumulation energy to the full extent.

3 Acknowledgements

Presented inductive modeling e nergetic systems self-organization control is confirmed by practical applications [ 1-3 ] .

References :

1. Chaikovskaya E.E. C ontrol of energetic systems operation on the basis of monitoring their capacity to work // Eastern-Europium journal of Enterprise Technologies. - 2006. - №3/2(21). - Р p. 48-52.

2. Chaikovskaya E.E . Control of co- ordination of heat production and consumption on the level of decision-making // Eastern-Europium journal of Enterprise Technologies. - 2007. - №2/3(26). - Р . 16-20.

3. Chaikovskaya E.E. Operation of power systems on the basis of intellectual control of heat – and mass transfer processes // VI Minsk International Heat and Mass Transfer Forum, 8-05 . - 2008 . - Р . 1-10.